WorldWideScience

Sample records for network health diagnosis

  1. The research of elevator health diagnosis method based on Bayesian network

    Science.gov (United States)

    Liu, Chang; Zhang, Xinzheng; Liu, Xindong; Chen, Can

    2017-08-01

    Elevator, as a complex mechanical system, is hard to determine the factors that affect components’ status. In accordance with this special characteristic, the Elevator Fault Diagnosis Model is proposed based on Bayesian Network in this paper. The method uses different samples of the elevator and adopts Monte Carlo inference mechanism for Bayesian Network Model structure and parameter learning. Eventually, an elevator fault diagnosis model based on Bayesian network is established, which accords with the theory of elevator operation. In this paper, we use different kinds of fault data samples to test the method. Experimental results demonstrate the higher accuracy of our method. This paper provides a good assistant method by means of Fault prediction and Health diagnosis of elevator system at present.

  2. Health Insurance Portability and Accountability Act-Compliant Ocular Telehealth Network for the Remote Diagnosis and Management of Diabetic Retinopathy

    Energy Technology Data Exchange (ETDEWEB)

    Li, Yaquin [University of Tennessee, Knoxville (UTK); Karnowski, Thomas Paul [ORNL; Tobin Jr, Kenneth William [ORNL; Giancardo, Luca [ORNL; Garg, Seema [University of North Carolina; Fox, Karen [Delta Health Alliance; Chaum, Edward [University of Tennessee, Knoxville (UTK)

    2011-01-01

    In this article, we present the design and implementation of a regional ocular telehealth network for remote assessment and management of diabetic retinopathy (DR), including the design requirements, network topology, protocol design, system work flow, graphics user interfaces, and performance evaluation. The Telemedical Retinal Image Analysis and Diagnosis Network is a computer-aided, image analysis telehealth paradigm for the diagnosis of DR and other retinal diseases using fundus images acquired from primary care end users delivering care to underserved patient populations in the mid-South and southeastern United States.

  3. Diagnosis of Subtraction Bugs Using Bayesian Networks

    Science.gov (United States)

    Lee, Jihyun; Corter, James E.

    2011-01-01

    Diagnosis of misconceptions or "bugs" in procedural skills is difficult because of their unstable nature. This study addresses this problem by proposing and evaluating a probability-based approach to the diagnosis of bugs in children's multicolumn subtraction performance using Bayesian networks. This approach assumes a causal network relating…

  4. Communication networks of men facing a diagnosis of prostate cancer.

    Science.gov (United States)

    Brown, Dot; Oetzel, John; Henderson, Alison

    2016-11-01

    This study seeks to identify the factors that shape the communication networks of men who face a potential diagnosis of prostate cancer, and how these factors relate to their disclosure about their changing health status. Men facing a potential diagnosis of prostate cancer are in a challenging situation; the support benefits of disclosing their changing health status to others in their communication networks is set against a backdrop of the potential stigma and uncertainty of the diagnosis. All men on a prostate biopsy waiting list were eligible for inclusion in an exploratory and interpretive study. Semi-structured interviews with 40 men explored their network structures and disclosure of health information. Thematic analysis highlighted the factors which contributed to their network structures and their disclosure about their health status. Four network factors shaped men's perspectives about disclosing their health status: (1) tie strength, comprising both strong and weak ties; (2) knowledgeable others, with a focus on medical professionals in the family; (3) homophily, which included other individuals with a similar medical condition; and (4) geographical proximity, with a preference for face-to-face communication. Communication networks influence men's disclosure of their health status and in particular weak ties with medical knowledge have an important role. Men who use the potential for support in their networks may experience improved psychosocial outcomes. Using these four network factors-tie strength, knowledgeable others, homophily or geographical proximity-to forecast men's willingness to disclose helps identify men who lack potential support and so are at risk of poor psychosocial health. Those with few strong ties or knowledgeable others in their networks may be in the at-risk cohort. The support provided in communication networks complements formal medical care from nurses and other health professionals, and encouraging patients to use their

  5. Advanced fault diagnosis methods in molecular networks.

    Science.gov (United States)

    Habibi, Iman; Emamian, Effat S; Abdi, Ali

    2014-01-01

    Analysis of the failure of cell signaling networks is an important topic in systems biology and has applications in target discovery and drug development. In this paper, some advanced methods for fault diagnosis in signaling networks are developed and then applied to a caspase network and an SHP2 network. The goal is to understand how, and to what extent, the dysfunction of molecules in a network contributes to the failure of the entire network. Network dysfunction (failure) is defined as failure to produce the expected outputs in response to the input signals. Vulnerability level of a molecule is defined as the probability of the network failure, when the molecule is dysfunctional. In this study, a method to calculate the vulnerability level of single molecules for different combinations of input signals is developed. Furthermore, a more complex yet biologically meaningful method for calculating the multi-fault vulnerability levels is suggested, in which two or more molecules are simultaneously dysfunctional. Finally, a method is developed for fault diagnosis of networks based on a ternary logic model, which considers three activity levels for a molecule instead of the previously published binary logic model, and provides equations for the vulnerabilities of molecules in a ternary framework. Multi-fault analysis shows that the pairs of molecules with high vulnerability typically include a highly vulnerable molecule identified by the single fault analysis. The ternary fault analysis for the caspase network shows that predictions obtained using the more complex ternary model are about the same as the predictions of the simpler binary approach. This study suggests that by increasing the number of activity levels the complexity of the model grows; however, the predictive power of the ternary model does not appear to be increased proportionally.

  6. First diagnosis and management of incontinence in older people with and without dementia in primary care: a cohort study using The Health Improvement Network primary care database.

    Directory of Open Access Journals (Sweden)

    Robert L Grant

    2013-08-01

    Full Text Available Dementia is one of the most disabling and burdensome diseases. Incontinence in people with dementia is distressing, adds to carer burden, and influences decisions to relocate people to care homes. Successful and safe management of incontinence in people with dementia presents additional challenges. The aim of this study was to investigate the rates of first diagnosis in primary care of urinary and faecal incontinence among people aged 60-89 with dementia, and the use of medication or indwelling catheters for urinary incontinence.We extracted data on 54,816 people aged 60-89 with dementia and an age-gender stratified sample of 205,795 people without dementia from 2001 to 2010 from The Health Improvement Network (THIN, a United Kingdom primary care database. THIN includes data on patients and primary care consultations but does not identify care home residents. Rate ratios were adjusted for age, sex, and co-morbidity using multilevel Poisson regression. The rates of first diagnosis per 1,000 person-years at risk (95% confidence interval for urinary incontinence in the dementia cohort, among men and women, respectively, were 42.3 (40.9-43.8 and 33.5 (32.6-34.5. In the non-dementia cohort, the rates were 19.8 (19.4-20.3 and 18.6 (18.2-18.9. The rates of first diagnosis for faecal incontinence in the dementia cohort were 11.1 (10.4-11.9 and 10.1 (9.6-10.6. In the non-dementia cohort, the rates were 3.1 (2.9-3.3 and 3.6 (3.5-3.8. The adjusted rate ratio for first diagnosis of urinary incontinence was 3.2 (2.7-3.7 in men and 2.7 (2.3-3.2 in women, and for faecal incontinence was 6.0 (5.1-7.0 in men and 4.5 (3.8-5.2 in women. The adjusted rate ratio for pharmacological treatment of urinary incontinence was 2.2 (1.4-3.7 for both genders, and for indwelling urinary catheters was 1.6 (1.3-1.9 in men and 2.3 (1.9-2.8 in women.Compared with those without a dementia diagnosis, those with a dementia diagnosis have approximately three times the rate of

  7. Artificial neural network cardiopulmonary modeling and diagnosis

    Science.gov (United States)

    Kangas, Lars J.; Keller, Paul E.

    1997-01-01

    The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis.

  8. First diagnosis and management of incontinence in older people with and without dementia in primary care: a cohort study using The Health Improvement Network primary care database

    National Research Council Canada - National Science Library

    Grant, Robert L; Drennan, Vari M; Rait, Greta; Petersen, Irene; Iliffe, Steve

    2013-01-01

    ... catheters for urinary incontinence. We extracted data on 54,816 people aged 60-89 with dementia and an age-gender stratified sample of 205,795 people without dementia from 2001 to 2010 from The Health Improvement Network (THIN...

  9. Tumor Diagnosis Using Backpropagation Neural Network Method

    Science.gov (United States)

    Ma, Lixing; Looney, Carl; Sukuta, Sydney; Bruch, Reinhard; Afanasyeva, Natalia

    1998-05-01

    For characterization of skin cancer, an artificial neural network (ANN) method has been developed to diagnose normal tissue, benign tumor and melanoma. The pattern recognition is based on a three-layer neural network fuzzy learning system. In this study, the input neuron data set is the Fourier Transform infrared (FT-IR)spectrum obtained by a new Fiberoptic Evanescent Wave Fourier Transform Infrared (FEW-FTIR) spectroscopy method in the range of 1480 to 1850 cm-1. Ten input features are extracted from the absorbency values in this region. A single hidden layer of neural nodes with sigmoids activation functions clusters the feature space into small subclasses and the output nodes are separated in different nonconvex classes to permit nonlinear discrimination of disease states. The output is classified as three classes: normal tissue, benign tumor and melanoma. The results obtained from the neural network pattern recognition are shown to be consistent with traditional medical diagnosis. Input features have also been extracted from the absorbency spectra using chemical factor analysis. These abstract features or factors are also used in the classification.

  10. Orthopedic Health: Joint Health and Care: Prevention, Symptoms, Diagnosis & Treatment

    Science.gov (United States)

    ... Orthopedic Health Joint Health and Care: Prevention, Symptoms, Diagnosis & Treatment Past Issues / Spring 2009 Table of Contents For ... may be used to help achieve an accurate diagnosis, including: ... joint for examination Treatment The only type of arthritis that can be ...

  11. Health Participatory Sensing Networks

    Directory of Open Access Journals (Sweden)

    Andrew Clarke

    2014-01-01

    Full Text Available The use of participatory sensing in relation to the capture of health-related data is rapidly becoming a possibility due to the widespread consumer adoption of emerging mobile computing technologies and sensing platforms. This has the potential to revolutionize data collection for population health, aspects of epidemiology, and health-related e-Science applications and as we will describe, provide new public health intervention capabilities, with the classifications and capabilities of such participatory sensing platforms only just beginning to be explored. Such a development will have important benefits for access to near real-time, large-scale, up to population-scale data collection. However, there are also numerous issues to be addressed first: provision of stringent anonymity and privacy within these methodologies, user interface issues, and the related issue of how to incentivize participants and address barriers/concerns over participation. To provide a step towards describing these aspects, in this paper we present a first classification of health participatory sensing models, a novel contribution to the literature, and provide a conceptual reference architecture for health participatory sensing networks (HPSNs and user interaction example case study.

  12. [psychenet - The Hamburg Network for Mental Health].

    Science.gov (United States)

    Härter, Martin; Brandes, Andreas; Hillebrandt, Bernd; Lambert, Martin

    2015-07-01

    With the research and development project psychenet: the Hamburg Network for Mental Health (2011 - 2015), the Federal Ministry of Education and Research contributes to strengthening healthcare regions in Germany by establishing new transsectoral cooperations and implementing evaluated innovations. More than 300 partners from research, health care, health industry and government in the Free and Hanseatic City of Hamburg are promoting innovative measures to improve the detection, diagnosis, and treatment for mental disorders. The main objective is to implement integrated healthcare networks based on evidence for effective treatment methods, deriving from high-quality research throughout five indications such as psychosis, depression, somatoform and functional syndromes, anorexia and bulimia and addiction illnesses in adolescence. Those networks are accompanied by additional measures, for example, for improving awareness, information and education for mental health, addressing occupational health or strengthening the participation of patients and their families suffering from mental illness. © Georg Thieme Verlag KG Stuttgart · New York.

  13. Artificial Neural Network System for Thyroid Diagnosis

    Directory of Open Access Journals (Sweden)

    Mazin Abdulrasool Hameed

    2017-05-01

    Full Text Available Thyroid disease is one of major causes of severe medical problems for human beings. Therefore, proper diagnosis of thyroid disease is considered as an important issue to determine treatment for patients. This paper focuses on using Artificial Neural Network (ANN as a significant technique of artificial intelligence to diagnose thyroid diseases. The continuous values of three laboratory blood tests are used as input signals to the proposed system of ANN. All types of thyroid diseases that may occur in patients are taken into account in design of system, as well as the high accuracy of the detection and categorization of thyroid diseases are considered in the system. A multilayer feedforward architecture of ANN is adopted in the proposed design, and the back propagation is selected as learning algorithm to accomplish the training process. The result of this research shows that the proposed ANN system is able to precisely diagnose thyroid disease, and can be exploited in practical uses. The system is simulated via MATLAB software to evaluate its performance

  14. The Usage of Neural Networks for the Medical Diagnosis

    OpenAIRE

    Malyshevska, Kateryna

    2009-01-01

    The problem of cancer diagnosis from multi-channel images using the neural networks is investigated. The goal of this work is to classify the different tissue types which are used to determine the cancer risk. The radial basis function networks and backpropagation neural networks are used for classification. The results of experiments are presented.

  15. Heart Health - Heart Disease: Symptoms, Diagnosis, Treatment

    Science.gov (United States)

    ... Bar Home Current Issue Past Issues Cover Story Heart Health Heart Disease: Symptoms, Diagnosis, Treatment Past Issues / Winter 2009 ... of this page please turn Javascript on. Most heart attacks happen when a clot in the coronary ...

  16. Dam health diagnosis and evaluation

    Science.gov (United States)

    Wu, Zhongru; Su, Huaizhi

    2005-06-01

    Based on the bionics principle in the life sciences field, we regard a dam as a vital and intelligent system. A bionics model is constructed to observe, diagnose and evaluate dam health. The model is composed of a sensing system (nerve), central processing unit (cerebrum) and decision-making implement (organism). In addition, the model, index system and engineering method on dam health assessment are presented. The proposed theories and methods are applied to evaluate dynamically the health of one concrete dam.

  17. Social Networking and Health

    OpenAIRE

    Kevin Curran; Michael Mc Hugh

    2013-01-01

    The rise of social networking has revolutionised how people communicate on a daily basis. In a world where more people are connecting to the internet, social networking services create an immediate communication link between users. Social networking sites provide multiple services which include emailing, instant messaging, uploading files, gaming and finding friends. Just as social networking has become a more popular method of communication in recent years, the ways in which people look afte...

  18. Hearing health network: a spatial analysis

    Directory of Open Access Journals (Sweden)

    Camila Ferreira de Rezende

    2015-06-01

    Full Text Available INTRODUCTION: In order to meet the demands of the patient population with hearing impairment, the Hearing Health Care Network was created, consisting of primary care actions of medium and high complexity. Spatial analysis through geoprocessing is a way to understand the organization of such services. OBJECTIVE: To analyze the organization of the Hearing Health Care Network of the State of Minas Gerais. METHODS: Cross-sectional analytical study using geoprocessing techniques. The absolute frequency and the frequency per 1000 inhabitants of the following variables were analyzed: assessment and diagnosis, selection and adaptation of hearing aids, follow-up, and speech therapy. The spatial analysis unit was the health micro-region. RESULTS: The assessment and diagnosis, selection, and adaptation of hearing aids and follow-up had a higher absolute number in the micro-regions with hearing health services. The follow-up procedure showed the lowest occurrence. Speech therapy showed higher occurrence in the state, both in absolute numbers, as well as per population. CONCLUSION: The use of geoprocessing techniques allowed the identification of the care flow as a function of the procedure performance frequency, population concentration, and territory distribution. All procedures offered by the Hearing Health Care Network are performed for users of all micro-regions of the state.

  19. Possibilistic networks for uncertainty knowledge processing in student diagnosis

    Directory of Open Access Journals (Sweden)

    Adina COCU

    2006-12-01

    Full Text Available In this paper, a possibilistic network implementation for uncertain knowledge modeling of the diagnostic process is proposed as a means to achieve student diagnosis in intelligent tutoring system. This approach is proposed in the object oriented programming domain for diagnosis of students learning errors and misconception. In this expertise domain dependencies between data exist that are encoded in the structure of network. Also, it is available qualitative information about these data which are represented and interpreted with qualitative approach of possibility theory. The aim of student diagnosis system is to ensure an adapted support for the student and to sustain the student in personalized learning process and errors explanation.

  20. Bayesian-network-based fault diagnosis methodology of subsea jumper

    Science.gov (United States)

    Cai, Baoping; Liu, Yonghong; Huang, Lei; Hu, Song; Xue, Haitao; Wang, Jiaxing

    2017-10-01

    The paper proposes a Bayesian-network-based real-time fault diagnosis methodology of M-shaped subsea jumper. Finite element models of a typical M-shaped subsea jumper system are built to get the data for diagnosis. Netica is Bayesian-network -based software and is used to construct diagnosis models of the jumper in two main loading conditions which are falling objects and seabed moving. The results show that the accuracy of falling objects diagnosis model with four faults is 100%, and the accuracy of seabed moving diagnosis model with two faults is also 100%. Combine the two models into one and the accuracy of combined model is 96.59%. The effectiveness of the proposed method is validated.

  1. Improving Robustness of Network Fault Diagnosis to Uncertainty in Observations

    DEFF Research Database (Denmark)

    Grønbæk, Lars Jesper; Schwefel, Hans-Peter; Ceccarelli, Andrea

    2010-01-01

    Performing decentralized network fault diagnosis based on network traffic is challenging. Besides inherent stochastic behaviour of observations, measurements may be subject to errors degrading diagnosis timeliness and accuracy. In this paper we present a novel approach in which we aim to mitigate...... issues of measurement errors by quantifying uncertainty. The uncertainty information is applied in the diagnostic component to improve its robustness. Three diagnosis components have been proposed based on the Hidden Markov Model formalism: (H0) representing a classical approach, (H1) a static...... compensation of (H0) to uncertainties and (H2) dynamically adapting diagnosis to uncertainty information. From uncertainty injection scenarios of added measurement noise we demonstrate how using uncertainty information can provide a structured approach of improving diagnosis....

  2. Social Networks and Health.

    Science.gov (United States)

    Perdiaris, Christos; Chardalias, Konstantinos; Magita, Andrianna; Mechili, Aggelos E; Diomidous, Marianna

    2015-01-01

    Nowadays the social networks have been developed into an advanced communications tool, which is important for all people to contact each other. These specific networks do offer lots of options as well as plenty of advantages and disadvantages. The social websites are many in number and titles, such as the facebook, the twitter, the bandoo etc. One of the most important function-mechanisms for the social network websites, are the marketing tools. The future goal is suggested to be the evolution of these programs. The development of these applications, which is going to lead into a new era for the social digital communication between the internet users, all around the globe.

  3. Software Health Management with Bayesian Networks

    Science.gov (United States)

    Mengshoel, Ole; Schumann, JOhann

    2011-01-01

    Most modern aircraft as well as other complex machinery is equipped with diagnostics systems for its major subsystems. During operation, sensors provide important information about the subsystem (e.g., the engine) and that information is used to detect and diagnose faults. Most of these systems focus on the monitoring of a mechanical, hydraulic, or electromechanical subsystem of the vehicle or machinery. Only recently, health management systems that monitor software have been developed. In this paper, we will discuss our approach of using Bayesian networks for Software Health Management (SWHM). We will discuss SWHM requirements, which make advanced reasoning capabilities for the detection and diagnosis important. Then we will present our approach to using Bayesian networks for the construction of health models that dynamically monitor a software system and is capable of detecting and diagnosing faults.

  4. Population health diagnosis with an ecohealth approach

    Science.gov (United States)

    Arenas-Monreal, Luz; Cortez-Lugo, Marlene; Parada-Toro, Irene; Pacheco-Magaña, Lilian E; Magaña-Valladares, Laura

    2015-01-01

    OBJECTIVE To analyze the characteristics of health diagnosis according to the ecohealth approach in rural and urban communities in Mexico. METHODS Health diagnosis were conducted in La Nopalera, from December 2007 to October 2008, and in Atlihuayan, from December 2010 to October 2011. The research was based on three principles of the ecohealth approach: transdisciplinarity, community participation, gender and equity. To collect information, a joint methodology and several techniques were used to stimulate the participation of inhabitants. The diagnostic exercise was carried out in five phases that went from collecting information to prioritization of problems. RESULTS The constitution of the transdisciplinary team, as well as the participation of the population and the principle of gender/equity were differentials between the communities. In the rural community, the active participation of inhabitants and authorities was achieved and the principles of transdisciplinarity and gender/equity were incorporated. CONCLUSIONS With all the difficulties that entails the boost in participation, the incorporation of gender/equity and transdisciplinarity in health diagnosis allowed a holistic public health approach closer to the needs of the population. PMID:26538099

  5. Population health diagnosis with an ecohealth approach.

    Science.gov (United States)

    Arenas-Monreal, Luz; Cortez-Lugo, Marlene; Parada-Toro, Irene; Pacheco-Magaña, Lilian E; Magaña-Valladares, Laura

    2015-01-01

    To analyze the characteristics of health diagnosis according to the ecohealth approach in rural and urban communities in Mexico. Health diagnosis were conducted in La Nopalera, from December 2007 to October 2008, and in Atlihuayan, from December 2010 to October 2011. The research was based on three principles of the ecohealth approach: transdisciplinarity, community participation, gender and equity. To collect information, a joint methodology and several techniques were used to stimulate the participation of inhabitants. The diagnostic exercise was carried out in five phases that went from collecting information to prioritization of problems. The constitution of the transdisciplinary team, as well as the participation of the population and the principle of gender/equity were differentials between the communities. In the rural community, the active participation of inhabitants and authorities was achieved and the principles of transdisciplinarity and gender/equity were incorporated. With all the difficulties that entails the boost in participation, the incorporation of gender/equity and transdisciplinarity in health diagnosis allowed a holistic public health approach closer to the needs of the population.

  6. Intelligent Fault Diagnosis in a Power Distribution Network

    Directory of Open Access Journals (Sweden)

    Oluleke O. Babayomi

    2016-01-01

    Full Text Available This paper presents a novel method of fault diagnosis by the use of fuzzy logic and neural network-based techniques for electric power fault detection, classification, and location in a power distribution network. A real network was used as a case study. The ten different types of line faults including single line-to-ground, line-to-line, double line-to-ground, and three-phase faults were investigated. The designed system has 89% accuracy for fault type identification. It also has 93% accuracy for fault location. The results indicate that the proposed technique is effective in detecting, classifying, and locating low impedance faults.

  7. Canadian Public Health Laboratory Network laboratory Guidelines for the Use of Serological Tests (excluding point-of-care tests for the Diagnosis of Syphilis in Canada

    Directory of Open Access Journals (Sweden)

    Paul N Levett

    2015-01-01

    Full Text Available Syphilis, caused by the bacterium Treponema pallidum subsp. pallidum, is an infection recognized since antiquity. It was first reported at the end of the 15th century in Europe. Infections may be sexually transmitted as well as spread from an infected mother to her fetus or through blood transfusions. The laboratory diagnosis of syphilis infection is complex. Because this organism cannot be cultured, serology is used as the principal diagnostic method. Some of the issues related to serological diagnoses are that antibodies take time to appear after infection, and serology screening tests require several secondary confirmatory tests that can produce complex results needing interpretation by experts in the field. Traditionally, syphilis screening was performed using either rapid plasma reagin or Venereal Disease Research Laboratory tests, and confirmed by treponemal tests such as MHA-TP, TPPA or FTA-Abs. Currently, that trend is reversed, ie, most of the laboratories in Canada now screen for syphilis using treponemal enzyme immunoassays and confirm the status of infection using rapid plasma reagin or Venereal Disease Research Laboratory tests; this approach is often referred to as the reverse algorithm. This chapter reviews guidelines for specimen types and sample collection, treponemal and non-treponemal tests utilized in Canada, the current status of serological tests for syphilis in Canada, the complexity of serological diagnosis of syphilis infection and serological testing algorithms. Both traditional and reverse sequence algorithms are recommended and the algorithm used should be based on a combination of local disease epidemiology, test volumes, performance of the proposed assays and available resources.

  8. Social networks and survival after breast cancer diagnosis.

    Science.gov (United States)

    Beasley, Jeannette M; Newcomb, Polly A; Trentham-Dietz, Amy; Hampton, John M; Ceballos, Rachel M; Titus-Ernstoff, Linda; Egan, Kathleen M; Holmes, Michelle D

    2010-12-01

    Evidence has been inconsistent regarding the impact of social networks on survival after breast cancer diagnosis. We prospectively examined the relation between components of social integration and survival in a large cohort of breast cancer survivors. Women (N=4,589) diagnosed with invasive breast cancer were recruited from a population-based, multi-center, case-control study. A median of 5.6 years (Interquartile Range 2.7-8.7) after breast cancer diagnosis, women completed a questionnaire on recent post-diagnosis social networks and other lifestyle factors. Social networks were measured using components of the Berkman-Syme Social Networks Index to create a measure of social connectedness. Based on a search of the National Death Index, 552 deaths (146 related to breast cancer) were identified. Adjusted hazard ratios (HR) and 95% confidence intervals (CI) were estimated using Cox proportional hazards regression. Higher scores on a composite measure of social connectedness as determined by the frequency of contacts with family and friends, attendance of religious services, and participation in community activities was associated with a 15-28% reduced risk of death from any cause (p-trend=0.02). Inverse trends were observed between all-cause mortality and frequency of attendance at religious services (p-trend=0.0001) and hours per week engaged in community activities (p-trend=0.0005). No material associations were identified between social networks and breast cancer-specific mortality. Engagement in activities outside the home was associated with lower overall mortality after breast cancer diagnosis.

  9. Privacy-Preserving Self-Helped Medical Diagnosis Scheme Based on Secure Two-Party Computation in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yi Sun

    2014-01-01

    Full Text Available With the continuing growth of wireless sensor networks in pervasive medical care, people pay more and more attention to privacy in medical monitoring, diagnosis, treatment, and patient care. On one hand, we expect the public health institutions to provide us with better service. On the other hand, we would not like to leak our personal health information to them. In order to balance this contradiction, in this paper we design a privacy-preserving self-helped medical diagnosis scheme based on secure two-party computation in wireless sensor networks so that patients can privately diagnose themselves by inputting a health card into a self-helped medical diagnosis ATM to obtain a diagnostic report just like drawing money from a bank ATM without revealing patients’ health information and doctors’ diagnostic skill. It makes secure self-helped disease diagnosis feasible and greatly benefits patients as well as relieving the heavy pressure of public health institutions.

  10. A Security Architecture for Health Information Networks

    OpenAIRE

    Kailar, Rajashekar

    2007-01-01

    Health information network security needs to balance exacting security controls with practicality, and ease of implementation in today’s healthcare enterprise. Recent work on ‘nationwide health information network’ architectures has sought to share highly confidential data over insecure networks such as the Internet. Using basic patterns of health network data flow and trust models to support secure communication between network nodes, we abstract network security requirements to a core set t...

  11. The Study of Maglev Train Control and Diagnosis Networks Based on Role Automation Decentralization

    National Research Council Canada - National Science Library

    LIU, Zhigang; WANG, Qi; TAN, Yongdong

    2008-01-01

    The control and diagnosis networks in Maglev Train are the most important parts. In the paper, the control and diagnosis network structures are discussed, and the disadvantages of them are described and analyzed...

  12. CDC National Environmental Public Health Tracking Network (Tracking Network)

    Data.gov (United States)

    U.S. Department of Health & Human Services — The National Environmental Public Health Tracking Network is a system of integrated health, exposure, and hazard information and data from a variety of national,...

  13. A cognitive fault diagnosis system for distributed sensor networks.

    Science.gov (United States)

    Alippi, Cesare; Ntalampiras, Stavros; Roveri, Manuel

    2013-08-01

    This paper introduces a novel cognitive fault diagnosis system (FDS) for distributed sensor networks that takes advantage of spatial and temporal relationships among sensors. The proposed FDS relies on a suitable functional graph representation of the network and a two-layer hierarchical architecture designed to promptly detect and isolate faults. The lower processing layer exploits a novel change detection test (CDT) based on hidden Markov models (HMMs) configured to detect variations in the relationships between couples of sensors. HMMs work in the parameter space of linear time-invariant dynamic systems, approximating, over time, the relationship between two sensors; changes in the approximating model are detected by inspecting the HMM likelihood. Information provided by the CDT layer is then passed to the cognitive one, which, by exploiting the graph representation of the network, aggregates information to discriminate among faults, changes in the environment, and false positives induced by the model bias of the HMMs.

  14. [Study of primary care health needs through family health diagnosis].

    Science.gov (United States)

    Torres-Arreola, Laura Pilar; Vladislavovna Doubova, Svetlana; Reyes-Morales, Hortensia; Villa-Barragán, Juan Pablo; Constantino-Casas, Patricia; Pérez-Cuevas, Ricardo

    2006-10-31

    To assess the health needs of the eligible public population of the Mexican Institute of Social Security (IMSS). Observational, descriptive, transversal study. Family Medicine Unit number 8 of the IMSS, in the city of Tlaxcala, Mexico. A sample of 1200 families using multi-stage sampling, between October 1999 and March 2000. The designed and validated questionnaire on "Family health diagnosis" was used. A 19.2% of the families had a very low socio-economic level, and 14.9% of subjects were not entitled to Social Security. Functional illiteracy in at least one member was found in 12.6% of the families. According to the family Apgar, 93% of families were functional and two-thirds of the families were classified as nuclear. About 51.1% and 36.9% of women used programs for detection of cervical/uterine and breast cancer, respectively. Only 25% of the adult population underwent the detection tests for diabetes mellitus and hypertension and 10.9% had a chronic disease. 56.4% of families considered the quality of health care good, and only 18.13% were satisfied with the care received. Identification of health needs through diagnosis of family health is useful as a basis for establishing a hierarchy of problems as well as for developing health programs that may facilitate greater equity in attention.

  15. Smart Brain Hemorrhage Diagnosis Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Santosh H. Suryawanshi

    2015-08-01

    Full Text Available Abstract The fundamental motivation behind this study is to identify the brain hemorrhage and to give accurate treatment so that death rate because of brain hemorrhage can be reduced. This project investigates the possibility of diagnosing brain hemorrhage using an image segmentation of CT scan images using watershed method and feeding of the appropriate inputs extracted from the brain CT image to an artificial neural network for classification. The output generated as the type of brain hemorrhages can be used to verify expert diagnosis and also as learning tool for trainee radiologists to minimize errors in current methods.

  16. Uncertainty management using bayesian networks in student knowledge diagnosis

    Directory of Open Access Journals (Sweden)

    Adina COCU

    2005-12-01

    Full Text Available In intelligent tutoring systems, student or user modeling implies dealing with imperfect and uncertain knowledge. One of the artificial intelligence techniques used for uncertainty management is that of Bayesian networks. This paradigm is recommended in the situation when exist dependencies between data and qualitative information about these data. In this work we present a student knowledge diagnosis model based on representation with Bayesian networks. The educational system incorporate a multimedia interface for accomplishes the testing tools. The results of testing sessions are represented and interpreted with probability theory in order to ensure an adapted support for the student. The aims of the computer assisted application that contains this diagnose module are to support the student in personalized learning process and errors explanation.

  17. A security architecture for health information networks.

    Science.gov (United States)

    Kailar, Rajashekar; Muralidhar, Vinod

    2007-10-11

    Health information network security needs to balance exacting security controls with practicality, and ease of implementation in today's healthcare enterprise. Recent work on 'nationwide health information network' architectures has sought to share highly confidential data over insecure networks such as the Internet. Using basic patterns of health network data flow and trust models to support secure communication between network nodes, we abstract network security requirements to a core set to enable secure inter-network data sharing. We propose a minimum set of security controls that can be implemented without needing major new technologies, but yet realize network security and privacy goals of confidentiality, integrity and availability. This framework combines a set of technology mechanisms with environmental controls, and is shown to be sufficient to counter commonly encountered network security threats adequately.

  18. Tumor diagnosis using the backpropagation neural network method

    Science.gov (United States)

    Ma, Lixing; Sukuta, Sydney; Bruch, Reinhard F.; Afanasyeva, Natalia I.; Looney, Carl G.

    1998-04-01

    For characterization of skin cancer, an artificial neural network method has been developed to diagnose normal tissue, benign tumor and melanoma. The pattern recognition is based on a three-layer neural network fuzzy learning system. In this study, the input neuron data set is the Fourier transform IR spectrum obtained by a new fiberoptic evanescent wave Fourier transform IR spectroscopy method in the range of 1480 to 1850 cm-1. Ten input features are extracted from the absorbency values in this region. A single hidden layer of neural nodes with sigmoids activation functions clusters the feature space into small subclasses and the output nodes are separated in different nonconvex classes to permit nonlinear discrimination of disease states. The output is classified as three classes: normal tissue, benign tumor and melanoma. The results obtained from the neural network pattern recognition are shown to be consistent with traditional medical diagnosis. Input features have also been extracted from the absorbency spectra using chemical factor analysis. These abstract features or factors are also used in the classification.

  19. A pilot study on diagnostic sensor networks for structure health monitoring.

    Science.gov (United States)

    2013-08-01

    The proposal was submitted in an effort to obtain some preliminary results on using sensor networks for real-time structure health : monitoring. The proposed work has twofold: to develop and validate an elective algorithm for the diagnosis of coupled...

  20. Application of artificial neural networks in computer-aided diagnosis.

    Science.gov (United States)

    Liu, Bei

    2015-01-01

    Computer-aided diagnosis is a diagnostic procedure in which a radiologist uses the outputs of computer analysis of medical images as a second opinion in the interpretation of medical images, either to help with lesion detection or to help determine if the lesion is benign or malignant. Artificial neural networks (ANNs) are usually employed to formulate the statistical models for computer analysis. Receiver operating characteristic curves are used to evaluate the performance of the ANN alone, as well as the diagnostic performance of radiologists who take into account the ANN output as a second opinion. In this chapter, we use mammograms to illustrate how an ANN model is trained, tested, and evaluated, and how a radiologist should use the ANN output as a second opinion in CAD.

  1. 78 FR 17418 - Rural Health Information Technology Network Development Grant

    Science.gov (United States)

    2013-03-21

    ... award under the Rural Health Information Technology Network Development Grant (RHITND) to Grace... relinquishing its fiduciary responsibilities for the Rural Health Information Technology Network Development... HUMAN SERVICES Health Resources and Services Administration Rural Health Information Technology Network...

  2. [Public health impact of a remote diagnosis system implemented in regional and district hospitals in Paraguay].

    Science.gov (United States)

    Galván, Pedro; Velázquez, Miguel; Benítez, Gualberto; Ortellado, José; Rivas, Ronald; Barrios, Antonio; Hilario, Enrique

    2017-06-08

    Determine the viability of a remote diagnosis system implemented to provide health care to remote and scattered populations in Paraguay. The study was conducted in all regional and general hospitals in Paraguay, and in the main district hospitals in the country's 18 health regions. Clinical data, tomographic images, sonography, and electrocardiograms (ECGs) of patients who needed a diagnosis by a specialized physician were entered into the system. This information was sent to specialists in diagnostic imaging and in cardiology for remote diagnosis and the report was then forwarded to the hospitals connected to the system. The cost-benefit and impact of the remote diagnosis tool was analyzed from the perspective of the National Health System. Between January 2014 and May 2015, a total of 34 096 remote diagnoses were made in 25 hospitals in the Ministry of Health's telemedicine system. The average unit cost of remote diagnosis was US$2.6 per ECG, tomography, and sonography, while the unit cost of "face-to-face" diagnosis was US$11.8 per ECG, US$68.6 per tomography, and US$21.5 per sonography. As a result of remote diagnosis, unit costs were 4.5 times lower for ECGs; 26.4 times lower for tomography, and 8.3 times lower for sonography. In monetary terms, implementation of the remote diagnosis system during the 16 months of the study led to average savings of US$2 420 037. Paraguay has a remote diagnosis system for electrocardiography, tomography, and sonography, using low-cost information and communications technologies (ICTs) based on free software that is scalable to other types of remote diagnostic studies of interest for public health. Implementation of remote diagnosis helped to strengthen the integrated network of health services and programs, enabling professionals to optimize their time and productivity, while improving quality, increasing access and equity, and reducing costs.

  3. Critical Review of Dual Diagnosis Training for Mental Health Professionals

    DEFF Research Database (Denmark)

    Pinderup, Pernille; Thylstrup, Birgitte; Hesse, Morten

    2016-01-01

    To review evidence on the effects of training programs in dual diagnosis treatment for mental health professionals. Three databases were searched. Included studies were evaluated by an adapted version of Kirkpatrick’s Training Evaluation Model, which evaluates participant perception of training...... level showed mixed results. Training mental health professionals in dual diagnosis treatment may have a positive effect on professional competencies and clinical practice. Any conclusion regarding the overall training effect is premature due to limitations in study designs. Future studies on the effects...... of dual diagnosis training programs for mental health professionals should involve control groups, validated measures, follow-ups, and patient outcomes....

  4. The need for European OneHealth/EcoHealth networks.

    Science.gov (United States)

    Keune, Hans; Flandroy, Lucette; Thys, Séverine; De Regge, Nick; Mori, Marcella; Antoine-Moussiaux, Nicolas; Vanhove, Maarten P M; Rebolledo, Javiera; Van Gucht, Steven; Deblauwe, Isra; Hiemstra, Wim; Häsler, Barbara; Binot, Aurélie; Savic, Sara; Ruegg, Simon R; De Vries, Sjerp; Garnier, Julie; van den Berg, Thierry

    2017-01-01

    Elaborating from the European One Health/Ecohealth (OH/EH) workshop that took place in fall 2016 and aimed to bring together different communities and explore collaborative potential, the creation of European networks focusing on the development of important OH/EH perspectives was a direct output from discussions at the end of some sessions, in particular: - A network on transdisciplinary One Health education. - A network integrating inputs from social sciences in One Health/EcoHealth actions and networks. - A network aiming at translating research findings on the Environment-Microbiome-Health axis into policy making, with a view to make healthy ecosystems a cost-effective disease prevention healthcare strategy. It was also suggested that a European Community of Practice could be initiated in order to support these several concrete networking initiatives, and to help to promote the building of other emerging initiatives.

  5. Diagnosis of Constant Faults in Read-Once Contact Networks over Finite Bases using Decision Trees

    KAUST Repository

    Busbait, Monther I.

    2014-05-01

    We study the depth of decision trees for diagnosis of constant faults in read-once contact networks over finite bases. This includes diagnosis of 0-1 faults, 0 faults and 1 faults. For any finite basis, we prove a linear upper bound on the minimum depth of decision tree for diagnosis of constant faults depending on the number of edges in a contact network over that basis. Also, we obtain asymptotic bounds on the depth of decision trees for diagnosis of each type of constant faults depending on the number of edges in contact networks in the worst case per basis. We study the set of indecomposable contact networks with up to 10 edges and obtain sharp coefficients for the linear upper bound for diagnosis of constant faults in contact networks over bases of these indecomposable contact networks. We use a set of algorithms, including one that we create, to obtain the sharp coefficients.

  6. An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis.

    Science.gov (United States)

    Li, Shaobo; Liu, Guokai; Tang, Xianghong; Lu, Jianguang; Hu, Jianjun

    2017-07-28

    Intelligent machine health monitoring and fault diagnosis are becoming increasingly important for modern manufacturing industries. Current fault diagnosis approaches mostly depend on expert-designed features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks and an improved Dempster-Shafer theory based evidence fusion. The convolutional neural networks take the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the vibration signals from two sensors as inputs. The improved D-S evidence theory is implemented via distance matrix from evidences and modified Gini Index. Extensive evaluations of the IDSCNN on the Case Western Reserve Dataset showed that our IDSCNN algorithm can achieve better fault diagnosis performance than existing machine learning methods by fusing complementary or conflicting evidences from different models and sensors and adapting to different load conditions.

  7. Network science and oral health research.

    Science.gov (United States)

    Maupome, Gerardo; McCranie, Ann

    2015-01-01

    The present overview of research methods describes a scientific enquiry paradigm that is well established in other disciplines, including health research, but that is fairly new to oral health research. Social networks analysis (SNA) or network science research is a set of relational methods purporting to identify and characterize the connections between members of a system or network, as well as the structure of the network. Persons and communities making up the members of networks have commonly been the focus of SNA studies but corporations or living organisms might just as well be organized in networks. SNA is grounded in both graphic imagery and computational models. SNA is based on the assumptions that features and structure of networks are amenable to characterization, that such information sheds light on the ways members of the network relate to each other (sharing information, diseases, norms, and so on), and that through these connections between members the overall network structure and characteristics are shaped. The overview resorts to examples specific to oral health themes and proposes a few general avenues for population-based research. © 2015 American Association of Public Health Dentistry.

  8. [Health networks for new immigrants in taiwan].

    Science.gov (United States)

    Yen, Fang-Tzu; Wu, Huei-Min

    2014-08-01

    Healthcare and studies related to new immigrants in Taiwan have been influenced by immigrant reproductive health management policy. Some nursing scholars have criticized the top-down approach as potentially not addressing the actual healthcare needs of these immigrants. Medical institutions are being called upon to provide culturally appropriate care. Using health networks as its conceptual framework, this paper explores the definition of health as perceived by recent immigrants to Taiwan and their perspectives on seeking and maintaining health. This paper uses participant observation and depth-interviews to assess how recent immigrants from Mainland China, Vietnam, and Indonesia seek health in their new homeland, evaluate the differences between the healthcare systems in their former and current countries, and recommend actions necessary to ensure the health and wellbeing of this population. The findings are grouped into three themes: "the differences between immigrants and Taiwanese in health care," "local health networks", and "transnational health networks." These themes reflect the views on health and health care of recent female immigrants to Taiwan. Through the actions and narratives of these immigrants, this paper suggests the priority concerns that immigrant agencies should address in order to maintain the health of this group. Additionally, findings give some insight into the gender and ethnic characteristics of immigrant health networks. Immigrants construct and rely upon social relations, cultural identity, and resources to maintain their wellbeing. This study contributes to transcultural nursing theory and to in-service training and helps medical practitioners and nurses provide culturally appropriate care.

  9. Malaria diagnosis and mapping with m-Health and geographic information systems (GIS: evidence from Uganda

    Directory of Open Access Journals (Sweden)

    Alberto Larocca

    2016-10-01

    Full Text Available Abstract Background Rural populations experience several barriers to accessing clinical facilities for malaria diagnosis. Increasing penetration of ICT and mobile-phones and subsequent m-Health applications can contribute overcoming such obstacles. Methods GIS is used to evaluate the feasibility of m-Health technologies as part of anti-malaria strategies. This study investigates where in Uganda: (1 malaria affects the largest number of people; (2 the application of m-Health protocol based on the mobile network has the highest potential impact. Results About 75% of the population affected by Plasmodium falciparum malaria have scarce access to healthcare facilities. The introduction of m-Health technologies should be based on the 2G protocol, as 3G mobile network coverage is still limited. The western border and the central-Southeast are the regions where m-Health could reach the largest percentage of the remote population. Six districts (Arua, Apac, Lira, Kamuli, Iganga, and Mubende could have the largest benefit because they account for about 28% of the remote population affected by falciparum malaria with access to the 2G mobile network. Conclusions The application of m-Health technologies could improve access to medical services for distant populations. Affordable remote malaria diagnosis could help to decongest health facilities, reducing costs and contagion. The combination of m-Health and GIS could provide real-time and geo-localized data transmission, improving anti-malarial strategies in Uganda. Scalability to other countries and diseases looks promising.

  10. Social networks in improvement of health care.

    Science.gov (United States)

    Masic, Izet; Sivic, Suad; Toromanovic, Selim; Borojevic, Tea; Pandza, Haris

    2012-01-01

    Social network is a social structure made of individuals or organizations associated with one or more types of interdependence (friendship, common interests, work, knowledge, prestige, etc.) which are the "nodes" of the network. Networks can be organized to exchange information, knowledge or financial assistance under the various interest groups in universities, workplaces and associations of citizens. Today the most popular and widely used networks are based on application of the Internet as the main ICT. Depending on the method of connection, their field of activity and expertise of those who participate in certain networks, the network can be classified into the following groups: a) Social Networks with personal physical connectivity (the citizens' associations, transplant networks, etc.), b) Global social internet network (Facebook, Twitter, Skype), c) specific health internet social network (forums, Health Care Forums, Healthcare Industry Forum), d) The health community internet network of non professionals (DailyStrength, CaringBridge, CarePages, MyFamilyHealth), e) Scientific social internet network (BiomedExperts, ResearchGate, iMedExchange), f) Social internet network which supported professionals (HealthBoards, Spas and Hope Association of Disabled and diabetic Enurgi), g) Scientific medical internet network databases in the system of scientific and technical information (CC, Pubmed/Medline, Excerpta Medica/EMBASE, ISI Web Knowledge, EBSCO, Index Copernicus, Social Science Index, etc.). The information in the network are exchanged in real time and in a way that has until recently been impossible in real life of people in the community. Networks allow tens of thousands of specific groups of people performing a series of social, professional and educational activities in the place of living and housing, place of work or other locations where individuals are. Network provides access to information related to education, health, nutrition, drugs, procedures

  11. Automated diagnosis of rolling bearings using MRA and neural networks

    Science.gov (United States)

    Castejón, C.; Lara, O.; García-Prada, J. C.

    2010-01-01

    Any industry needs an efficient predictive plan in order to optimize the management of resources and improve the economy of the plant by reducing unnecessary costs and increasing the level of safety. A great percentage of breakdowns in productive processes are caused by bearings. They begin to deteriorate from early stages of their functional life, also called the incipient level. This manuscript develops an automated diagnosis of rolling bearings based on the analysis and classification of signature vibrations. The novelty of this work is the application of the methodology proposed for data collected from a quasi-real industrial machine, where rolling bearings support the radial and axial loads the bearings are designed for. Multiresolution analysis (MRA) is used in a first stage in order to extract the most interesting features from signals. Features will be used in a second stage as inputs of a supervised neural network (NN) for classification purposes. Experimental results carried out in a real system show the soundness of the method which detects four bearing conditions (normal, inner race fault, outer race fault and ball fault) in a very incipient stage.

  12. Neural network feature selection for breast cancer diagnosis

    Science.gov (United States)

    Kocur, Catherine M.; Rogers, Steven K.; Bauer, Kenneth W., Jr.; Steppe, Jean M.; Hoffmeister, Jeffrey W.

    1995-04-01

    More than 50 million women over the age of 40 are currently at risk for breast cancer in the United States. Computer-aided diagnosis, as a second opinion to radiologists, will aid in decreasing the number of false readings of mammograms. Neural network benefits are exploited at both the classification and feature selection stages in the development of a computer-aided breast cancer diagnostic system. The multilayer perceptron is used to classify and contrast three features (angular second moment, eigenmasses, and wavelets) developed to distinguish benign from malignant lesion in a database of 94 difficult-to-diagnose digitized microcalcification cases. System performance of 74 percent correct classifications is achieved. Feature selection techniques are presented which further improve performance. Neural and decision boundary-based methods are implemented, compared, and validated to isolate and remove useless features. The contribution from this analysis is an increase to 88 percent correct classification in system performance. These feature selection techniques can also process risk factor data.

  13. Motor Fault Diagnosis Based on Short-time Fourier Transform and Convolutional Neural Network

    Science.gov (United States)

    Wang, Li-Hua; Zhao, Xiao-Ping; Wu, Jia-Xin; Xie, Yang-Yang; Zhang, Yong-Hong

    2017-11-01

    With the rapid development of mechanical equipment, the mechanical health monitoring field has entered the era of big data. However, the method of manual feature extraction has the disadvantages of low efficiency and poor accuracy, when handling big data. In this study, the research object was the asynchronous motor in the drivetrain diagnostics simulator system. The vibration signals of different fault motors were collected. The raw signal was pretreated using short time Fourier transform (STFT) to obtain the corresponding time-frequency map. Then, the feature of the time-frequency map was adaptively extracted by using a convolutional neural network (CNN). The effects of the pretreatment method, and the hyper parameters of network diagnostic accuracy, were investigated experimentally. The experimental results showed that the influence of the preprocessing method is small, and that the batch-size is the main factor affecting accuracy and training efficiency. By investigating feature visualization, it was shown that, in the case of big data, the extracted CNN features can represent complex mapping relationships between signal and health status, and can also overcome the prior knowledge and engineering experience requirement for feature extraction, which is used by traditional diagnosis methods. This paper proposes a new method, based on STFT and CNN, which can complete motor fault diagnosis tasks more intelligently and accurately.

  14. Social networking sites and adolescent health.

    Science.gov (United States)

    Moreno, Megan A; Kolb, Jennifer

    2012-06-01

    Social networking sites are popular among and consistently used by adolescents. These sites present benefits as well as risks to adolescent health. Recently, pediatric providers have also considered the benefits and risks of using social networking sites in their own practices. Copyright © 2012 Elsevier Inc. All rights reserved.

  15. Embedding Design in a Mental Health Network

    OpenAIRE

    Pierri, Paola; Warwick, Laura; Garber, Jake

    2016-01-01

    Service Design in Mind (SDiM) is a programme run by Mind, the national mental health charity. The programme aims to embed service design techniques and methods into a network of local voluntary organisations that deliver mental health services. This case study describes how the programme, based on the idea that everybody designs and everyone can be a designer, aimed to create a diffused design culture (Manzini, 2015) across the charity and its network. By capitalising on existing internal des...

  16. Service network analysis for agricultural mental health

    Directory of Open Access Journals (Sweden)

    Fuller Jeffrey D

    2009-05-01

    Full Text Available Abstract Background Farmers represent a subgroup of rural and remote communities at higher risk of suicide attributed to insecure economic futures, self-reliant cultures and poor access to health services. Early intervention models are required that tap into existing farming networks. This study describes service networks in rural shires that relate to the mental health needs of farming families. This serves as a baseline to inform service network improvements. Methods A network survey of mental health related links between agricultural support, health and other human services in four drought declared shires in comparable districts in rural New South Wales, Australia. Mental health links covered information exchange, referral recommendations and program development. Results 87 agencies from 111 (78% completed a survey. 79% indicated that two thirds of their clients needed assistance for mental health related problems. The highest mean number of interagency links concerned information exchange and the frequency of these links between sectors was monthly to three monthly. The effectiveness of agricultural support and health sector links were rated as less effective by the agricultural support sector than by the health sector (p Conclusion Aligning with agricultural agencies is important to build effective mental health service pathways to address the needs of farming populations. Work is required to ensure that these agricultural support agencies have operational and effective links to primary mental health care services. Network analysis provides a baseline to inform this work. With interventions such as local mental health training and joint service planning to promote network development we would expect to see over time an increase in the mean number of links, the frequency in which these links are used and the rated effectiveness of these links.

  17. The Structure and Quality of Social Network Support among Mental Health Consumers of Clubhouse Programs

    Science.gov (United States)

    Pernice-Duca, Francesca M.

    2008-01-01

    This study explored the structure and quality of social network support among a group of adult consumers of community-based mental health programs known as "clubhouses". The structure and quality of social network support was also examined by diagnosis, specifically between consumers living with and without schizophrenia. The study…

  18. Implementing nutrition diagnosis at a multisite health care organization.

    Science.gov (United States)

    Van Heukelom, Holly; Fraser, Valli; Koh, Jiak-Chin; McQueen, Kay; Vogt, Kara; Johnson, Frances

    2011-01-01

    The American Dietetic Association Nutrition Care Process (NCP) is designed to improve patient care and interdisciplinary communication through the consistent use of standardized nutrition language. Supported by Dietitians of Canada, the NCP has been gaining prominence across Canada. In spring 2009, registered dietitians at Providence Health Care, an academic, multisite health care organization in Vancouver, British Columbia, began using the NCP with a focus on nutrition diagnosis. The success of nutrition diagnosis at Providence Health Care has depended on support from the Clinical Nutrition Department leadership, commitment from the NCP champions, regularly scheduled lunch-and-learn sessions, revised nutrition assessment forms with a section for nutrition diagnosis statements, and the Pocket Guide for International Dietetics & Nutrition Terminology (IDNT) Reference Manual. Audit results from June through August 2010 showed a 92% nutrition diagnosis completion rate for acute-care and long-term care sites within Providence Health Care. Ongoing audits will be used to evaluate the accuracy and quality of nutrition diagnosis statements. This evaluation will allow Providence Health Care dietitians to move forward with nutrition intervention.

  19. Data for decision making in networked health

    Directory of Open Access Journals (Sweden)

    Christian Bourret

    2006-06-01

    Full Text Available In developed countries, nowadays we live in a networked society: a society of information, knowledge and services (Castells, 1996, with strong specificities in the Health field (Bourret, 2003, Silber, 2003. The World Health Organization (WHO has outlined the importance of information for improving health for all. However, financial resources remain limited. Health costs represent 11% of GNP in France, Germany, Switzerland and Canada, 14% in the USA, and 7.5% in Spain and the United Kingdom. Governments, local powers, health or insurance organizations therefore face difficult choices in terms of opportunities and priorities, and for that they need specific and valuable data. Firstly, this paper provide a comprehensive overview of our networked society and the appointment of ICT (Information and Communication Technologies and Health (in other words e-Health in a perspective of needs and uses at the micro, meso, and macro levels. We point out the main challenges of development of Nationwide Health Information Network both in the US, UK and France. Then we analyze the main issues about data for Decision Making in Networked Health: coordination and evaluation. In the last sections, we use an Information System perspective to investigate the three interoperability layers (micro, meso and macro. We analyze the requirements and challenges to design an interoperability global architecture which supports different kinds of interactions; then we focus on the harmonization efforts provided at several levels. Finally, we identify common methodological and engineering issues.

  20. Water Diagnosis in Shrimp Aquaculture based on Neural Network

    Science.gov (United States)

    Carbajal Hernández, J. J.; Sánchez Fernández, L. P.

    2007-05-01

    In many countries, the shrimp aquaculture has not advanced computational systems to supervise the artificial habitat of the farms and laboratories. A computational system of this type helps significantly to improve the environmental conditions and to elevate the production and its quality. The main idea of this study is the creation of a system using an artificial neural network (ANN), which can help to recognize patterns of problems and their evolution in shrimp aquaculture, and thus to respond with greater rapidity against the negative effects. Bad control on the shrimp artificial habitat produces organisms with high stress and as consequence losses in their defenses. It generate low nutrition, low reproduction or worse still, they prearrange to acquire lethal diseases. The proposed system helps to control this problem. Environmental variables as pH, temperature, salinity, dissolved oxygen and turbidity have an important effect in the suitable growth of the shrimps and influence in their health. However, the exact mathematical model of this relationship is unspecified; an ANN is useful for establishing a relationship between these variables and to classify a status that describes a problem into the farm. The data classification is made to recognize and to quantify two states within the pool: a) Normal: Everything is well. b) Risk: One, some or all environmental variables are outside of the allowed interval, which generates problems. The neural network will have to recognize the state and to quantify it, in others words, how normal or risky it is, which allows finding trend of the water quality. A study was developed for designing a software tool that allows recognizing the status of the water quality and control problems for the environment into the pond.

  1. Advanced neural network-based computational schemes for robust fault diagnosis

    CERN Document Server

    Mrugalski, Marcin

    2014-01-01

    The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems. A part of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault diagnosis schemes as well as the importance of robustness. The book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. The book is also devoted to advanced schemes of description of neural model uncertainty. In particular, the methods of computation of neural networks uncertainty with robust parameter estimation are presented. Moreover, a novel approach for system identification with the state-space GMDH neural network is delivered. All the concepts described in this book are illustrated by both simple academic illustrative examples and practica...

  2. Social network fragmentation and community health.

    Science.gov (United States)

    Chami, Goylette F; Ahnert, Sebastian E; Kabatereine, Narcis B; Tukahebwa, Edridah M

    2017-09-05

    Community health interventions often seek to intentionally destroy paths between individuals to prevent the spread of infectious diseases. Immunizing individuals through direct vaccination or the provision of health education prevents pathogen transmission and the propagation of misinformation concerning medical treatments. However, it remains an open question whether network-based strategies should be used in place of conventional field approaches to target individuals for medical treatment in low-income countries. We collected complete friendship and health advice networks in 17 rural villages of Mayuge District, Uganda. Here we show that acquaintance algorithms, i.e., selecting neighbors of randomly selected nodes, were systematically more efficient in fragmenting all networks than targeting well-established community roles, i.e., health workers, village government members, and schoolteachers. Additionally, community roles were not good proxy indicators of physical proximity to other households or connections to many sick people. We also show that acquaintance algorithms were effective in offsetting potential noncompliance with deworming treatments for 16,357 individuals during mass drug administration (MDA). Health advice networks were destroyed more easily than friendship networks. Only an average of 32% of nodes were removed from health advice networks to reduce the percentage of nodes at risk for refusing treatment in MDA to below 25%. Treatment compliance of at least 75% is needed in MDA to control human morbidity attributable to parasitic worms and progress toward elimination. Our findings point toward the potential use of network-based approaches as an alternative to role-based strategies for targeting individuals in rural health interventions.

  3. Do governance choices matter in health care networks?: an exploratory configuration study of health care networks

    Science.gov (United States)

    2013-01-01

    Background Health care networks are widely used and accepted as an organizational form that enables integrated care as well as dealing with complex matters in health care. However, research on the governance of health care networks lags behind. The research aim of our study is to explore the type and importance of governance structure and governance mechanisms for network effectiveness. Methods The study has a multiple case study design and covers 22 health care networks. Using a configuration view, combinations of network governance and other network characteristics were studied on the level of the network. Based on interview and questionnaire data, network characteristics were identified and patterns in the data looked for. Results Neither a dominant (or optimal) governance structure or mechanism nor a perfect fit among governance and other characteristics were revealed, but a number of characteristics that need further study might be related to effective networks such as the role of governmental agencies, legitimacy, and relational, hierarchical, and contractual governance mechanisms as complementary factors. Conclusions Although the results emphasize the situational character of network governance and effectiveness, they give practitioners in the health care sector indications of which factors might be more or less crucial for network effectiveness. PMID:23800334

  4. Do governance choices matter in health care networks?: an exploratory configuration study of health care networks.

    Science.gov (United States)

    Willem, Annick; Gemmel, Paul

    2013-06-24

    Health care networks are widely used and accepted as an organizational form that enables integrated care as well as dealing with complex matters in health care. However, research on the governance of health care networks lags behind. The research aim of our study is to explore the type and importance of governance structure and governance mechanisms for network effectiveness. The study has a multiple case study design and covers 22 health care networks. Using a configuration view, combinations of network governance and other network characteristics were studied on the level of the network. Based on interview and questionnaire data, network characteristics were identified and patterns in the data looked for. Neither a dominant (or optimal) governance structure or mechanism nor a perfect fit among governance and other characteristics were revealed, but a number of characteristics that need further study might be related to effective networks such as the role of governmental agencies, legitimacy, and relational, hierarchical, and contractual governance mechanisms as complementary factors. Although the results emphasize the situational character of network governance and effectiveness, they give practitioners in the health care sector indications of which factors might be more or less crucial for network effectiveness.

  5. Auxiliary health diagnosis method for lead-acid battery

    Energy Technology Data Exchange (ETDEWEB)

    Sun, Yu-Hua; Jou, Hurng-Liahng; Wu, Kuen-Der [Department of Electrical Engineering, National Kaohsiung University of Applied Sciences, 415 Chien-Kung Road, Kaohsiung 80782 (China); Wu, Jinn-Chang [Department of Microelectronic Engineering, National Kaohsiung Marine University, 142, Haijhuan Road, Nanzih District, Kaohsiung 81143 (China)

    2010-12-15

    This paper proposes a health auxiliary diagnosis method for the lead-acid battery unit. The proposed method is based on Approximate Entropy (ApEn). Since ApEn can quantify the regularity of a data sequence and the discharging curve for a health lead-acid battery unit is smooth, the proposed method can detect the degradation of battery unit caused by the internal short, opening of internal shorted or cell undergoing reversal using the distorted discharging curve. Aging experiments for the lead-acid battery are developed to verify the proposed method. The experimental results verify that the proposed health auxiliary diagnosis method can diagnosis the degradation of battery unit caused by the internal short, opening of internal shorted or cell undergoing reversal. (author)

  6. Diagnosis of constant faults in read-once contact networks over finite bases

    KAUST Repository

    Busbait, Monther I.

    2015-03-01

    We study the depth of decision trees for diagnosis of constant 0 and 1 faults in read-once contact networks over finite bases containing only indecomposable networks. For each basis, we obtain a linear upper bound on the minimum depth of decision trees depending on the number of edges in the networks. For bases containing networks with at most 10 edges we find coefficients for linear bounds which are close to sharp. © 2014 Elsevier B.V. All rights reserved.

  7. Diagnosis of three types of constant faults in read-once contact networks over finite bases

    KAUST Repository

    Busbait, Monther I.

    2016-03-24

    We study the depth of decision trees for diagnosis of three types of constant faults in read-once contact networks over finite bases containing only indecomposable networks. For each basis and each type of faults, we obtain a linear upper bound on the minimum depth of decision trees depending on the number of edges in networks. For bases containing networks with at most 10 edges, we find sharp coefficients for linear bounds.

  8. 77 FR 62243 - Rural Health Network Development Program

    Science.gov (United States)

    2012-10-12

    ... Administration Rural Health Network Development Program AGENCY: Health Resources and Services Administration...-competitive replacement award under the Rural Health Network Development Program to the Siloam Springs... through the Rural Health Network Development Grant Program are to improve the capacity of network members...

  9. 42 CFR 485.603 - Rural health network.

    Science.gov (United States)

    2010-10-01

    ... 42 Public Health 5 2010-10-01 2010-10-01 false Rural health network. 485.603 Section 485.603... Participation: Critical Access Hospitals (CAHs) § 485.603 Rural health network. A rural health network is an... quality assurance with at least— (1) One hospital that is a member of the network when applicable; (2) One...

  10. Improved probabilistic neural networks with self-adaptive strategies for transformer fault diagnosis problem

    Directory of Open Access Journals (Sweden)

    Jiao-Hong Yi

    2016-01-01

    Full Text Available Probabilistic neural network has successfully solved all kinds of engineering problems in various fields since it is proposed. In probabilistic neural network, Spread has great influence on its performance, and probabilistic neural network will generate bad prediction results if it is improperly selected. It is difficult to select the optimal manually. In this article, a variant of probabilistic neural network with self-adaptive strategy, called self-adaptive probabilistic neural network, is proposed. In self-adaptive probabilistic neural network, Spread can be self-adaptively adjusted and selected and then the best selected Spread is used to guide the self-adaptive probabilistic neural network train and test. In addition, two simplified strategies are incorporated into the proposed self-adaptive probabilistic neural network with the aim of further improving its performance and then two versions of simplified self-adaptive probabilistic neural network (simplified self-adaptive probabilistic neural networks 1 and 2 are proposed. The variants of self-adaptive probabilistic neural networks are further applied to solve the transformer fault diagnosis problem. By comparing them with basic probabilistic neural network, and the traditional back propagation, extreme learning machine, general regression neural network, and self-adaptive extreme learning machine, the results have experimentally proven that self-adaptive probabilistic neural networks have a more accurate prediction and better generalization performance when addressing the transformer fault diagnosis problem.

  11. Model-Based Fault Diagnosis in Electric Drive Inverters Using Artificial Neural Network

    National Research Council Canada - National Science Library

    Masrur, Abul; Chen, ZhiHang; Zhang, Baifang; Jia, Hongbin; Murphey, Yi-Lu

    2006-01-01

    .... A normal model and various faulted models of the inverter-motor combination were developed, and voltages and current signals were generated from those models to train an artificial neural network for fault diagnosis...

  12. Research on Big Data Attribute Selection Method in Submarine Optical Fiber Network Fault Diagnosis Database

    Directory of Open Access Journals (Sweden)

    Chen Ganlang

    2017-11-01

    Full Text Available At present, in the fault diagnosis database of submarine optical fiber network, the attribute selection of large data is completed by detecting the attributes of the data, the accuracy of large data attribute selection cannot be guaranteed. In this paper, a large data attribute selection method based on support vector machines (SVM for fault diagnosis database of submarine optical fiber network is proposed. Mining large data in the database of optical fiber network fault diagnosis, and calculate its attribute weight, attribute classification is completed according to attribute weight, so as to complete attribute selection of large data. Experimental results prove that ,the proposed method can improve the accuracy of large data attribute selection in fault diagnosis database of submarine optical fiber network, and has high use value.

  13. Breast Cancer Diagnosis using Artificial Neural Networks with Extreme Learning Techniques

    OpenAIRE

    Chandra Prasetyo Utomo; Aan Kardiana; Rika Yuliwulandari

    2014-01-01

    Breast cancer is the second cause of dead among women. Early detection followed by appropriate cancer treatment can reduce the deadly risk. Medical professionals can make mistakes while identifying a disease. The help of technology such as data mining and machine learning can substantially improve the diagnosis accuracy. Artificial Neural Networks (ANN) has been widely used in intelligent breast cancer diagnosis. However, the standard Gradient-Based Back Propagation Artificial Neural Networks...

  14. Neural Network Expert System in the Application of Tower Fault Diagnosis

    Science.gov (United States)

    Liu, Xiaoyang; Xia, Zhongwu; Tao, Zhiyong; Zhao, Zhenlian

    For the corresponding fuzzy relationship between the fault symptoms and the fault causes in the process of tower crane operation, this paper puts forward a kind of rapid new method of fast detection and diagnosis for common fault based on neural network expert system. This paper makes full use of expert system and neural network advantages, and briefly introduces the structure, function, algorithm and realization of the adopted system. Results show that the new algorithm is feasible and can achieve rapid faults diagnosis.

  15. The Usage Of Artificial Neural Networks Method In The Diagnosis Of Rheumatoid Arthritis

    OpenAIRE

    Tok, Kadir; Saritas, Ismail

    2016-01-01

    In this study, artificial neural networks (ANN) method is used for the diagnosis of rheumatoid arthritis in order to support medical diagnostics. For the diagnosis of rheumatoid arthritis, backpropagation algorithm was examined in Matlab R2015b environment in artificial neural networks. With the system, the data in a data set, which are received from the patients with rheumatoid arthritis and from the people who are not suffering from rheumatoid arthritis, are classified successfully. Also, A...

  16. Privacy policies for health social networking sites.

    Science.gov (United States)

    Li, Jingquan

    2013-01-01

    Health social networking sites (HSNS), virtual communities where users connect with each other around common problems and share relevant health data, have been increasingly adopted by medical professionals and patients. The growing use of HSNS like Sermo and PatientsLikeMe has prompted public concerns about the risks that such online data-sharing platforms pose to the privacy and security of personal health data. This paper articulates a set of privacy risks introduced by social networking in health care and presents a practical example that demonstrates how the risks might be intrinsic to some HSNS. The aim of this study is to identify and sketch the policy implications of using HSNS and how policy makers and stakeholders should elaborate upon them to protect the privacy of online health data.

  17. Social networks of experientially similar others: formation, activation, and consequences of network ties on the health care experience.

    Science.gov (United States)

    Gage, Elizabeth A

    2013-10-01

    Research documents that interactions among experientially similar others (individuals facing a common stressor) shape health care behavior and ultimately health outcomes. However, we have little understanding of how ties among experientially similar others are formed, what resources and information flows through these networks, and how network embeddedness shapes health care behavior. This paper uses in-depth interviews with 76 parents of pediatric cancer patients to examine network ties among experientially similar others after a serious medical diagnosis. Interviews were conducted between August 2009 and May 2011. Findings demonstrate that many parents formed ties with other families experiencing pediatric cancer, and that information and resources were exchanged during the everyday activities associated with their child's care. Network flows contained emotional support, caregiving strategies, information about second opinions, health-related knowledge, and strategies for navigating the health care system. Diffusion of information, resources, and support occurred through explicit processes (direct information and support exchanges) and implicit processes (parents learning through observing other families). Network flows among parents shaped parents' perceptions of the health care experience and their role in their child's care. These findings contribute to the social networks and social support literatures by elucidating the mechanisms through which network ties among experientially similar others influence health care behavior and experiences. Copyright © 2012 Elsevier Ltd. All rights reserved.

  18. Establishing a laboratory network of influenza diagnosis in Indonesia: an experience from the avian flu (H5N1 outbreak

    Directory of Open Access Journals (Sweden)

    Setiawaty V

    2012-08-01

    Full Text Available Vivi Setiawaty, Krisna NA Pangesti, Ondri D SampurnoNational Institute of Health Research and Development, Ministry of Health, the Republic of Indonesia, Jakarta, IndonesiaAbstract: Indonesia has been part of the global influenza surveillance since the establishment of a National Influenza Center (NIC at the National Institute of Health Research and Development (NIHRD by the Indonesian Ministry of Health in 1975. When the outbreak of avian influenza A (H5N1 occurred, the NIC and US Naval Medical Research Unit 2 were the only diagnostic laboratories equipped for etiology confirmation. The large geographical area of the Republic of Indonesia poses a real challenge to provide prompt and accurate diagnosis nationally. This was the main reason to establish a laboratory network for H5N1 diagnosis in Indonesia. Currently, 44 laboratories have been included in the network capable of performing polymerase chain reaction testing for influenza A. Diagnostic equipment and standard procedures of biosafety and biosecurity of handling specimens have been adopted largely from World Health Organization recommendations.Keywords: influenza, laboratory, networking

  19. Railway Track Circuit Fault Diagnosis Using Recurrent Neural Networks

    NARCIS (Netherlands)

    de Bruin, T.D.; Verbert, K.A.J.; Babuska, R.

    2017-01-01

    Timely detection and identification of faults in railway track circuits are crucial for the safety and availability of railway networks. In this paper, the use of the long-short-term memory (LSTM) recurrent neural network is proposed to accomplish these tasks based on the commonly available

  20. Railway track circuit fault diagnosis using recurrent neural networks

    NARCIS (Netherlands)

    de Bruin, T.D.; Verbert, K.A.J.; Babuska, R.

    2017-01-01

    Timely detection and identification of faults in railway track circuits are crucial for the safety and availability of railway networks. In this paper, the use of the long-short-term memory (LSTM) recurrent neural network is proposed to accomplish these tasks based on the commonly available

  1. Disk hernia and spondylolisthesis diagnosis using biomechanical features and neural network.

    Science.gov (United States)

    Oyedotun, Oyebade K; Olaniyi, Ebenezer O; Khashman, Adnan

    2016-01-01

    Artificial neural networks have found applications in various areas of medical diagnosis. The capability of neural networks to learn medical data, mining useful and complex relationships that exist between attributes has earned it a major domain in decision support systems. This paper proposes a fast automatic system for the diagnosis of disk hernia and spondylolisthesis using biomechanical features and neural network. Such systems as described within this work allow the diagnosis of new cases using trained neural networks; patients are classified as either having disk hernia, spondylolisthesis, or normal. Generally, both disk hernia and spondylolisthesis present similar symptoms; hence, diagnosis is prone to inter-misclassification error. This work is significant in that the proposed systems are capable of making fast decisions on such somewhat difficult diagnoses with reasonable accuracies. Feedforward neural network and radial basis function networks are trained on data obtained from a public database. The results obtained within this research are promising and show that neural networks can find applications as efficient and effective expert systems for the diagnosis of disk hernia and spondylolisthesis.

  2. Understanding power relationships in health care networks.

    Science.gov (United States)

    Addicott, Rachael; Ferlie, Ewan

    2007-01-01

    The purpose of this paper is to show that networks are emerging as a new, innovative organisational form in the UK public sector. The emergence of more network-based modes of organisation is apparent across many public services in the UK but has been particularly evident in the health sector or NHS. Cancer services represent an important and early example, where managed clinical networks (MCNs) for cancer have been established by the UK National Health Service (NHS) as a means of streamlining patient pathways and fostering the flow of knowledge and good practice between the many different professions and organisations involved in care. There is very little understanding of the role of power in public sector networks, and in particular MCNs. This paper aims to explore and theorise the nature of power relations within a network model of governance. The paper discusses evidence from five case studies of MCNs for cancer in London. The findings in this paper demonstrate that a model of bounded pluralism can be used to understand power relations within London MCNs. However, power over the development of policy and strategic direction is instead exerted in a top-down manner by the government (e.g. Department of Health) and its associated national bodies. The paper supports the argument that the introduction of rhetoric of a more collaborative approach to the management of public services has not been enough to destabilise the embedded managerialist framework. This paper uses empirical data from five case studies of managed clinical networks to theorise the nature of power relations in the development and implementation of network reform in cancer services. Also, there is limited understanding of the nature of power relations in network relationships, particularly in relation to the public sector.

  3. Distributed Diagnosis in Uncertain Environments Using Dynamic Bayesian Networks

    Data.gov (United States)

    National Aeronautics and Space Administration — This paper presents a distributed Bayesian fault diagnosis scheme for physical systems. Our diagnoser design is based on a procedure for factoring the global system...

  4. Four Challenges That Global Health Networks Face

    Directory of Open Access Journals (Sweden)

    Jeremy Shiffman

    2017-04-01

    Full Text Available Global health networks, webs of individuals and organizations with a shared concern for a particular condition, have proliferated over the past quarter century. They differ in their effectiveness, a factor that may help explain why resource allocations vary across health conditions and do not correspond closely with disease burden. Drawing on findings from recently concluded studies of eight global health networks—addressing alcohol harm, early childhood development (ECD, maternal mortality, neonatal mortality, pneumonia, surgically-treatable conditions, tobacco use, and tuberculosis—I identify four challenges that networks face in generating attention and resources for the conditions that concern them. The first is problem definition: generating consensus on what the problem is and how it should be addressed. The second is positioning: portraying the issue in ways that inspire external audiences to act. The third is coalition-building: forging alliances with these external actors, particularly ones outside the health sector. The fourth is governance: establishing institutions to facilitate collective action. Research indicates that global health networks that effectively tackle these challenges are more likely to garner support to address the conditions that concern them. In addition to the effectiveness of networks, I also consider their legitimacy, identifying reasons both to affirm and to question their right to exert power.

  5. An interpretation of neural networks as inference engines with application to transformer failure diagnosis

    Energy Technology Data Exchange (ETDEWEB)

    Castro, Adriana R. Garcez; Miranda, Vladimiro [Instituto de Engenharia de Sistemas e Computadores do Porto, INESC Porto (Portugal)

    2005-12-01

    An artificial neural network concept has been developed for transformer fault diagnosis using dissolved gas-in-oil analysis (DGA). A new methodology for mapping the neural network into a rule-based inference system is described. This mapping makes explicit the knowledge implicitly captured by the neural network during the learning stage, by transforming it into a Fuzzy Inference System. Some studies are reported, illustrating the good results obtained. (author)

  6. A belief network approach for development of a nuclear power plant diagnosis system

    Energy Technology Data Exchange (ETDEWEB)

    Hwang, I. K.; Kim, J. T.; Lee, D. Y.; Jung, C. H.; Kim, J. Y.; Lee, J. S.; Ham, C. S. [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

    1998-12-31

    Belief network (or Bayesian network) based on Bayes` rule in probabilistic theory can be applied to the reasoning of diagnostic system. This paper describes the basic theory of concept and feasibility of using the network for diagnosis of nuclear power plants. An example shows that the probabilities of root causes of a failure are calculated from the measured or believed evidences. 6 refs., 3 figs. (Author)

  7. The Application of Radial Basis Function (RBF) Neural Network for Mechanical Fault Diagnosis of Gearbox

    Science.gov (United States)

    Wang, Pengbo

    2017-11-01

    In this paper, the radial basis function (RBF) neural network is used for the mechanical fault diagnosis of a gearbox. We introduce the basic principles of the RBF neural network which is used for pattern classification and features a fast learning pace and strong nonlinear mapping capability; thus, it can be employed for fault diagnosis. The gearbox is a widely-used piece of equipment in engineering, and diagnosing mechanical faults is of great significance for engineers. A numerical example is presented to demonstrate the capability of the proposed method. The results indicate that the mechanical faults of a gearbox can be correctly diagnosed with a trained RBF neural network.

  8. Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data

    Science.gov (United States)

    Jia, Feng; Lei, Yaguo; Lin, Jing; Zhou, Xin; Lu, Na

    2016-05-01

    Aiming to promptly process the massive fault data and automatically provide accurate diagnosis results, numerous studies have been conducted on intelligent fault diagnosis of rotating machinery. Among these studies, the methods based on artificial neural networks (ANNs) are commonly used, which employ signal processing techniques for extracting features and further input the features to ANNs for classifying faults. Though these methods did work in intelligent fault diagnosis of rotating machinery, they still have two deficiencies. (1) The features are manually extracted depending on much prior knowledge about signal processing techniques and diagnostic expertise. In addition, these manual features are extracted according to a specific diagnosis issue and probably unsuitable for other issues. (2) The ANNs adopted in these methods have shallow architectures, which limits the capacity of ANNs to learn the complex non-linear relationships in fault diagnosis issues. As a breakthrough in artificial intelligence, deep learning holds the potential to overcome the aforementioned deficiencies. Through deep learning, deep neural networks (DNNs) with deep architectures, instead of shallow ones, could be established to mine the useful information from raw data and approximate complex non-linear functions. Based on DNNs, a novel intelligent method is proposed in this paper to overcome the deficiencies of the aforementioned intelligent diagnosis methods. The effectiveness of the proposed method is validated using datasets from rolling element bearings and planetary gearboxes. These datasets contain massive measured signals involving different health conditions under various operating conditions. The diagnosis results show that the proposed method is able to not only adaptively mine available fault characteristics from the measured signals, but also obtain superior diagnosis accuracy compared with the existing methods.

  9. Signs for early diagnosis of heart failure in primary health care

    Science.gov (United States)

    Devroey, Dirk; Van Casteren, Viviane

    2011-01-01

    Objective The current guidelines for the diagnosis of heart failure (HF) are based on studies of hospital-based patients. The aim of this study is to describe the symptoms, clinical signs, and diagnostic procedures confirming the diagnosis of HF in primary health care. Materials/subjects and methods Data were prospectively collected during a 2-year period by a nationwide network of sentinel practices. All adult patients without known HF, for which the diagnosis of HF was clinically suspected for the first time, were registered. When diagnosed, HF was confirmed after 1 month. Results 754 patients with a suspicion of HF were recorded. The diagnosis of HF was confirmed for 74% of the patients. The average age of the patients with confirmed HF was 77.7 years, and for those without HF 75.6 years (P = 0.018). From a logistic regression, breathlessness on exercise (P signs most associated with HF, were pulmonary rales (P signs may occur in patients with HF. However, the occurrence of peripheral edema, breathlessness on exercise, or pulmonary rales, are highly suggestive for HF when diagnosed in primary health care, as is the case in hospital-admitted patients. The diagnosis of HF was often left unconfirmed by an echocardiogram and/or an electrocardiogram. PMID:21966224

  10. Metabolic resting-state brain networks in health and disease.

    Science.gov (United States)

    Spetsieris, Phoebe G; Ko, Ji Hyun; Tang, Chris C; Nazem, Amir; Sako, Wataru; Peng, Shichun; Ma, Yilong; Dhawan, Vijay; Eidelberg, David

    2015-02-24

    The delineation of resting state networks (RSNs) in the human brain relies on the analysis of temporal fluctuations in functional MRI signal, representing a small fraction of total neuronal activity. Here, we used metabolic PET, which maps nonfluctuating signals related to total activity, to identify and validate reproducible RSN topographies in healthy and disease populations. In healthy subjects, the dominant (first component) metabolic RSN was topographically similar to the default mode network (DMN). In contrast, in Parkinson's disease (PD), this RSN was subordinated to an independent disease-related pattern. Network functionality was assessed by quantifying metabolic RSN expression in cerebral blood flow PET scans acquired at rest and during task performance. Consistent task-related deactivation of the "DMN-like" dominant metabolic RSN was observed in healthy subjects and early PD patients; in contrast, the subordinate RSNs were activated during task performance. Network deactivation was reduced in advanced PD; this abnormality was partially corrected by dopaminergic therapy. Time-course comparisons of DMN loss in longitudinal resting metabolic scans from PD and Alzheimer's disease subjects illustrated that significant reductions appeared later for PD, in parallel with the development of cognitive dysfunction. In contrast, in Alzheimer's disease significant reductions in network expression were already present at diagnosis, progressing over time. Metabolic imaging can directly provide useful information regarding the resting organization of the brain in health and disease.

  11. An artificial multilayer perceptron neural network for diagnosis of proximal dental caries.

    Science.gov (United States)

    Devito, Karina Lopes; de Souza Barbosa, Flávio; Felippe Filho, Waldir Neme

    2008-12-01

    To evaluate if the application of an artificial intelligence model, a multilayer perceptron neural network, improves the radiographic diagnosis of proximal caries. One hundred sixty radiographic images of proximal surfaces of extracted human teeth were assessed regarding the presence of caries by 25 examiners. Examination of the radiographs was used to feed the neural network, and the corresponding teeth were sectioned and assessed under optical microscope (gold standard). This gold standard served to teach the neural network to diagnose caries on the basis of the radiographic exams. To gauge the network's capacity for generalization, i.e., its performance with new cases, data were divided into 3 subgroups for training, test, and cross-validation. The area under the receiver operating characteristic (ROC) curve allowed comparison of efficacy between network and examiner diagnosis. For the best of the 25 examiners, the ROC curve area was 0.717, whereas network diagnosis achieved an ROC curve area of 0.884, indicating a sizeable improvement in proximal caries diagnosis. Considering all examiners, the diagnostic improvement using the neural network was 39.4%.

  12. Management continuity in local health networks

    Directory of Open Access Journals (Sweden)

    Mylaine Breton

    2012-04-01

    Full Text Available Introduction: Patients increasingly receive care from multiple providers in a variety of settings. They expect management continuity that crosses boundaries and bridges gaps in the healthcare system. To our knowledge, little research has been done to assess coordination across organizational and professional boundaries from the patients' perspective. Our objective was to assess whether greater local health network integration is associated with management continuity as perceived by patients. Method: We used the data from a research project on the development and validation of a generic and comprehensive continuity measurement instrument that can be applied to a variety of patient conditions and settings. We used the results of a cross-sectional survey conducted in 2009 with 256 patients in two local health networks in Quebec, Canada. We compared four aspects of management continuity between two contrasting network types (highly integrated vs. poorly integrated. Results: The scores obtained in the highly integrated network are better than those of the poorly integrated network on all dimensions of management continuity (coordinator role, role clarity and coordination between clinics, and information gaps between providers except for experience of care plan. Conclusion: Some aspects of care coordination among professionals and organizations are noticed by patients and may be valid indicators to assess care coordination.

  13. [Health Economic Evaluations within the Hamburg Network for Mental Health].

    Science.gov (United States)

    König, Hans-Helmut; Grochtdreis, Thomas; Brettschneider, Christian

    2015-07-01

    Within the Hamburg Network for Mental Health, cost-effectiveness analyses of collaborative care models are conducted. After providing an overview of the international literature on the cost-effectiveness of collaborative care for mental disorders, this article describes the rationale, aims and methods of the cost-effectiveness analyses conducted within the Hamburg Network for Mental Health. Proof of cost-effectiveness is expected to promote the transfer of collaborative care models into routine care. © Georg Thieme Verlag KG Stuttgart · New York.

  14. A Learning Health Care System Using Computer-Aided Diagnosis.

    Science.gov (United States)

    Cahan, Amos; Cimino, James J

    2017-03-08

    Physicians intuitively apply pattern recognition when evaluating a patient. Rational diagnosis making requires that clinical patterns be put in the context of disease prior probability, yet physicians often exhibit flawed probabilistic reasoning. Difficulties in making a diagnosis are reflected in the high rates of deadly and costly diagnostic errors. Introduced 6 decades ago, computerized diagnosis support systems are still not widely used by internists. These systems cannot efficiently recognize patterns and are unable to consider the base rate of potential diagnoses. We review the limitations of current computer-aided diagnosis support systems. We then portray future diagnosis support systems and provide a conceptual framework for their development. We argue for capturing physician knowledge using a novel knowledge representation model of the clinical picture. This model (based on structured patient presentation patterns) holds not only symptoms and signs but also their temporal and semantic interrelations. We call for the collection of crowdsourced, automatically deidentified, structured patient patterns as means to support distributed knowledge accumulation and maintenance. In this approach, each structured patient pattern adds to a self-growing and -maintaining knowledge base, sharing the experience of physicians worldwide. Besides supporting diagnosis by relating the symptoms and signs with the final diagnosis recorded, the collective pattern map can also provide disease base-rate estimates and real-time surveillance for early detection of outbreaks. We explain how health care in resource-limited settings can benefit from using this approach and how it can be applied to provide feedback-rich medical education for both students and practitioners. ©Amos Cahan, James J Cimino. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 08.03.2017.

  15. A telecommunications journey rural health network.

    Science.gov (United States)

    Moore, Joe

    2012-01-01

    Utilizing a multi-gigabit statewide fiber healthcare network, Radiology Consultants of Iowa (RCI) set out to provide instantaneous service to their rural, critical access, hospital partners. RCIs idea was to assemble a collection of technologies and services that would even out workflow, reduce time on the road, and provide superior service. These technologies included PACS, voice recognition enabled dictation, HL7 interface technology, an imaging system for digitizing paper and prior films, and modern communication networks. The Iowa Rural Health Telecommunication Project was undertaken to form a system that all critical access hospitals would participate in, allowing RCI radiologists the efficiency of "any image, anywhere, anytime".

  16. A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network.

    Science.gov (United States)

    Liu, Zengkai; Liu, Yonghong; Shan, Hongkai; Cai, Baoping; Huang, Qing

    2015-01-01

    This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information.

  17. Online social networking and mental health.

    Science.gov (United States)

    Pantic, Igor

    2014-10-01

    During the past decade, online social networking has caused profound changes in the way people communicate and interact. It is unclear, however, whether some of these changes may affect certain normal aspects of human behavior and cause psychiatric disorders. Several studies have indicated that the prolonged use of social networking sites (SNS), such as Facebook, may be related to signs and symptoms of depression. In addition, some authors have indicated that certain SNS activities might be associated with low self-esteem, especially in children and adolescents. Other studies have presented opposite results in terms of positive impact of social networking on self-esteem. The relationship between SNS use and mental problems to this day remains controversial, and research on this issue is faced with numerous challenges. This concise review focuses on the recent findings regarding the suggested connection between SNS and mental health issues such as depressive symptoms, changes in self-esteem, and Internet addiction.

  18. Online Social Networking and Mental Health

    Science.gov (United States)

    2014-01-01

    Abstract During the past decade, online social networking has caused profound changes in the way people communicate and interact. It is unclear, however, whether some of these changes may affect certain normal aspects of human behavior and cause psychiatric disorders. Several studies have indicated that the prolonged use of social networking sites (SNS), such as Facebook, may be related to signs and symptoms of depression. In addition, some authors have indicated that certain SNS activities might be associated with low self-esteem, especially in children and adolescents. Other studies have presented opposite results in terms of positive impact of social networking on self-esteem. The relationship between SNS use and mental problems to this day remains controversial, and research on this issue is faced with numerous challenges. This concise review focuses on the recent findings regarding the suggested connection between SNS and mental health issues such as depressive symptoms, changes in self-esteem, and Internet addiction. PMID:25192305

  19. Image compression for medical diagnosis using neural networks

    OpenAIRE

    Lanzarini, Laura Cristina; Vargas Camacho, María Teresa; Flores Badrán, Amado; De Giusti, Armando Eduardo

    2000-01-01

    Images compression is a widely studied topic. Conventional situations offer variable compression ratios depending on the image in question and, in general, do not yield good results for images that are rich in tones. This work is an application of images compression of patient s computed tomographies using neural networks, which allows to carry out both compression and decompression of the images with a fixed ratio of 8:1 and a loss of 2%. Facultad de Informática

  20. Network Monitoring and Diagnosis Based on Available Bandwidth Measurement

    Science.gov (United States)

    2006-05-01

    encouragements helped me pass those tough early days in the US. I would also like to thank my officemates Julio Lopez and Rajesh Balan, both system experts. With...tradeoffs of structured overlays in a dynamic non-transitive network. In MIT 6.829 Fall 2003 class project, December 2003. [52] Ramesh Govindan and Vern ...using packet quartets. In ACM SIGCOMM Internet Measurement Workshop 2002, 2002. [92] Vern Paxson. Measurements and Analysis of End-to-End Internet

  1. Structural health monitoring using wireless sensor networks

    Science.gov (United States)

    Sreevallabhan, K.; Nikhil Chand, B.; Ramasamy, Sudha

    2017-11-01

    Monitoring and analysing health of large structures like bridges, dams, buildings and heavy machinery is important for safety, economical, operational, making prior protective measures, and repair and maintenance point of view. In recent years there is growing demand for such larger structures which in turn make people focus more on safety. By using Microelectromechanical Systems (MEMS) Accelerometer we can perform Structural Health Monitoring by studying the dynamic response through measure of ambient vibrations and strong motion of such structures. By using Wireless Sensor Networks (WSN) we can embed these sensors in wireless networks which helps us to transmit data wirelessly thus we can measure the data wirelessly at any remote location. This in turn reduces heavy wiring which is a cost effective as well as time consuming process to lay those wires. In this paper we developed WSN based MEMS-accelerometer for Structural to test the results in the railway bridge near VIT University, Vellore campus.

  2. SOM neural network fault diagnosis method of polymerization kettle equipment optimized by improved PSO algorithm.

    Science.gov (United States)

    Wang, Jie-sheng; Li, Shu-xia; Gao, Jie

    2014-01-01

    For meeting the real-time fault diagnosis and the optimization monitoring requirements of the polymerization kettle in the polyvinyl chloride resin (PVC) production process, a fault diagnosis strategy based on the self-organizing map (SOM) neural network is proposed. Firstly, a mapping between the polymerization process data and the fault pattern is established by analyzing the production technology of polymerization kettle equipment. The particle swarm optimization (PSO) algorithm with a new dynamical adjustment method of inertial weights is adopted to optimize the structural parameters of SOM neural network. The fault pattern classification of the polymerization kettle equipment is to realize the nonlinear mapping from symptom set to fault set according to the given symptom set. Finally, the simulation experiments of fault diagnosis are conducted by combining with the industrial on-site historical data of the polymerization kettle and the simulation results show that the proposed PSO-SOM fault diagnosis strategy is effective.

  3. SOM Neural Network Fault Diagnosis Method of Polymerization Kettle Equipment Optimized by Improved PSO Algorithm

    Directory of Open Access Journals (Sweden)

    Jie-sheng Wang

    2014-01-01

    Full Text Available For meeting the real-time fault diagnosis and the optimization monitoring requirements of the polymerization kettle in the polyvinyl chloride resin (PVC production process, a fault diagnosis strategy based on the self-organizing map (SOM neural network is proposed. Firstly, a mapping between the polymerization process data and the fault pattern is established by analyzing the production technology of polymerization kettle equipment. The particle swarm optimization (PSO algorithm with a new dynamical adjustment method of inertial weights is adopted to optimize the structural parameters of SOM neural network. The fault pattern classification of the polymerization kettle equipment is to realize the nonlinear mapping from symptom set to fault set according to the given symptom set. Finally, the simulation experiments of fault diagnosis are conducted by combining with the industrial on-site historical data of the polymerization kettle and the simulation results show that the proposed PSO-SOM fault diagnosis strategy is effective.

  4. Investigations on Incipient Fault Diagnosis of Power Transformer Using Neural Networks and Adaptive Neurofuzzy Inference System

    Directory of Open Access Journals (Sweden)

    Nandkumar Wagh

    2014-01-01

    Full Text Available Continuity of power supply is of utmost importance to the consumers and is only possible by coordination and reliable operation of power system components. Power transformer is such a prime equipment of the transmission and distribution system and needs to be continuously monitored for its well-being. Since ratio methods cannot provide correct diagnosis due to the borderline problems and the probability of existence of multiple faults, artificial intelligence could be the best approach. Dissolved gas analysis (DGA interpretation may provide an insight into the developing incipient faults and is adopted as the preliminary diagnosis tool. In the proposed work, a comparison of the diagnosis ability of backpropagation (BP, radial basis function (RBF neural network, and adaptive neurofuzzy inference system (ANFIS has been investigated and the diagnosis results in terms of error measure, accuracy, network training time, and number of iterations are presented.

  5. Chaotic Extension Neural Network-Based Fault Diagnosis Method for Solar Photovoltaic Systems

    Directory of Open Access Journals (Sweden)

    Kuo-Nan Yu

    2014-01-01

    Full Text Available At present, the solar photovoltaic system is extensively used. However, once a fault occurs, it is inspected manually, which is not economical. In order to remedy the defect of unavailable fault diagnosis at any irradiance and temperature in the literature with chaos synchronization based intelligent fault diagnosis for photovoltaic systems proposed by Hsieh et al., this study proposed a chaotic extension fault diagnosis method combined with error back propagation neural network to overcome this problem. It used the nn toolbox of matlab 2010 for simulation and comparison, measured current irradiance and temperature, and used the maximum power point tracking (MPPT for chaotic extraction of eigenvalue. The range of extension field was determined by neural network. Finally, the voltage eigenvalue obtained from current temperature and irradiance was used for the fault diagnosis. Comparing the diagnostic rates with the results by Hsieh et al., this scheme can obtain better diagnostic rates when the irradiances or the temperatures are changed.

  6. Health and the Structure of Adolescent Social Networks

    Science.gov (United States)

    Haas, Steven A.; Schaefer, David R.; Kornienko, Olga

    2010-01-01

    Much research has explored the role of social networks in promoting health through the provision of social support. However, little work has examined how social networks themselves may be structured by health. This article investigates the link between individuals' health and the characteristics of their social network positions.We first develop…

  7. Network solutions for home health care applications.

    Science.gov (United States)

    Herzog, Almut; Lind, Leili

    2003-01-01

    The growing number of the elderly in industrialised countries is increasing the pressure on respective health care systems. This is one reason for recent trends in the development and expansion of home health care organisations. With Internet access available to everyone and the advent of wireless technologies, advanced telehomecare is a possibility for a large proportion of the population. In the near future, one of the authors plans to implement a home health care infrastructure for patients with congestive heart failure and patients with chronic obstructive pulmonary disease. The system is meant to support regular and ad-hoc measurements of medical parameters in patient homes and transmission of measurement data to the home health care provider. In this paper we look at network technologies that connect sensors and input devices in the patient home to a home health care provider. We consider wireless and Internet technologies from functional and security-related perspectives and arrive at a recommendation for our system. Security and usability aspects of the proposed network infrastructures are explored with special focus on their impact on the patient home.

  8. Mental Health and Social Networks After Disaster.

    Science.gov (United States)

    Bryant, Richard A; Gallagher, H Colin; Gibbs, Lisa; Pattison, Philippa; MacDougall, Colin; Harms, Louise; Block, Karen; Baker, Elyse; Sinnott, Vikki; Ireton, Greg; Richardson, John; Forbes, David; Lusher, Dean

    2017-03-01

    Although disasters are a major cause of mental health problems and typically affect large numbers of people and communities, little is known about how social structures affect mental health after a disaster. The authors assessed the extent to which mental health outcomes after disaster are associated with social network structures. In a community-based cohort study of survivors of a major bushfire disaster, participants (N=558) were assessed for probable posttraumatic stress disorder (PTSD) and probable depression. Social networks were assessed by asking participants to nominate people with whom they felt personally close. These nominations were used to construct a social network map that showed each participant's ties to other participants they nominated and also to other participants who nominated them. This map was then analyzed for prevailing patterns of mental health outcomes. Depression risk was higher for participants who reported fewer social connections, were connected to other depressed people, or were connected to people who had left their community. PTSD risk was higher if fewer people reported being connected with the participant, if those who felt close to the participant had higher levels of property loss, or if the participant was linked to others who were themselves not interconnected. Interestingly, being connected to other people who in turn were reciprocally close to each other was associated with a lower risk of PTSD. These findings provide the first evidence of disorder-specific patterns in relation to one's social connections after disaster. Depression appears to co-occur in linked individuals, whereas PTSD risk is increased with social fragmentation. These patterns underscore the need to adopt a sociocentric perspective of postdisaster mental health in order to better understand the potential for societal interventions in the wake of disaster.

  9. Neural Networks and Fault Probability Evaluation for Diagnosis Issues

    Science.gov (United States)

    Lefebvre, Dimitri; Guersi, Noureddine

    2014-01-01

    This paper presents a new FDI technique for fault detection and isolation in unknown nonlinear systems. The objective of the research is to construct and analyze residuals by means of artificial intelligence and probabilistic methods. Artificial neural networks are first used for modeling issues. Neural networks models are designed for learning the fault-free and the faulty behaviors of the considered systems. Once the residuals generated, an evaluation using probabilistic criteria is applied to them to determine what is the most likely fault among a set of candidate faults. The study also includes a comparison between the contributions of these tools and their limitations, particularly through the establishment of quantitative indicators to assess their performance. According to the computation of a confidence factor, the proposed method is suitable to evaluate the reliability of the FDI decision. The approach is applied to detect and isolate 19 fault candidates in the DAMADICS benchmark. The results obtained with the proposed scheme are compared with the results obtained according to a usual thresholding method. PMID:25132845

  10. Neural Networks and Fault Probability Evaluation for Diagnosis Issues

    Directory of Open Access Journals (Sweden)

    Yahia Kourd

    2014-01-01

    Full Text Available This paper presents a new FDI technique for fault detection and isolation in unknown nonlinear systems. The objective of the research is to construct and analyze residuals by means of artificial intelligence and probabilistic methods. Artificial neural networks are first used for modeling issues. Neural networks models are designed for learning the fault-free and the faulty behaviors of the considered systems. Once the residuals generated, an evaluation using probabilistic criteria is applied to them to determine what is the most likely fault among a set of candidate faults. The study also includes a comparison between the contributions of these tools and their limitations, particularly through the establishment of quantitative indicators to assess their performance. According to the computation of a confidence factor, the proposed method is suitable to evaluate the reliability of the FDI decision. The approach is applied to detect and isolate 19 fault candidates in the DAMADICS benchmark. The results obtained with the proposed scheme are compared with the results obtained according to a usual thresholding method.

  11. [Local public health networks. Apropos of an experience].

    Science.gov (United States)

    Guix, Joan; Bocio, Ana; Ferràs, Joaquim; Margalef, Jordi; Osanz, Anna C; Serrano, Mónica; Sentenà, Anna

    2013-01-01

    Public health action on a territory is complex and requires the involvement of multiple actors, who do not always act coordinately. Networks of organizations structures including the whole of the local actors facilitate the generation of synergies and enable greater effectiveness and efficiency of the joint action from the different actors on a same landscape. We present 3 years experience of four Public Health Committees in a region of Catalonia (Spain), composed by the main actors in public health planning. Each of the committees is organized on a plenary and working groups on issues arising from the regional health diagnosis, and coincident with the Health Plan of the Region. Coordination in no case implies the loss or dilution of the firm of the actor generator of intervention initiative in public health, but their empowerment and collaboration by the other actors. In conclusion welcomes the creation of a culture of collaboration and synergies between the different organizations concerned. Lack of specificity is observed in establishing operational objectives, and the need for greater coordination and involvement of the components of the various working groups. Copyright © 2012 SESPAS. Published by Elsevier Espana. All rights reserved.

  12. [Diagnosis of visual impairment: information requirements for health professionals].

    Science.gov (United States)

    Tamditi, K

    2011-01-01

    For some years now, the Braille League has been thinking about ways of improving information of the public with visual impairment about the assistance and services that it can seek. The conclusion that this information was inadequate was highlighted by all those in charge of assisting people with disabilities. As one of the key moments is the announcement of the diagnosis, the association wanted to contact health professionals in order to measure the level of information in their possession and pass on their expectations. Two approaches were used. First, there was a questionnaire that ophthalmologists completed at the Ophtalmologica Belgica 2010 conference. The topics raised were very diverse: their professional context, conditions of announcement, information given to patients, information requested by patients, and their wishes for the future. Moreover, semi-structured interviews enabled various areas of the study to be examined in greater depth: definition of impairment, conditions of announcement of the diagnosis, non-medical aspects, conclusion about lack of information and avenues for improvement. This study confirmed that ophthalmologists lack information on the subject of the psychosocial components of disability and have poor awareness of the existing aids and services. After the presentation of the results, a series of recommendations was made so that the announcement of the diagnosis could be optimal, so as to enable patients to embark on a process of adaptation and acceptance of their visual impairment.

  13. Uganda Health Information Network (UHIN) - Phase IV | IDRC ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Health services in five participating districts are using the Uganda Health Information Network (UHIN) to send and receive disease surveillance data, health management reports, reports on drug supplies and use, and continuing education materials. This phase aims to fully integrate the Network into the Ministry of Health ...

  14. Regional cross national networks for education and training in health

    DEFF Research Database (Denmark)

    Nøhr, Christian; Bygholm, Ann; Hejlesen, Ole

    The paper argues that the education activities in health informatics should be established in net-works covering regions with comparable health care systems involving one or more comparable countries.......The paper argues that the education activities in health informatics should be established in net-works covering regions with comparable health care systems involving one or more comparable countries....

  15. Diagnosis and Reconfiguration using Bayesian Networks: An Electrical Power System Case Study

    Science.gov (United States)

    Knox, W. Bradley; Mengshoel, Ole

    2009-01-01

    Automated diagnosis and reconfiguration are important computational techniques that aim to minimize human intervention in autonomous systems. In this paper, we develop novel techniques and models in the context of diagnosis and reconfiguration reasoning using causal Bayesian networks (BNs). We take as starting point a successful diagnostic approach, using a static BN developed for a real-world electrical power system. We discuss in this paper the extension of this diagnostic approach along two dimensions, namely: (i) from a static BN to a dynamic BN; and (ii) from a diagnostic task to a reconfiguration task. More specifically, we discuss the auto-generation of a dynamic Bayesian network from a static Bayesian network. In addition, we discuss subtle, but important, differences between Bayesian networks when used for diagnosis versus reconfiguration. We discuss a novel reconfiguration agent, which models a system causally, including effects of actions through time, using a dynamic Bayesian network. Though the techniques we discuss are general, we demonstrate them in the context of electrical power systems (EPSs) for aircraft and spacecraft. EPSs are vital subsystems on-board aircraft and spacecraft, and many incidents and accidents of these vehicles have been attributed to EPS failures. We discuss a case study that provides initial but promising results for our approach in the setting of electrical power systems.

  16. Using Bayesian Networks for Candidate Generation in Consistency-based Diagnosis

    Science.gov (United States)

    Narasimhan, Sriram; Mengshoel, Ole

    2008-01-01

    Consistency-based diagnosis relies heavily on the assumption that discrepancies between model predictions and sensor observations can be detected accurately. When sources of uncertainty like sensor noise and model abstraction exist robust schemes have to be designed to make a binary decision on whether predictions are consistent with observations. This risks the occurrence of false alarms and missed alarms when an erroneous decision is made. Moreover when multiple sensors (with differing sensing properties) are available the degree of match between predictions and observations can be used to guide the search for fault candidates. In this paper we propose a novel approach to handle this problem using Bayesian networks. In the consistency- based diagnosis formulation, automatically generated Bayesian networks are used to encode a probabilistic measure of fit between predictions and observations. A Bayesian network inference algorithm is used to compute most probable fault candidates.

  17. Artificial neural networks versus bivariate logistic regression in prediction diagnosis of patients with hypertension and diabetes.

    Science.gov (United States)

    Adavi, Mehdi; Salehi, Masoud; Roudbari, Masoud

    2016-01-01

    Diabetes and hypertension are important non-communicable diseases and their prevalence is important for health authorities. The aim of this study was to determine the predictive precision of the bivariate Logistic Regression (LR) and Artificial Neutral Network (ANN) in concurrent diagnosis of diabetes and hypertension. This cross-sectional study was performed with 12000 Iranian people in 2013 using stratified- cluster sampling. The research questionnaire included information on hypertension and diabetes and their risk factors. A perceptron ANN with two hidden layers was applied to data. To build a joint LR model and ANN, SAS 9.2 and Matlab software were used. The AUC was used to find the higher accurate model for predicting diabetes and hypertension. The variables of gender, type of cooking oil, physical activity, family history, age, passive smokers and obesity entered to the LR model and ANN. The odds ratios of affliction to both diabetes and hypertension is high in females, users of solid oil, with no physical activity, with positive family history, age of equal or higher than 55, passive smokers and those with obesity. The AUC for LR model and ANN were 0.78 (p=0.039) and 0.86 (p=0.046), respectively. The best model for concurrent affliction to hypertension and diabetes is ANN which has higher accuracy than the bivariate LR model.

  18. Comparison of the Helicobacter Pylori Diagnosis Methods with Analytic Network Process

    Directory of Open Access Journals (Sweden)

    Hacer KONAKLI

    2015-11-01

    Full Text Available Helicobacter pylori is infecting %70-80 of the world’s population and is assumed to cause gastric diseases. Diagnosis of the bacteria is crucial for the treatment of the bacteria related infections. Histology, culture, urea breath test, stool antigen test some of the diagnosis methods each having specific strength and weaknesses as sensitivity, specificity, cost, easiness, time, effectiveness in the treatment and laboratory requirements. In this study, three of the commonly used H. pylori diagnosis methods: histology, culture and urea breath test, are evaluated with Analytic network process (ANP and the rank of the criteria and alternatives are obtained. The evaluation of the methods and the rank of the diagnosis methods can reduce time, cost, and validity of the test results.

  19. Improved Diagnosis and Care for Rare Diseases through Implementation of Precision Public Health Framework.

    Science.gov (United States)

    Baynam, Gareth; Bowman, Faye; Lister, Karla; Walker, Caroline E; Pachter, Nicholas; Goldblatt, Jack; Boycott, Kym M; Gahl, William A; Kosaki, Kenjiro; Adachi, Takeya; Ishii, Ken; Mahede, Trinity; McKenzie, Fiona; Townshend, Sharron; Slee, Jennie; Kiraly-Borri, Cathy; Vasudevan, Anand; Hawkins, Anne; Broley, Stephanie; Schofield, Lyn; Verhoef, Hedwig; Groza, Tudor; Zankl, Andreas; Robinson, Peter N; Haendel, Melissa; Brudno, Michael; Mattick, John S; Dinger, Marcel E; Roscioli, Tony; Cowley, Mark J; Olry, Annie; Hanauer, Marc; Alkuraya, Fowzan S; Taruscio, Domenica; Posada de la Paz, Manuel; Lochmüller, Hanns; Bushby, Kate; Thompson, Rachel; Hedley, Victoria; Lasko, Paul; Mina, Kym; Beilby, John; Tifft, Cynthia; Davis, Mark; Laing, Nigel G; Julkowska, Daria; Le Cam, Yann; Terry, Sharon F; Kaufmann, Petra; Eerola, Iiro; Norstedt, Irene; Rath, Ana; Suematsu, Makoto; Groft, Stephen C; Austin, Christopher P; Draghia-Akli, Ruxandra; Weeramanthri, Tarun S; Molster, Caron; Dawkins, Hugh J S

    2017-01-01

    Public health relies on technologies to produce and analyse data, as well as effectively develop and implement policies and practices. An example is the public health practice of epidemiology, which relies on computational technology to monitor the health status of populations, identify disadvantaged or at risk population groups and thereby inform health policy and priority setting. Critical to achieving health improvements for the underserved population of people living with rare diseases is early diagnosis and best care. In the rare diseases field, the vast majority of diseases are caused by destructive but previously difficult to identify protein-coding gene mutations. The reduction in cost of genetic testing and advances in the clinical use of genome sequencing, data science and imaging are converging to provide more precise understandings of the 'person-time-place' triad. That is: who is affected (people); when the disease is occurring (time); and where the disease is occurring (place). Consequently we are witnessing a paradigm shift in public health policy and practice towards 'precision public health'.Patient and stakeholder engagement has informed the need for a national public health policy framework for rare diseases. The engagement approach in different countries has produced highly comparable outcomes and objectives. Knowledge and experience sharing across the international rare diseases networks and partnerships has informed the development of the Western Australian Rare Diseases Strategic Framework 2015-2018 (RD Framework) and Australian government health briefings on the need for a National plan.The RD Framework is guiding the translation of genomic and other technologies into the Western Australian health system, leading to greater precision in diagnostic pathways and care, and is an example of how a precision public health framework can improve health outcomes for the rare diseases population.Five vignettes are used to illustrate how policy

  20. Multi-threshold white matter structural networks fusion for accurate diagnosis of Tourette syndrome children

    Science.gov (United States)

    Wen, Hongwei; Liu, Yue; Wang, Shengpei; Li, Zuoyong; Zhang, Jishui; Peng, Yun; He, Huiguang

    2017-03-01

    Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder. To date, TS is still misdiagnosed due to its varied presentation and lacking of obvious clinical symptoms. Therefore, studies of objective imaging biomarkers are of great importance for early TS diagnosis. As tic generation has been linked to disturbed structural networks, and many efforts have been made recently to investigate brain functional or structural networks using machine learning methods, for the purpose of disease diagnosis. However, few studies were related to TS and some drawbacks still existed in them. Therefore, we propose a novel classification framework integrating a multi-threshold strategy and a network fusion scheme to address the preexisting drawbacks. Here we used diffusion MRI probabilistic tractography to construct the structural networks of 44 TS children and 48 healthy children. We ameliorated the similarity network fusion algorithm specially to fuse the multi-threshold structural networks. Graph theoretical analysis was then implemented, and nodal degree, nodal efficiency and nodal betweenness centrality were selected as features. Finally, support vector machine recursive feature extraction (SVM-RFE) algorithm was used for feature selection, and then optimal features are fed into SVM to automatically discriminate TS children from controls. We achieved a high accuracy of 89.13% evaluated by a nested cross validation, demonstrated the superior performance of our framework over other comparison methods. The involved discriminative regions for classification primarily located in the basal ganglia and frontal cortico-cortical networks, all highly related to the pathology of TS. Together, our study may provide potential neuroimaging biomarkers for early-stage TS diagnosis.

  1. Community Health Global Network and Sustainable Development

    Directory of Open Access Journals (Sweden)

    Rebekah Young

    2016-01-01

    Full Text Available With the achievements, failures and passing of the Millennium Development Goals (MDG, the world has turned its eyes to the Sustainable Development Goals (SDG, designed to foster sustainable social, economic and environmental development over the next 15 years.(1 Community-led initiatives are increasingly being recognised as playing a key role in realising sustainable community development and in the aspirations of universal healthcare.(2 In many parts of the world, faith-based organisations are some of the main players in community-led development and health care.(3 Community Health Global Network (CHGN creates links between organisations, with the purpose being to encourage communities to recognise their assets and abilities, identify shared concerns and discover solutions together, in order to define and lead their futures in sustainable ways.(4 CHGN has facilitated the development of collaborative groups of health and development initiatives called ‘Clusters’ in several countries including India, Bangladesh, Kenya, Tanzania, Zambia and Myanmar. In March 2016 these Clusters met together in an International Forum, to share learnings, experiences, challenges, achievements and to encourage one another. Discussions held throughout the forum suggest that the CHGN model is helping to promote effective, sustainable development and health care provision on both a local and a global scale.

  2. Fault Diagnosis from Raw Sensor Data Using Deep Neural Networks Considering Temporal Coherence.

    Science.gov (United States)

    Zhang, Ran; Peng, Zhen; Wu, Lifeng; Yao, Beibei; Guan, Yong

    2017-03-09

    Intelligent condition monitoring and fault diagnosis by analyzing the sensor data can assure the safety of machinery. Conventional fault diagnosis and classification methods usually implement pretreatments to decrease noise and extract some time domain or frequency domain features from raw time series sensor data. Then, some classifiers are utilized to make diagnosis. However, these conventional fault diagnosis approaches suffer from the expertise of feature selection and they do not consider the temporal coherence of time series data. This paper proposes a fault diagnosis model based on Deep Neural Networks (DNN). The model can directly recognize raw time series sensor data without feature selection and signal processing. It also takes advantage of the temporal coherence of the data. Firstly, raw time series training data collected by sensors are used to train the DNN until the cost function of DNN gets the minimal value; Secondly, test data are used to test the classification accuracy of the DNN on local time series data. Finally, fault diagnosis considering temporal coherence with former time series data is implemented. Experimental results show that the classification accuracy of bearing faults can get 100%. The proposed fault diagnosis approach is effective in recognizing the type of bearing faults.

  3. Combined Geometric and Neural Network Approach to Generic Fault Diagnosis in Satellite Actuators and Sensors

    DEFF Research Database (Denmark)

    Baldi, P.; Blanke, Mogens; Castaldi, P.

    2016-01-01

    This paper presents a novel scheme for diagnosis of faults affecting the sensors measuring the satellite attitude, body angular velocity and flywheel spin rates as well as defects related to the control torques provided by satellite reaction wheels. A nonlinear geometric design is used to avoid...... that aerodynamic disturbance torques have unwanted influence on the residuals exploited for fault detection and isolation. Radial basis function neural networks are used to obtain fault estimation filters that do not need a priori information about the fault internal models. Simulation results are based...... on a detailed nonlinear satellite model with embedded disturbance description. The results document the efficacy of the proposed diagnosis scheme....

  4. Challenges for Game Addiction as a Mental Health Diagnosis

    DEFF Research Database (Denmark)

    Nielsen, Rune Kristian; Aarseth, Espen; Poulsen, Arne

    2014-01-01

    . The validity of the prevalent instruments used to assess the prevalence of computer game addiction is examined in a cross-disciplinary context. The argument of the project is that research on computer game addiction is limited by mono-disciplinary approaches that fail to capture significant nuances at the cost...... of validity of results and instruments. The lack of communication between researchers has resulted in qualitative research that deny the existence of computer game addiction and quantitative research that assert the existence and prevalence of the phenomenon. Qualitative research cannot claim to capture......In this paper, we outline the proposed PhD project: "Challenges for Game Addiction as a Mental Health Diagnosis". The project aims to bridge gaps between the perspectives, theories and data of current research trajectories that engage with the concept of game addiction; from psychology, psychiatry...

  5. Challenges for Game Addiction as a Mental Health Diagnosis

    DEFF Research Database (Denmark)

    Nielsen, Rune K.L.; Aarseth, Espen; Poulsen, Arne

    , cognitive neuroscience to media and game studies. The project has several proposed outcomes. Based on a review of the literature, the adequacy of 'game addiction' as a concept is questioned. The concept is further discussed in a historical perspective of game related pathologies and media/moral panics......In this paper, we outline the proposed PhD project: "Challenges for Game Addiction as a Mental Health Diagnosis". The project aims to bridge gaps between the perspectives, theories and data of current research trajectories that engage with the concept of game addiction; from psychology, psychiatry....... The validity of the prevalent instruments used to assess the prevalence of computer game addiction is examined in a cross-disciplinary context. The argument of the project is that research on computer game addiction is limited by mono-disciplinary approaches that fail to capture significant nuances at the cost...

  6. Social networks, social support mechanisms, and quality of life after breast cancer diagnosis

    Science.gov (United States)

    Kroenke, Candyce H; Kwan, Marilyn L.; Neugut, Alfred I.; Ergas, Isaac J.; Wright, Jaime D.; Caan, Bette J.; Hershman, Dawn; Kushi, Lawrence H.

    2013-01-01

    Purpose We examined mechanisms through which social relationships influence quality of life (QOL) in breast cancer survivors. Methods This study included 3,139 women from the Pathways Study who were diagnosed with breast cancer from 2006-2011 and provided data on social networks (presence of spouse or intimate partner, religious/social ties, volunteering, and numbers of close friends and relatives), social support (tangible, emotional/informational, affection, positive social interaction), and quality of life (QOL), measured by the FACT-B, approximately two months post-diagnosis. We used logistic models to evaluate associations between social network size, social support, and lower vs. higher than median QOL scores. We further stratified by stage at diagnosis and treatment. Results In multivariate-adjusted analyses, women who were characterized as socially isolated had significantly lower FACT-B (OR=2.18, 95%CI:1.72-2.77), physical well-being (WB) (OR=1.61, 95%CI:1.27-2.03), functional WB (OR=2.08, 95%CI:1.65-2.63), social WB (OR=3.46, 95%CI:2.73-4.39), and emotional WB (OR=1.67, 95%CI:1.33-2.11) scores and higher breast cancer symptoms (OR=1.48, 95%CI:1.18-1.87), compared with socially integrated women. Each social network member independently predicted higher QOL. Simultaneous adjustment for social networks and social support partially attenuated associations between social networks and QOL. The strongest mediator and type of social support that was most predictive of QOL outcomes was “positive social interaction”. However, each type of support was important depending on outcome, stage, and treatment status. Conclusions Larger social networks and greater social support were related to higher QOL after a diagnosis of breast cancer. Effective social support interventions need to evolve beyond social-emotional interventions and need to account for disease severity and treatment status. PMID:23657404

  7. Application of artificial neural network model combined with four biomarkers in auxiliary diagnosis of lung cancer.

    Science.gov (United States)

    Duan, Xiaoran; Yang, Yongli; Tan, Shanjuan; Wang, Sihua; Feng, Xiaolei; Cui, Liuxin; Feng, Feifei; Yu, Songcheng; Wang, Wei; Wu, Yongjun

    2017-08-01

    The purpose of the study was to explore the application of artificial neural network model in the auxiliary diagnosis of lung cancer and compare the effects of back-propagation (BP) neural network with Fisher discrimination model for lung cancer screening by the combined detections of four biomarkers of p16, RASSF1A and FHIT gene promoter methylation levels and the relative telomere length. Real-time quantitative methylation-specific PCR was used to detect the levels of three-gene promoter methylation, and real-time PCR method was applied to determine the relative telomere length. BP neural network and Fisher discrimination analysis were used to establish the discrimination diagnosis model. The levels of three-gene promoter methylation in patients with lung cancer were significantly higher than those of the normal controls. The values of Z(P) in two groups were 2.641 (0.008), 2.075 (0.038) and 3.044 (0.002), respectively. The relative telomere lengths of patients with lung cancer (0.93 ± 0.32) were significantly lower than those of the normal controls (1.16 ± 0.57), t = 4.072, P neural network were 0.670 (0.569-0.761) and 0.760 (0.664-0.840). The AUC of BP neural network was higher than that of Fisher discrimination analysis, and Z(P) was 0.76. Four biomarkers are associated with lung cancer. BP neural network model for the prediction of lung cancer is better than Fisher discrimination analysis, and it can provide an excellent and intelligent diagnosis tool for lung cancer.

  8. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals.

    Science.gov (United States)

    Acharya, U Rajendra; Oh, Shu Lih; Hagiwara, Yuki; Tan, Jen Hong; Adeli, Hojjat

    2017-09-27

    An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of epilepsy. The EEG signal contains information about the electrical activity of the brain. Traditionally, neurologists employ direct visual inspection to identify epileptiform abnormalities. This technique can be time-consuming, limited by technical artifact, provides variable results secondary to reader expertise level, and is limited in identifying abnormalities. Therefore, it is essential to develop a computer-aided diagnosis (CAD) system to automatically distinguish the class of these EEG signals using machine learning techniques. This is the first study to employ the convolutional neural network (CNN) for analysis of EEG signals. In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes. The proposed technique achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.

  9. Developing Large-Scale Bayesian Networks by Composition: Fault Diagnosis of Electrical Power Systems in Aircraft and Spacecraft

    Science.gov (United States)

    Mengshoel, Ole Jakob; Poll, Scott; Kurtoglu, Tolga

    2009-01-01

    In this paper, we investigate the use of Bayesian networks to construct large-scale diagnostic systems. In particular, we consider the development of large-scale Bayesian networks by composition. This compositional approach reflects how (often redundant) subsystems are architected to form systems such as electrical power systems. We develop high-level specifications, Bayesian networks, clique trees, and arithmetic circuits representing 24 different electrical power systems. The largest among these 24 Bayesian networks contains over 1,000 random variables. Another BN represents the real-world electrical power system ADAPT, which is representative of electrical power systems deployed in aerospace vehicles. In addition to demonstrating the scalability of the compositional approach, we briefly report on experimental results from the diagnostic competition DXC, where the ProADAPT team, using techniques discussed here, obtained the highest scores in both Tier 1 (among 9 international competitors) and Tier 2 (among 6 international competitors) of the industrial track. While we consider diagnosis of power systems specifically, we believe this work is relevant to other system health management problems, in particular in dependable systems such as aircraft and spacecraft. (See CASI ID 20100021910 for supplemental data disk.)

  10. Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness.

    Science.gov (United States)

    Chennu, Srivas; Annen, Jitka; Wannez, Sarah; Thibaut, Aurore; Chatelle, Camille; Cassol, Helena; Martens, Géraldine; Schnakers, Caroline; Gosseries, Olivia; Menon, David; Laureys, Steven

    2017-08-01

    Recent advances in functional neuroimaging have demonstrated novel potential for informing diagnosis and prognosis in the unresponsive wakeful syndrome and minimally conscious states. However, these technologies come with considerable expense and difficulty, limiting the possibility of wider clinical application in patients. Here, we show that high density electroencephalography, collected from 104 patients measured at rest, can provide valuable information about brain connectivity that correlates with behaviour and functional neuroimaging. Using graph theory, we visualize and quantify spectral connectivity estimated from electroencephalography as a dense brain network. Our findings demonstrate that key quantitative metrics of these networks correlate with the continuum of behavioural recovery in patients, ranging from those diagnosed as unresponsive, through those who have emerged from minimally conscious, to the fully conscious locked-in syndrome. In particular, a network metric indexing the presence of densely interconnected central hubs of connectivity discriminated behavioural consciousness with accuracy comparable to that achieved by expert assessment with positron emission tomography. We also show that this metric correlates strongly with brain metabolism. Further, with classification analysis, we predict the behavioural diagnosis, brain metabolism and 1-year clinical outcome of individual patients. Finally, we demonstrate that assessments of brain networks show robust connectivity in patients diagnosed as unresponsive by clinical consensus, but later rediagnosed as minimally conscious with the Coma Recovery Scale-Revised. Classification analysis of their brain network identified each of these misdiagnosed patients as minimally conscious, corroborating their behavioural diagnoses. If deployed at the bedside in the clinical context, such network measurements could complement systematic behavioural assessment and help reduce the high misdiagnosis rate reported

  11. Unsupervised Fault Diagnosis of a Gear Transmission Chain Using a Deep Belief Network.

    Science.gov (United States)

    He, Jun; Yang, Shixi; Gan, Chunbiao

    2017-07-04

    Artificial intelligence (AI) techniques, which can effectively analyze massive amounts of fault data and automatically provide accurate diagnosis results, have been widely applied to fault diagnosis of rotating machinery. Conventional AI methods are applied using features selected by a human operator, which are manually extracted based on diagnostic techniques and field expertise. However, developing robust features for each diagnostic purpose is often labour-intensive and time-consuming, and the features extracted for one specific task may be unsuitable for others. In this paper, a novel AI method based on a deep belief network (DBN) is proposed for the unsupervised fault diagnosis of a gear transmission chain, and the genetic algorithm is used to optimize the structural parameters of the network. Compared to the conventional AI methods, the proposed method can adaptively exploit robust features related to the faults by unsupervised feature learning, thus requires less prior knowledge about signal processing techniques and diagnostic expertise. Besides, it is more powerful at modelling complex structured data. The effectiveness of the proposed method is validated using datasets from rolling bearings and gearbox. To show the superiority of the proposed method, its performance is compared with two well-known classifiers, i.e., back propagation neural network (BPNN) and support vector machine (SVM). The fault classification accuracies are 99.26% for rolling bearings and 100% for gearbox when using the proposed method, which are much higher than that of the other two methods.

  12. Clinical phenotype network: the underlying mechanism for personalized diagnosis and treatment of traditional Chinese medicine.

    Science.gov (United States)

    Zhou, Xuezhong; Li, Yubing; Peng, Yonghong; Hu, Jingqing; Zhang, Runshun; He, Liyun; Wang, Yinghui; Jiang, Lijie; Yan, Shiyan; Li, Peng; Xie, Qi; Liu, Baoyan

    2014-09-01

    Traditional Chinese medicine (TCM) investigates the clinical diagnosis and treatment regularities in a typical schema of personalized medicine, which means that individualized patients with same diseases would obtain distinct diagnosis and optimal treatment from different TCM physicians. This principle has been recognized and adhered by TCM clinical practitioners for thousands of years. However, the underlying mechanisms of TCM personalized medicine are not fully investigated so far and remained unknown. This paper discusses framework of TCM personalized medicine in classic literatures and in real-world clinical settings, and investigates the underlying mechanisms of TCM personalized medicine from the perspectives of network medicine. Based on 246 well-designed outpatient records on insomnia, by evaluating the personal biases of manifestation observation and preferences of herb prescriptions, we noted significant similarities between each herb prescriptions and symptom similarities between each encounters. To investigate the underlying mechanisms of TCM personalized medicine, we constructed a clinical phenotype network (CPN), in which the clinical phenotype entities like symptoms and diagnoses are presented as nodes and the correlation between these entities as links. This CPN is used to investigate the promiscuous boundary of syndromes and the co-occurrence of symptoms. The small-world topological characteristics are noted in the CPN with high clustering structures, which provide insight on the rationality of TCM personalized diagnosis and treatment. The investigation on this network would help us to gain understanding on the underlying mechanism of TCM personalized medicine and would propose a new perspective for the refinement of the TCM individualized clinical skills.

  13. Sensor fault diagnosis based on discrete wavelet transform and BP neural network

    Science.gov (United States)

    Liu, Quan; Jiang, Xuemei

    2005-11-01

    Sensor technology is one of three major pillars of the modern information technology. With the extensive application of sensor, the dependability of the sensor is paid more and more attention. The development of sensor faults diagnose technology offers strong guarantee for using the sensor reliably. In this paper, the application of combining the wavelet and BP neural networks to sensors failure detection is studied, and a novel diagnosis method based on discrete wavelet transform and BP neural network was proposed to detect and identify sensor abrupt fault. Since wavelet transform can accurately localize sensor signal characteristics both in time and frequency domain, it is very suitable for non-stationary signal analysis. After discrete wavelet transform analysis for sensor output, eigenvector of energy changing rate was extracted, and classification of sensor fault was conducted by using BP neural network. The proposed method does not need construction of sensor model and measurement of sensor input. Hence redundant data can be reduced by omitting some wavelet coefficients and the capability of fault detection can be improved. Sensor fault diagnosis is simulated by the computer. Through a large amount of simulated examples it indicates that the sensors fault diagnosis method based on the theory of wavelet has characteristic such as good sensitivity, high accuracy rate and robust ability to overcome noise. Simulation results proved the effectiveness of this method.

  14. Unsupervised Fault Diagnosis of a Gear Transmission Chain Using a Deep Belief Network

    Directory of Open Access Journals (Sweden)

    Jun He

    2017-07-01

    Full Text Available Artificial intelligence (AI techniques, which can effectively analyze massive amounts of fault data and automatically provide accurate diagnosis results, have been widely applied to fault diagnosis of rotating machinery. Conventional AI methods are applied using features selected by a human operator, which are manually extracted based on diagnostic techniques and field expertise. However, developing robust features for each diagnostic purpose is often labour-intensive and time-consuming, and the features extracted for one specific task may be unsuitable for others. In this paper, a novel AI method based on a deep belief network (DBN is proposed for the unsupervised fault diagnosis of a gear transmission chain, and the genetic algorithm is used to optimize the structural parameters of the network. Compared to the conventional AI methods, the proposed method can adaptively exploit robust features related to the faults by unsupervised feature learning, thus requires less prior knowledge about signal processing techniques and diagnostic expertise. Besides, it is more powerful at modelling complex structured data. The effectiveness of the proposed method is validated using datasets from rolling bearings and gearbox. To show the superiority of the proposed method, its performance is compared with two well-known classifiers, i.e., back propagation neural network (BPNN and support vector machine (SVM. The fault classification accuracies are 99.26% for rolling bearings and 100% for gearbox when using the proposed method, which are much higher than that of the other two methods.

  15. Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network.

    Science.gov (United States)

    Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing

    2016-01-08

    A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method.

  16. Tufts academic health information network: concept and scenario.

    Science.gov (United States)

    Stearns, N S

    1986-04-01

    Tufts University School of Medicine's new health sciences education building, the Arthur M. Sackler Center for Health Communications, will house a modern medical library and computer center, classrooms, auditoria, and media facilities. The building will also serve as the center for an information and communication network linking the medical school and adjacent New England Medical Center, Tufts' primary teaching hospital, with Tufts Associated Teaching Hospitals throughout New England. Ultimately, the Tufts network will join other gateway networks, information resource facilities, health care institutions, and medical schools throughout the world. The center and the network are intended to facilitate and improve the education of health professionals, the delivery of health care to patients, the conduct of research, and the implementation of administrative management approaches that should provide more efficient utilization of resources and save dollars. A model and scenario show how health care delivery and health care education are integrated through better use of information transfer technologies by health information specialists, practitioners, and educators.

  17. Developing an Intelligent System for Diagnosis of Asthma Based on Artificial Neural Network.

    Science.gov (United States)

    Alizadeh, Behrouz; Safdari, Reza; Zolnoori, Maryam; Bashiri, Azadeh

    2015-08-01

    Lack of proper diagnosis and inadequate treatment of asthma, leads to physical and financial complications. This study aimed to use data mining techniques and creating a neural network intelligent system for diagnosis of asthma. The study population is the patients who had visited one of the Lung Clinics in Tehran. Data were analyzed using the SPSS statistical tool and the chi-square Pearson's coefficient was the basis of decision making for data ranking. The considered neural network is trained using back propagation learning technique. According to the analysis performed by means of SPSS to select the top factors, 13 effective factors were selected, in different performances, data was mixed in various forms, so the different modes was made for training the data and testing networks and in all different modes, the network was able to predict correctly 100% of all cases. Using data mining methods before the design structure of system, aimed to reduce the data dimension and the optimum choice of the data, will lead to a more accurate system. So considering the data mining approaches due to the nature of medical data is necessary.

  18. Health and Maintenance Status Determination and Predictive Fault Diagnosis System Project

    Data.gov (United States)

    National Aeronautics and Space Administration — The objective of this project is to demonstrate intelligent health and maintenance status determination and predictive fault diagnosis techniques for NASA rocket...

  19. Educational and Gender Differences in Health Behavior Changes After a Gateway Diagnosis.

    Science.gov (United States)

    Hernandez, Elaine M; Margolis, Rachel; Hummer, Robert A

    2018-03-01

    Hypertension represents a gateway diagnosis to more serious health problems that occur as people age. We examine educational differences in three health behavior changes people often make after receiving this diagnosis in middle or older age, and test whether these educational differences depend on (a) the complexity of the health behavior change and (b) gender. We use data from the Health and Retirement Study and conduct logistic regression analysis to examine the likelihood of modifying health behaviors post diagnosis. We find educational differences in three behavior changes-antihypertensive medication use, smoking cessation, and physical activity initiation-after a hypertension diagnosis. These educational differences in health behaviors were stronger among women compared with men. Upon receiving a hypertension diagnosis, education is a more important predictor of behavior changes for women compared with men, which may help explain gender differences in the socioeconomic gradient in health in the United States.

  20. Social Network Types and Mental Health Among LGBT Older Adults.

    Science.gov (United States)

    Kim, Hyun-Jun; Fredriksen-Goldsen, Karen I; Bryan, Amanda E B; Muraco, Anna

    2017-02-01

    This study was designed to identify social network types among lesbian, gay, bisexual, and transgender (LGBT) older adults and examine the relationship between social network type and mental health. We analyzed the 2014 survey data of LGBT adults aged 50 and older (N = 2,450) from Aging with Pride: National Health, Aging, and Sexuality/Gender Study. Latent profile analyses were conducted to identify clusters of social network ties based on 11 indicators. Multiple regression analysis was performed to examine the association between social network types and mental health. We found five social network types. Ordered from greatest to least access to family, friend, and other non-family network ties, they were diverse, diverse/no children, immediate family-focused, friend-centered/restricted, and fully restricted. The friend-centered/restricted (33%) and diverse/no children network types (31%) were the most prevalent. Among individuals with the friend-centered/restricted type, access to social networks was limited to friends, and across both types children were not present. The least prevalent type was the fully restricted network type (6%). Social network type was significantly associated with mental health, after controlling for background characteristics and total social network size; those with the fully restricted type showed the poorest mental health. Unique social network types (diverse/no children and friend-centered/restricted) emerge among LGBT older adults. Moreover, individuals with fully restricted social networks are at particular risk due to heightened health needs and limited social resources. This study highlights the importance of understanding heterogeneous social relations and developing tailored interventions to promote social connectedness and mental health in LGBT older adults. © The Author 2017. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  1. Can mental health interventions change social networks? A systematic review

    OpenAIRE

    Anderson, Kimberley; Laxhman, Neelam; Priebe, Stefan

    2015-01-01

    Background Social networks of patients with psychosis can provide social support, and improve health and social outcomes, including quality of life. However, patients with psychosis often live rather isolated with very limited social networks. Evidence for interventions targeting symptoms or social skills, are largely unsuccessful at improving social networks indirectly. As an alternative, interventions may directly focus on expanding networks. In this systematic review, we assessed what inte...

  2. Efficient health care service delivery using network analysis: a case ...

    African Journals Online (AJOL)

    Efficient health care service delivery using network analysis: a case study of Kwara State, Nigeria. ... Ethiopian Journal of Environmental Studies and Management ... This paper addresses challenges with prompt health care delivery using Network Analysis of Critical Path Model (CPM) to plan the hospital capacity with a ...

  3. A Case for Open Network Health Systems: Systems as Networks in Public Mental Health

    Directory of Open Access Journals (Sweden)

    Michael Grant Rhodes

    2017-03-01

    Full Text Available Increases in incidents involving so-called confused persons have brought attention to the potential costs of recent changes to public mental health (PMH services in the Netherlands. Decentralized under the (Community Participation Act (2014, local governments must find resources to compensate for reduced central funding to such services or “innovate.” But innovation, even when pressure for change is intense, is difficult. This perspective paper describes experience during and after an investigation into a particularly violent incident and murder. The aim was to provide recommendations to improve the functioning of local PMH services. The investigation concluded that no specific failure by an individual professional or service provider facility led to the murder. Instead, also as a result of the Participation Act that severed communication lines between individuals and organizations, information sharing failures were likely to have reduced system level capacity to identify risks. The methods and analytical frameworks employed to reach this conclusion, also lead to discussion as to the plausibility of an unconventional solution. If improving communication is the primary problem, non-hierarchical information, and organizational networks arise as possible and innovative system solutions. The proposal for debate is that traditional “health system” definitions, literature and narratives, and operating assumptions in public (mental health are ‘locked in’ constraining technical and organization innovations. If we view a “health system” as an adaptive system of economic and social “networks,” it becomes clear that the current orthodox solution, the so-called integrated health system, typically results in a “centralized hierarchical” or “tree” network. An overlooked alternative that breaks out of the established policy narratives is the view of a ‘health systems’ as a non-hierarchical organizational structure or

  4. Diagnosis of embankment dam distresses using Bayesian networks. Part I. Global-level characteristics based on a dam distress database

    National Research Council Canada - National Science Library

    Zhang, L. M; Xu, Y; Jia, J. S; Zhao, C

    2011-01-01

    .... The main objective of this paper is to develop a robust probability-based tool using Bayesian networks for the diagnosis of embankment dam distresses at the global level based on past dam distress data...

  5. MobiHealth: Ambulant Patient Monitoring Over Public Wireless Networks

    NARCIS (Netherlands)

    Konstantas, D.; van Halteren, Aart; Bults, Richard G.A.; Wac, K.E.; Jones, Valerie M.; Widya, I.A.; Herzog, Rainer

    2004-01-01

    The use of health BANs together with advanced wireless communications enables remote management of chronic conditions and detection of health emergencies whilst maximising patient mobility. MobiHealth1,2 has developed a generic Body Area Network (BAN) for healthcare and an m-health service platform.

  6. Diagnosis of mental disorder in adults and increased use of health services in four outpatient settings.

    Science.gov (United States)

    Hoeper, E W; Nycz, G R; Regier, D A; Goldberg, I D; Jacobson, A; Hankin, J

    1980-02-01

    The differential use of medical services by patients with and those without a diagnosis of mental disorder was examined in four adult populations by age, sex, diagnosis, and medical department used. The four settings offered comprehensive services to patients who varied greatly in socioeconomic status. In all four settings patients with a diagnosis of mental disorder used all services and general health services more than patients without such a diagnosis. Results document increased medical morbidity and a greater likelihood of a diagnosis of an ill-defined condition in patients with mental disorder than that found in patients without a diagnosis of mental disorder.

  7. Computer-Aided Diagnosis of Parkinson's Disease Using Enhanced Probabilistic Neural Network.

    Science.gov (United States)

    Hirschauer, Thomas J; Adeli, Hojjat; Buford, John A

    2015-11-01

    Early and accurate diagnosis of Parkinson's disease (PD) remains challenging. Neuropathological studies using brain bank specimens have estimated that a large percentages of clinical diagnoses of PD may be incorrect especially in the early stages. In this paper, a comprehensive computer model is presented for the diagnosis of PD based on motor, non-motor, and neuroimaging features using the recently-developed enhanced probabilistic neural network (EPNN). The model is tested for differentiating PD patients from those with scans without evidence of dopaminergic deficit (SWEDDs) using the Parkinson's Progression Markers Initiative (PPMI) database, an observational, multi-center study designed to identify PD biomarkers for diagnosis and disease progression. The results are compared to four other commonly-used machine learning algorithms: the probabilistic neural network (PNN), support vector machine (SVM), k-nearest neighbors (k-NN) algorithm, and classification tree (CT). The EPNN had the highest classification accuracy at 92.5% followed by the PNN (91.6%), k-NN (90.8%) and CT (90.2%). The EPNN exhibited an accuracy of 98.6% when classifying healthy control (HC) versus PD, higher than any previous studies.

  8. Wireless sensor networks for structural health monitoring

    CERN Document Server

    Cao, Jiannong

    2016-01-01

    This brief covers the emerging area of wireless sensor network (WSN)-based structural health monitoring (SHM) systems, and introduces the authors’ WSN-based platform called SenetSHM. It helps the reader differentiate specific requirements of SHM applications from other traditional WSN applications, and demonstrates how these requirements are addressed by using a series of systematic approaches. The brief serves as a practical guide, explaining both the state-of-the-art technologies in domain-specific applications of WSNs, as well as the methodologies used to address the specific requirements for a WSN application. In particular, the brief offers instruction for problem formulation and problem solving based on the authors’ own experiences implementing SenetSHM. Seven concise chapters cover the development of hardware and software design of SenetSHM, as well as in-field experiments conducted while testing the platform. The brief’s exploration of the SenetSHM platform is a valuable feature for civil engine...

  9. A study on Fault Diagnosis Method of Rolling Bearing Based on Wavelet Packet and Improved BP Neural Network

    Science.gov (United States)

    Song, Mengmeng; Song, Haixia; Xiao, Shungen

    2017-12-01

    In this paper, rolling bearing fault diagnosis method is proposed based on wavelet packet threshold de-noising and improved BP neural network. It achieves the goal of signal de-noising by setting the appropriate threshold, and then the denoised signal is decomposed into three layers by wavelet packet. The energy characteristics of the 8 frequency bands are calculated respectively. Levenberg-Maquardt algorithm which is improved the traditional BP neural network to improve the diagnosis efficiency of BP neural network, is proposed. Taking the outer ring fault of rolling bearings as an example, the experimental results show that the wavelet packet threshold de-noising can effectively improve the signal-to-noise ratio. Compared with the traditional BP neural network, the improved BP neural network has better diagnosis efficiency.

  10. Mental health network governance: comparative analysis across Canadian regions

    Science.gov (United States)

    Wiktorowicz, Mary E; Fleury, Marie-Josée; Adair, Carol E; Lesage, Alain; Goldner, Elliot; Peters, Suzanne

    2010-01-01

    Objective Modes of governance were compared in ten local mental health networks in diverse contexts (rural/urban and regionalized/non-regionalized) to clarify the governance processes that foster inter-organizational collaboration and the conditions that support them. Methods Case studies of ten local mental health networks were developed using qualitative methods of document review, semi-structured interviews and focus groups that incorporated provincial policy, network and organizational levels of analysis. Results Mental health networks adopted either a corporate structure, mutual adjustment or an alliance governance model. A corporate structure supported by regionalization offered the most direct means for local governance to attain inter-organizational collaboration. The likelihood that networks with an alliance model developed coordination processes depended on the presence of the following conditions: a moderate number of organizations, goal consensus and trust among the organizations, and network-level competencies. In the small and mid-sized urban networks where these conditions were met their alliance realized the inter-organizational collaboration sought. In the large urban and rural networks where these conditions were not met, externally brokered forms of network governance were required to support alliance based models. Discussion In metropolitan and rural networks with such shared forms of network governance as an alliance or voluntary mutual adjustment, external mediation by a regional or provincial authority was an important lever to foster inter-organizational collaboration. PMID:21289999

  11. A Modular Neural Network Scheme Applied to Fault Diagnosis in Electric Power Systems

    Science.gov (United States)

    Flores, Agustín; Morant, Francisco

    2014-01-01

    This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system. PMID:25610897

  12. A Modular Neural Network Scheme Applied to Fault Diagnosis in Electric Power Systems

    Directory of Open Access Journals (Sweden)

    Agustín Flores

    2014-01-01

    Full Text Available This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.

  13. Health Monitoring Using Wireless Sensor Network: "A Matlab Approach"

    OpenAIRE

    Okeke, David Chukwuemeka

    2016-01-01

    A wireless sensor network consists of locally distributed independent sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants. Wireless Body Area Network (WBANs) represents a promising trend in wearable health monitoring systems. WBANs promise to revolutionize health monitoring and offer continuous and omnipresent moving health monitoring at the least level of obtrusiveness, resulting in an increase in user’s...

  14. Tracks: A National Environmental Public Health Tracking Network Overview

    Centers for Disease Control (CDC) Podcasts

    2009-08-04

    In this podcast, Dr. Mike McGeehin, Director of CDC's Division of Environmental Hazards and Health Effects, provides an overview of the National Environmental Public Health Tracking Network. It highlights the Tracking Network's goal, how it will improve public health, its audience, and much more.  Created: 8/4/2009 by Centers for Disease Control and Prevention (CDC).   Date Released: 8/4/2009.

  15. Early diagnosis and treatment of uncomplicated malaria and patterns of health seeking in Vietnam

    NARCIS (Netherlands)

    Giao, Phan T.; Vries, Peter J.; Binh, Tran Q.; Nam, Nguyen V.; Kager, Piet A.

    2005-01-01

    Early diagnosis and treatment of malaria (EDTM) is a key component of malaria control. The success of EDTM depends on health seeking behaviour and the quality of the health service. This study assessed self-diagnosis, treatment and treatment delay after the introduction of EDTM in 1993. In southern

  16. Fault Diagnosis Based on Chemical Sensor Data with an Active Deep Neural Network.

    Science.gov (United States)

    Jiang, Peng; Hu, Zhixin; Liu, Jun; Yu, Shanen; Wu, Feng

    2016-10-13

    Big sensor data provide significant potential for chemical fault diagnosis, which involves the baseline values of security, stability and reliability in chemical processes. A deep neural network (DNN) with novel active learning for inducing chemical fault diagnosis is presented in this study. It is a method using large amount of chemical sensor data, which is a combination of deep learning and active learning criterion to target the difficulty of consecutive fault diagnosis. DNN with deep architectures, instead of shallow ones, could be developed through deep learning to learn a suitable feature representation from raw sensor data in an unsupervised manner using stacked denoising auto-encoder (SDAE) and work through a layer-by-layer successive learning process. The features are added to the top Softmax regression layer to construct the discriminative fault characteristics for diagnosis in a supervised manner. Considering the expensive and time consuming labeling of sensor data in chemical applications, in contrast to the available methods, we employ a novel active learning criterion for the particularity of chemical processes, which is a combination of Best vs. Second Best criterion (BvSB) and a Lowest False Positive criterion (LFP), for further fine-tuning of diagnosis model in an active manner rather than passive manner. That is, we allow models to rank the most informative sensor data to be labeled for updating the DNN parameters during the interaction phase. The effectiveness of the proposed method is validated in two well-known industrial datasets. Results indicate that the proposed method can obtain superior diagnosis accuracy and provide significant performance improvement in accuracy and false positive rate with less labeled chemical sensor data by further active learning compared with existing methods.

  17. Fault Diagnosis Based on Chemical Sensor Data with an Active Deep Neural Network

    Science.gov (United States)

    Jiang, Peng; Hu, Zhixin; Liu, Jun; Yu, Shanen; Wu, Feng

    2016-01-01

    Big sensor data provide significant potential for chemical fault diagnosis, which involves the baseline values of security, stability and reliability in chemical processes. A deep neural network (DNN) with novel active learning for inducing chemical fault diagnosis is presented in this study. It is a method using large amount of chemical sensor data, which is a combination of deep learning and active learning criterion to target the difficulty of consecutive fault diagnosis. DNN with deep architectures, instead of shallow ones, could be developed through deep learning to learn a suitable feature representation from raw sensor data in an unsupervised manner using stacked denoising auto-encoder (SDAE) and work through a layer-by-layer successive learning process. The features are added to the top Softmax regression layer to construct the discriminative fault characteristics for diagnosis in a supervised manner. Considering the expensive and time consuming labeling of sensor data in chemical applications, in contrast to the available methods, we employ a novel active learning criterion for the particularity of chemical processes, which is a combination of Best vs. Second Best criterion (BvSB) and a Lowest False Positive criterion (LFP), for further fine-tuning of diagnosis model in an active manner rather than passive manner. That is, we allow models to rank the most informative sensor data to be labeled for updating the DNN parameters during the interaction phase. The effectiveness of the proposed method is validated in two well-known industrial datasets. Results indicate that the proposed method can obtain superior diagnosis accuracy and provide significant performance improvement in accuracy and false positive rate with less labeled chemical sensor data by further active learning compared with existing methods. PMID:27754386

  18. Bearing Fault Diagnosis Based on Deep Belief Network and Multisensor Information Fusion

    Directory of Open Access Journals (Sweden)

    Jie Tao

    2016-01-01

    Full Text Available In the rolling bearing fault diagnosis, the vibration signal of single sensor is usually nonstationary and noisy, which contains very little useful information, and impacts the accuracy of fault diagnosis. In order to solve the problem, this paper presents a novel fault diagnosis method using multivibration signals and deep belief network (DBN. By utilizing the DBN’s learning ability, the proposed method can adaptively fuse multifeature data and identify various bearing faults. Firstly, multiple vibration signals are acquainted from various fault bearings. Secondly, some time-domain characteristics are extracted from original signals of each individual sensor. Finally, the features data of all sensors are put into the DBN and generate an appropriate classifier to complete fault diagnosis. In order to demonstrate the effectiveness of multivibration signals, experiments are carried out on the individual sensor with the same conditions and procedure. At the same time, the method is compared with SVM, KNN, and BPNN methods. The results show that the DBN-based method is able to not only adaptively fuse multisensor data, but also obtain higher identification accuracy than other methods.

  19. Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease.

    Science.gov (United States)

    Shi, Jun; Zheng, Xiao; Li, Yan; Zhang, Qi; Ying, Shihui

    2018-01-01

    The accurate diagnosis of Alzheimer's disease (AD) and its early stage, i.e., mild cognitive impairment, is essential for timely treatment and possible delay of AD. Fusion of multimodal neuroimaging data, such as magnetic resonance imaging (MRI) and positron emission tomography (PET), has shown its effectiveness for AD diagnosis. The deep polynomial networks (DPN) is a recently proposed deep learning algorithm, which performs well on both large-scale and small-size datasets. In this study, a multimodal stacked DPN (MM-SDPN) algorithm, which MM-SDPN consists of two-stage SDPNs, is proposed to fuse and learn feature representation from multimodal neuroimaging data for AD diagnosis. Specifically speaking, two SDPNs are first used to learn high-level features of MRI and PET, respectively, which are then fed to another SDPN to fuse multimodal neuroimaging information. The proposed MM-SDPN algorithm is applied to the ADNI dataset to conduct both binary classification and multiclass classification tasks. Experimental results indicate that MM-SDPN is superior over the state-of-the-art multimodal feature-learning-based algorithms for AD diagnosis.

  20. Rolling bearing fault diagnosis using adaptive deep belief network with dual-tree complex wavelet packet.

    Science.gov (United States)

    Shao, Haidong; Jiang, Hongkai; Wang, Fuan; Wang, Yanan

    2017-07-01

    Automatic and accurate identification of rolling bearing fault categories, especially for the fault severities and compound faults, is a challenge in rotating machinery fault diagnosis. For this purpose, a novel method called adaptive deep belief network (DBN) with dual-tree complex wavelet packet (DTCWPT) is developed in this paper. DTCWPT is used to preprocess the vibration signals to refine the fault characteristics information, and an original feature set is designed from each frequency-band signal of DTCWPT. An adaptive DBN is constructed to improve the convergence rate and identification accuracy with multiple stacked adaptive restricted Boltzmann machines (RBMs). The proposed method is applied to the fault diagnosis of rolling bearings. The results confirm that the proposed method is more effective than the existing methods. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  1. Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data.

    Science.gov (United States)

    Sun, Wenqing; Tseng, Tzu-Liang Bill; Zhang, Jianying; Qian, Wei

    2017-04-01

    In this study we developed a graph based semi-supervised learning (SSL) scheme using deep convolutional neural network (CNN) for breast cancer diagnosis. CNN usually needs a large amount of labeled data for training and fine tuning the parameters, and our proposed scheme only requires a small portion of labeled data in training set. Four modules were included in the diagnosis system: data weighing, feature selection, dividing co-training data labeling, and CNN. 3158 region of interests (ROIs) with each containing a mass extracted from 1874 pairs of mammogram images were used for this study. Among them 100 ROIs were treated as labeled data while the rest were treated as unlabeled. The area under the curve (AUC) observed in our study was 0.8818, and the accuracy of CNN is 0.8243 using the mixed labeled and unlabeled data. Copyright © 2016. Published by Elsevier Ltd.

  2. Empowering health personnel for decentralized health planning in India: The Public Health Resource Network

    Directory of Open Access Journals (Sweden)

    Prasad Vandana

    2009-07-01

    Full Text Available Abstract The Public Health Resource Network is an innovative distance-learning course in training, motivating, empowering and building a network of health personnel from government and civil society groups. Its aim is to build human resource capacity for strengthening decentralized health planning, especially at the district level, to improve accountability of health systems, elicit community participation for health, ensure equitable and accessible health facilities and to bring about convergence in programmes and services. The question confronting health systems in India is how best to reform, revitalize and resource primary health systems to deliver different levels of service aligned to local realities, ensuring universal coverage, equitable access, efficiency and effectiveness, through an empowered cadre of health personnel. To achieve these outcomes it is essential that health planning be decentralized. Districts vary widely according to the specific needs of their population, and even more so in terms of existing interventions and available resources. Strategies, therefore, have to be district-specific, not only because health needs vary, but also because people's perceptions and capacities to intervene and implement programmes vary. In centrally designed plans there is little scope for such adaptation and contextualization, and hence decentralized planning becomes crucial. To undertake these initiatives, there is a strong need for trained, motivated, empowered and networked health personnel. It is precisely at this level that a lack of technical knowledge and skills and the absence of a supportive network or adequate educational opportunities impede personnel from making improvements. The absence of in-service training and of training curricula that reflect field realities also adds to this, discouraging health workers from pursuing effective strategies. The Public Health Resource Network is thus an attempt to reach out to motivated

  3. Diagnosis of compliance of health care product processing in Primary Health Care

    Directory of Open Access Journals (Sweden)

    Camila Eugenia Roseira

    Full Text Available ABSTRACT Objective: identify the compliance of health care product processing in Primary Health Care and assess possible differences in the compliance among the services characterized as Primary Health Care Service and Family Health Service. Method: quantitative, observational, descriptive and inferential study with the application of structure, process and outcome indicators of the health care product processing at ten services in an interior city of the State of São Paulo - Brazil. Results: for all indicators, the compliance indices were inferior to the ideal levels. No statistically significant difference was found in the indicators between the two types of services investigated. The health care product cleaning indicators obtained the lowest compliance index, while the indicator technical-operational resources for the preparation, conditioning, disinfection/sterilization, storage and distribution of health care products obtained the best index. Conclusion: the diagnosis of compliance of health care product processing at the services assessed indicates that the quality of the process is jeopardized, as no results close to ideal levels were obtained at any service. In addition, no statistically significant difference in these indicators was found between the two types of services studied.

  4. Social network analysis of public health programs to measure partnership.

    Science.gov (United States)

    Schoen, Martin W; Moreland-Russell, Sarah; Prewitt, Kim; Carothers, Bobbi J

    2014-12-01

    In order to prevent chronic diseases, community-based programs are encouraged to take an ecological approach to public health promotion and involve many diverse partners. Little is known about measuring partnership in implementing public health strategies. We collected data from 23 Missouri communities in early 2012 that received funding from three separate programs to prevent obesity and/or reduce tobacco use. While all of these funding programs encourage partnership, only the Social Innovation for Missouri (SIM) program included a focus on building community capacity and enhancing collaboration. Social network analysis techniques were used to understand contact and collaboration networks in community organizations. Measurements of average degree, density, degree centralization, and betweenness centralization were calculated for each network. Because of the various sizes of the networks, we conducted comparative analyses with and without adjustment for network size. SIM programs had increased measurements of average degree for partner collaboration and larger networks. When controlling for network size, SIM groups had higher measures of network density and lower measures of degree centralization and betweenness centralization. SIM collaboration networks were more dense and less centralized, indicating increased partnership. The methods described in this paper can be used to compare partnership in community networks of various sizes. Further research is necessary to define causal mechanisms of partnership development and their relationship to public health outcomes. Copyright © 2014 Elsevier Ltd. All rights reserved.

  5. Artificial neural networks and prostate cancer--tools for diagnosis and management.

    Science.gov (United States)

    Hu, Xinhai; Cammann, Henning; Meyer, Hellmuth-A; Miller, Kurt; Jung, Klaus; Stephan, Carsten

    2013-03-01

    Artificial neural networks (ANNs) are mathematical models that are based on biological neural networks and are composed of interconnected groups of artificial neurons. ANNs are used to map and predict outcomes in complex relationships between given 'inputs' and sought-after 'outputs' and can also be used find patterns in datasets. In medicine, ANN applications have been used in cancer diagnosis, staging and recurrence prediction since the mid-1990s, when an enormous effort was initiated, especially in prostate cancer detection. Modern ANNs can incorporate new biomarkers and imaging data to improve their predictive power and can offer a number of advantages as clinical decision making tools, such as easy handling of distribution-free input parameters. Most importantly, ANNs consider nonlinear relationships among input data that cannot always be recognized by conventional analyses. In the future, complex medical diagnostic and treatment decisions will be increasingly based on ANNs and other multivariate models.

  6. EXPERIMENT BASED FAULT DIAGNOSIS ON BOTTLE FILLING PLANT WITH LVQ ARTIFICIAL NEURAL NETWORK ALGORITHM

    Directory of Open Access Journals (Sweden)

    Mustafa DEMETGÜL

    2008-01-01

    Full Text Available In this study, an artificial neural network is developed to find an error rapidly on pneumatic system. Also the ANN prevents the system versus the failure. The error on the experimental bottle filling plant can be defined without any interference using analog values taken from pressure sensors and linear potentiometers. The sensors and potentiometers are placed on different places of the plant. Neural network diagnosis faults on plant, where no bottle, cap closing cylinder B is not working, bottle cap closing cylinder C is not working, air pressure is not sufficient, water is not filling and low air pressure faults. The fault is diagnosed by artificial neural network with LVQ. It is possible to find an failure by using normal programming or PLC. The reason offing Artificial Neural Network is to give a information where the fault is. However, ANN can be used for different systems. The aim is to find the fault by using ANN simultaneously. In this situation, the error taken place on the pneumatic system is collected by a data acquisition card. It is observed that the algorithm is very capable program for many industrial plants which have mechatronic systems.

  7. Predictive models for pressure ulcers from intensive care unit electronic health records using Bayesian networks.

    Science.gov (United States)

    Kaewprag, Pacharmon; Newton, Cheryl; Vermillion, Brenda; Hyun, Sookyung; Huang, Kun; Machiraju, Raghu

    2017-07-05

    We develop predictive models enabling clinicians to better understand and explore patient clinical data along with risk factors for pressure ulcers in intensive care unit patients from electronic health record data. Identifying accurate risk factors of pressure ulcers is essential to determining appropriate prevention strategies; in this work we examine medication, diagnosis, and traditional Braden pressure ulcer assessment scale measurements as patient features. In order to predict pressure ulcer incidence and better understand the structure of related risk factors, we construct Bayesian networks from patient features. Bayesian network nodes (features) and edges (conditional dependencies) are simplified with statistical network techniques. Upon reviewing a network visualization of our model, our clinician collaborators were able to identify strong relationships between risk factors widely recognized as associated with pressure ulcers. We present a three-stage framework for predictive analysis of patient clinical data: 1) Developing electronic health record feature extraction functions with assistance of clinicians, 2) simplifying features, and 3) building Bayesian network predictive models. We evaluate all combinations of Bayesian network models from different search algorithms, scoring functions, prior structure initializations, and sets of features. From the EHRs of 7,717 ICU patients, we construct Bayesian network predictive models from 86 medication, diagnosis, and Braden scale features. Our model not only identifies known and suspected high PU risk factors, but also substantially increases sensitivity of the prediction - nearly three times higher comparing to logistical regression models - without sacrificing the overall accuracy. We visualize a representative model with which our clinician collaborators identify strong relationships between risk factors widely recognized as associated with pressure ulcers. Given the strong adverse effect of pressure ulcers

  8. Active Fault Diagnosis and Assessment for Aircraft Health Management Project

    Data.gov (United States)

    National Aeronautics and Space Administration — To address the NASA LaRC need for innovative methods and tools for the diagnosis of aircraft faults and failures, Physical Optics Corporation (POC) proposes to...

  9. Rwanda Health and Education Information Network (OASIS-RHEIN ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Rwanda Health and Education Information Network (OASIS-RHEIN). Partners in Health (PIH), ... As a result, the Ministry of Health has decided to roll out OpenMRS nationally to track patient-level medical information for improved healthcare delivery and human capacity development. Rolling out OpenMRS will require that ...

  10. Rwanda Health and Education Information Network (OASIS-RHEIN ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Rwanda Health and Education Information Network (OASIS-RHEIN). Partners in Health (PIH), ... The second component will build the capacity of nursing staff to provide better care at the front line by means of an electronic learning platform integrated into the health system infrastructure. Nurses will be upgraded through a ...

  11. The use of fuzzy backpropagation neural networks for the early diagnosis of hypoxic ischemic encephalopathy in newborns.

    Science.gov (United States)

    Li, Liu; Liqing, Huo; Hongru, Lu; Feng, Zhang; Chongxun, Zheng; Pokhrel, Shami; Jie, Zhang

    2011-01-01

    To establish an early diagnostic system for hypoxic ischemic encephalopathy (HIE) in newborns based on artificial neural networks and to determine its feasibility. Based on published research as well as preliminary studies in our laboratory, multiple noninvasive indicators with high sensitivity and specificity were selected for the early diagnosis of HIE and employed in the present study, which incorporates fuzzy logic with artificial neural networks. The analysis of the diagnostic results from the fuzzy neural network experiments with 140 cases of HIE showed a correct recognition rate of 100% in all training samples and a correct recognition rate of 95% in all the test samples, indicating a misdiagnosis rate of 5%. A preliminary model using fuzzy backpropagation neural networks based on a composite index of clinical indicators was established and its accuracy for the early diagnosis of HIE was validated. Therefore, this method provides a convenient tool for the early clinical diagnosis of HIE.

  12. The Use of Fuzzy BackPropagation Neural Networks for the Early Diagnosis of Hypoxic Ischemic Encephalopathy in Newborns

    Directory of Open Access Journals (Sweden)

    Liu Li

    2011-01-01

    Full Text Available Objective. To establish an early diagnostic system for hypoxic ischemic encephalopathy (HIE in newborns based on artificial neural networks and to determine its feasibility. Methods. Based on published research as well as preliminary studies in our laboratory, multiple noninvasive indicators with high sensitivity and specificity were selected for the early diagnosis of HIE and employed in the present study, which incorporates fuzzy logic with artificial neural networks. Results. The analysis of the diagnostic results from the fuzzy neural network experiments with 140 cases of HIE showed a correct recognition rate of 100% in all training samples and a correct recognition rate of 95% in all the test samples, indicating a misdiagnosis rate of 5%. Conclusion. A preliminary model using fuzzy backpropagation neural networks based on a composite index of clinical indicators was established and its accuracy for the early diagnosis of HIE was validated. Therefore, this method provides a convenient tool for the early clinical diagnosis of HIE.

  13. Digital Networked Information Society and Public Health: Problems and Promises of Networked Health Communication of Lay Publics.

    Science.gov (United States)

    Kim, Jeong-Nam

    2018-01-01

    This special issue of Health Communication compiles 10 articles to laud the promise and yet confront the problems in the digital networked information society related to public health. We present this anthology of symphony and cacophony of lay individuals' communicative actions in a digital networked information society. The collection of problems and promise of the new digital world may be a cornerstone joining two worlds-pre- and postdigital network society-and we hope this special issue will help better shape our future states of public health.

  14. [Analysing a public health service network's managerial competence].

    Science.gov (United States)

    Huerta-Riveros, Patricia C; Leyton-Pavez, Carolina E; Saldia-Barahona, Héctor

    2009-12-01

    Analysing the effectiveness of training-action methodology in developing and strengthening the skills required by public health network managerial staff. The study evaluated an educational programme (pre- and post- evaluation being applied within the programme's framework and conditions, without controlled conditions) in which 37 managerial staff from the Talcahuano Health Service Network (forming part of the Public Health Assistance Network) participated in the Managerial Team Training programme, 2007 and 2008, run by the Bío-Bío University's Entrepreneurial Science Faculty in Chile. A skill-based self-evaluation instrument was applied on two different occasions. The results revealed a lack of management skills-based training in network managerial teams and the need for it through training-action methodology which stimulates such needed managerial skills. Acquiring these skills will lead to providing users with a quality service through better management practice in public health establishments.

  15. Air Quality Measures on the National Environmental Health Tracking Network

    Data.gov (United States)

    U.S. Department of Health & Human Services — The Environmental Protection Agency (EPA) provides air pollution data about ozone and particulate matter (PM2.5) to CDC for the Tracking Network. The EPA maintains a...

  16. Health Provider Networks, Quality and Costs

    NARCIS (Netherlands)

    Boone, J.; Schottmuller, C.

    2015-01-01

    We provide a modeling framework to think about selective contracting in the health care sector. Two health care providers differ in quality and costs. When buying health insurance, consumers observe neither provider quality nor costs. We derive an equilibrium where health insurers signal provider

  17. Health provider networks, quality and costs

    NARCIS (Netherlands)

    Boone, Jan; Schottmuller, C.

    2015-01-01

    We provide a modeling framework to think about selective contracting in the health care sector. Two health care providers differ in quality and costs. When buying health insurance, consumers observe neither provider quality nor costs. We derive an equilibrium where health insurers signal provider

  18. Detection and diagnosis of colitis on computed tomography using deep convolutional neural networks.

    Science.gov (United States)

    Liu, Jiamin; Wang, David; Lu, Le; Wei, Zhuoshi; Kim, Lauren; Turkbey, Evrim B; Sahiner, Berkman; Petrick, Nicholas A; Summers, Ronald M

    2017-09-01

    Colitis refers to inflammation of the inner lining of the colon that is frequently associated with infection and allergic reactions. In this paper, we propose deep convolutional neural networks methods for lesion-level colitis detection and a support vector machine (SVM) classifier for patient-level colitis diagnosis on routine abdominal CT scans. The recently developed Faster Region-based Convolutional Neural Network (Faster RCNN) is utilized for lesion-level colitis detection. For each 2D slice, rectangular region proposals are generated by region proposal networks (RPN). Then, each region proposal is jointly classified and refined by a softmax classifier and bounding-box regressor. Two convolutional neural networks, eight layers of ZF net and 16 layers of VGG net are compared for colitis detection. Finally, for each patient, the detections on all 2D slices are collected and a SVM classifier is applied to develop a patient-level diagnosis. We trained and evaluated our method with 80 colitis patients and 80 normal cases using 4 × 4-fold cross validation. For lesion-level colitis detection, with ZF net, the mean of average precisions (mAP) were 48.7% and 50.9% for RCNN and Faster RCNN, respectively. The detection system achieved sensitivities of 51.4% and 54.0% at two false positives per patient for RCNN and Faster RCNN, respectively. With VGG net, Faster RCNN increased the mAP to 56.9% and increased the sensitivity to 58.4% at two false positive per patient. For patient-level colitis diagnosis, with ZF net, the average areas under the ROC curve (AUC) were 0.978 ± 0.009 and 0.984 ± 0.008 for RCNN and Faster RCNN method, respectively. The difference was not statistically significant with P = 0.18. At the optimal operating point, the RCNN method correctly identified 90.4% (72.3/80) of the colitis patients and 94.0% (75.2/80) of normal cases. The sensitivity improved to 91.6% (73.3/80) and the specificity improved to 95.0% (76.0/80) for the Faster RCNN

  19. Fault Diagnosis and Detection in Industrial Motor Network Environment Using Knowledge-Level Modelling Technique

    Directory of Open Access Journals (Sweden)

    Saud Altaf

    2017-01-01

    Full Text Available In this paper, broken rotor bar (BRB fault is investigated by utilizing the Motor Current Signature Analysis (MCSA method. In industrial environment, induction motor is very symmetrical, and it may have obvious electrical signal components at different fault frequencies due to their manufacturing errors, inappropriate motor installation, and other influencing factors. The misalignment experiments revealed that improper motor installation could lead to an unexpected frequency peak, which will affect the motor fault diagnosis process. Furthermore, manufacturing and operating noisy environment could also disturb the motor fault diagnosis process. This paper presents efficient supervised Artificial Neural Network (ANN learning technique that is able to identify fault type when situation of diagnosis is uncertain. Significant features are taken out from the electric current which are based on the different frequency points and associated amplitude values with fault type. The simulation results showed that the proposed technique was able to diagnose the target fault type. The ANN architecture worked well with selecting of significant number of feature data sets. It seemed that, to the results, accuracy in fault detection with features vector has been achieved through classification performance and confusion error percentage is acceptable between healthy and faulty condition of motor.

  20. An integrated approach to planetary gearbox fault diagnosis using deep belief networks

    Science.gov (United States)

    Chen, Haizhou; Wang, Jiaxu; Tang, Baoping; Xiao, Ke; Li, Junyang

    2017-02-01

    Aiming at improving the accuracy of planetary gearbox fault diagnosis, an integrated scheme based on dimensionality reduction method and deep belief networks (DBNs) is presented in this paper. Firstly, the acquired vibration signals are decomposed into mono-component called intrinsic mode functions (IMFs) through ensemble empirical mode decomposition (EEMD), and then Teager-Kaiser energy operator (TKEO) is used to track the instantaneous amplitude (IA) and instantaneous frequency (IF) of a mono-component amplitude modulation (AM) and frequency modulation (FM) signal. Secondly, a high dimensional feature set is constructed through extracting statistical features from six different signal groups. Then, an integrated dimensionality reduction method combining feature selection and feature extraction techniques is proposed to yield a more sensitive and lower dimensional feature set, which not only reduces the computation burden for fault diagnosis but also improves the separability of the samples by integrating the label information. Further, the low dimensional feature set is fed into DBNs classifier to identify the fault types using the optimal parameters selected by particle swarm optimization algorithm (PSO). Finally, two independent cases study of planetary gearbox fault diagnosis are carried out on test rig, and the results show that the proposed method provides higher accuracy in comparison with the existing methods.

  1. Diagnosis of sustainable collaboration in health promotion – a case study

    Directory of Open Access Journals (Sweden)

    van der Sar Rosalie

    2008-11-01

    Full Text Available Abstract Background Collaborations are important to health promotion in addressing multi-party problems. Interest in collaborative processes in health promotion is rising, but still lacks monitoring instruments. The authors developed the DIagnosis of Sustainable Collaboration (DISC model to enable comprehensive monitoring of public health collaboratives. The model focuses on opportunities and impediments for collaborative change, based on evidence from interorganizational collaboration, organizational behavior and planned organizational change. To illustrate and assess the DISC-model, the 2003/2004 application of the model to the Dutch whole-school health promotion collaboration is described. Methods The study combined quantitative research, using a cross-sectional survey, with qualitative research using the personal interview methodology and document analysis. A DISC-based survey was sent to 55 stakeholders in whole-school health promotion in one Dutch region. The survey consisted of 22 scales with 3 to 8 items. Only scales with a reliability score of 0.60 were accepted. The analysis provided for comparisons between stakeholders from education, public service and public health. The survey was followed by approaching 14 stakeholders for a semi-structured DISC-based interview. As the interviews were timed after the survey, the interviews were used to clarify unexpected and unclear outcomes of the survey as well. Additionally, a DISC-based document analysis was conducted including minutes of meetings, project descriptions and correspondence with schools and municipalities. Results Response of the survey was 77% and of the interviews 86%. Significant differences between respondents of different domains were found for the following scales: organizational characteristics scale, the change strategies, network development, project management, willingness to commit and innovative actions and adaptations. The interviews provided a more specific picture

  2. Dissemination of health information through social networks: twitter and antibiotics.

    Science.gov (United States)

    Scanfeld, Daniel; Scanfeld, Vanessa; Larson, Elaine L

    2010-04-01

    This study reviewed Twitter status updates mentioning "antibiotic(s)" to determine overarching categories and explore evidence of misunderstanding or misuse of antibiotics. One thousand Twitter status updates mentioning antibiotic(s) were randomly selected for content analysis and categorization. To explore cases of potential misunderstanding or misuse, these status updates were mined for co-occurrence of the following terms: "cold + antibiotic(s)," "extra + antibiotic(s)," "flu + antibiotic(s)," "leftover + antibiotic(s)," and "share + antibiotic(s)" and reviewed to confirm evidence of misuse or misunderstanding. Of the 1000 status updates, 971 were categorized into 11 groups: general use (n = 289), advice/information (n = 157), side effects/negative reactions (n = 113), diagnosis (n = 102), resistance (n = 92), misunderstanding and/or misuse (n = 55), positive reactions (n = 48), animals (n = 46), other (n = 42), wanting/needing (n = 19), and cost (n = 8). Cases of misunderstanding or abuse were identified for the following combinations: "flu + antibiotic(s)" (n = 345), "cold + antibiotic(s)" (n = 302), "leftover + antibiotic(s)" (n = 23), "share + antibiotic(s)" (n = 10), and "extra + antibiotic(s)" (n = 7). Social media sites offer means of health information sharing. Further study is warranted to explore how such networks may provide a venue to identify misuse or misunderstanding of antibiotics, promote positive behavior change, disseminate valid information, and explore how such tools can be used to gather real-time health data. 2010 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Mosby, Inc. All rights reserved.

  3. Brazilian mothers with HIV: experiences with diagnosis and treatment in a human rights based health care system.

    Science.gov (United States)

    Jerome, Jessica Scott; Galvao, Marli Teresinha Gimeniz; Lindau, Stacy Tessler

    2012-01-01

    Drawing on in-depth interviews with a group of urban poor HIV-positive mothers in Northeastern Brazil, this essay examines their experiences with HIV medical diagnosis and treatment. It argues that strong social and religious networks as well as the Universal HIV treatment program provide Northeastern Brazilian mothers with forms of support that may be absent in other countries. It further suggests that more research be done to determine how particular forms of health care, such as the human rights-based approach that Brazil has taken to HIV/AIDS, inform patient-provider relationships.

  4. From seismic network optimization to real-time diagnosis of magma migration

    Science.gov (United States)

    Taisne, B.; Aoki, Y.

    2013-12-01

    Triggering mechanism of a seismic swarm has to be identified with great confidence in real time. Crisis response will not be the same whether magma is involved or not. The method based on the Seismic Amplitude Ratio Analysis enables a rapid and unambiguous diagnosis to detect migrating micro-seismicity. Combined with other measurements, this migrating seismicity could be linked to complex motions of magma within the volcanic edifice. The beauty of this method lies in the fact that the ratio of seismic energy recorded at different stations is independent of the seismic energy radiated at the source. Drastic changes in attenuation are unlikely to occur at the time scale of magma intrusion, therefore temporal evolutions in the measured ratio have to be explained by a change in the source location. Based on a simple assumption this technique can be used to assess the potential of existing monitoring seismic network to detect migrating events in real-time. It can also be used to design monitoring seismic network based on the available number of sensors as well as from field constraints. Network capability also depends on the noise level at each station, therefore this noise is used to define the magnitude threshold that can be detected as a function of the distance.

  5. Computer-Aided Diagnosis Based on Convolutional Neural Network System for Colorectal Polyp Classification: Preliminary Experience.

    Science.gov (United States)

    Komeda, Yoriaki; Handa, Hisashi; Watanabe, Tomohiro; Nomura, Takanobu; Kitahashi, Misaki; Sakurai, Toshiharu; Okamoto, Ayana; Minami, Tomohiro; Kono, Masashi; Arizumi, Tadaaki; Takenaka, Mamoru; Hagiwara, Satoru; Matsui, Shigenaga; Nishida, Naoshi; Kashida, Hiroshi; Kudo, Masatoshi

    2017-01-01

    Computer-aided diagnosis (CAD) is becoming a next-generation tool for the diagnosis of human disease. CAD for colon polyps has been suggested as a particularly useful tool for trainee colonoscopists, as the use of a CAD system avoids the complications associated with endoscopic resections. In addition to conventional CAD, a convolutional neural network (CNN) system utilizing artificial intelligence (AI) has been developing rapidly over the past 5 years. We attempted to generate a unique CNN-CAD system with an AI function that studied endoscopic images extracted from movies obtained with colonoscopes used in routine examinations. Here, we report our preliminary results of this novel CNN-CAD system for the diagnosis of colon polyps. A total of 1,200 images from cases of colonoscopy performed between January 2010 and December 2016 at Kindai University Hospital were used. These images were extracted from the video of actual endoscopic examinations. Additional video images from 10 cases of unlearned processes were retrospectively assessed in a pilot study. They were simply diagnosed as either an adenomatous or nonadenomatous polyp. The number of images used by AI to learn to distinguish adenomatous from nonadenomatous was 1,200:600. These images were extracted from the videos of actual endoscopic examinations. The size of each image was adjusted to 256 × 256 pixels. A 10-hold cross-validation was carried out. The accuracy of the 10-hold cross-validation is 0.751, where the accuracy is the ratio of the number of correct answers over the number of all the answers produced by the CNN. The decisions by the CNN were correct in 7 of 10 cases. A CNN-CAD system using routine colonoscopy might be useful for the rapid diagnosis of colorectal polyp classification. Further prospective studies in an in vivo setting are required to confirm the effectiveness of a CNN-CAD system in routine colonoscopy. © 2017 S. Karger AG, Basel.

  6. Quebec mental health services networks: models and implementation

    Directory of Open Access Journals (Sweden)

    Marie-Josée Fleury

    2005-06-01

    Full Text Available Purpose: In the transformation of health care systems, the introduction of integrated service networks is considered to be one of the main solutions for enhancing efficiency. In the last few years, a wealth of literature has emerged on the topic of services integration. However, the question of how integrated service networks should be modelled to suit different implementation contexts has barely been touched. To fill that gap, this article presents four models for the organization of mental health integrated networks. Data sources: The proposed models are drawn from three recently published studies on mental health integrated services in the province of Quebec (Canada with the author as principal investigator. Description: Following an explanation of the concept of integrated service network and a description of the Quebec context for mental health networks, the models, applicable in all settings: rural, urban or semi-urban, and metropolitan, and summarized in four figures, are presented. Discussion and conclusion: To apply the models successfully, the necessity of rallying all the actors of a system, from the strategic, tactical and operational levels, according to the type of integration involved: functional/administrative, clinical and physician-system is highlighted. The importance of formalizing activities among organizations and actors in a network and reinforcing the governing mechanisms at the local level is also underlined. Finally, a number of integration strategies and key conditions of success to operationalize integrated service networks are suggested.

  7. Malaria diagnosis and treatment amongst health workers in ...

    African Journals Online (AJOL)

    Folders successfully traced were 1012; in 92 percent investigations for malaria were requested while 32 percent had differential diagnosis. Out of the 931 malaria investigations requested, 30percent did the tests and positive results were 94.9 percent. Presumptive treatment was 98 percent. Majority (83.3%) received ACTs.

  8. Diagnosis of vaginal infection in pregnancy | Botha | Health SA ...

    African Journals Online (AJOL)

    In 51,4% of the cases the diagnosis differed. Candida albicans infection was diagnosed by 10 respondents, while 3 actually had Trichomonas vaginalis infection and seven had Gardnerella vaginalis infection. Trichomonas vaginalis infection was diagnosed in 26 cases, while 15 were actually due to Candida albicans and ...

  9. EARLY DIAGNOSIS OF CRANIOSYNOSTOSIS IN INFANTS AT PRIMARY HEALTH CARE

    Directory of Open Access Journals (Sweden)

    Skoric Jasmina

    2014-12-01

    Full Text Available Craniosynostosis or premature fusion of one or more cranial sutures in infants disturbs normal brain growth. This condition causes abnormal skull configuration, increased intracranial pressure, headache, strabismus, blurred vision, blindness, psychomotor retardation. The diagnosis of craniosynostosis is very simple. Pediatricians should routinely assess neurological status and measure head circumference and anterior fontanelle. When necessary, ultrasound of CNS, X-ray and cranial CT scan can be done. When it comes to this condition, early diagnosis and surgical intervention are of utmost importance. In this paper, we have presented a case on craniosynostosis in a female infant, discovered in the third month of life during systematic review that included measurement of head circumference, palpation of anterior fontanelle and cranial sutures. The child was referred to a neurosurgeon who performed the CT scan of endocranium and confirmed the initial diagnosis of craniosynostosis. With head circumference of 40 cm and fused anterior fontanelle, the surgery was timely performed at the sixth month of life due to early diagnosis.

  10. Early diagnosis of craniosynostosis in infants at primary health care

    Directory of Open Access Journals (Sweden)

    Skoric Jasmina

    2014-12-01

    Full Text Available Craniosynostosis or premature fusion of one or more cranial sutures in infants disturbs normal brain growth. This condition causes abnormal skull configuration, increased intracranial pressure, headache, strabismus, blurred vision, blindness, psychomotor retardation. The diagnosis of craniosynostosis is very simple. Pediatricians should routinely assess neurological status and measure head circumference and anterior fontanelle. When necessary, ultrasound of CNS, X-ray and cranial CT scan can be done. When it comes to this condition, early diagnosis and surgical intervention are of utmost importance. In this paper, we have presented a case on craniosynostosis in a female infant, discovered in the third month of life during systematic review that included measurement of head circumference, palpation of anterior fontanelle and cranial sutures. The child was referred to a neurosurgeon who performed the CT scan of endocranium and confirmed the initial diagnosis of craniosynostosis. With head circumference of 40 cm and fused anterior fontanelle, the surgery was timely performed at the sixth month of life due to early diagnosis.

  11. Four health data networks illustrate the potential for a shared national multipurpose big-data network.

    Science.gov (United States)

    Curtis, Lesley H; Brown, Jeffrey; Platt, Richard

    2014-07-01

    Information in electronic health data that are drawn from large populations of patients is transforming health care, public health practice, and clinical research. This article describes our experience in developing data networks that repurpose electronic health records and administrative data. The four programs we feature are the Food and Drug Administration's Mini-Sentinel program (which focuses on medical product safety), the National Patient-Centered Clinical Research Network (PCORnet, comparative effectiveness research), the National Institutes of Health's Health Care Systems Research Collaboratory Distributed Research Network (biomedical research), and ESPnet (public health surveillance). Challenges to these uses of electronic health data include understanding the factors driving the collection, coding, and preservation of the data; the extensive customization of different systems that collect similar data; the fragmentation of the US health care delivery system and its records; and privacy and proprietary considerations. We view these four programs as examples of the first stage in the development of a shared national big-data resource that leverages the investments of many agencies and organizations for the benefit of multiple networks and users. Project HOPE—The People-to-People Health Foundation, Inc.

  12. Networked Biomedical System for Ubiquitous Health Monitoring

    Directory of Open Access Journals (Sweden)

    Arjan Durresi

    2008-01-01

    Full Text Available We propose a distributed system that enables global and ubiquitous health monitoring of patients. The biomedical data will be collected by wearable health diagnostic devices, which will include various types of sensors and will be transmitted towards the corresponding Health Monitoring Centers. The permanent medical data of patients will be kept in the corresponding Home Data Bases, while the measured biomedical data will be sent to the Visitor Health Monitor Center and Visitor Data Base that serves the area of present location of the patient. By combining the measured biomedical data and the permanent medical data, Health Medical Centers will be able to coordinate the needed actions and help the local medical teams to make quickly the best decisions that could be crucial for the patient health, and that can reduce the cost of health service.

  13. Using Social Network Analysis to Assess Mentorship and Collaboration in a Public Health Network.

    Science.gov (United States)

    Petrescu-Prahova, Miruna; Belza, Basia; Leith, Katherine; Allen, Peg; Coe, Norma B; Anderson, Lynda A

    2015-08-20

    Addressing chronic disease burden requires the creation of collaborative networks to promote systemic changes and engage stakeholders. Although many such networks exist, they are rarely assessed with tools that account for their complexity. This study examined the structure of mentorship and collaboration relationships among members of the Healthy Aging Research Network (HAN) using social network analysis (SNA). We invited 97 HAN members and partners to complete an online social network survey that included closed-ended questions about HAN-specific mentorship and collaboration during the previous 12 months. Collaboration was measured by examining the activity of the network on 6 types of products: published articles, in-progress manuscripts, grant applications, tools, research projects, and presentations. We computed network-level measures such as density, number of components, and centralization to assess the cohesiveness of the network. Sixty-three respondents completed the survey (response rate, 65%). Responses, which included information about collaboration with nonrespondents, suggested that 74% of HAN members were connected through mentorship ties and that all 97 members were connected through at least one form of collaboration. Mentorship and collaboration ties were present both within and across boundaries of HAN member organizations. SNA of public health collaborative networks provides understanding about the structure of relationships that are formed as a result of participation in network activities. This approach may offer members and funders a way to assess the impact of such networks that goes beyond simply measuring products and participation at the individual level.

  14. No longer simply a Practice-based Research Network (PBRN) health improvement networks.

    Science.gov (United States)

    Williams, Robert L; Rhyne, Robert L

    2011-01-01

    While primary care Practice-based Research Networks are best known for their original, research purpose, evidence accumulating over the last several years is demonstrating broader values of these collaborations. Studies have demonstrated their role in quality improvement and practice change, in continuing professional education, in clinician retention in medically underserved areas, and in facilitating transition of primary care organization. A role in informing and facilitating health policy development is also suggested. Taking into account this more robust potential, we propose a new title, the Health Improvement Network, and a new vision for Practice-based Research Networks.

  15. The discourse of health managers on aspects related to the delay in tuberculosis diagnosis

    Directory of Open Access Journals (Sweden)

    Lenilde Duarte de Sa

    2013-10-01

    Full Text Available The aim of this study was to analyze the discourse of health managers on aspects related to delay in tuberculosis diagnosis. This was a qualitative research study, conducted with 16 Family Health Unit managers. The empirical data were obtained through semi-structured interviews. The analysis was based on the theoretical framework of the French school of discourse analysis. According to the managers’ statements, the delay in tuberculosis diagnosis is related to patient and health service aspects. As for patient aspects, managers report fear, prejudice and lack of information as factors that may promote a delayed diagnosis. Regarding health service aspects, structural problems and lack of professional skills were reported. The discourse of managers should be considered to qualify tuberculosis control actions and to prevent delays in diagnosis.

  16. How to enhance the efficacy of health network growth.

    Science.gov (United States)

    Weil, T P

    2000-01-01

    In almost every American metropolitan area, health executives are busily enhancing the efficacy of their health networks by corporately restructuring so that their organization can become a fiscally and politically powerful oligopoly or a regulated monopoly. When the formation of these alliances are initially announced by the local media, they are reported to be vehicles to enhance access, social equity and quality of care, and to reduce costs. Since an increasing number of these health networks are currently experiencing fiscal, cultural and other difficulties, it is critical to study: (a) what factors should be considered when developing an effective and efficient health network?; (b) what are the practical issues in their strategic formation and management so they eventually achieve their full potential?; and (c) why will some divestitures among these health networks occur and how will these corporate 'spin offs' impact on consumers, providers, insurers and governmental agencies? Within the next decade the United States will face some inevitable economic difficulties. At that time, enhancing access and reducing costs will become more critical issues for health networks. These alliances may then need to become more responsive to consumer pressures as the Americans shift their political proclivities from the current quasi-competitive to a more quasi-regulatory position. In this context, the use of global budgetary targets is discussed as a possible option in the United States to constrain costs, an approach used in almost all other western industrialized nations.

  17. Evaluating the rural health placements of the Rural Support Network ...

    African Journals Online (AJOL)

    Evaluating the rural health placements of the Rural Support Network at the Faculty of Health Sciences, University of Cape Town. ... The importance of community empowerment and of connecting and building relationships with communities was also emphasised. Challenges pertained to conflict within groups, incidents of ...

  18. One Health in social networks and social media.

    Science.gov (United States)

    Mekaru, S R; Brownstein, J S

    2014-08-01

    In the rapidly evolving world of social media, social networks, mobile applications and citizen science, online communities can develop organically and separately from larger or more established organisations. The One Health online community is experiencing expansion from both the bottom up and the top down. In this paper, the authors review social media's strengths and weaknesses, earlier work examining Internet resources for One Health, the current state of One Health in social media (e.g. Facebook, Twitter, YouTube) and online social networking sites (e.g. LinkedIn and ResearchGate), as well as social media in One Health-related citizen science projects. While One Health has a fairly strong presence on websites, its social media presence is more limited and has an uneven geographic distribution. In work following the Stone Mountain Meeting,the One Health Global Network Task Force Report recommended the creation of an online community of practice. Professional social networks as well as the strategic use of social media should be employed in this effort. Finally, One Health-related research projects using volunteers (citizen science) often use social media to enhance their recruitment. Including these researchers in a community of practitioners would take full advantage of their existing social media presence. In conclusion, the interactive nature of social media, combined with increasing global Internet access, provides the One Health community with opportunities to meaningfully expand their community and promote their message.

  19. Strengthening the Indonesia's Health Policy Network to Promote ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Strengthening the Indonesia's Health Policy Network to Promote Equity and Social Protection. Despite sustained economic growth and efforts to expand universal health coverage in Indonesia, many poor people still have little or no access to proper healthcare services. Indeed, healthcare provision remains uneven and of ...

  20. Social Networks, Interpersonal Social Support, and Health Outcomes: A Health Communication Perspective

    OpenAIRE

    Wright, Kevin

    2016-01-01

    This manuscript discusses the development, impact, and several major research findings of studies in the area of social network support and health outcomes. The review focuses largely on the development of online social support networks and the ways in which they may interact with face-to-face support networks to influence physical and psychological health outcomes. The manuscript discusses this area, and it presents a research agenda for future work in this area from an Associate Editor’s pe...

  1. Brain Network Activation Technology Does Not Assist with Concussion Diagnosis and Return to Play in Football Athletes

    OpenAIRE

    Broglio, Steven P; Richelle Williams; Andrew Lapointe; Ashley Rettmann; Brandon Moore; Meehan, Sean K; Eckner, James T.

    2017-01-01

    Background Concussion diagnosis and management remains a largely subjective process. This investigation sought to evaluate the utility of a novel neuroelectric measure for concussion diagnosis and return to play decision-making. Hypothesis Brain Network Activation (BNA) scores obtained within 72-h of injury will be lower than the athlete’s preseason evaluation and that of a matched control athlete; and the BNA will demonstrate ongoing declines at the return to play and post-season ...

  2. An Intelligent Gear Fault Diagnosis Methodology Using a Complex Wavelet Enhanced Convolutional Neural Network.

    Science.gov (United States)

    Sun, Weifang; Yao, Bin; Zeng, Nianyin; Chen, Binqiang; He, Yuchao; Cao, Xincheng; He, Wangpeng

    2017-07-12

    As a typical example of large and complex mechanical systems, rotating machinery is prone to diversified sorts of mechanical faults. Among these faults, one of the prominent causes of malfunction is generated in gear transmission chains. Although they can be collected via vibration signals, the fault signatures are always submerged in overwhelming interfering contents. Therefore, identifying the critical fault's characteristic signal is far from an easy task. In order to improve the recognition accuracy of a fault's characteristic signal, a novel intelligent fault diagnosis method is presented. In this method, a dual-tree complex wavelet transform (DTCWT) is employed to acquire the multiscale signal's features. In addition, a convolutional neural network (CNN) approach is utilized to automatically recognise a fault feature from the multiscale signal features. The experiment results of the recognition for gear faults show the feasibility and effectiveness of the proposed method, especially in the gear's weak fault features.

  3. Brain Functional Network in Alzheimer's Disease: Diagnostic Markers for Diagnosis and Monitoring

    Directory of Open Access Journals (Sweden)

    Guido Rodriguez

    2011-01-01

    Full Text Available Alzheimer's disease (AD is the most common type of dementia that is clinically characterized by the presence of memory impairment and later by impairment in other cognitive domains. The clinical diagnosis is based on interviews with the patient and his/her relatives and on neuropsychological assessment, which are also used to monitor cognitive decline over time. Several biomarkers have been proposed for detecting AD in its earliest stages, that is, in the predementia stage. In an attempt to find noninvasive biomarkers, researchers have investigated the feasibility of neuroimaging tools, such as MR, SPECT, and FDG-PET imaging, as well as neurophysiological measurements using EEG. In this paper, we investigate the brain functional networks in AD, focusing on main neurophysiological techniques, integrating with most relevant functional brain imaging findings.

  4. Employment and comparison of different Artificial Neural Networks for epilepsy diagnosis from EEG signals.

    Science.gov (United States)

    Sezer, Esma; Işik, Hakan; Saracoğlu, Esra

    2012-02-01

    In this study, it has been intended to analyze Electroencephalography (EEG) signals by Wavelet Transform (WT) for diagnosis of epilepsy, to employ various Artificial Neural Networks (ANNs) for the signals' automatic classification. Furthermore, carrying out a performance comparison has been aimed. Three EEG signals have been decomposed into frequency sub bands by WT and the feature vectors have been extracted from these sub bands. In order to reduce the sizes of the extracted feature vectors, Principal Component Analysis (PCA) method has been applied when necessary and these feature vectors have been classified by five different ANNs as either epileptic or healthy. The performance evaluation has been carried out by conducting ROC analysis for the used ANN models that and their comparisons have also been included.

  5. An adaptive deep convolutional neural network for rolling bearing fault diagnosis

    Science.gov (United States)

    Fuan, Wang; Hongkai, Jiang; Haidong, Shao; Wenjing, Duan; Shuaipeng, Wu

    2017-09-01

    The working conditions of rolling bearings usually is very complex, which makes it difficult to diagnose rolling bearing faults. In this paper, a novel method called the adaptive deep convolutional neural network (CNN) is proposed for rolling bearing fault diagnosis. Firstly, to get rid of manual feature extraction, the deep CNN model is initialized for automatic feature learning. Secondly, to adapt to different signal characteristics, the main parameters of the deep CNN model are determined with a particle swarm optimization method. Thirdly, to evaluate the feature learning ability of the proposed method, t-distributed stochastic neighbor embedding (t-SNE) is further adopted to visualize the hierarchical feature learning process. The proposed method is applied to diagnose rolling bearing faults, and the results confirm that the proposed method is more effective and robust than other intelligent methods.

  6. Networked Learning and Network Science: Potential Applications to Health Professionals' Continuing Education and Development.

    Science.gov (United States)

    Margolis, Alvaro; Parboosingh, John

    2015-01-01

    Prior interpersonal relationships and interactivity among members of professional associations may impact the learning process in continuing medical education (CME). On the other hand, CME programs that encourage interactivity between participants may impact structures and behaviors in these professional associations. With the advent of information and communication technologies, new communication spaces have emerged that have the potential to enhance networked learning in national and international professional associations and increase the effectiveness of CME for health professionals. In this article, network science, based on the application of network theory and other theories, is proposed as an approach to better understand the contribution networking and interactivity between health professionals in professional communities make to their learning and adoption of new practices over time. © 2015 The Alliance for Continuing Education in the Health Professions, the Society for Academic Continuing Medical Education, and the Council on Continuing Medical Education, Association for Hospital Medical Education.

  7. A microfluidic platform with a flow-balanced fluidic network for osteoarthritis diagnosis

    Science.gov (United States)

    Kim, Kangil; Park, Yoo Min; Yoon, Hyun C.; Yang, Sang Sik

    2013-05-01

    Osteoarthritis (OA) is one of the most common human diseases, and the occurrence of OA is likely to increase with the increase of population ages. The diagnosis of OA is based on patientrelevant measures, structural measures, and measurement of biomarkers that are released through joint metabolism. Traditionally, radiography or magnetic resonance imaging (MRI) is used to diagnose OA and predict its course. However, diagnostic imaging in OA provides only indirect information on pathology and treatment response. A sensing of OA based on the detection of biomarkers insignificantly improves the accuracy and sensitivity of diagnosis and reduces the cost compared with that of radiography or MRI. In our former study, we proposed microfluidic platform to detect biomarker of OA. But the platform can detect only one biomarker because it has one microfluidic channel. In this report, we proposes microfluidic platform that can detect several biomarkers. The proposed platform has three layers. The bottom layer has gold patterns on a Si substrate for optical sensing. The middle layer and top layer were fabricated by polydimethysiloxane (PDMS) using soft-lithography. The middle layer has four channels connecting top layer to bottom layer. The top layer consists of one sample injection inlet, and four antibody injection inlets. To this end, we designed a flow-balanced microfluidic network using analogy between electric and hydraulic systems. Also, the designed microfluidic network was confirmed by finite element model (FEM) analysis using COMSOL FEMLAB. To verify the efficiency of fabricated platform, the optical sensing test was performed to detect biomarker of OA using fluorescence microscope. We used cartilage oligomeric matrix protein (COMP) as biomarker because it reflects specific changes in joint tissues. The platform successfully detected various concentration of COMP (0, 100, 500, 1000 ng/ml) at each chamber. The effectiveness of the microfluidic platform was verified

  8. The application of artificial neural network model in the non-invasive diagnosis of liver fibrosis

    Directory of Open Access Journals (Sweden)

    Bo LI

    2012-12-01

    Full Text Available Objective  To construct and evaluate an artificial neural network (ANN model as a new non-invasive diagnostic method for clinical assessment of liver fibrosis at early stage. Methods  The model was set up and tested among 683 chronic hepatitis B (CHB patients, with authentic positive clinical biopsy results, proved to have liver fibrosis or cirrhosis, admitted to 302 Hospital of PLA from May 2008 to March 2011. Among 683 samples, 504 samples were diagnosed as cirrhosis as a result of CHB, and 179 liver fibrosis due to other liver diseases. 134 out of 683 patients were included in training group by stratified sampling, and the others for verification. Six items (age, AST, PTS, PLT, GGT and DBil were selected as input layer indexes to set up the model for evaluation. Results  The ANN model for diagnosis of liver fibrosis was set up. The diagnostic accuracy was 77.4%, sensitivity was 76.8%, and specificity was 77.8%. Its Kappa concordance tests showed the diagnosis result of the model was consistent with biopsy result (Kappa index=0.534. The accuracy, sensitivity and specificity of CHB patients were 80.4%, 79.9% and 80.7% (Kappa index=0.598 respectively, and those for other liver diseases were 67.9%, 64.3% and 69.7% (Kappa index=0.316. Conclusion  The artificial neural network model established by the authors demonstrates its high sensitivity and specificity as a new non-invasive diagnostic method for liver fibrosis induced by HBV infection. However, it shows limited diagnostic reliability to fibrosis as a result of other liver diseases.

  9. Combined expert system/neural networks method for process fault diagnosis

    Science.gov (United States)

    Reifman, Jaques; Wei, Thomas Y. C.

    1995-01-01

    A two-level hierarchical approach for process fault diagnosis is an operating system employs a function-oriented approach at a first level and a component characteristic-oriented approach at a second level, where the decision-making procedure is structured in order of decreasing intelligence with increasing precision. At the first level, the diagnostic method is general and has knowledge of the overall process including a wide variety of plant transients and the functional behavior of the process components. An expert system classifies malfunctions by function to narrow the diagnostic focus to a particular set of possible faulty components that could be responsible for the detected functional misbehavior of the operating system. At the second level, the diagnostic method limits its scope to component malfunctions, using more detailed knowledge of component characteristics. Trained artificial neural networks are used to further narrow the diagnosis and to uniquely identify the faulty component by classifying the abnormal condition data as a failure of one of the hypothesized components through component characteristics. Once an anomaly is detected, the hierarchical structure is used to successively narrow the diagnostic focus from a function misbehavior, i.e., a function oriented approach, until the fault can be determined, i.e., a component characteristic-oriented approach.

  10. Combined expert system/neural networks method for process fault diagnosis

    Science.gov (United States)

    Reifman, J.; Wei, T.Y.C.

    1995-08-15

    A two-level hierarchical approach for process fault diagnosis of an operating system employs a function-oriented approach at a first level and a component characteristic-oriented approach at a second level, where the decision-making procedure is structured in order of decreasing intelligence with increasing precision. At the first level, the diagnostic method is general and has knowledge of the overall process including a wide variety of plant transients and the functional behavior of the process components. An expert system classifies malfunctions by function to narrow the diagnostic focus to a particular set of possible faulty components that could be responsible for the detected functional misbehavior of the operating system. At the second level, the diagnostic method limits its scope to component malfunctions, using more detailed knowledge of component characteristics. Trained artificial neural networks are used to further narrow the diagnosis and to uniquely identify the faulty component by classifying the abnormal condition data as a failure of one of the hypothesized components through component characteristics. Once an anomaly is detected, the hierarchical structure is used to successively narrow the diagnostic focus from a function misbehavior, i.e., a function oriented approach, until the fault can be determined, i.e., a component characteristic-oriented approach. 9 figs.

  11. Fault Feature Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs Network

    Directory of Open Access Journals (Sweden)

    Kai Chen

    2017-01-01

    Full Text Available A gear transmission system is a complex nonstationary and nonlinear time-varying coupling system. When faults occur on gear system, it is difficult to extract the fault feature. In this paper, a novel fault diagnosis method based on ensemble empirical mode decomposition (EEMD and Deep Briefs Network (DBN is proposed to treat the vibration signals measured from gearbox. The original data is decomposed into a set of intrinsic mode functions (IMFs using EEMD, and then main IMFs were chosen for reconstructed signal to suppress abnormal interference from noise. The reconstructed signals were regarded as input of DBN to identify gearbox working states and fault types. To verify the effectiveness of the EEMD-DBN in detecting the faults, a series of gear fault simulate experiments at different states were carried out. Results showed that the proposed method which coupled EEMD and DBN can improve the accuracy of gear fault identification and it is capable of applying to fault diagnosis in practical application.

  12. Artificial neural network application for space station power system fault diagnosis

    Science.gov (United States)

    Momoh, James A.; Oliver, Walter E.; Dias, Lakshman G.

    1995-01-01

    This study presents a methodology for fault diagnosis using a Two-Stage Artificial Neural Network Clustering Algorithm. Previously, SPICE models of a 5-bus DC power distribution system with assumed constant output power during contingencies from the DDCU were used to evaluate the ANN's fault diagnosis capabilities. This on-going study uses EMTP models of the components (distribution lines, SPDU, TPDU, loads) and power sources (DDCU) of Space Station Alpha's electrical Power Distribution System as a basis for the ANN fault diagnostic tool. The results from the two studies are contrasted. In the event of a major fault, ground controllers need the ability to identify the type of fault, isolate the fault to the orbital replaceable unit level and provide the necessary information for the power management expert system to optimally determine a degraded-mode load schedule. To accomplish these goals, the electrical power distribution system's architecture can be subdivided into three major classes: DC-DC converter to loads, DC Switching Unit (DCSU) to Main bus Switching Unit (MBSU), and Power Sources to DCSU. Each class which has its own electrical characteristics and operations, requires a unique fault analysis philosophy. This study identifies these philosophies as Riddles 1, 2 and 3 respectively. The results of the on-going study addresses Riddle-1. It is concluded in this study that the combination of the EMTP models of the DDCU, distribution cables and electrical loads yields a more accurate model of the behavior and in addition yielded more accurate fault diagnosis using ANN versus the results obtained with the SPICE models.

  13. Diagnosis Of Malaria By Community Health Workers In Nigeria | Eke ...

    African Journals Online (AJOL)

    Objective : The introduction of primary health care made Nigeria, a developing country, train and retrain community health workers to work all over the country especially in the rural communities where there is dearth of doctors. Despite their training and experience many people are skeptical of their competence to diagnose ...

  14. European consensus statement on diagnosis and treatment of adult ADHD: the European Network adult ADHD

    LENUS (Irish Health Repository)

    Kooij, Sandra JJ

    2010-09-03

    Abstract Background Attention deficit hyperactivity disorder (ADHD) is among the most common psychiatric disorders of childhood that persists into adulthood in the majority of cases. The evidence on persistence poses several difficulties for adult psychiatry considering the lack of expertise for diagnostic assessment, limited treatment options and patient facilities across Europe. Methods The European Network Adult ADHD, founded in 2003, aims to increase awareness of this disorder and improve knowledge and patient care for adults with ADHD across Europe. This Consensus Statement is one of the actions taken by the European Network Adult ADHD in order to support the clinician with research evidence and clinical experience from 18 European countries in which ADHD in adults is recognised and treated. Results Besides information on the genetics and neurobiology of ADHD, three major questions are addressed in this statement: (1) What is the clinical picture of ADHD in adults? (2) How can ADHD in adults be properly diagnosed? (3) How should ADHD in adults be effectively treated? Conclusions ADHD often presents as an impairing lifelong condition in adults, yet it is currently underdiagnosed and treated in many European countries, leading to ineffective treatment and higher costs of illness. Expertise in diagnostic assessment and treatment of ADHD in adults must increase in psychiatry. Instruments for screening and diagnosis of ADHD in adults are available and appropriate treatments exist, although more research is needed in this age group.

  15. Empirical mode decomposition and neural networks on FPGA for fault diagnosis in induction motors.

    Science.gov (United States)

    Camarena-Martinez, David; Valtierra-Rodriguez, Martin; Garcia-Perez, Arturo; Osornio-Rios, Roque Alfredo; Romero-Troncoso, Rene de Jesus

    2014-01-01

    Nowadays, many industrial applications require online systems that combine several processing techniques in order to offer solutions to complex problems as the case of detection and classification of multiple faults in induction motors. In this work, a novel digital structure to implement the empirical mode decomposition (EMD) for processing nonstationary and nonlinear signals using the full spline-cubic function is presented; besides, it is combined with an adaptive linear network (ADALINE)-based frequency estimator and a feed forward neural network (FFNN)-based classifier to provide an intelligent methodology for the automatic diagnosis during the startup transient of motor faults such as: one and two broken rotor bars, bearing defects, and unbalance. Moreover, the overall methodology implementation into a field-programmable gate array (FPGA) allows an online and real-time operation, thanks to its parallelism and high-performance capabilities as a system-on-a-chip (SoC) solution. The detection and classification results show the effectiveness of the proposed fused techniques; besides, the high precision and minimum resource usage of the developed digital structures make them a suitable and low-cost solution for this and many other industrial applications.

  16. Empirical Mode Decomposition and Neural Networks on FPGA for Fault Diagnosis in Induction Motors

    Directory of Open Access Journals (Sweden)

    David Camarena-Martinez

    2014-01-01

    Full Text Available Nowadays, many industrial applications require online systems that combine several processing techniques in order to offer solutions to complex problems as the case of detection and classification of multiple faults in induction motors. In this work, a novel digital structure to implement the empirical mode decomposition (EMD for processing nonstationary and nonlinear signals using the full spline-cubic function is presented; besides, it is combined with an adaptive linear network (ADALINE-based frequency estimator and a feed forward neural network (FFNN-based classifier to provide an intelligent methodology for the automatic diagnosis during the startup transient of motor faults such as: one and two broken rotor bars, bearing defects, and unbalance. Moreover, the overall methodology implementation into a field-programmable gate array (FPGA allows an online and real-time operation, thanks to its parallelism and high-performance capabilities as a system-on-a-chip (SoC solution. The detection and classification results show the effectiveness of the proposed fused techniques; besides, the high precision and minimum resource usage of the developed digital structures make them a suitable and low-cost solution for this and many other industrial applications.

  17. European consensus statement on diagnosis and treatment of adult ADHD: The European Network Adult ADHD

    Science.gov (United States)

    2010-01-01

    Background Attention deficit hyperactivity disorder (ADHD) is among the most common psychiatric disorders of childhood that persists into adulthood in the majority of cases. The evidence on persistence poses several difficulties for adult psychiatry considering the lack of expertise for diagnostic assessment, limited treatment options and patient facilities across Europe. Methods The European Network Adult ADHD, founded in 2003, aims to increase awareness of this disorder and improve knowledge and patient care for adults with ADHD across Europe. This Consensus Statement is one of the actions taken by the European Network Adult ADHD in order to support the clinician with research evidence and clinical experience from 18 European countries in which ADHD in adults is recognised and treated. Results Besides information on the genetics and neurobiology of ADHD, three major questions are addressed in this statement: (1) What is the clinical picture of ADHD in adults? (2) How can ADHD in adults be properly diagnosed? (3) How should ADHD in adults be effectively treated? Conclusions ADHD often presents as an impairing lifelong condition in adults, yet it is currently underdiagnosed and treated in many European countries, leading to ineffective treatment and higher costs of illness. Expertise in diagnostic assessment and treatment of ADHD in adults must increase in psychiatry. Instruments for screening and diagnosis of ADHD in adults are available and appropriate treatments exist, although more research is needed in this age group. PMID:20815868

  18. A pre-trained convolutional neural network based method for thyroid nodule diagnosis.

    Science.gov (United States)

    Ma, Jinlian; Wu, Fa; Zhu, Jiang; Xu, Dong; Kong, Dexing

    2017-01-01

    In ultrasound images, most thyroid nodules are in heterogeneous appearances with various internal components and also have vague boundaries, so it is difficult for physicians to discriminate malignant thyroid nodules from benign ones. In this study, we propose a hybrid method for thyroid nodule diagnosis, which is a fusion of two pre-trained convolutional neural networks (CNNs) with different convolutional layers and fully-connected layers. Firstly, the two networks pre-trained with ImageNet database are separately trained. Secondly, we fuse feature maps learned by trained convolutional filters, pooling and normalization operations of the two CNNs. Finally, with the fused feature maps, a softmax classifier is used to diagnose thyroid nodules. The proposed method is validated on 15,000 ultrasound images collected from two local hospitals. Experiment results show that the proposed CNN based methods can accurately and effectively diagnose thyroid nodules. In addition, the fusion of the two CNN based models lead to significant performance improvement, with an accuracy of 83.02%±0.72%. These demonstrate the potential clinical applications of this method. Copyright © 2016 Elsevier B.V. All rights reserved.

  19. Assessment of a national network: the case of the French teacher training colleges' health education network.

    Science.gov (United States)

    Guével, Marie-Renée; Jourdan, Didier

    2009-06-01

    The French teacher training colleges' health education (HE) network was set up in 2005 to encourage the inclusion of HE in courses for primary and secondary school teachers. A systematic process of monitoring the activity and the impact of this initiative was implemented. This analysis was systematically compared with the perceptions of teaching staff involved in the network. This paper assesses the network after 2 years using documents produced and interviews with 24 coordinators. Twenty-nine teacher training colleges out of a total of 31 are involved in the network. The network has helped to create links between teacher training colleges, extend HE training and encourage partnerships with other public health organizations. By 2007, HE was included in courses offered by 19 teacher training colleges as opposed to only 3 in 2005. This study not only showed the positive impact of the network but also revealed issues in its management and presented new challenges to ensure the effectiveness of the network. The network has succeeded in attracting and training trainers who were already providing or were interested in HE. Reaching other trainers who are not familiar with HE remains a challenge for the future.

  20. Social Networks and Health Knowledge in India

    DEFF Research Database (Denmark)

    Blunch, Niels-Hugo; Datta Gupta, Nabanita

    Addressing several methodological shortcomings of the previous literature, this paper explore the relationship among health knowledge and caste and religion and a number of important mediating factors in India, estimating causal impacts through a combination of instrumental variables and matching...

  1. Network, anatomical, and non-imaging measures for the prediction of ADHD diagnosis in individual subjects

    Directory of Open Access Journals (Sweden)

    Jason W Bohland

    2012-12-01

    Full Text Available Brain imaging methods have long held promise as diagnostic aids for neuropsychiatric conditions with complex behavioral phenotypes such as Attention-Deficit/Hyperactivity Disorder. This promise has largely been unrealized, at least partly due to the heterogeneity of clinical populations and the small sample size of many studies. A large, multi-center dataset provided by the ADHD-200 Consortium affords new opportunities to test methods for individual diagnosis based on MRI-observable structural brain attributes and functional interactions observable from resting state fMRI. In this study, we systematically calculated a large set of standard and new quantitative markers from individual subject datasets. These features (>12,000 per subject consisted of local anatomical attributes such as cortical thickness and structure volumes and both local and global resting state network measures. Three methods were used to compute graphs representing interdependencies between activations in different brain areas, and a full set of network features was derived from each. Of these, features derived from the inverse of the time series covariance matrix, under an L1-norm regularization penalty, proved most powerful. Anatomical and network feature sets were used individually, and combined with non-imaging phenotypic features from each subject. Machine learning algorithms were used to rank attributes, and performance was assessed under cross-validation and on a separate test set of 168 subjects for a variety of feature set combinations. While non-imaging features gave highest performance in cross-validation, the addition of imaging features in sufficient numbers led to improved generalization to new data. Stratification by gender also proved to be a fruitful strategy to improve classifier performance. We describe the overall approach used, compare the predictive power of different classes of features, and describe the most impactful features in relation to the

  2. Shifts in the architecture of the Nationwide Health Information Network.

    Science.gov (United States)

    Lenert, Leslie; Sundwall, David; Lenert, Michael Edward

    2012-01-01

    In the midst of a US $30 billion USD investment in the Nationwide Health Information Network (NwHIN) and electronic health records systems, a significant change in the architecture of the NwHIN is taking place. Prior to 2010, the focus of information exchange in the NwHIN was the Regional Health Information Organization (RHIO). Since 2010, the Office of the National Coordinator (ONC) has been sponsoring policies that promote an internet-like architecture that encourages point to-point information exchange and private health information exchange networks. The net effect of these activities is to undercut the limited business model for RHIOs, decreasing the likelihood of their success, while making the NwHIN dependent on nascent technologies for community level functions such as record locator services. These changes may impact the health of patients and communities. Independent, scientifically focused debate is needed on the wisdom of ONC's proposed changes in its strategy for the NwHIN.

  3. Changes in Female Support Network Systems and Adaptation after Breast Cancer Diagnosis: Differences between Older and Younger Patients

    Science.gov (United States)

    Ashida, Sato; Palmquist, Aunchalee E. L.; Basen-Engquist, Karen; Singletary, S. Eva; Koehly, Laura M.

    2009-01-01

    Purpose: This study evaluates the changes in social networks of older and younger breast cancer patients over a 6-month period following their first diagnosis and how such modifications are associated with changes in the patients' mood state. Design and Methods: Newly diagnosed breast cancer patients were interviewed shortly after their diagnosis…

  4. Diagnosis of Breast Cancer using a Combination of Genetic Algorithm and Artificial Neural Network in Medical Infrared Thermal Imaging

    Directory of Open Access Journals (Sweden)

    Hossein Ghayoumi zadeh

    2013-03-01

    Full Text Available Introduction This study is an effort to diagnose breast cancer by processing the quantitative and qualitative information obtained from medical infrared imaging. The medical infrared imaging is free from any harmful radiation and it is one of the best advantages of the proposed method. By analyzing this information, the best diagnostic parameters among the available parameters are selected and its sensitivity and precision in cancer diagnosis is improved by utilizing genetic algorithm and artificial neural network. Materials and Methods In this research, the necessary information is obtained from thermal imaging of 200 people, and 8 diagnostic parameters are extracted from these images by the research team. Then these 8 parameters are used as input of our proposed combinatorial model which is formed using artificial neural network and genetic algorithm. Results Our results have revealed that comparison of the breast areas; thermal pattern and kurtosis are the most important parameters in breast cancer diagnosis from proposed medical infrared imaging. The proposed combinatorial model with a 50% sensitivity, 75% specificity and, 70% accuracy shows good precision in cancer diagnosis. Conclusion The main goal of this article is to describe the capability of infrared imaging in preliminary diagnosis of breast cancer. This method is beneficial to patients with and without symptoms. The results indicate that the proposed combinatorial model produces optimum and efficacious parameters in comparison to other parameters and can improve the capability and power of globalizing the artificial neural network. This will help physicians in more accurate diagnosis of this type of cancer.

  5. Health impact assessment of cycling network expansions in European cities.

    Science.gov (United States)

    Mueller, Natalie; Rojas-Rueda, David; Salmon, Maëlle; Martinez, David; Ambros, Albert; Brand, Christian; de Nazelle, Audrey; Dons, Evi; Gaupp-Berghausen, Mailin; Gerike, Regine; Götschi, Thomas; Iacorossi, Francesco; Int Panis, Luc; Kahlmeier, Sonja; Raser, Elisabeth; Nieuwenhuijsen, Mark

    2018-01-09

    We conducted a health impact assessment (HIA) of cycling network expansions in seven European cities. We modeled the association between cycling network length and cycling mode share and estimated health impacts of the expansion of cycling networks. First, we performed a non-linear least square regression to assess the relationship between cycling network length and cycling mode share for 167 European cities. Second, we conducted a quantitative HIA for the seven cities of different scenarios (S) assessing how an expansion of the cycling network [i.e. 10% (S1); 50% (S2); 100% (S3), and all-streets (S4)] would lead to an increase in cycling mode share and estimated mortality impacts thereof. We quantified mortality impacts for changes in physical activity, air pollution and traffic incidents. Third, we conducted a cost-benefit analysis. The cycling network length was associated with a cycling mode share of up to 24.7% in European cities. The all-streets scenario (S4) produced greatest benefits through increases in cycling for London with 1,210 premature deaths (95% CI: 447-1,972) avoidable annually, followed by Rome (433; 95% CI: 170-695), Barcelona (248; 95% CI: 86-410), Vienna (146; 95% CI: 40-252), Zurich (58; 95% CI: 16-100) and Antwerp (7; 95% CI: 3-11). The largest cost-benefit ratios were found for the 10% increase in cycling networks (S1). If all 167 European cities achieved a cycling mode share of 24.7% over 10,000 premature deaths could be avoided annually. In European cities, expansions of cycling networks were associated with increases in cycling and estimated to provide health and economic benefits. Copyright © 2018 Elsevier Inc. All rights reserved.

  6. "I would never want to have a mental health diagnosis on my record": A survey of female physicians on mental health diagnosis, treatment, and reporting.

    Science.gov (United States)

    Gold, Katherine J; Andrew, Louise B; Goldman, Edward B; Schwenk, Thomas L

    Physicians have high rates of suicide and depression. Most state medical boards require disclosure of mental health problems on physician licensing applications, which has been theorized to increase stigma about mental health and prevent help-seeking among physicians. We surveyed a convenience sample of female physician-parents on a closed Facebook group. The anonymous 24-question survey asked about mental health history and treatment, perceptions of stigma, opinions about state licensing questions on mental health, and personal experiences with reporting. 2106 women responded, representing all 50 states and the District of Columbia. Most respondents were aged 30-59. Almost 50% of women believed that they had met the criteria for mental illness but had not sought treatment. Key reasons for avoiding care included a belief they could manage independently, limited time, fear of reporting to a medical licensing board, and the belief that diagnosis was embarrassing or shameful. Only 6% of physicians with formal diagnosis or treatment of mental illness had disclosed to their state. Women physicians report substantial and persistent fear regarding stigma which inhibits both treatment and disclosure. Licensing questions, particularly those asking about a diagnosis or treatment rather than functional impairment may contribute to treatment reluctance. Copyright © 2016 Elsevier Inc. All rights reserved.

  7. A numerically-enhanced machine learning approach to damage diagnosis using a Lamb wave sensing network

    Science.gov (United States)

    Sbarufatti, C.; Manson, G.; Worden, K.

    2014-09-01

    This paper describes a methodology for the design of a model-based diagnostic unit. The objective of the work is to define a suitable procedure for the design and verification of diagnostic performance in a simulated environment, trying to maximise the generalisation capability of pattern recognition algorithms when tested with real experimental signals. The system is designed and experimentally verified to solve the fatigue crack damage localisation and assessment problems in a realistic, though rather idealised, Structural Health Monitoring (SHM) framework. The study is applied to a piezoelectric Lamb wave sensor network and is validated experimentally on a simple aluminium skin. The analytically-derived dispersion curves for Lamb wave propagation in aluminium are used in order to determine the wave velocities and thus their arrival time at given sensors. The Local Interaction Simulation Approach (LISA) is used to simulate the entire waveform propagation. Once the agreement between analytical, numerical and experimental data is verified on a baseline undamaged condition, the parametric LISA model has been iteratively run, varying the position and the length of a crack on an aluminium skin panel, generating the virtual experience necessary to train a supervised learning regressor based on Artificial Neural Networks (ANNs). After the algorithm structure has been statistically optimised, the network sensitivity to input variations has been evaluated on simulated signals through a technique inspired by information gap theory. Real Lamb wave signals are then processed into the algorithm, providing feasible real-time indication of damage characteristics.

  8. Brain Network Activation Technology Does Not Assist with Concussion Diagnosis and Return to Play in Football Athletes

    Directory of Open Access Journals (Sweden)

    Steven P. Broglio

    2017-06-01

    Full Text Available BackgroundConcussion diagnosis and management remains a largely subjective process. This investigation sought to evaluate the utility of a novel neuroelectric measure for concussion diagnosis and return to play decision-making.HypothesisBrain Network Activation (BNA scores obtained within 72-h of injury will be lower than the athlete’s preseason evaluation and that of a matched control athlete; and the BNA will demonstrate ongoing declines at the return to play and post-season time points, while standard measures will have returned to pre-injury and control athlete levels.DesignCase–control study.MethodsFootball athletes with a diagnosed concussion (n = 8 and matched control football athletes (n = 8 completed a preseason evaluation of cognitive (i.e., Cogstate Computerized Cognitive Assessment Tool and neuroelectric function (i.e., BNA, clinical reaction time, SCAT3 self-reported symptoms, and quality of life (i.e., Health Behavior Inventory and Satisfaction with Life Scale. Following a diagnosed concussion, injured and control athletes completed post-injury evaluations within 72-h, once asymptomatic, and at the conclusion of the football season.ResultsCase analysis of the neuroelectric assessment failed to provide improved diagnostics beyond traditional clinical measures. Statistical analyses indicated significant BNA improvements in the concussed and control groups from baseline to the asymptomatic timepoint.ConclusionWith additional attention being placed on rapid and accurate concussion diagnostics and return to play decision-making, the addition of a novel neuroelectric assessment does not appear to provide additional clinical benefit at this time. Clinicians should continue to follow the recommendations for the clinical management of concussion with the assessment of the symptom, cognitive, and motor control domains.

  9. Health care use after diagnosis of cancer in children.

    NARCIS (Netherlands)

    Heins, M.J.; Lorenzi, M.F.; Korevaar, J.C.; McBride, M.L.

    2014-01-01

    Purpose: Young patients with cancer often require extensive care during and shortly after cancer treatment for medical, psychosocial and educational problems. Approximately 85% are treated by an oncologist; however, their additional health care in this phase has barely been studied. The role of the

  10. Exploring mobile health in a private online social network.

    Science.gov (United States)

    Memon, Qurban A; Mustafa, Asma Fayes

    2015-01-01

    Health information is very vulnerable. Certain individuals or corporate organisations will continue to steal it similar to bank account data once data is on wireless channels. Once health information is part of a social network, corresponding privacy issues also surface. Insufficiently trained employees at hospitals that pay less attention to creating a privacy-aware culture will suffer loss when mobile devices containing health information are lost, stolen or sniffed. In this work, a social network system is explored as a m-health system from a privacy perspective. A model is developed within a framework of data-driven privacy and implemented on Android operating system. In order to check feasibility of the proposed model, a prototype application is developed on Facebook for different services, including: i) sharing user location; ii) showing nearby friends; iii) calculating and sharing distance moved, and calories burned; iv) calculating, tracking and sharing user heart rate; etc.

  11. Methods for inferring health-related social networks among coworkers from online communication patterns

    National Research Council Canada - National Science Library

    Matthews, Luke J; DeWan, Peter; Rula, Elizabeth Y

    2013-01-01

    .... Health traits have been shown to correlate across friendship ties, but evaluating network effects in large coworker populations presents the challenge of obtaining sufficiently comprehensive network data...

  12. Malignant pleural mesothelioma: incidence, etiology, diagnosis, treatment, and occupational health.

    Science.gov (United States)

    Neumann, Volker; Löseke, Stefan; Nowak, Dennis; Herth, Felix J F; Tannapfel, Andrea

    2013-05-01

    The incidence of malignant mesothelioma in Germany is about 20 cases per million persons per year. Its association with asbestos exposure, usually occupational, has been unequivocally demonstrated. Even though the industrial use of asbestos was forbidden many years ago, new cases of mesothelioma continue to appear because of the long latency of the disease (median, 50 years). Its diagnosis and treatment still present a major challenge for ambulatory and in-hospital care and will do so for years to come. This article is based on a selective review of the literature, along with data from the German Mesothelioma Register. 1397 people died of mesothelioma in Germany in 2010. A plateau in the incidence of the disease is predicted between 2015 and 2030. Most mesotheliomas arise from the pleura. The histological subtype and the Karnofsky score are the main prognostic factors. Only limited data are now available to guide treatment with a combination of the available methods (chemotherapy, surgery, radiotherapy). The prognosis is still poor, with a median survival time of only 12 months. Symptom control and the preservation of the patient's quality of life are the main aspects of care for patients with mesothelioma. The incidence of mesothelioma is not expected to drop in the next few years. The available treatments are chemotherapy, surgery, and radiotherapy. Specialized treatment centers now increasingly provide multimodal therapy for treatment of mesothelioma.

  13. A Bayesian network decision model for supporting the diagnosis of dementia, Alzheimer׳s disease and mild cognitive impairment.

    Science.gov (United States)

    Seixas, Flávio Luiz; Zadrozny, Bianca; Laks, Jerson; Conci, Aura; Muchaluat Saade, Débora Christina

    2014-08-01

    Population aging has been occurring as a global phenomenon with heterogeneous consequences in both developed and developing countries. Neurodegenerative diseases, such as Alzheimer׳s Disease (AD), have high prevalence in the elderly population. Early diagnosis of this type of disease allows early treatment and improves patient quality of life. This paper proposes a Bayesian network decision model for supporting diagnosis of dementia, AD and Mild Cognitive Impairment (MCI). Bayesian networks are well-suited for representing uncertainty and causality, which are both present in clinical domains. The proposed Bayesian network was modeled using a combination of expert knowledge and data-oriented modeling. The network structure was built based on current diagnostic criteria and input from physicians who are experts in this domain. The network parameters were estimated using a supervised learning algorithm from a dataset of real clinical cases. The dataset contains data from patients and normal controls from the Duke University Medical Center (Washington, USA) and the Center for Alzheimer׳s Disease and Related Disorders (at the Institute of Psychiatry of the Federal University of Rio de Janeiro, Brazil). The dataset attributes consist of predisposal factors, neuropsychological test results, patient demographic data, symptoms and signs. The decision model was evaluated using quantitative methods and a sensitivity analysis. In conclusion, the proposed Bayesian network showed better results for diagnosis of dementia, AD and MCI when compared to most of the other well-known classifiers. Moreover, it provides additional useful information to physicians, such as the contribution of certain factors to diagnosis. Copyright © 2014 Elsevier Ltd. All rights reserved.

  14. Socialising Health Burden Through Different Network Topologies: A Simulation Study.

    Science.gov (United States)

    Peacock, Adrian; Cheung, Anthony; Kim, Peter; Poon, Simon K

    2017-01-01

    An aging population and the expectation of premium quality health services combined with the increasing economic burden of the healthcare system requires a paradigm shift toward patient oriented healthcare. The guardian angel theory described by Szolovits [1] explores the notion of enlisting patients as primary providers of information and motivation to patients with similar clinical history through social connections. In this study, an agent based model was developed to simulate to explore how individuals are affected through their levels of intrinsic positivity. Ring, point-to-point (paired buddy), and random networks were modelled, with individuals able to send messages to each other given their levels of variables positivity and motivation. Of the 3 modelled networks it is apparent that the ring network provides the most equal, collective improvement in positivity and motivation for all users. Further study into other network topologies should be undertaken in the future.

  15. How do people with long-term mental health problems negotiate relationships with network members at times of crisis?

    Science.gov (United States)

    Walker, Sandra; Kennedy, Anne; Vassilev, Ivaylo; Rogers, Anne

    2017-10-10

    Social network processes impact on the genesis and management of mental health problems. There is currently less understanding of the way people negotiate networked relationships in times of crisis compared to how they manage at other times. This paper explores the patterns and nature of personal network involvement at times of crises and how these may differ from day-to-day networks of recovery and maintenance. Semi-structured interviews with 25 participants with a diagnosis of long-term mental health (MH) problems drawn from recovery settings in the south of England. Interviews centred on personal network mapping of members and resources providing support. The mapping interviews explored the work of network members and changes in times of crisis. Interviews were recorded, transcribed and analysed using a framework analysis. Three key themes were identified: the fluidity of network relationality between crisis and recovery; isolation as a means of crises management; leaning towards peer support. Personal network input retreated at times of crisis often as result of "ejection" from the network by participants who used self-isolation as a personal management strategy in an attempt to deal with crises. Peer support is considered useful during a crisis, whilst the role of services was viewed with some ambiguity. Social networks membership, and type and depth of involvement, is subject to change between times of crisis and everyday support. This has implications for managing mental health in terms of engaging with network support differently in times of crises versus recovery and everyday living. © 2017 The Authors Health Expectations Published by John Wiley & Sons Ltd.

  16. Physical and mental health trajectories of cancer patients and caregivers across the year post-diagnosis: a dyadic investigation.

    Science.gov (United States)

    Shaffer, Kelly M; Kim, Youngmee; Carver, Charles S

    2016-06-01

    Evidence suggests interdependence between cancer patients' and their caregivers' physical and mental health. However, the extent to which caregivers' health relates to their patients' recovery, or patients' health affects their caregivers' outcomes, is largely unknown. This dyadic investigation reports the relations between cancer patients' and their caregivers' physical and mental health trajectories during the year following diagnosis. Ninety-two colorectal cancer patient-caregiver dyads completed questionnaires at 2, 6 and 12 months post-diagnosis. Self-reported physical and mental health using the Medical Outcomes Study Short Form Health Survey-12. Patients reported improved physical health over the year following their diagnosis, whereas caregivers reported declining physical health. Patients with lower mental health at diagnosis had stagnated physical health recovery. Caregivers' physical health declined most noticeably among those reporting low mental health at diagnosis and whose patients reported low physical health at diagnosis. Findings suggest targeting health interventions to cancer patients and caregivers reporting poor mental health at diagnosis may mitigate their long-term physical morbidity. Limited evidence of dyadic interdependence between patients' and caregivers' physical and mental health trajectories suggests future studies are warranted to identify psychosocial and medical characteristics moderating the relations between patients' and caregivers' health.

  17. Health behaviour changes after diagnosis of chronic illness among Canadians aged 50 or older

    OpenAIRE

    Newsom, Jason T.; Huguet, Nathalie; Ramage-Morin, Pamela L.; McCarthy, Michael J.; Bernier, Julie; Kaplan, Mark S.; McFarland, Bentson H.

    2012-01-01

    Changes in health behaviours (smoking, physical activity, alcohol consumption, and fruit and vegetable consumption) after diagnosis of chronic health conditions (heart disease, cancer, stroke, respiratory disease, and diabetes) were examined among Canadians aged 50 or older. Results from 12 years of longitudinal data from the Canadian National Population Health Survey indicated relatively modest changes in behaviour. Although significant decreases in smoking were observed among all groups exc...

  18. Health Monitoring and Diagnosis of Solid Rocket Motors with Bore Cracks

    Science.gov (United States)

    2015-11-01

    Technical Paper 3. DATES COVERED (From - To) January 2014-February 2015 4. TITLE AND SUBTITLE Health Monitoring and Diagnosis of Solid Rocket Motors with... rocket motors at various storage temperatures. Capabilities of a rocket motor health monitoring system are assessed based on the assumption that the...system can detect critical bore cracks in solid rocket motors. 15. SUBJECT TERMS structural health monitoring (SHM) · structural integrity · damage

  19. Maryland's Special Populations Cancer Network: cancer health disparities reduction model.

    Science.gov (United States)

    Baquet, Claudia R; Mack, Kelly M; Bramble, Joy; DeShields, Mary; Datcher, Delores; Savoy, Mervin; Hummel, Kery; Mishra, Shiraz I; Brooks, Sandra E; Boykin-Brown, Stephanie

    2005-05-01

    Cancer in Maryland is a serious health concern for minority and underserved populations in rural and urban areas. This report describes the National Cancer Institute (NCI) supported Maryland Special Populations Cancer Network (MSPN), a community-academic partnership. The MSPN's priority populations include African Americans, Native Americans, and other medically underserved residents of rural and urban areas. The MSPN has established a community infrastructure through formal collaborations with several community partners located in Baltimore City, the rural Eastern Shore, and Southern and Western Maryland, and among the Piscataway Conoy Tribe and the other 27 Native American Tribes in Maryland. Key partners also include the University of Maryland Eastern Shore and the University of Maryland Statewide Health Network. The MSPN has implemented innovative and successful programs in cancer health disparities research, outreach, and training; clinical trials education, health disparities policy, and resource leveraging. The MSPN addresses the goal of the NCI and the Department of Health and Human Services (DHHS) to reduce and eventually eliminate cancer health disparities. Community-academic partnerships are the foundation of this successful network.

  20. The Dutch sentinel practice network: relevance for public health policy.

    NARCIS (Netherlands)

    Bartelds, A.I.M.; Fracheboud, J.; Zee, J. van der

    1989-01-01

    The Dutch sentinal practice network: relevance for public health policy, considers the now 20-year history of the Continuous Morbidity Registration Sentinel Stations the Netherlands. The book consists of two parts. In the first part general aspects are discussed: the origin of the project at the end

  1. Evaluating the rural health placements of the Rural Support Network ...

    African Journals Online (AJOL)

    2011-10-19

    Oct 19, 2011 ... by organising individual and group placements in rural hospitals during vacations. This paper describes a qualitative evaluation, from the students' perspective, of the 2010 RSN rural health placements in order to make recommendations for the. Abstract. Objectives: The Rural Support Network (RSN) is an ...

  2. Evidence Informed Policy Network (EVIPNet) for Better Health ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Evidence Informed Policy Network (EVIPNet) for Better Health Policymaking in sub-Saharan Africa. Research results have no value unless they are made available for due consideration by practitioners and policymakers. Scientific articles are not enough. There is need to package research results for a wider audience and ...

  3. Social network analysis predicts health behaviours and self-reported health in African villages.

    Directory of Open Access Journals (Sweden)

    Goylette F Chami

    Full Text Available The provision of healthcare in rural African communities is a highly complex and largely unsolved problem. Two main difficulties are the identification of individuals that are most likely affected by disease and the prediction of responses to health interventions. Social networks have been shown to capture health outcomes in a variety of contexts. Yet, it is an open question as to what extent social network analysis can identify and distinguish among households that are most likely to report poor health and those most likely to respond to positive behavioural influences. We use data from seven highly remote, post-conflict villages in Liberia and compare two prominent network measures: in-degree and betweenness. We define in-degree as the frequency in which members from one household are named by another household as a friends. Betweenness is defined as the proportion of shortest friendship paths between any two households in a network that traverses a particular household. We find that in-degree explains the number of ill family members, whereas betweenness explains engagement in preventative health. In-degree and betweenness independently explained self-reported health and behaviour, respectively. Further, we find that betweenness predicts susceptibility to, instead of influence over, good health behaviours. The results suggest that targeting households based on network measures rather than health status may be effective for promoting the uptake of health interventions in rural poor villages.

  4. Social network analysis predicts health behaviours and self-reported health in African villages.

    Science.gov (United States)

    Chami, Goylette F; Ahnert, Sebastian E; Voors, Maarten J; Kontoleon, Andreas A

    2014-01-01

    The provision of healthcare in rural African communities is a highly complex and largely unsolved problem. Two main difficulties are the identification of individuals that are most likely affected by disease and the prediction of responses to health interventions. Social networks have been shown to capture health outcomes in a variety of contexts. Yet, it is an open question as to what extent social network analysis can identify and distinguish among households that are most likely to report poor health and those most likely to respond to positive behavioural influences. We use data from seven highly remote, post-conflict villages in Liberia and compare two prominent network measures: in-degree and betweenness. We define in-degree as the frequency in which members from one household are named by another household as a friends. Betweenness is defined as the proportion of shortest friendship paths between any two households in a network that traverses a particular household. We find that in-degree explains the number of ill family members, whereas betweenness explains engagement in preventative health. In-degree and betweenness independently explained self-reported health and behaviour, respectively. Further, we find that betweenness predicts susceptibility to, instead of influence over, good health behaviours. The results suggest that targeting households based on network measures rather than health status may be effective for promoting the uptake of health interventions in rural poor villages.

  5. Automated diagnosis of prostate cancer in multi-parametric MRI based on multimodal convolutional neural networks

    Science.gov (United States)

    Le, Minh Hung; Chen, Jingyu; Wang, Liang; Wang, Zhiwei; Liu, Wenyu; (Tim Cheng, Kwang-Ting; Yang, Xin

    2017-08-01

    Automated methods for prostate cancer (PCa) diagnosis in multi-parametric magnetic resonance imaging (MP-MRIs) are critical for alleviating requirements for interpretation of radiographs while helping to improve diagnostic accuracy (Artan et al 2010 IEEE Trans. Image Process. 19 2444-55, Litjens et al 2014 IEEE Trans. Med. Imaging 33 1083-92, Liu et al 2013 SPIE Medical Imaging (International Society for Optics and Photonics) p 86701G, Moradi et al 2012 J. Magn. Reson. Imaging 35 1403-13, Niaf et al 2014 IEEE Trans. Image Process. 23 979-91, Niaf et al 2012 Phys. Med. Biol. 57 3833, Peng et al 2013a SPIE Medical Imaging (International Society for Optics and Photonics) p 86701H, Peng et al 2013b Radiology 267 787-96, Wang et al 2014 BioMed. Res. Int. 2014). This paper presents an automated method based on multimodal convolutional neural networks (CNNs) for two PCa diagnostic tasks: (1) distinguishing between cancerous and noncancerous tissues and (2) distinguishing between clinically significant (CS) and indolent PCa. Specifically, our multimodal CNNs effectively fuse apparent diffusion coefficients (ADCs) and T2-weighted MP-MRI images (T2WIs). To effectively fuse ADCs and T2WIs we design a new similarity loss function to enforce consistent features being extracted from both ADCs and T2WIs. The similarity loss is combined with the conventional classification loss functions and integrated into the back-propagation procedure of CNN training. The similarity loss enables better fusion results than existing methods as the feature learning processes of both modalities are mutually guided, jointly facilitating CNN to ‘see’ the true visual patterns of PCa. The classification results of multimodal CNNs are further combined with the results based on handcrafted features using a support vector machine classifier. To achieve a satisfactory accuracy for clinical use, we comprehensively investigate three critical factors which could greatly affect the performance of our

  6. Comparison of sputum collection methods for tuberculosis diagnosis: a systematic review and pairwise and network meta-analysis.

    Science.gov (United States)

    Datta, Sumona; Shah, Lena; Gilman, Robert H; Evans, Carlton A

    2017-08-01

    The performance of laboratory tests to diagnose pulmonary tuberculosis is dependent on the quality of the sputum sample tested. The relative merits of sputum collection methods to improve tuberculosis diagnosis are poorly characterised. We therefore aimed to investigate the effects of sputum collection methods on tuberculosis diagnosis. We did a systematic review and meta-analysis to investigate whether non-invasive sputum collection methods in people aged at least 12 years improve the diagnostic performance of laboratory testing for pulmonary tuberculosis. We searched PubMed, Google Scholar, ProQuest, Web of Science, CINAHL, and Embase up to April 14, 2017, to identify relevant experimental, case-control, or cohort studies. We analysed data by pairwise meta-analyses with a random-effects model and by network meta-analysis. All diagnostic performance data were calculated at the sputum-sample level, except where authors only reported data at the individual patient-level. Heterogeneity was assessed, with potential causes identified by logistic meta-regression. We identified 23 eligible studies published between 1959 and 2017, involving 8967 participants who provided 19 252 sputum samples. Brief, on-demand spot sputum collection was the main reference standard. Pooled sputum collection increased tuberculosis diagnosis by microscopy (odds ratio [OR] 1·6, 95% CI 1·3-1·9, p<0·0001) or culture (1·7, 1·2-2·4, p=0·01). Providing instructions to the patient before sputum collection, during observed collection, or together with physiotherapy assistance increased diagnostic performance by microscopy (OR 1·6, 95% CI 1·3-2·0, p<0·0001). Collecting early morning sputum did not significantly increase diagnostic performance of microscopy (OR 1·5, 95% CI 0·9-2·6, p=0·2) or culture (1·4, 0·9-2·4, p=0·2). Network meta-analysis confirmed these findings, and revealed that both pooled and instructed spot sputum collections were similarly effective techniques for

  7. An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox

    Directory of Open Access Journals (Sweden)

    Luyang Jing

    2017-02-01

    Full Text Available A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1 the feature extraction from various types of sensory data and (2 the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN and a support vector machine (SVM, are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment.

  8. An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox.

    Science.gov (United States)

    Jing, Luyang; Wang, Taiyong; Zhao, Ming; Wang, Peng

    2017-02-21

    A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment.

  9. An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox

    Science.gov (United States)

    Jing, Luyang; Wang, Taiyong; Zhao, Ming; Wang, Peng

    2017-01-01

    A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment. PMID:28230767

  10. LORD: a phenotype-genotype semantically integrated biomedical data tool to support rare disease diagnosis coding in health information systems.

    Science.gov (United States)

    Choquet, Remy; Maaroufi, Meriem; Fonjallaz, Yannick; de Carrara, Albane; Vandenbussche, Pierre-Yves; Dhombres, Ferdinand; Landais, Paul

    Characterizing a rare disease diagnosis for a given patient is often made through expert's networks. It is a complex task that could evolve over time depending on the natural history of the disease and the evolution of the scientific knowledge. Most rare diseases have genetic causes and recent improvements of sequencing techniques contribute to the discovery of many new diseases every year. Diagnosis coding in the rare disease field requires data from multiple knowledge bases to be aggregated in order to offer the clinician a global information space from possible diagnosis to clinical signs (phenotypes) and known genetic mutations (genotype). Nowadays, the major barrier to the coding activity is the lack of consolidation of such information scattered in different thesaurus such as Orphanet, OMIM or HPO. The Linking Open data for Rare Diseases (LORD) web portal we developed stands as the first attempt to fill this gap by offering an integrated view of 8,400 rare diseases linked to more than 14,500 signs and 3,270 genes. The application provides a browsing feature to navigate through the relationships between diseases, signs and genes, and some Application Programming Interfaces to help its integration in health information systems in routine.

  11. The South Eastern Europe Health Network: A model for regional collaboration in public health

    Directory of Open Access Journals (Sweden)

    Maria Ruseva

    2015-12-01

    Full Text Available Inter-country alliances, articulated through regional approaches, have increasingly been used to drive economic development and social progress in the past several decades. The South Eastern Europe Health Network (SEEHN stands out among these types of initiatives for the tangible improvements it has achieved in regional governance for health, with several important lessons for public health leaders worldwide. This review paper, written by several key participants in SEEHN operation, follows the main milestones in network development, including its foundation under the Stability Pact’s Initiative for Social Cohesion and the three ministerial forums that have shaped its evolution, in order to show how it can constitute a model for regional collaboration in public health. Herewith we summarise the main accomplishments of the network and highlight the keys to its success, drawing lessons that both international bodies and other regions may use in their own design of collaborative initiatives in health and in other areas of public policy.

  12. COORDINATION OF THE PSYCHOSOCIAL CARE NETWORK FOR MENTAL HEALTH CRISIS

    Directory of Open Access Journals (Sweden)

    Karoline Andrade

    2016-09-01

    Full Text Available This research aimed to investigate the coordination of the psychosocial care network (RAPS for mental health crisis care, in its workers’ view. It is a descriptive exploratory study with qualitative approach. The study was carried out from 62 portfolios made by the students of the Mental Health Crisis and Urgency Course, who answered the reflective question: "Considering your workplace as a point of RAPS / RUE, describe, reflect and write a text with the synthesis regarding the articulation with the other network points in the reality of your municipality". The data were analyzed according to Thematic Content Analysis method suggested by Bardin, which comprises three phases: Pre - analysis, Material Exploration and Treatment of the Information, inference and interpretation. As a result, three thematic categories were identified: Referral, the traditional way of referring to specialized care, which is associated to a more fragmented care process; Matrix support, the current proposal of collaborative care, a joint strategy that contributes to the complex care demanded by mental health services users; and new strategies for network care, exemplified by meetings or sessions that discuss new ways to enable the network care.

  13. Analog neural network-based helicopter gearbox health monitoring system.

    Science.gov (United States)

    Monsen, P T; Dzwonczyk, M; Manolakos, E S

    1995-12-01

    The development of a reliable helicopter gearbox health monitoring system (HMS) has been the subject of considerable research over the past 15 years. The deployment of such a system could lead to a significant saving in lives and vehicles as well as dramatically reduce the cost of helicopter maintenance. Recent research results indicate that a neural network-based system could provide a viable solution to the problem. This paper presents two neural network-based realizations of an HMS system. A hybrid (digital/analog) neural system is proposed as an extremely accurate off-line monitoring tool used to reduce helicopter gearbox maintenance costs. In addition, an all analog neural network is proposed as a real-time helicopter gearbox fault monitor that can exploit the ability of an analog neural network to directly compute the discrete Fourier transform (DFT) as a sum of weighted samples. Hardware performance results are obtained using the Integrated Neural Computing Architecture (INCA/1) analog neural network platform that was designed and developed at The Charles Stark Draper Laboratory. The results indicate that it is possible to achieve a 100% fault detection rate with 0% false alarm rate by performing a DFT directly on the first layer of INCA/1 followed by a small-size two-layer feed-forward neural network and a simple post-processing majority voting stage.

  14. Social networks and mental health among people living with human immunodeficiency virus (HIV) in Johannesburg, South Africa.

    Science.gov (United States)

    Odek, Willis Omondi

    2014-01-01

    People living with human immunodeficiency virus (PLHIV) in developing countries can live longer due to improved treatment access, and a deeper understanding of determinants of their quality of life is critical. This study assessed the link between social capital, operationally defined in terms of social networks (group-based and personal social networks) and access to network resources (access to material and non-material resources and social support) and health-related quality of life (HRQoL) among 554 (55% female) adults on HIV treatment through South Africa's public health system. Female study participants were involved with more group-based social networks but had fewer personal social networks in comparison to males. Access to network resources was higher among females and those from larger households but lower among older study participants. Experience of social support significantly increased with household economic status and duration at current residence. Social capital indicators were unrelated to HIV disease status indicators, including duration since diagnosis, CD4 count and viral load. Only a minority (13%) of study participants took part in groups formed by and for predominantly PLHIV (HIV support groups), and participation in such groups was unrelated to their mental or physical health. Personal rather than group-linked social networks and access to network resources were significantly associated with mental but not physical health, after controlling for sociodemographic characteristics. The findings of limited participation in HIV support groups and that the participation in such groups was not significantly associated with physical or mental health may suggest efforts among PLHIV in South Africa to normalise HIV as a chronic illness through broad-based rather than HIV-status bounded social participation, as a strategy for deflecting stigma. Further research is required to examine the effects of HIV treatment on social networking and participation

  15. Linear and Non-Linear Associations of Gonorrhea Diagnosis Rates with Social Determinants of Health

    Directory of Open Access Journals (Sweden)

    Hazel D. Dean

    2012-09-01

    Full Text Available Identifying how social determinants of health (SDH influence the burden of disease in communities and populations is critically important to determine how to target public health interventions and move toward health equity. A holistic approach to disease prevention involves understanding the combined effects of individual, social, health system, and environmental determinants on geographic area-based disease burden. Using 2006–2008 gonorrhea surveillance data from the National Notifiable Sexually Transmitted Disease Surveillance and SDH variables from the American Community Survey, we calculated the diagnosis rate for each geographic area and analyzed the associations between those rates and the SDH and demographic variables. The estimated product moment correlation (PMC between gonorrhea rate and SDH variables ranged from 0.11 to 0.83. Proportions of the population that were black, of minority race/ethnicity, and unmarried, were each strongly correlated with gonorrhea diagnosis rates. The population density, female proportion, and proportion below the poverty level were moderately correlated with gonorrhea diagnosis rate. To better understand relationships among SDH, demographic variables, and gonorrhea diagnosis rates, more geographic area-based estimates of additional variables are required. With the availability of more SDH variables and methods that distinguish linear from non-linear associations, geographic area-based analysis of disease incidence and SDH can add value to public health prevention and control programs.

  16. Development of an online tool for public health: the European Public Health Law Network.

    Science.gov (United States)

    Basak, P

    2011-09-01

    The European Public Health Law Network was established in 2007 as part of the European Union (EU) co-funded Public Health Law Flu project. The aims of the website consisted of designing an interactive network of specialist information and encouraging an exchange of expertise amongst members. The website sought to appeal to academics, public health professionals and lawyers. The Public Health Law Flu project team designed and managed the website. Registered network members were recruited through publicity, advertising and word of mouth. Details of the network were sent to health organizations and universities throughout Europe. Corresponding website links attracted many new visitors. Publications, news, events and a pandemic glossary became popular features on the site. Although the website initially focused only on pandemic diseases it has grown into a multidisciplinary website covering a range of public health law topics. The network contains over 700 publications divided into 28 public health law categories. News, events, front page content, legislation and the francophone section are updated on a regular basis. Since 2007 the website has received over 15,000 views from 156 countries. Newsletter subscribers have risen to 304. There are now 723 followers on the associated Twitter site. The European Public Health Law Network has been a successful and innovative site in the area of public health law. Interest in the site continues to grow. Future funding can contribute to a bigger site with interactive features and pages in a wider variety of languages to attract a wider global audience. Copyright © 2011 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

  17. Patient-Centered Network of Learning Health Systems: Developing a resource for clinical translational research.

    Science.gov (United States)

    Finney Rutten, L J; Alexander, A; Embi, P J; Flores, G; Friedman, C; Haller, I V; Haug, P; Jensen, D; Khosla, S; Runger, G; Shah, N D; Winden, T; Roger, V L

    2017-02-01

    The Learning Health System Network clinical data research network includes academic medical centers, health-care systems, public health departments, and health plans, and is designed to facilitate outcomes research, pragmatic trials, comparative effectiveness research, and evaluation of population health interventions. The Learning Health System Network is 1 of 13 clinical data research networks assembled to create, in partnership with 20 patient-powered research networks, a National Patient-Centered Clinical Research Network. Herein, we describe the Learning Health System Network as an emerging resource for translational research, providing details on the governance and organizational structure of the network, the key milestones of the current funding period, and challenges and opportunities for collaborative science leveraging the network.

  18. Understanding change in global health policy: ideas, discourse and networks.

    Science.gov (United States)

    Harmer, Andrew

    2011-01-01

    How is radical change in global health policy possible? Material factors such as economics or human resources are important, but ideational factors such as ideas and discourse play an important role as well. In this paper, I apply a theoretical framework to show how discourse made it possible for public and private actors to fundamentally change their way of working together--to shift from international public and private interactions to global health partnerships (GHPs)--and in the process create a new institutional mechanism for governing global health. Drawing on insights from constructivist analysis, I demonstrate how discourse justified, legitimised, communicated and coordinated ideas about the practice of GHPs through a concentrated network of partnership pioneers. As attention from health policy analysts turns increasingly to ideational explanations for answers to global health problems, this paper contributes to the debate by showing how, precisely, discourse makes change possible.

  19. The city, territoriality and networks in mental health policies

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    Luciana Assis Costa

    2014-09-01

    Full Text Available The understanding of territory, made evident by a decentralized, local based, and non-institutionalized mental health model, is a fundamental element in building a renewed network. The objective of this essay is to understand how mental health policies gradually favor local actions, organized in terms of territories, to develop strategies of care that support the new model of mental health. From this perspective, the aim of this research is to reflect on the possibilities of establishing new social relations that can, in fact, widen the sense of community belonging in the daily living of those presenting mental health conditions. This study draws from theoretical concepts and frameworks of the social sciences, describing the diverse positions held by the main schools of urban sociology with regards to the understanding of territories. The multiple conceptions of territories and their relations to mental health are analyzed. Historical data about mental health in Brazil show a heterogeneous development of mental health policies in different areas of the country. Finally, social inclusion in the cities depends on an effective expansion of territory-based mental health services, as well as an amplification of the access to consumer goods and services not necessarily connected to health care, but to basic social and civil rights. Hopefully, new rules of social interaction will not be restricted to the mental health universe, but will promote new encounters in the urban space, with respect for differences and appreciation of diversity.

  20. Specification of requirements for health social-network as Personal Health Record (PHR system

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    Mozhgan Tanhapour

    2015-09-01

    Conclusion: The proposed set of requirements are qualitatively compared with the other similar systems. Using the proposed health social network that provides PHR capabilities for its users will have an irrefutable impact on quality and efficiency of patient-centered care, and play an important role in improving the health of society.

  1. Identifying personal health experience tweets with deep neural networks.

    Science.gov (United States)

    Keyuan Jiang; Gupta, Ravish; Gupta, Matrika; Calix, Ricardo A; Bernard, Gordon R

    2017-07-01

    Twitter, as a social media platform, has become an increasingly useful data source for health surveillance studies, and personal health experiences shared on Twitter provide valuable information to the surveillance. Twitter data are known for their irregular usages of languages and informal short texts due to the 140 character limit, and for their noisiness such that majority of the posts are irrelevant to any particular health surveillance. These factors pose challenges in identifying personal health experience tweets from the Twitter data. In this study, we designed deep neural networks with 3 different architectural configurations, and after training them with a corpus of 8,770 annotated tweets, we used them to predict personal experience tweets from a set of 821 annotate tweets. Our results demonstrated a significant amount of improvement in predicting personal health experience tweets by deep neural networks over that by conventional classifiers: 37.5% in accuracy, 31.1% in precision, and 53.6% in recall. We believe that our method can be utilized in various health surveillance studies using Twitter as a data source.

  2. Artificial neural networks for the diagnosis of aggressive periodontitis trained by immunologic parameters.

    Directory of Open Access Journals (Sweden)

    Georgios Papantonopoulos

    Full Text Available There is neither a single clinical, microbiological, histopathological or genetic test, nor combinations of them, to discriminate aggressive periodontitis (AgP from chronic periodontitis (CP patients. We aimed to estimate probability density functions of clinical and immunologic datasets derived from periodontitis patients and construct artificial neural networks (ANNs to correctly classify patients into AgP or CP class. The fit of probability distributions on the datasets was tested by the Akaike information criterion (AIC. ANNs were trained by cross entropy (CE values estimated between probabilities of showing certain levels of immunologic parameters and a reference mode probability proposed by kernel density estimation (KDE. The weight decay regularization parameter of the ANNs was determined by 10-fold cross-validation. Possible evidence for 2 clusters of patients on cross-sectional and longitudinal bone loss measurements were revealed by KDE. Two to 7 clusters were shown on datasets of CD4/CD8 ratio, CD3, monocyte, eosinophil, neutrophil and lymphocyte counts, IL-1, IL-2, IL-4, INF-γ and TNF-α level from monocytes, antibody levels against A. actinomycetemcomitans (A.a. and P.gingivalis (P.g.. ANNs gave 90%-98% accuracy in classifying patients into either AgP or CP. The best overall prediction was given by an ANN with CE of monocyte, eosinophil, neutrophil counts and CD4/CD8 ratio as inputs. ANNs can be powerful in classifying periodontitis patients into AgP or CP, when fed by CE values based on KDE. Therefore ANNs can be employed for accurate diagnosis of AgP or CP by using relatively simple and conveniently obtained parameters, like leukocyte counts in peripheral blood. This will allow clinicians to better adapt specific treatment protocols for their AgP and CP patients.

  3. City networks collaboration and planning for health and sustainability

    CERN Document Server

    Migdalas, Athanasios; Rassia, Stamatina; Pardalos, Panos

    2017-01-01

    Sustainable development within urban and rural areas, transportation systems, logistics, supply chain management, urban health, social services, and architectural design are taken into consideration in the cohesive network models provided in this book. The ideas, methods, and models presented consider city landscapes and quality of life conditions based on mathematical network models and optimization. Interdisciplinary Works from prominent researchers in mathematical modeling, optimization, architecture, engineering, and physics are featured in this volume to promote health and well-being through design.   Specific topics include: -          Current technology that form the basis of future living in smart cities -          Interdisciplinary design and networking of large-scale urban systems  -          Network communication and route traffic optimization -          Carbon dioxide emission reduction -          Closed-loop logistics chain management and operation ...

  4. Investigation of Wireless Sensor Networks for Structural Health Monitoring

    Directory of Open Access Journals (Sweden)

    Ping Wang

    2012-01-01

    Full Text Available Wireless sensor networks (WSNs are one of the most able technologies in the structural health monitoring (SHM field. Through intelligent, self-organising means, the contents of this paper will test a variety of different objects and different working principles of sensor nodes connected into a network and integrated with data processing functions. In this paper the key issues of WSN applied in SHM are discussed, including the integration of different types of sensors with different operational modalities, sampling frequencies, issues of transmission bandwidth, real-time ability, and wireless transmitter frequency. Furthermore, the topology, data fusion, integration, energy saving, and self-powering nature of different systems will be investigated. In the FP7 project “Health Monitoring of Offshore Wind Farms,” the above issues are explored.

  5. The network of the Regional Health Observatories in France

    Directory of Open Access Journals (Sweden)

    Bernard Ledésert

    2004-06-01

    Full Text Available The Regional Health Observatories (RHOs were created in France in the 1980’s. They exist in each of the 26
    regions. Their principal mission is to assist in the decision making process by the undertaking of in-depth
    analyses of the populations’ health status. To achieve this aim they catalogue and validate existing data,
    promote surveys when there is a lack of information, disseminate the collected information and conduct
    evaluations of public health interventions.
    Around 400 people work in the observatories (241 full-time equivalents with various expertise including
    public health doctors, statisticians, sociologists, demographers, geographers, economists etc. More than
    40% of the budget of the RHOs comes from the government while 21% comes from the local executive.
    RHOs work with all the regional and local institutions concerned with health programs and policies. The
    main role of the RHOs in this context is to identify the health needs, to describe health related behaviours
    and to describe the utilisation of the health care system. The analysis of the different kinds of work
    conducted shows the great diversity of the subjects treated by the observatories.
    The creation in 1989 of the “Fédération Nationale des Observatoires Régionaux de Santé” (FNORS
    strengthened the links within the RHOs. Its role is to represent the RHO network and to facilitate the
    harmonisation of the inter-RHOs projects. Recently, the FNORS increase its position to represent RHOs at the
    European level by taking part in EU projects and to promote the creation of a European network of regional
    health observatories.

  6. Computer-Aided Diagnosis of Parkinson's Disease Using Complex-Valued Neural Networks and mRMR Feature Selection Algorithm.

    Science.gov (United States)

    Peker, Musa; Sen, Baha; Delen, Dursun

    2015-01-01

    Parkinson's disease (PD) is a neurological disorder which has a significant social and economic impact. PD is diagnosed by clinical observation and evaluations, coupled with a PD rating scale. However, these methods may be insufficient, especially in the initial phase of the disease. The processes are tedious and time-consuming, and hence systems that can automatically offer a diagnosis are needed. In this study, a novel method for the diagnosis of PD is proposed. Biomedical sound measurements obtained from continuous phonation samples were used as attributes. First, a minimum redundancy maximum relevance (mRMR) attribute selection algorithm was applied for the identification of the effective attributes. After conversion to a complex number, the resulting attributes are presented as input data to the complex-valued artificial neural network (CVANN). The proposed novel system might be a powerful tool for effective diagnosis of PD.

  7. Fault detection and diagnosis of a gearbox in marine propulsion systems using bispectrum analysis and artificial neural networks

    Science.gov (United States)

    Li, Zhixiong; Yan, Xinping; Yuan, Chengqing; Zhao, Jiangbin; Peng, Zhongxiao

    2011-03-01

    A marine propulsion system is a very complicated system composed of many mechanical components. As a result, the vibration signal of a gearbox in the system is strongly coupled with the vibration signatures of other components including a diesel engine and main shaft. It is therefore imperative to assess the coupling effect on diagnostic reliability in the process of gear fault diagnosis. For this reason, a fault detection and diagnosis method based on bispectrum analysis and artificial neural networks (ANNs) was proposed for the gearbox with consideration given to the impact of the other components in marine propulsion systems. To monitor the gear conditions, the bispectrum analysis was first employed to detect gear faults. The amplitude-frequency plots containing gear characteristic signals were then attained based on the bispectrum technique, which could be regarded as an index actualizing forepart gear faults diagnosis. Both the back propagation neural network (BPNN) and the radial-basis function neural network (RBFNN) were applied to identify the states of the gearbox. The numeric and experimental test results show the bispectral patterns of varying gear fault severities are different so that distinct fault features of the vibrant signal of a marine gearbox can be extracted effectively using the bispectrum, and the ANN classification method has achieved high detection accuracy. Hence, the proposed diagnostic techniques have the capability of diagnosing marine gear faults in the earlier phases, and thus have application importance.

  8. Fault detection and diagnosis for non-Gaussian stochastic distribution systems with time delays via RBF neural networks.

    Science.gov (United States)

    Yi, Qu; Zhan-ming, Li; Er-chao, Li

    2012-11-01

    A new fault detection and diagnosis (FDD) problem via the output probability density functions (PDFs) for non-gausian stochastic distribution systems (SDSs) is investigated. The PDFs can be approximated by radial basis functions (RBFs) neural networks. Different from conventional FDD problems, the measured information for FDD is the output stochastic distributions and the stochastic variables involved are not confined to Gaussian ones. A (RBFs) neural network technique is proposed so that the output PDFs can be formulated in terms of the dynamic weighings of the RBFs neural network. In this work, a nonlinear adaptive observer-based fault detection and diagnosis algorithm is presented by introducing the tuning parameter so that the residual is as sensitive as possible to the fault. Stability and Convergency analysis is performed in fault detection and fault diagnosis analysis for the error dynamic system. At last, an illustrated example is given to demonstrate the efficiency of the proposed algorithm, and satisfactory results have been obtained. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

  9. Your Health Buddies Matter: Preferential Selection and Social Influence on Weight Management in an Online Health Social Network.

    Science.gov (United States)

    Meng, Jingbo

    2016-12-01

    A growing number of online social networks are designed with the intention to promote health by providing virtual space wherein individuals can seek and share information and support with similar others. Research has shown that real-world social networks have a significant influence on one's health behavior and outcomes. However, there is a dearth of studies on how individuals form social networks in virtual space and whether such online social networks exert any impact on individuals' health outcomes. Built on the Multi-Theoretical Multilevel (MTML) framework and drawing from literature on social influence, this study examined the mechanisms underlying the formation of an online health social network and empirically tested social influence on individual health outcomes through the network. Situated in a weight management social networking site, the study tracked a health buddy network of 709 users and their weight management activities and outcomes for 4 months. Actor-based modeling was used to test the joint dynamics of preferential selection and social influence among health buddies. The results showed that baseline, inbreeding, and health status homophily significantly predicted preferential selection of health buddies in the weight management social networking site, whereas self-interest in seeking experiential health information did not. The study also found peer influence of online health buddy networks on individual weight outcomes, such that an individual's odds of losing weight increased if, on average, the individual's health buddies were losing weight.

  10. Social Networks and Health: A Systematic Review of Sociocentric Network Studies in Low- and Middle-Income Countries

    Science.gov (United States)

    Perkins, Jessica M; Subramanian, S V; Christakis, Nicholas A

    2015-01-01

    In low- and middle-income countries (LMICs), naturally occurring social networks may be particularly vital to health outcomes as extended webs of social ties often are the principal source of various resources. Understanding how social network structure, and influential individuals within the network, may amplify the effects of interventions in LMICs, by creating, for example, cascade effects to non-targeted participants, presents an opportunity to improve the efficiency and effectiveness of public health interventions in such settings. We conducted a systematic review of PubMed, Econlit, Sociological Abstracts, and PsycINFO to identify a sample of 17 sociocentric network papers (arising from 10 studies) that specifically examined health issues in LMICs. We also separately selected to review 19 sociocentric network papers (arising from 10 other studies) on development topics related to wellbeing in LMICs. First, to provide a methodological resource, we discuss the sociocentric network study designs employed in the selected papers, and then provide a catalog of 105 name generator questions used to measure social ties across all the LMIC network papers (including both ego- and sociocentric network papers) cited in this review. Second, we show that network composition, individual network centrality, and network structure are associated with important health behaviors and health and development outcomes in different contexts across multiple levels of analysis and across distinct network types. Lastly, we highlight the opportunities for health researchers and practitioners in LMICs to 1) design effective studies and interventions in LMICs that account for the sociocentric network positions of certain individuals and overall network structure, 2) measure the spread of outcomes or intervention externalities, and 3) enhance the effectiveness and efficiency of aid based on knowledge of social structure. In summary, human health and wellbeing are connected through complex

  11. Social networks and health: a systematic review of sociocentric network studies in low- and middle-income countries.

    Science.gov (United States)

    Perkins, Jessica M; Subramanian, S V; Christakis, Nicholas A

    2015-01-01

    In low- and middle-income countries (LMICs), naturally occurring social networks may be particularly vital to health outcomes as extended webs of social ties often are the principal source of various resources. Understanding how social network structure, and influential individuals within the network, may amplify the effects of interventions in LMICs, by creating, for example, cascade effects to non-targeted participants, presents an opportunity to improve the efficiency and effectiveness of public health interventions in such settings. We conducted a systematic review of PubMed, Econlit, Sociological Abstracts, and PsycINFO to identify a sample of 17 sociocentric network papers (arising from 10 studies) that specifically examined health issues in LMICs. We also separately selected to review 19 sociocentric network papers (arising from 10 other studies) on development topics related to wellbeing in LMICs. First, to provide a methodological resource, we discuss the sociocentric network study designs employed in the selected papers, and then provide a catalog of 105 name generator questions used to measure social ties across all the LMIC network papers (including both ego- and sociocentric network papers) cited in this review. Second, we show that network composition, individual network centrality, and network structure are associated with important health behaviors and health and development outcomes in different contexts across multiple levels of analysis and across distinct network types. Lastly, we highlight the opportunities for health researchers and practitioners in LMICs to 1) design effective studies and interventions in LMICs that account for the sociocentric network positions of certain individuals and overall network structure, 2) measure the spread of outcomes or intervention externalities, and 3) enhance the effectiveness and efficiency of aid based on knowledge of social structure. In summary, human health and wellbeing are connected through complex

  12. New accreditation program: university health network's experience with Qmentum.

    Science.gov (United States)

    Tepfers, Anita; Hruska, Christa; Stone, Justin; Moser, Jane

    2009-01-01

    In 2008, University Health Network was surveyed using Accreditation Canada's new Qmentum program. The following article describes UHN's experience rolling out the program to over 12,000 staff, physicians and volunteers. The article also outlines key challenges and lessons learned by the multi-site organization, with a focus on staff engagement, on-site survey preparation and sustainability moving forward. Staff feedback on the Qmentum program was extremely positive, and forecast results from Accreditation Canada were excellent.

  13. Health impact assessment in a network of European cities.

    Science.gov (United States)

    Ison, Erica

    2013-10-01

    The methodology of health impact assessment (HIA) was introduced as one of four core themes for Phase IV (2003-2008) of the World Health Organization European Healthy Cities Network (WHO-EHCN). Four objectives for HIA were set at the beginning of the phase. We report on the results of the evaluation of introducing and implementing this methodology in cities from countries across Europe with widely differing economies and sociopolitical contexts. Two main sources of data were used: a general questionnaire designed for the Phase IV evaluation and the annual reporting template for 2007-2008. Sources of bias included the proportion of non-responders and the requirement to communicate in English. Main barriers to the introduction and implementation of HIA were a lack of skill, knowledge and experience of HIA, the newness of the concept, the lack of a legal basis for implementation and a lack of political support. Main facilitating factors were political support, training in HIA, collaboration with an academic/public health institution or local health agency, a pre-existing culture of intersectoral working, a supportive national policy context, access to WHO materials about or expertise in HIA and membership of the WHO-EHCN, HIA Sub-Network or a National Network. The majority of respondents did not feel that they had had the resources, knowledge or experience to achieve all of the objectives set for HIA in Phase IV. The cities that appear to have been most successful at introducing and implementing HIA had pre-existing experience of HIA, came from a country with a history of applying HIA, were HIA Sub-Network members or had made a commitment to implementing HIA during successive years of Phase IV. Although HIA was recognised as an important component of Healthy Cities' work, the experience in the WHO-EHCN underscores the need for political buy-in, capacity building and adequate resourcing for the introduction and implementation of HIA to be successful.

  14. Health Care Engagement Among LGBT Older Adults: The Role of Depression Diagnosis and Symptomatology.

    Science.gov (United States)

    Shiu, Chengshi; Kim, Hyun-Jun; Fredriksen-Goldsen, Karen

    2017-02-01

    Optimal engagement in health care plays a critical role in the success of disease prevention and treatment, particularly for older adults who are often in greater need of health care services. However, to date, there is still limited knowledge about the relationship between depression and health care engagement among lesbian, gay, bisexual, and transgender (LGBT) older adults. This study utilized data from Aging with Pride: National Health, Aging, Sexuality/Gender Study, from the 2014 survey with 2,450 LGBT adults 50 years old and older. Multiple-variable regression was utilized to evaluate relationships between three indicators of health care engagement and four depression groups after controlling for background characteristics and discrimination in health care. Health care engagement indicators were "not using preventive care," "not seeking care when needed," and "difficulty in adhering to treatments." Depression groups were defined by depression diagnosis and symptomatology, including Diagnosed-Symptomatic group (Diag-Sympt), Diagnosed-Nonsymptomatic group (Diag-NoSympt), Nondiagnosed-Symptomatic group (NoDiag-Sympt), and Nondiagnosed-Nonsymptomatic group (NoDiag-NoSympt). Depression groups displayed different patterns and levels of health care engagement. The Diag-Sympt group displayed the highest "difficulty in adhering to treatments." Diag-NoSympt group displayed the lowest "not using preventive care." The NoDiag-Sympt group reported the highest "not using preventive care" and "not seeking care when needed." The NoDiag-NoSympt group had the lowest "not seeking care when needed" and "difficulty in adhering to treatments." Depression diagnosis and symptomatology are jointly associated with health care engagement among LGBT older adults. Interventions aiming to promote health care engagement among this population should simultaneously consider both depression diagnosis and symptomatology. © The Author 2017. Published by Oxford University Press on behalf of The

  15. Diagnosis of compliance of health care product processing in Primary Health Care.

    Science.gov (United States)

    Roseira, Camila Eugenia; Silva, Darlyani Mariano da; Passos, Isis Pienta Batista Dias; Orlandi, Fabiana Souza; Padoveze, Maria Clara; Figueiredo, Rosely Moralez de

    2016-11-21

    identify the compliance of health care product processing in Primary Health Care and assess possible differences in the compliance among the services characterized as Primary Health Care Service and Family Health Service. quantitative, observational, descriptive and inferential study with the application of structure, process and outcome indicators of the health care product processing at ten services in an interior city of the State of São Paulo - Brazil. for all indicators, the compliance indices were inferior to the ideal levels. No statistically significant difference was found in the indicators between the two types of services investigated. The health care product cleaning indicators obtained the lowest compliance index, while the indicator technical-operational resources for the preparation, conditioning, disinfection/sterilization, storage and distribution of health care products obtained the best index. the diagnosis of compliance of health care product processing at the services assessed indicates that the quality of the process is jeopardized, as no results close to ideal levels were obtained at any service. In addition, no statistically significant difference in these indicators was found between the two types of services studied. identificar a conformidade do processamento de produtos para saúde na Atenção Primária à Saúde e avaliar possível diferença na conformidade entre as unidades caracterizadas como Unidade Básica de Saúde e Unidade Saúde da Família. estudo quantitativo, observacional, descritivo e inferencial, com a aplicação de indicadores de estrutura, processo e resultado referentes ao processamento de produtos para a saúde em dez unidades, de um município do interior de São Paulo - Brasil. todos os indicadores obtiveram índice de conformidade inferior ao ideal. Não houve diferença estatisticamente significante nos indicadores entre os dois tipos de unidades investigadas, sendo o indicador de limpeza de produtos para sa

  16. Reducing age of autism diagnosis: developmental social neuroscience meets public health challenge.

    Science.gov (United States)

    Klin, Ami; Klaiman, Cheryl; Jones, Warren

    2015-02-25

    Autism spectrum disorder (autism) is a highly prevalent and heterogeneous family of neurodevelopmental disorders of genetic origins with potentially devastating implications for child, family, health and educational systems. Despite advances in paper-and-pencil screening and in standardization of diagnostic procedures, diagnosis of autism in the US still hovers around the ages of four or five years, later still in disadvantaged communities, and several years after the age of two to three years when the condition can be reliably diagnosed by expert clinicians. As early detection and treatment are two of the most important factors optimizing outcome, and given that diagnosis is typically a necessary condition for families to have access to early treatment, reducing age of diagnosis has become one of the greatest priorities of the field. Recent advances in developmental social neuroscience promise the advent of cost-effective and community-viable, performance-based procedures, and suggest a complementary method for promoting universal screening and much greater access to the diagnosis process. Small but critical studies have already reported on experiments that differentiate groups of children at risk for autism from controls, and at least one study so far could predict diagnostic classification and level of disability on the basis of a brief experiment. Although the road to translating such procedures into effective devices for screening and diagnosis is still a long one, and premature claims should be avoided, this effort could be critical in addressing this worldwide public health challenge.

  17. Intelligent Wireless Sensor Networks for System Health Monitoring

    Science.gov (United States)

    Alena, Rick

    2011-01-01

    Wireless sensor networks (WSN) based on the IEEE 802.15.4 Personal Area Network (PAN) standard are finding increasing use in the home automation and emerging smart energy markets. The network and application layers, based on the ZigBee 2007 Standard, provide a convenient framework for component-based software that supports customer solutions from multiple vendors. WSNs provide the inherent fault tolerance required for aerospace applications. The Discovery and Systems Health Group at NASA Ames Research Center has been developing WSN technology for use aboard aircraft and spacecraft for System Health Monitoring of structures and life support systems using funding from the NASA Engineering and Safety Center and Exploration Technology Development and Demonstration Program. This technology provides key advantages for low-power, low-cost ancillary sensing systems particularly across pressure interfaces and in areas where it is difficult to run wires. Intelligence for sensor networks could be defined as the capability of forming dynamic sensor networks, allowing high-level application software to identify and address any sensor that joined the network without the use of any centralized database defining the sensors characteristics. The IEEE 1451 Standard defines methods for the management of intelligent sensor systems and the IEEE 1451.4 section defines Transducer Electronic Datasheets (TEDS), which contain key information regarding the sensor characteristics such as name, description, serial number, calibration information and user information such as location within a vehicle. By locating the TEDS information on the wireless sensor itself and enabling access to this information base from the application software, the application can identify the sensor unambiguously and interpret and present the sensor data stream without reference to any other information. The application software is able to read the status of each sensor module, responding in real-time to changes of

  18. Family health history communication networks of older adults: importance of social relationships and disease perceptions.

    Science.gov (United States)

    Ashida, Sato; Kaphingst, Kimberly A; Goodman, Melody; Schafer, Ellen J

    2013-10-01

    Older individuals play a critical role in disseminating family health history (FHH) information that can facilitate disease prevention among younger family members. This study evaluated the characteristics of older adults and their familial networks associated with two types of communication (have shared and intend to share new FHH information with family members) to inform public health efforts to facilitate FHH dissemination. Information on 970 social network members enumerated by 99 seniors (aged 57 years and older) at 3 senior centers in Memphis, Tennessee, through face-to-face interviews was analyzed. Participants shared FHH information with 27.5% of the network members; 54.7% of children and 24.4% of siblings. Two-level logistic regression models showed that participants had shared FHH with those to whom they provided emotional support (odds ratio [OR] = 1.836) and felt close to (OR = 1.757). Network-members were more likely to have received FHH from participants with a cancer diagnosis (OR = 2.617) and higher familiarity with (OR = 1.380) and importance of sharing FHH with family (OR = 1.474). Participants intended to share new FHH with those who provide tangible support to (OR = 1.804) and were very close to them (OR = 2.112). Members with whom participants intend to share new FHH were more likely to belong to the network of participants with higher perceived severity if family members encountered heart disease (OR = 1.329). Many first-degree relatives were not informed of FHH. Perceptions about FHH and disease risk as well as quality of social relationships may play roles in whether seniors communicate FHH with their families. Future studies may consider influencing these perceptions and relationships.

  19. The Central American Network for Disaster and Health Information.

    Science.gov (United States)

    Arnesen, Stacey J; Cid, Victor H; Scott, John C; Perez, Ricardo; Zervaas, Dave

    2007-07-01

    This paper describes an international outreach program to support rebuilding Central America's health information infrastructure after several natural disasters in the region, including Hurricane Mitch in 1998 and two major earthquakes in 2001. The National Library of Medicine joined forces with the Pan American Health Organization/World Health Organization, the United Nations International Strategy for Disaster Reduction, and the Regional Center of Disaster Information for Latin America and the Caribbean (CRID) to strengthen libraries and information centers in Central America and improve the availability of and access to health and disaster information in the region by developing the Central American Network for Disaster and Health Information (CANDHI). Through CRID, the program created ten disaster health information centers in medical libraries and disaster-related organizations in six countries. This project served as a catalyst for the modernization of several medical libraries in Central America. The resulting CANDHI provides much needed electronic access to public health "gray literature" on disasters, as well as access to numerous health information resources. CANDHI members assist their institutions and countries in a variety of disaster preparedness activities through collecting and disseminating information.

  20. Methodology developed for the energy-productive diagnosis and evaluation in health buildings

    Energy Technology Data Exchange (ETDEWEB)

    Martini, I.; Discoli, C.; Rosenfeld, E. [Instituto de Estudios del Habitat (IDEHAB), Facultad de Arquitectura y Urbanismo, Universidad Nacional de La Plata, La Plata, Buenos Aires (Argentina)

    2007-07-01

    The public health network in Argentina consists of a wide variety of buildings presenting a complex system of services and structures. In order to modulate and study the energy behaviour of each type of health facility, a database of Energy-Productive Building Modules (Modulos Edilicios Energeticos Productivos: MEEP) was built. This involved evaluating the interactions among physical spaces, building envelope, infrastructure, and equipment usage with the energy consumption, for each specialty service provided in the most common buildings present in the health service network. The MEEP database enables investigators to: (i) Obtain detailed information on each facility. (ii) Identify variables critical to an energy consumption perspective. (iii) Detect areas of over consumption and/or inadequate infrastructure. (iv) Gather essential reference material for the design of health facilities and other similar sectors. The information of each MEEP can be summarized in typological charts. (author)

  1. Can mental health interventions change social networks? A systematic review.

    Science.gov (United States)

    Anderson, Kimberley; Laxhman, Neelam; Priebe, Stefan

    2015-11-21

    Social networks of patients with psychosis can provide social support, and improve health and social outcomes, including quality of life. However, patients with psychosis often live rather isolated with very limited social networks. Evidence for interventions targeting symptoms or social skills, are largely unsuccessful at improving social networks indirectly. As an alternative, interventions may directly focus on expanding networks. In this systematic review, we assessed what interventions have previously been tested for this and to what extent they have been effective. A systematic review was conducted of randomised controlled trials, testing psychosocial interventions designed to directly increase the social networks of patients with psychosis. Searches of five online databases (PsycINFO, CINAHL, Cochrane Database, MEDLINE, Embase), hand searching of grey literature, and both forward and backward snowballing of key papers were conducted and completed on 12 December 2014. Trial reports were included if they were written in English, the social network size was the primary outcome, participants were ≥ 18 years old and diagnosed with a psychotic disorder. Five studies (n = 631 patients) met the complete inclusion criteria. Studies were from different countries and published since 2008. Four trials had significant positive results, i.e. an observable increase in patients' social network size at the end of the intervention. The interventions included: guided peer support, a volunteer partner scheme, supported engagement in social activity, dog-assisted integrative psychological therapy and psychosocial skills training. Other important elements featured were the presence of a professional, and a focus on friendships and peers outside of services and the immediate family. Despite the small number and heterogeneity of included studies, the results suggest that interventions directly targeting social isolation can be effective and achieve a meaningful increase

  2. Post-diagnosis social networks, and lifestyle and treatment factors in the After Breast Cancer Pooling Project.

    Science.gov (United States)

    Kroenke, Candyce H; Michael, Yvonne L; Shu, Xiao-Ou; Poole, Elizabeth M; Kwan, Marilyn L; Nechuta, Sarah; Caan, Bette J; Pierce, John P; Chen, Wendy Y

    2017-04-01

    Larger social networks have been associated with better breast cancer survival. To investigate potential mediators, we evaluated associations of social network size and diversity with lifestyle and treatment factors associated with prognosis. We included 9331 women from the After Breast Cancer Pooling Project who provided data on social networks within approximately two years following diagnosis. A social network index was derived from information about the presence of a spouse or intimate partner, religious ties, community participation, friendship ties, and numbers of living relatives. Diversity was assessed as variety of ties, independent of size. We used logistic regression to evaluate associations with outcomes and evaluated whether effect estimates differed using meta-analytic techniques. Associations were similar across cohorts though analyses of smoking and alcohol included US cohorts only because of low prevalence of these behaviors in the Shanghai cohort. Socially isolated women were more likely to be obese (OR = 1.21, 95% CI:1.03-1.42), have low physical activity (socially integrated women. Among node positive cases from three cohorts, socially isolated women were more likely not to receive chemotherapy (OR = 2.10, 95% CI:1.30-3.39); associations differed in a fourth cohort. Other associations (nonsignificant) were consistent with less intensive treatment in socially isolated women. Low social network diversity was independently associated with more adverse lifestyle, but not clinical, factors. Small, less diverse social networks measured post-diagnosis were associated with more adverse lifestyle factors and less intensive cancer treatment. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  3. [Transfer of Care Innovations from the Hamburg Network for Mental Health to other Health Regions].

    Science.gov (United States)

    Tokar, Oksana; Dörbecker, Regine; Böhmann, Tilo; Härter, Martin

    2015-07-01

    The goal of this paper is to present the research conducted for systemizing network elements and analyzing their interconnection that emerged during the establishment and functioning of health care innovation project of psychenet - the Hamburg Network for Mental Health.Semi-structured manual-based face-to-face interviews with project researchers and leaders were conducted. The gathered data was validated and updated several times during the project duration. The results include a systematic description of 186 network elements developed during the overall project and respective subprojects. The elements were consolidated in a web-based database and integrated into the psychenet.de public website. A clustering of elements was conducted and modules of elements were generated based on the interconnection between the related elements.The systematic description of network elements as well as determination of their interconnection and dependency can play an important role in understanding the emergence and functioning of integrated mental health networks. The innovative medical networks prove to be complex service systems and urge for a grounded application of integration techniques in order to be successfully transferred and adopted in other regions. © Georg Thieme Verlag KG Stuttgart · New York.

  4. Dual diagnosis discourse in Victoria Australia: the responsiveness of mental health services.

    Science.gov (United States)

    Roberts, Bridget M; Maybery, Darryl

    2014-01-01

    In recent decades, psychiatric services have been challenged to be more responsive to patients' coexisting problems, in particular those concerning substance use. In Australia this has been referred to as a "No Wrong Door" approach. This paper explores the meanings of this move for the acute mental health sector, including attitudes toward a No Wrong Door approach to people with a dual diagnosis of mental illness and substance use disorder.   This qualitative study involved a review of the research literatures, analysis of policy documents, and interviews with 19 key informants in a case study of the State of Victoria, Australia.   The analysis resulted in two broad themes surrounding the implications of dual diagnosis discourse for the mental health sector. The first involves progress regarding the concept of No Wrong Door with subthemes including interprofessional cultural conflicts, intersectoral professional status issues, terminology, problem definition, perspectives on serious mental illness, the role of the client, and pharmacological treatment. The second overarching theme focuses upon informants' thoughts on future directions for the sector and highlights divided opinion on the implications of dual diagnosis discourse for the mental health service and social care systems.   While the perspectives on system change and multiple issues such as resource concerns and cultural clashes are presented here, the informants in this study also gave clear guidance for the future of dual diagnosis work in the mental health sector (e.g., focusing on orienting services toward consumer strengths and recovery), along with recommendations for future research. This paper contributes to the small body of qualitative research on the history and course of efforts to develop appropriate practice in mental health services with regard to patients who have substance use problems and other mental health disorders.

  5. European health telematics networks for positron emission tomography

    Energy Technology Data Exchange (ETDEWEB)

    Kontaxakis, George [Universidad Politecnica de Madrid, ETSI Telecomunicacion, Madrid 28040 (Spain)]. E-mail: g.kontaxakis@upm.es; Pozo, Miguel Angel [Centro PET Complutense, Madrid 28040 (Spain); Universidad Complutense de Madrid, Instituto Pluridisciplinar, Madrid 28040 (Spain); Ohl, Roland [MedCom Gesellschaft fuer medizinische Bildverarbeitung mbH, Darmstadt 64283 (Germany); Visvikis, Dimitris [U650 INSERM, Lab. du Traitement de L' Information Medicale, University of Brest Occidentale, CHU Morvan, Brest 29609 (France); Sachpazidis, Ilias [Fraunhofer Institute for Computer Graphics, Darmstadt 64283 (Germany); Ortega, Fernando [Fundacion Instituto Valenciano de Oncologia, Valencia 46009 (Spain); Guerra, Pedro [Universidad Politecnica de Madrid, ETSI Telecomunicacion, Madrid 28040 (Spain); Cheze-Le Rest, Catherine [Dept. Medicine Nucleaire, CHU Morvan, Brest 29609 (France); Selby, Peter [MedCom Gesellschaft fuer medizinische Bildverarbeitung mbH, Darmstadt 64283 (Germany); Pan, Leyun [German Cancer Research Centre, Clinical Cooperation Unit Nuclear Medicine, Heidelberg 69120 (Germany); Diaz, Javier [Fundacion Instituto Valenciano de Oncologia, Valencia 46009 (Spain); Dimitrakopoulou-Strauss, Antonia [German Cancer Research Centre, Clinical Cooperation Unit Nuclear Medicine, Heidelberg 69120 (Germany); Santos, Andres [Universidad Politecnica de Madrid, ETSI Telecomunicacion, Madrid 28040 (Spain); Strauss, Ludwig [German Cancer Research Centre, Clinical Cooperation Unit Nuclear Medicine, Heidelberg 69120 (Germany); Sakas, Georgios [MedCom Gesellschaft fuer medizinische Bildverarbeitung mbH, Darmstadt 64283 (Germany); Fraunhofer Institute for Computer Graphics, Darmstadt 64283 (Germany)

    2006-12-20

    A pilot network of positron emission tomography centers across Europe has been setup employing telemedicine services. The primary aim is to bring all PET centers in Europe (and beyond) closer, by integrating advanced medical imaging technology and health telematics networks applications into a single, easy to operate health telematics platform, which allows secure transmission of medical data via a variety of telecommunications channels and fosters the cooperation between professionals in the field. The platform runs on PCs with Windows 2000/XP and incorporates advanced techniques for image visualization, analysis and fusion. The communication between two connected workstations is based on a TCP/IP connection secured by secure socket layers and virtual private network or jabber protocols. A teleconsultation can be online (with both physicians physically present) or offline (via transmission of messages which contain image data and other information). An interface sharing protocol enables online teleconsultations even over low bandwidth connections. This initiative promotes the cooperation and improved communication between nuclear medicine professionals, offering options for second opinion and training. It permits physicians to remotely consult patient data, even if they are away from the physical examination site.

  6. European health telematics networks for positron emission tomography

    Science.gov (United States)

    Kontaxakis, George; Pozo, Miguel Angel; Ohl, Roland; Visvikis, Dimitris; Sachpazidis, Ilias; Ortega, Fernando; Guerra, Pedro; Cheze-Le Rest, Catherine; Selby, Peter; Pan, Leyun; Diaz, Javier; Dimitrakopoulou-Strauss, Antonia; Santos, Andres; Strauss, Ludwig; Sakas, Georgios

    2006-12-01

    A pilot network of positron emission tomography centers across Europe has been setup employing telemedicine services. The primary aim is to bring all PET centers in Europe (and beyond) closer, by integrating advanced medical imaging technology and health telematics networks applications into a single, easy to operate health telematics platform, which allows secure transmission of medical data via a variety of telecommunications channels and fosters the cooperation between professionals in the field. The platform runs on PCs with Windows 2000/XP and incorporates advanced techniques for image visualization, analysis and fusion. The communication between two connected workstations is based on a TCP/IP connection secured by secure socket layers and virtual private network or jabber protocols. A teleconsultation can be online (with both physicians physically present) or offline (via transmission of messages which contain image data and other information). An interface sharing protocol enables online teleconsultations even over low bandwidth connections. This initiative promotes the cooperation and improved communication between nuclear medicine professionals, offering options for second opinion and training. It permits physicians to remotely consult patient data, even if they are away from the physical examination site.

  7. Coordination in networks for improved mental health service

    Directory of Open Access Journals (Sweden)

    Johan Hansson

    2010-08-01

    Full Text Available Background: Well-organised clinical cooperation between health and social services has been difficult to achieve in Sweden as in other countries. Purpose: This paper presents an empirical study of a mental health coordination network in one area in Stockholm. The aim was to describe the development and nature of coordination within a mental health and social care consortium and to assess the impact on care processes and client outcomes. Method: Data was gathered through interviews with coordina­tors from three rehabilitation units. The interviews focused on coordination activities aimed at supporting the clients’ needs and investigated how the coordinators acted according to the consortium's holistic approach. Data on The Camberwell Assess­ment of Need (CAN-S showing clients’ satisfaction was used to assess on set of outcomes. Findings: The findings revealed different coordination activities and factors both helping and hindering the network coordination activities. One factor helping was the history of local and personal informal cooperation and shared responsibilities evident. Unclear roles and routines hindered cooperation Practical value: The contribution is an empirical example and a model for organisations establishing structures for network coordination. One lesson for current policy about integrated health care is to adapt and implement ”pair coordinators” where full structural integration is not possible. Another lesson, based on the idea of patient quality by coordinated care, is specific to adapt the work of the local psychiatric addictive team – an independent special team in the psychiatric outpatient care serving psychotic clients with complex addictive problems.

  8. Coordination in networks for improved mental health service

    Directory of Open Access Journals (Sweden)

    Johan Hansson

    2010-08-01

    Full Text Available Background: Well-organised clinical cooperation between health and social services has been difficult to achieve in Sweden as in other countries.Purpose: This paper presents an empirical study of a mental health coordination network in one area in Stockholm. The aim was to describe the development and nature of coordination within a mental health and social care consortium and to assess the impact on care processes and client outcomes.Method: Data was gathered through interviews with coordina­tors from three rehabilitation units. The interviews focused on coordination activities aimed at supporting the clients’ needs and investigated how the coordinators acted according to the consortium's holistic approach. Data on The Camberwell Assess­ment of Need (CAN-S showing clients’ satisfaction was used to assess on set of outcomes. Findings: The findings revealed different coordination activities and factors both helping and hindering the network coordination activities. One factor helping was the history of local and personal informal cooperation and shared responsibilities evident. Unclear roles and routines hindered cooperationPractical value: The contribution is an empirical example and a model for organisations establishing structures for network coordination. One lesson for current policy about integrated health care is to adapt and implement ”pair coordinators” where full structural integration is not possible. Another lesson, based on the idea of patient quality by coordinated care, is specific to adapt the work of the local psychiatric addictive team – an independent special team in the psychiatric outpatient care serving psychotic clients with complex addictive problems.

  9. Mobile Network Data for Public Health: Opportunities and Challenges

    Science.gov (United States)

    Oliver, Nuria; Matic, Aleksandar; Frias-Martinez, Enrique

    2015-01-01

    The ubiquity of mobile phones worldwide is generating an unprecedented amount of human behavioral data both at an individual and aggregated levels. The study of this data as a rich source of information about human behavior emerged almost a decade ago. Since then, it has grown into a fertile area of research named computational social sciences with a wide variety of applications in different fields such as social networks, urban and transport planning, economic development, emergency relief, and, recently, public health. In this paper, we briefly describe the state of the art on using mobile phone data for public health, and present the opportunities and challenges that this kind of data presents for public health. PMID:26301211

  10. Social Network Analysis of Elders' Health Literacy and their Use of Online Health Information.

    Science.gov (United States)

    Jang, Haeran; An, Ji-Young

    2014-07-01

    Utilizing social network analysis, this study aimed to analyze the main keywords in the literature regarding the health literacy of and the use of online health information by aged persons over 65. Medical Subject Heading keywords were extracted from articles on the PubMed database of the National Library of Medicine. For health literacy, 110 articles out of 361 were initially extracted. Seventy-one keywords out of 1,021 were finally selected after removing repeated keywords and applying pruning. Regarding the use of online health information, 19 articles out of 26 were selected. One hundred forty-four keywords were initially extracted. After removing the repeated keywords, 74 keywords were finally selected. Health literacy was found to be strongly connected with 'Health knowledge, attitudes, practices' and 'Patient education as topic.' 'Computer literacy' had strong connections with 'Internet' and 'Attitude towards computers.' 'Computer literacy' was connected to 'Health literacy,' and was studied according to the parameters 'Attitude towards health' and 'Patient education as topic.' The use of online health information was strongly connected with 'Health knowledge, attitudes, practices,' 'Consumer health information,' 'Patient education as topic,' etc. In the network, 'Computer literacy' was connected with 'Health education,' 'Patient satisfaction,' 'Self-efficacy,' 'Attitude to computer,' etc. Research on older citizens' health literacy and their use of online health information was conducted together with study of computer literacy, patient education, attitude towards health, health education, patient satisfaction, etc. In particular, self-efficacy was noted as an important keyword. Further research should be conducted to identify the effective outcomes of self-efficacy in the area of interest.

  11. Effect of obstructive sleep apnea diagnosis on health related quality of life.

    Science.gov (United States)

    Isidoro, Serena Iacono; Salvaggio, Adriana; Lo Bue, Anna; Romano, Salvatore; Marrone, Oreste; Insalaco, Giuseppe

    2015-05-29

    Perceived Health Related Quality of Life (HRQoL) is impaired in obstructive sleep apnea (OSA). To our knowledge, no study has analyzed the effect of OSA diagnosis communication on HRQoL. We evaluated self-perceived HRQoL in patients afferent to our sleep center, in order to examine the effect of the diagnosis disclosure on their HRQoL. Two hundred ninety-seven consecutive outpatients (227 M) (mean age 54.1 ± 11.6 yrs, range 23-80 yrs) were evaluated, before first clinical visit and nocturnal diagnostic examination (Time A), and after diagnosis disclosure (Time B), with two self-reported questionnaires for HRQoL assessment: Psychological General Well-Being Index (PGWBI), consisting of anxiety, depressed mood, positive well-being, self-control, general health, vitality subscales, and 12-Item Short-Form Health Survey (SF-12), comprising Physical (PCS) and Mental Component Summaries (MCS). Comparison of mean HRQoL scores at Time A with reference values, showed worse scores. Mean PGWBI Total and subscales scores improved at Time B. Similar improvement was observed for SF-12 MCS (p = 0.0148), but nor for SF-12 PCS. At Time B, Anxiety, Depression and Well-being PGWBI subscales became similar to reference values, while the scores in the other PGWBI subscales and SF-12 remained worse. Comparison between males and females showed higher HRQoL values for males at both times. Score changes were independent from age, gender, BMI, AHI, TSat90 and excessive daytime sleepiness. Diagnosis communication improves patients' HRQoL, regardless of the severity. Changes in HRQoL after diagnosis disclosure may be due to patients' motivation for medical check and diagnostic expectations.

  12. Patient Health Monitoring Using Wireless Body Area Network

    Directory of Open Access Journals (Sweden)

    Hsu Myat Thwe

    2015-06-01

    Full Text Available Abstract Nowadays remote patient health monitoring using wireless technology plays very vigorous role in a society. Wireless technology helps monitoring of physiological parameters like body temperature heart rate respiration blood pressure and ECG. The main aim of this paper is to propose a wireless sensor network system in which both heart rate and body temperature ofmultiplepatients can monitor on PC at the same time via RF network. The proposed prototype system includes two sensor nodes and receiver node base station. The sensor nodes are able to transmit data to receiver using wireless nRF transceiver module.The nRF transceiver module is used to transfer the data from microcontroller to PC and a graphical user interface GUI is developed to display the measured data and save to database. This system can provide very cheaper easier and quick respondent history of patient.

  13. Dental health economics and diagnosis related groups/casemix in Indonesian dentistry

    Directory of Open Access Journals (Sweden)

    Ronnie Rivany

    2009-12-01

    Full Text Available Background: Dental Health Economics is a branch of transdiciplinary science that refers to the Economic and Public Health science. On the other hand, in other developed countries, Diagnosis Related Groups (DRG’s /Casemix has been used as a basic in creating the same perception between providers, patients and insurance companies in many aspects such as health planning, healthcare financing and quality assurance. Purpose: The objective of this review is to propose a new paradigm of economics to be applied in Indonesian Dentistry. Reviews: The Dental Health Economics should be considered as an important aspect in Indonesian Dentistry, which is used to determine the dental treatment fee based on unit cost, cost containment, and cost recovery rate analysis. Referring to Australian Refined Diagnosis Related Group, health care industry in Indonesia has starting to try a more structured way in grouping disease pattern in order to come up with more precise health care services to their patients. The on going development of Indonesian DRG’s is meant to confirm the disease pattern and partition. Conclusion: The development of Indonesian DRG’s concept, especially the Dental & Oral Disorders, needs a new paradigm, so the practitioners and academics could group and calculate the unit cost from each dental treatment according to the Indonesian DRG version (INA-DRG’s.

  14. Experiences of The Network: Towards Unity for Health Women and Health Taskforce.

    Science.gov (United States)

    Gonzalez de Leon, D; Lewis, J

    2009-08-01

    Women's health is an often neglected component of health professions education despite the well-documented need to improve the health status of women, especially in low income countries. This paper was written on behalf of all members of The Network: Towards Unity for Health Women and Health Taskforce (WHTF) which unites leaders in women's health and higher education from different countries around the world. The WHTF objectives include teaching health providers the skills and knowledge necessary to improve care to women; encouraging universities to partner with community women's groups; promoting the inclusion of women's rights and gender issues in curricula; and cultivating leadership among female health professions students. The main goal of the paper is to provide an overview of the collaborative processes, the accomplishments and the lessons learned in this project since the early 1990s. It includes the history and evolution of the Taskforce; the importance of human rights and gender issues in approaching women's health; teaching tools--the Women and Health Learning Package (WHLP); implementation of WHLP in health professions education and community settings; and main outcomes and future challenges. The WHLP was implemented in fourteen universities and seven university community programs. A new edition of WHLP will be available in 2009. The WHTF is a model of south-south collaboration in health professions education and community programs to improve women's health. It has been successful in reaching universities and communities all over the globe and provides a model for other education, health and community issues.

  15. Single and combined fault diagnosis of reciprocating compressor valves using a hybrid deep belief network

    NARCIS (Netherlands)

    Tran, Van Tung; Thobiani, Faisal Al; Tinga, Tiedo; Ball, Andrew David; Niu, Gang

    2017-01-01

    In this paper, a hybrid deep belief network is proposed to diagnose single and combined faults of suction and discharge valves in a reciprocating compressor. This hybrid integrates the deep belief network structured by multiple stacked restricted Boltzmann machines for pre-training and simplified

  16. Oral health considerations in anorexia and bulimia nervosa. 1. Symptomatology and diagnosis.

    Science.gov (United States)

    Bassiouny, Mohamed A

    2017-01-01

    Eating disorders have captured the attention of medical and dental professionals as well as the public for decades and continue to raise concern today. The literature devoted to anorexia and bulimia highlights myriad psychological, systemic, and dental health complications. Dental practitioners are in a unique position to discover early manifestations of these disorders. The present article reviews anorexia and bulimia, summarizing telltale behavioral traits, systemic manifestations, and dental features to facilitate recognition and enable accurate diagnosis.

  17. Decentralised fault diagnosis of large-scale systems: Application to water transport networks

    OpenAIRE

    Puig, Vicenç; Ocampo-Martinez, Carlos

    2015-01-01

    In this paper, a decentralised fault diagnosis approach for large-scale systems is proposed. This approach is based on obtaining a set of local diagnosers using the analytical redundancy relation (ARRs) approach. The proposed approach starts with obtaining the set of ARRs of the system yielding into an equivalent graph. From that graph, the graph partitioning problem is solved obtaining a set of ARRs for each local diagnoser. Finally, a decentralised fault diagnosis strategy is proposed an...

  18. From diagnosis through survivorship: health-care experiences of colorectal cancer survivors with ostomies

    Science.gov (United States)

    Grant, Marcia; McMullen, Carmit K.; Altschuler, Andrea; Mohler, M. Jane; Hornbrook, Mark C.; Herrinton, Lisa J.; Krouse, Robert S.

    2014-01-01

    Purpose The journey from diagnosis through treatment to survivorship can be challenging for colorectal cancer (CRC) survivors with permanent ostomies. Memories of both the positive and negative health-care interactions can persist years after the initial diagnosis and treatment. The purpose of this paper is to describe the health-care experiences of long-term (>5 years) CRC survivors with ostomies. Methods Thirty-three CRC survivors with ostomies who were members of Kaiser Permanente, an integrated care organization, in Oregon, southwestern Washington and northern California participated in eight focus groups. Discussions from the focus groups were recorded, transcribed, and analyzed for potential categories and themes. Results Health-care-related themes described CRC survivors’ experiences with diagnosis, treatment decision-making, initial experiences with ostomy, and survivorship. Participants discussed both positive and negative health-care-related experiences, including the need for continued access to trained nurses for ostomy self-care, access to peer support, and resources related to managing persistent, debilitating symptoms. Conclusions Long-term CRC survivors with ostomies have both positive and negative health-care experiences, regardless of health-related quality of life (HRQOL) and gender. Long-term support mechanisms and quality survivorship care that CRC survivors with ostomies can access are needed to promote positive adjustments and improved HRQOL. Structured abstract The current literature in CRC survivor-ship suggests that HRQOL concerns can persist years after treatment completion. The coordination of care to manage persistent late- and long-term effects are still lacking for CRC survivors living with an ostomy. Findings from this qualitative analysis will aid in the development of support strategies that foster more positive adjustments for CRC survivors living with an ostomy and support their ongoing ostomy-related needs. PMID:24442998

  19. Choosing your network: social preferences in an online health community.

    Science.gov (United States)

    Centola, Damon; van de Rijt, Arnout

    2015-01-01

    A growing number of online health communities offer individuals the opportunity to receive information, advice, and support from peers. Recent studies have demonstrated that these new online contacts can be important informational resources, and can even exert significant influence on individuals' behavior in various contexts. However little is known about how people select their health contacts in these virtual domains. This is because selection preferences in peer networks are notoriously difficult to detect. In existing networks, unobserved pressures on tie formation--such as common organizational memberships, introductions to friends of friends, or limitations on accessibility--may mistakenly be interpreted as individual preferences for interacting/not interacting with others. We address these issues by adopting a social media approach to studying network formation. We study social selection using an in vivo study within an online exercise program, in which anonymous participants have equal opportunities for initiating relationships with other program members. This design allows us to identify individuals' preferences for health contacts, and to evaluate what these preferences imply for members' access to new kinds of health information, and for the kinds of social influences to which they are exposed. The study was conducted within a goal-oriented fitness competition, in which participation was greatest among a small core of active individuals. Our results show that the active participants displayed indifference to the fitness and exercise profiles of others, disregarding information about others' fitness levels, exercise preferences, and workout experiences, instead selecting partners almost entirely on the basis of similarities on gender, age, and BMI. Interestingly, the findings suggest that rather than expanding and diversifying their sources of health information, participants' choices limited the value of their online resources by selecting contacts

  20. A Study on the Effects of Sympathetic Skin Response Parameters in Diagnosis of Fibromyalgia Using Artificial Neural Networks.

    Science.gov (United States)

    Ozkan, Ozhan; Yildiz, Murat; Arslan, Evren; Yildiz, Sedat; Bilgin, Suleyman; Akkus, Selami; Koyuncuoglu, Hasan R; Koklukaya, Etem

    2016-03-01

    Fibromyalgia syndrome (FMS), usually observed commonly in females over age 30, is a rheumatic disease accompanied by extensive chronic pain. In the diagnosis of the disease non-objective psychological tests and physiological tests and laboratory test results are evaluated and clinical experiences stand out. However, these tests are insufficient in differentiating FMS with similar diseases that demonstrate symptoms of extensive pain. Thus, objective tests that would help the diagnosis are needed. This study analyzes the effect of sympathetic skin response (SSR) parameters on the auxiliary tests used in FMS diagnosis, the laboratory tests and physiological tests. The study was conducted in Suleyman Demirel University, Faculty of Medicine, Physical Medicine and Rehabilitation Clinic in Turkey with 60 patients diagnosed with FMS for the first time and a control group of 30 healthy individuals. In the study all participants underwent laboratory tests (blood tests), certain physiological tests (pulsation, skin temperature, respiration) and SSR measurements. The test data and SSR parameters obtained were classified using artificial neural network (ANN). Finally, in the ANN framework, where only laboratory and physiological test results were used as input, a simulation result of 96.51 % was obtained, which demonstrated diagnostic accuracy. This data, with the addition of SSR parameter values obtained increased to 97.67 %. This result including SSR parameters - meaning a higher diagnostic accuracy - demonstrated that SSR could be a new auxillary diagnostic method that could be used in the diagnosis of FMS.

  1. Social network analysis predicts health behaviours and self-reported health in African villages

    NARCIS (Netherlands)

    Chami, G.F.; Ahnert, S.E.; Voors, M.J.; Kontoleon, A.A.

    2014-01-01

    The provision of healthcare in rural African communities is a highly complex and largely unsolved problem. Two main difficulties are the identification of individuals that are most likely affected by disease and the prediction of responses to health interventions. Social networks have been shown to

  2. A motivational health companion in the home as part of an intelligent health monitoring sensor network

    NARCIS (Netherlands)

    Evers, V.; Wildvuur, S.; Kröse, B.

    2010-01-01

    This paper describes our work in progress to develop a personal monitoring system that can monitor the physical and emotional condition of a patient by using contextual information from a sensor network, provide the patient with feedback concerning their health status and motivate the patient to

  3. [Networking, Coordination and Responsibilities based on Health Regionsplus: New Health Political Approaches and Developments in Bavaria].

    Science.gov (United States)

    Hollederer, A; Eicher, A; Pfister, F; Stühler, K; Wildner, M

    2017-08-01

    For many health and health-care problems in the population, there is a need for professional management and coordination instruments as well as a competent local network. The new "Health Regions(plus)" in Bavaria offer such a structure. This new concept is presented in the following article. The "Health Regions(plus)" aim to improve the population's health, the health-related quality of life, equity in health, as well as to further develop the local health care. The Bavarian State Ministry of Health and Care will support up to 24 regions with a funding of up to 50 000 Euro yearly per "Health Region(plus)" until the end of 2019. The structure of "Health Regions(plus)" implies the establishment of a coordinating agency that works as a "motor", a health forum on the strategic level and relevant working groups. "Health Regions(plus)" involve all relevant stakeholders of the regional health system and are chaired by the district administrator or mayor. They work primarily in the fields of health care and prevention/health promotion but can also pursue other region-specific fields. The Bavarian Health and Food Safety Authority supports and evaluates the "Health Regions(plus)". There is also a coordinating office which organises the exchange of information and experience among the "Health Regions(plus)". Although such a comprehensive regional approach does not change the statutory decision-making structures and responsibilities it does offer the communities an instrument to involve local needs in their decision-making processes. © Georg Thieme Verlag KG Stuttgart · New York.

  4. Bearings Fault Diagnosis Based on Convolutional Neural Networks with 2-D Representation of Vibration Signals as Input

    Directory of Open Access Journals (Sweden)

    Zhang Wei

    2017-01-01

    Full Text Available Periodic vibration signals captured by the accelerometers carry rich information for bearing fault diagnosis. Existing methods mostly rely on hand-crafted time-consuming preprocessing of data to acquire suitable features. In this paper, we use an easy and effective method to transform the 1-D temporal vibration signal into a 2-D image. With the signal image, convolutional Neural Network (CNN is used to train the raw vibration data. As powerful feature extractor and classifier for image recognition, CNN can learn to acquire features most suitable for the classification task by being trained. With the image format of vibration signals, the neuron in fully-connected layer of CNN can see farther and capture the periodic feature of signals. According to the results of the experiments, when fed in enough training samples, the proposed method outperforms other common methods. The proposed method can also be applied to solve intelligent diagnosis problems of other machine systems.

  5. Fault Diagnosis System of Induction Motors Based on Neural Network and Genetic Algorithm Using Stator Current Signals

    Directory of Open Access Journals (Sweden)

    Tian Han

    2006-01-01

    Full Text Available This paper proposes an online fault diagnosis system for induction motors through the combination of discrete wavelet transform (DWT, feature extraction, genetic algorithm (GA, and neural network (ANN techniques. The wavelet transform improves the signal-to-noise ratio during a preprocessing. Features are extracted from motor stator current, while reducing data transfers and making online application available. GA is used to select the most significant features from the whole feature database and optimize the ANN structure parameter. Optimized ANN is trained and tested by the selected features of the measurement data of stator current. The combination of advanced techniques reduces the learning time and increases the diagnosis accuracy. The efficiency of the proposed system is demonstrated through motor faults of electrical and mechanical origins on the induction motors. The results of the test indicate that the proposed system is promising for the real-time application.

  6. Networks as a type of social entrepreneurship to advance population health.

    Science.gov (United States)

    Wei-Skillern, Jane

    2010-11-01

    A detailed case study from the field of social entrepreneurship is used to illustrate the network approach, which does not require more resources but rather makes better use of existing resources. Leaders in public health can use networks to overcome some of the barriers that inhibit the widespread adoption of a population health approach to community health. Public health leaders who embrace social entrepreneurship may be better able to accomplish their missions by building their networks rather than just their organizations.

  7. Wearable sweat detector device design for health monitoring and clinical diagnosis

    Science.gov (United States)

    Wu, Qiuchen; Zhang, Xiaodong; Tian, Bihao; Zhang, Hongyan; Yu, Yang; Wang, Ming

    2017-06-01

    Miniaturized sensor is necessary part for wearable detector for biomedical applications. Wearable detector device is indispensable for online health care. This paper presents a concept of an wearable digital health monitoring device design for sweat analysis. The flexible sensor is developed to quantify the amount of hydrogen ions in sweat and skin temperature in real time. The detection system includes pH sensor, temperature sensor, signal processing module, power source, microprocessor, display module and so on. The sweat monitoring device is designed for sport monitoring or clinical diagnosis.

  8. Avian Colibacillosis and Salmonellosis: A Closer Look at Epidemiology, Pathogenesis, Diagnosis, Control and Public Health Concerns

    Directory of Open Access Journals (Sweden)

    S. M. Lutful Kabir

    2010-01-01

    Full Text Available Avian colibacillosis and salmonellosis are considered to be the major bacterial diseases in the poultry industry world-wide. Colibacillosis and salmonellosis are the most common avian diseases that are communicable to humans. This article provides the vital information on the epidemiology, pathogenesis, diagnosis, control and public health concerns of avian colibacillosis and salmonellosis. A better understanding of the information addressed in this review article will assist the poultry researchers and the poultry industry in continuing to make progress in reducing and eliminating avian colibacillosis and salmonellosis from the poultry flocks, thereby reducing potential hazards to the public health posed by these bacterial diseases.

  9. Effect of health belief model and health promotion model on breast cancer early diagnosis behavior: a systematic review.

    Science.gov (United States)

    Ersin, Fatma; Bahar, Zuhal

    2011-01-01

    Breast cancer is an important public health problem on the grounds that it is frequently seen and it is a fatal disease. The objective of this systematic analysis is to indicate the effects of interventions performed by nurses by using the Health Belief Model (HBM) and Health Promotion Model (HPM) on the breast cancer early diagnosis behaviors and on the components of the Health Belief Model and Health Promotion Model. The reveiw was created in line with the Centre for Reviews and Dissemination guide dated 2009 (CRD) and developed by York University National Institute of Health Researches. Review was conducted by using PUBMED, OVID, EBSCO and COCHRANE databases. Six hundred seventy eight studies (PUBMED: 236, OVID: 162, EBSCO: 175, COCHRANE:105) were found in total at the end of the review. Abstracts and full texts of these six hundred seventy eight studies were evaluated in terms of inclusion and exclusion criteria and 9 studies were determined to meet the criteria. Samplings of the studies varied between ninety four and one thousand six hundred fifty five. It was detected in the studies that educations provided by taking the theories as basis became effective on the breast cancer early diagnosis behaviors. When the literature is examined, it is observed that the experimental researches which compare the concepts of Health Belief Model (HBM) and Health Promotion Model (HPM) preoperatively and postoperatively and show the effect of these concepts on education and are conducted by nurses are limited in number. Randomized controlled studies which compare HBM and HPM concepts preoperatively and postoperatively and show the efficiency of the interventions can be useful in evaluating the efficiency of the interventions.

  10. Networking among young global health researchers through an intensive training approach: a mixed methods exploratory study

    Science.gov (United States)

    2014-01-01

    Background Networks are increasingly regarded as essential in health research aimed at influencing practice and policies. Less research has focused on the role networking can play in researchers’ careers and its broader impacts on capacity strengthening in health research. We used the Canadian Coalition for Global Health Research (CCGHR) annual Summer Institute for New Global Health Researchers (SIs) as an opportunity to explore networking among new global health researchers. Methods A mixed-methods exploratory study was conducted among SI alumni and facilitators who had participated in at least one SI between 2004 and 2010. Alumni and facilitators completed an online short questionnaire, and a subset participated in an in-depth interview. Thematic analysis of the qualitative data was triangulated with quantitative results and CCGHR reports on SIs. Synthesis occurred through the development of a process model relevant to networking through the SIs. Results Through networking at the SIs, participants experienced decreased isolation and strengthened working relationships. Participants accessed new knowledge, opportunities, and resources through networking during the SI. Post-SI, participants reported ongoing contact and collaboration, although most participants desired more opportunities for interaction. They made suggestions for structural supports to networking among new global health researchers. Conclusions Networking at the SI contributed positively to opportunities for individuals, and contributed to the formation of a network of global health researchers. Intentional inclusion of networking in health research capacity strengthening initiatives, with supportive resources and infrastructure could create dynamic, sustainable networks accessible to global health researchers around the world. PMID:24460819

  11. Networking among young global health researchers through an intensive training approach: a mixed methods exploratory study.

    Science.gov (United States)

    Lenters, Lindsey M; Cole, Donald C; Godoy-Ruiz, Paula

    2014-01-25

    Networks are increasingly regarded as essential in health research aimed at influencing practice and policies. Less research has focused on the role networking can play in researchers' careers and its broader impacts on capacity strengthening in health research. We used the Canadian Coalition for Global Health Research (CCGHR) annual Summer Institute for New Global Health Researchers (SIs) as an opportunity to explore networking among new global health researchers. A mixed-methods exploratory study was conducted among SI alumni and facilitators who had participated in at least one SI between 2004 and 2010. Alumni and facilitators completed an online short questionnaire, and a subset participated in an in-depth interview. Thematic analysis of the qualitative data was triangulated with quantitative results and CCGHR reports on SIs. Synthesis occurred through the development of a process model relevant to networking through the SIs. Through networking at the SIs, participants experienced decreased isolation and strengthened working relationships. Participants accessed new knowledge, opportunities, and resources through networking during the SI. Post-SI, participants reported ongoing contact and collaboration, although most participants desired more opportunities for interaction. They made suggestions for structural supports to networking among new global health researchers. Networking at the SI contributed positively to opportunities for individuals, and contributed to the formation of a network of global health researchers. Intentional inclusion of networking in health research capacity strengthening initiatives, with supportive resources and infrastructure could create dynamic, sustainable networks accessible to global health researchers around the world.

  12. A diagnosis model for early Tourette syndrome children based on brain structural network characteristics

    Science.gov (United States)

    Wen, Hongwei; Liu, Yue; Wang, Jieqiong; Zhang, Jishui; Peng, Yun; He, Huiguang

    2016-03-01

    Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder characterized by the presence of multiple motor and vocal tics. Tic generation has been linked to disturbed networks of brain areas involved in planning, controlling and execution of action. The aim of our work is to select topological characteristics of structural network which were most efficient for estimating the classification models to identify early TS children. Here we employed the diffusion tensor imaging (DTI) and deterministic tractography to construct the structural networks of 44 TS children and 48 age and gender matched healthy children. We calculated four different connection matrices (fiber number, mean FA, averaged fiber length weighted and binary matrices) and then applied graph theoretical methods to extract the regional nodal characteristics of structural network. For each weighted or binary network, nodal degree, nodal efficiency and nodal betweenness were selected as features. Support Vector Machine Recursive Feature Extraction (SVM-RFE) algorithm was used to estimate the best feature subset for classification. The accuracy of 88.26% evaluated by a nested cross validation was achieved on combing best feature subset of each network characteristic. The identified discriminative brain nodes mostly located in the basal ganglia and frontal cortico-cortical networks involved in TS children which was associated with tic severity. Our study holds promise for early identification and predicting prognosis of TS children.

  13. Discrimination against people with a mental health diagnosis: qualitative analysis of reported experiences.

    Science.gov (United States)

    Hamilton, Sarah; Lewis-Holmes, Elanor; Pinfold, Vanessa; Henderson, Claire; Rose, Diana; Thornicroft, Graham

    2014-04-01

    Discrimination towards people with a mental health diagnosis has public health implications. Recently, efforts have been made to tackle discrimination through campaigns and education. Understanding experiences of discrimination is vital in targeting efforts effectively. The study aimed to explore experiences of reported discrimination described by service users in a national survey in England. Structured telephone interviews were conducted with 537 mental health service users, randomly selected from five National Health Service Trusts in England. Interviews asked about experiences of discrimination in different life areas. Twenty-three interviews were audio-recorded and qualitatively analysed to develop a typology of discrimination experiences. We identified seven types: organisational decisions; mistreatment; social distancing; stereotyping; lack of understanding; dismissiveness; and over-protectiveness. Discrimination should be understood as occurring within social relationships and influenced by expectations of contact within these relationships. A better understanding of these processes can help target more effective messages in anti-discrimination campaigns.

  14. Time from pre-eclampsia diagnosis to delivery affects future health prospects of children

    DEFF Research Database (Denmark)

    Hollegaard, Birgitte; Lykke, Jacob A; Boomsma, Jacobus Jan

    2017-01-01

    Background and objectives: Pre-eclampsia often has detrimental health effects for pregnant women and their fetuses, but whether exposure in the womb has long-term health-consequences for children as they grow up remains poorly understood. We assessed overall morbidity of children following exposu...... in the complex clinical management of mild pre-eclampsia.......Background and objectives: Pre-eclampsia often has detrimental health effects for pregnant women and their fetuses, but whether exposure in the womb has long-term health-consequences for children as they grow up remains poorly understood. We assessed overall morbidity of children following exposure...... to either mild or severe pre-eclampsia up to 30 years after birth and related disease risks to duration of exposure, i.e. the time from diagnosis to delivery. Methodology: We did a registry-based retrospective cohort study in Denmark covering the years 1979-2009, using the separate diagnoses of mild...

  15. [Coverage by the Family Health Strategy and diagnosis of syphilis in pregnancy and congenital syphilis].

    Science.gov (United States)

    Saraceni, Valéria; Miranda, Angélica Espinosa

    2012-03-01

    This paper aimed to correlate syphilis in pregnancy and congenital syphilis with coverage of the Family Health Strategy (FHS), based on available data in the national health information systems. The syphilis notification estimates were calculated according to the Sentinel Childbirth Study for 2004 under the Ministry of Health and the data were obtained from the websites of the Health Surveillance Secretariat and Healthcare Secretariat, for the year 2008. The ratios between observed and estimated gestational syphilis and congenital syphilis were not statistically correlated with population coverage by the FHS (r = -0.28 and r = -0.40, respectively). The FHS is a privileged area for prenatal care and logically a source of compulsory notification of syphilis in pregnancy. By combining diagnosis with adequate treatment of syphilis in pregnant women and their partners, the FHS becomes a prime instrument for eliminating congenital syphilis in Brazil. Expanding the FHS coverage and quality of care are essential for achieving this goal.

  16. Beyond Networks: Health, Crime, and Migration in Mexico

    Directory of Open Access Journals (Sweden)

    Jose N. Martinez

    2014-01-01

    Full Text Available Two rounds of a longitudinal survey from Mexico, representative at the national, urban, rural, and regional level, are used to examine the determinants of local, domestic, and international migration. Aside from the typical covariates in the migration decision, this study considers health conditions, crime, and individual’s perspectives on life as explanatory variables. Coefficient estimates for most health variables do not offer significant support to the healthy migrant hypothesis. In terms of crime, the results suggest that females respond to worsening safety conditions in Mexico by migrating domestically, but not abroad. The decision to migrate domestically or abroad for males is not statistically correlated with increases in crime. Overall, having access to international migration networks continues to play a significant role in the decision to migrate to the US.

  17. The Impact of Online Social Networks on Health and Health Systems: A Scoping Review and Case Studies.

    Science.gov (United States)

    Griffiths, Frances; Dobermann, Tim; Cave, Jonathan A K; Thorogood, Margaret; Johnson, Samantha; Salamatian, Kavé; Gomez Olive, Francis X; Goudge, Jane

    2015-12-01

    Interaction through online social networks potentially results in the contestation of prevailing ideas about health and health care, and to mass protest where health is put at risk or health care provision is wanting. Through a review of the academic literature and case studies of four social networking health sites (PatientsLikeMe, Mumsnet, Treatment Action Campaign, and My Pro Ana), we establish the extent to which this phenomenon is documented, seek evidence of the prevalence and character of health-related networks, and explore their structure, function, participants, and impact, seeking to understand how they came into being and how they sustain themselves. Results indicate mass protest is not arising from these established health-related networking platforms. There is evidence of changes in policy following campaigning activity prompted by experiences shared through social networking such as improved National Health Service care for miscarriage (a Mumsnet campaign). Platform owners and managers have considerable power to shape these campaigns. Social networking is also influencing health policy indirectly through increasing awareness and so demand for health care. Transient social networking about health on platforms such as Twitter were not included as case studies but may be where the most radical or destabilizing influence on health care policy might arise.

  18. Classification of Microcalcifications for the Diagnosis of Breast Cancer Using Artificial Neural Networks

    National Research Council Canada - National Science Library

    Wu, Yuzheng

    1997-01-01

    .... A convolution neural network (CNN) was employed to classify benign and malignant microcalcifications in the radiographs of pathological specimen that were digitized at a high resolution of 21 microns x 21 microns...

  19. Support networks and people with physical disabilities: social inclusion and access to health services

    National Research Council Canada - National Science Library

    Holanda, Cristina Marques de Almeida; De Andrade, Fabienne Louise Juvêncio Paes; Bezerra, Maria Aparecida; Nascimento, João Paulo da Silva; Neves, Robson da Fonseca; Alves, Simone Bezerra; Ribeiro, Kátia Suely Queiroz Silva

    2015-01-01

    This study seeks to identify the formation of social support networks of people with physical disabilities, and how these networks can help facilitate access to health services and promote social inclusion...

  20. Diagnosis of Cervical Cancer Using the Median M-Type Radial Basis Function (MMRBF) Neural Network

    Science.gov (United States)

    Gómez-Mayorga, Margarita E.; Gallegos-Funes, Francisco J.; de-La-Rosa-Vázquez, José M.; Cruz-Santiago, Rene; Ponomaryov, Volodymyr

    The automatic analysis of Pap smear microscopic images is one of the most interesting fields in biomedical image processing. In this paper we present the capability of the Median M-Type Radial Basis Function (MMRBF) neural network in the classification of cervical cancer cells. From simulation results we observe that the MMRBF neural network has better classification capabilities in comparison with the Median RBF algorithm used as comparative.

  1. Implementation of the care programme approach across health and social services for dual diagnosis clients.

    Science.gov (United States)

    Kelly, Michael; Humphrey, Charlotte

    2013-12-01

    Care for clients with mental health problems and concurrent intellectual disability (dual diagnosis) is currently expected to be provided through the care programme approach (CPA), an approach to provide care to people with mental health problems in secondary mental health services. When CPA was originally introduced into UK mental health services in the 1990s, its implementation was slow and problematic, being hampered in part by problems occurring at a strategic level as health and social service organizations attempted to integrate complex systems. This article reports on a study of a more recent attempt to implement CPA for dual diagnosis clients in one mental health foundation trust, aiming to gauge progress and identify factors at the strategic level that were helping or hindering progress this time round. The study took place in a mental health National Health Service (NHS) Foundation Trust in a large English city, which was implementing a joint mental health and intellectual disability CPA policy across five of its constituent boroughs. Semi-structured interviews with key informants at Trust and borough levels focused on the Trust's overall strategy for implementing CPA and on how it was being put into practice at the front line. Documentary analysis and the administration of the Partnership Assessment Tool were also undertaken. Data were analysed using a framework approach. Progress in implementing CPA varied but overall was extremely limited in all the boroughs. The study identified six key contextual challenges that significantly hindered the implementation progress. These included organizational complexity; arrangements for governance and accountability; competing priorities; financial constraints; high staff turnover and complex information and IT systems. The only element of policy linked to CPA that had been widely taken up was the Greenlight Framework and Audit Toolkit (GLTK). The fact that the toolkit had targets and penalties associated with its

  2. Primary Health Care: care coordinator in regionalized networks?

    Science.gov (United States)

    Almeida, Patty Fidelis de; Santos, Adriano Maia Dos

    2016-12-22

    To analyze the breadth of care coordination by Primary Health Care in three health regions. This is a quantitative and qualitative case study. Thirty-one semi-structured interviews with municipal, regional and state managers were carried out, besides a cross-sectional survey with the administration of questionnaires to physicians (74), nurses (127), and a representative sample of users (1,590) of Estratégia Saúde da Família (Family Health Strategy) in three municipal centers of health regions in the state of Bahia. Primary Health Care as first contact of preference faced strong competition from hospital outpatient and emergency services outside the network. Issues related to access to and provision of specialized care were aggravated by dependence on the private sector in the regions, despite progress observed in institutionalizing flows starting out from Primary Health Care. The counter-referral system was deficient and interprofessional communication was scarce, especially concerning services provided by the contracted network. Coordination capacity is affected both by the fragmentation of the regional network and intrinsic problems in Primary Health Care, which poorly supported in its essential attributes. Although the health regions have common problems, Primary Health Care remains a subject confined to municipal boundaries. Analisar o alcance da coordenação do cuidado pela Atenção Primária à Saúde em três regiões de saúde. Trata-se de estudo de caso, com abordagem quantitativa e qualitativa. Foram realizadas 31 entrevistas semiestruturadas com gestores municipais, regionais e estaduais e estudo transversal com aplicação de questionários para médicos (74), enfermeiros (127) e amostra representativa de usuários (1.590) da Estratégia Saúde da Família em três municípios-sede de regiões de saúde do estado da Bahia. A função de porta de entrada preferencial pela Atenção Primária à Saúde deparava-se com forte concorrência de servi

  3. Combined Geometric and Neural Network Approach to Generic Fault Diagnosis in Satellite Reaction Wheels

    DEFF Research Database (Denmark)

    Baldi, P.; Blanke, Mogens; Castaldi, P.

    2015-01-01

    to allow design of generalized fault estimation filters, which do not need a priori information about the faults internal model. Simulation results with a detailed nonlinear spacecraft model, which includes disturbances, show that the proposed diagnosis scheme can deal with faults affecting both reaction......This paper suggests a novel diagnosis scheme for detection, isolation and estimation of faults affecting satellite reaction wheels. Both spin rate measurements and actuation torque defects are dealt with. The proposed system consists of a fault detection and isolation module composed by a bank...

  4. Network resiliency through memory health monitoring and proactive management

    Science.gov (United States)

    Andrade Costa, Carlos H.; Cher, Chen-Yong; Park, Yoonho; Rosenburg, Bryan S.; Ryu, Kyung D.

    2017-11-21

    A method for managing a network queue memory includes receiving sensor information about the network queue memory, predicting a memory failure in the network queue memory based on the sensor information, and outputting a notification through a plurality of nodes forming a network and using the network queue memory, the notification configuring communications between the nodes.

  5. Health, Dietary Habits, and Achievement Motivation in College Students With Self-Reported ADHD Diagnosis.

    Science.gov (United States)

    Merkt, Julia; Gawrilow, Caterina

    2016-09-01

    The present study aimed to investigate aspects of health and motivation in a subpopulation of college students with ADHD. Seventy-seven college students with self-reported ADHD (49 women; M age = 25.82, SD = 4.62) and 120 college students without ADHD (65 women; M age = 25.17, SD = 5.41) participated in an online survey assessing their health, dietary habits, and achievement motivation. College students with ADHD showed impairment in psychological functioning, impairment in their mental health, and reported more ambition and less self-control. Furthermore, we found gender differences: Women with ADHD reported worse psychological functioning, and the gender differences in obsessive-compulsive behavior and compensatory effort were mediated by the timing of diagnosis. College students, especially women, with ADHD struggle with health-related issues. Some of these gender differences might be due to under diagnosis of girls in childhood. Differences in achievement motivation might indicate compensatory mechanisms. © The Author(s) 2014.

  6. Social networking in online support groups for health: how online social networking benefits patients.

    Science.gov (United States)

    Chung, Jae Eun

    2014-01-01

    An increasing number of online support groups (OSGs) have embraced the features of social networking. So far, little is known about how patients use and benefit from these features. By implementing the uses-and-gratifications framework, the author conducted an online survey with current users of OSGs to examine associations among motivation, use of specific features of OSG, and support outcomes. Findings suggest that OSG users make selective use of varied features depending on their needs, and that perceptions of receiving emotional and informational support are associated more with the use of some features than others. For example, those with strong motivation for social interaction use diverse features of OSG and make one-to-one connections with other users by friending. In contrast, those with strong motivation for information seeking limit their use primarily to discussion boards. Results also show that online social networking features, such as friending and sharing of personal stories on blogs, are helpful in satisfying the need for emotional support. The present study sheds light on online social networking features in the context of health-related OSGs and provides practical lessons on how to improve the capacity of OSGs to serve the needs of their users.

  7. Leveraging Social Networks to Support Reproductive Health and Economic Wellbeing among Guatemalan Maya Women

    Science.gov (United States)

    Prescott, Alexandra S.; Luippold-Roge, Genevieve P.; Gurman, Tilly A.

    2016-01-01

    Objective: Maya women in Guatemala are disproportionately affected by poverty and negative reproductive health outcomes. Although social networks are valued in many Indigenous cultures, few studies have explored whether health education programmes can leverage these networks to improve reproductive health and economic wellbeing. Design: This…

  8. A Novel Network for Mentoring Family Physicians on Mental Health Issues Using E-Mail

    Science.gov (United States)

    Hunter, Jon J.; Rockman, Patricia; Gingrich, Nadine; Silveira, Jose; Salach, Lena

    2008-01-01

    Objective: Family practitioners are significant providers of mental health care and routinely report difficulty acquiring timely support in this area. The Collaborative Mental Health Care Network assembled groups of family practitioners and provided them with mental health practitioner mentors. This article addresses communication in the Network,…

  9. Adaptation of the oral health version of an instrument for diagnosing the healthcare network?s stage of development

    National Research Council Canada - National Science Library

    Leal, Daniele Lopes; Paiva, Saul Martins; Werneck, Marcos Azeredo Furquim; Oliveira, Ana Cristina Borges de

    2014-01-01

    .... The current study aimed to describe the stages in the adaptation of the oral healthcare version of an instrument to evaluate the stage of development in the healthcare network under the Unified National Health System (SUS...

  10. The financial performance of hospitals belonging to health networks and systems.

    Science.gov (United States)

    Bazzoli, G J; Chan, B; Shortell, S M; D'Aunno, T

    2000-01-01

    The U.S. health industry is experiencing substantial restructuring through ownership consolidation and development of new forms of interorganizational relationships. Using an established taxonomy of health networks and systems, this paper develops and tests four hypotheses related to hospital financial performance. Consistent with our predictions, we find that hospitals in health systems that had unified ownership generally had better financial performance than hospitals in contractually based health networks. Among health network hospitals, those belonging to highly centralized networks had better financial performance than those belonging to more decentralized networks. However, health system hospitals in moderately centralized systems performed better than those in highly centralized systems. Finally, hospitals in networks or systems with little differentiation or centralization experienced the poorest financial performance. These results are consistent with resource dependence, transaction cost economics, and institutional theories of organizational behavior, and provide a conceptual and empirical baseline for future research.

  11. Health behaviour changes after diagnosis of chronic illness among Canadians aged 50 or older.

    Science.gov (United States)

    Newson, Jason T; Huguet, Nathalie; Ramage-Morin, Pamela L; McCarthy, Michael J; Bernier, Julie; Kaplan, Mark S; McFarland, Bentson H

    2012-12-01

    Changes in health behaviours (smoking, physical activity, alcohol consumption, and fruit and vegetable consumption) after diagnosis of chronic health conditions (heart disease, cancer, stroke, respiratory disease, and diabetes) were examined among Canadians aged 50 or older. Results from 12 years of longitudinal data from the Canadian National Population Health Survey indicated relatively modest changes in behaviour. Although significant decreases in smoking were observed among all groups except those with respiratory disease, at least 75% of smokers did not quit. No significant changes emerged in the percentage meeting physical activity recommendations, except those with diabetes, or in excessive alcohol consumption, except those with diabetes and respiratory disease. The percentage reporting the recommended minimum fruit and vegetable intake did not increase significantly among any group.

  12. Automated Differential Diagnosis of Early Parkinsonism Using Metabolic Brain Networks: A Validation Study.

    Science.gov (United States)

    Tripathi, Madhavi; Tang, Chris C; Feigin, Andrew; De Lucia, Ivana; Nazem, Amir; Dhawan, Vijay; Eidelberg, David

    2016-01-01

    The differentiation of idiopathic Parkinson disease (IPD) from multiple system atrophy (MSA) and progressive supranuclear palsy (PSP), the most common atypical parkinsonian look-alike syndromes (APS), can be clinically challenging. In these disorders, diagnostic inaccuracy is more frequent early in the clinical course when signs and symptoms are mild. Diagnostic inaccuracy may be particularly relevant in trials of potential disease-modifying agents, which typically involve participants with early clinical manifestations. In an initial study, we developed a probabilistic algorithm to classify subjects with clinical parkinsonism but uncertain diagnosis based on the expression of metabolic covariance patterns for IPD, MSA, and PSP. Classifications based on this algorithm agreed closely with final clinical diagnosis. Nonetheless, blinded prospective validation is required before routine use of the algorithm can be considered. We used metabolic imaging to study an independent cohort of 129 parkinsonian subjects with uncertain diagnosis; 77 (60%) had symptoms for 2 y or less at the time of imaging. After imaging, subjects were followed by blinded movement disorders specialists for an average of 2.2 y before final diagnosis was made. When the algorithm was applied to the individual scan data, the probabilities of IPD, MSA, and PSP were computed and used to classify each of the subjects. The resulting image-based classifications were then compared with the final clinical diagnosis. IPD subjects were distinguished from APS with 94% specificity and 96% positive predictive value (PPV) using the original 2-level logistic classification algorithm. The algorithm achieved 90% specificity and 85% PPV for MSA and 94% specificity and 94% PPV for PSP. The diagnostic accuracy was similarly high (specificity and PPV > 90%) for parkinsonian subjects with short symptom duration. In addition, 25 subjects were classified as level I indeterminate parkinsonism and 4 more subjects as level II

  13. Cutting State Diagnosis for Shearer through the Vibration of Rocker Transmission Part with an Improved Probabilistic Neural Network

    Directory of Open Access Journals (Sweden)

    Lei Si

    2016-04-01

    Full Text Available In order to achieve more accurate and reliable identification of shearer cutting state, this paper employs the vibration of rocker transmission part and proposes a diagnosis method based on a probabilistic neural network (PNN and fruit fly optimization algorithm (FOA. The original FOA is modified with a multi-swarm strategy to enhance the search performance and the modified FOA is utilized to optimize the smoothing parameters of the PNN. The vibration signals of rocker transmission part are decomposed by the ensemble empirical mode decomposition and the Kullback-Leibler divergence is used to choose several appropriate components. Forty-five features are extracted to estimate the decomposed components and original signal, and the distance-based evaluation approach is employed to select a subset of state-sensitive features by removing the irrelevant features. Finally, the effectiveness of the proposed method is demonstrated via the simulation studies of shearer cutting state diagnosis and the comparison results indicate that the proposed method outperforms the competing methods in terms of diagnosis accuracy.

  14. Personal social networks and health: conceptual and clinical implications of their reciprocal impact.

    Science.gov (United States)

    Sluzki, Carlos E

    2010-03-01

    Social networks affect positively or negatively a person's health, and a person's health affects, in turn, the network's availability. This article discusses this double dynamic, recommends the routine exploration of patients' social networks, and offers a mapping tool that allows detection of strengths and weaknesses of those processes so as to facilitate interventions that improve the social support's health-enhancing effect. Copyright 2010 APA, all rights reserved

  15. Polycentrism in Global Health Governance Scholarship Comment on "Four Challenges That Global Health Networks Face".

    Science.gov (United States)

    Tosun, Jale

    2017-05-23

    Drawing on an in-depth analysis of eight global health networks, a recent essay in this journal argued that global health networks face four challenges to their effectiveness: problem definition, positioning, coalition-building, and governance. While sharing the argument of the essay concerned, in this commentary, we argue that these analytical concepts can be used to explicate a concept that has implicitly been used in global health governance scholarship for quite a few years. While already prominent in the discussion of climate change governance, for instance, global health governance scholarship could make progress by looking at global health governance as being polycentric. Concisely, polycentric forms of governance mix scales, mechanisms, and actors. Drawing on the essay, we propose a polycentric approach to the study of global health governance that incorporates coalitionbuilding tactics, internal governance and global political priority as explanatory factors. © 2018 The Author(s); Published by Kerman University of Medical Sciences. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

  16. Capsule endoscopy in diagnosis of small bowel diseases: a health technology assessment.

    Science.gov (United States)

    Li, Xiang Lian; Shen, Jian Tong; Li, You Ping; Tang, Cheng Wei; Huang, Li Bin; Li, Cui Cui; Yu, Jia Jie; Wang, Ying Jiang; Yang, Zong Xia

    2014-05-01

    Capsule endoscopy (CE) has been widely used in the diagnosis of small bowel disease (SBD) in the world. To bring CE into the national health insurance directory, and intensify its popularization in primary hospital, the government needs high-quality HTA evidence for decision makers. We were appointed by the National Health and Family Planning Commission of China to evaluate the effectiveness, safety, economy, and applicability of CE in the diagnosis of SBD, to provide the best currently available evidence for decision making. We searched the Cochrane Library (Issue 8, 2013), PubMed, EMbase, INAHTA, VIP, CBM, CNKI and WanFang Data. All confirmed or suspected SBD patients with diagnosis by CE versus other alternative therapies were considered. Health technology assessments (HTAs), systematic reviews (SRs), meta-analyses, randomized controlled trials (RCTs), guidelines and economic studies were included. Two investigators selected studies, assessed the quality and extracted data independently, and a descriptive analysis was used. We included 4 HTAs, 11 SRs/meta-analyses, 2 RCTs, 5 guidelines, and 10 economic studies for assessment. The results showed that the disease detection rate of CE was higher than that of many other traditional technologies and that the main adverse event for CE was retention (0.7% to 3.0%). These results were consistent with those of the guidelines. Comprehensive results of economic studies showed the superiority of CE compared with other technologies. As the first choice, CE can decrease potential costs, especially when used in outpatients. (i) CE has advantages in diagnostic yield, safety, and cost in the diagnosis of SBD, but some limitations exist. It still needs more high-quality evidence on CE diagnosis accuracy. (ii) When the government approves the introduction of CE in a hospital, many factors must be considered, such as the local disease burden, clinical demand, ability to pay, and staff. At the same time, it is necessary to

  17. Prevention, diagnosis, and treatment of tuberculosis in children and mothers: evidence for action for maternal, neonatal, and child health services

    National Research Council Canada - National Science Library

    Getahun, Haileyesus; Sculier, Delphine; Sismanidis, Charalambos; Grzemska, Malgorzata; Raviglione, Mario

    2012-01-01

    ...), and maternal tuberculosis increases the vertical transmission of HIV. Tuberculosis prevention, diagnosis, and treatment services should be included as key interventions in the integrated management of pregnancy and child health...

  18. Sharing for Health: A Study of Chinese Adolescents' Experiences and Perspectives on Using Social Network Sites to Share Health Information.

    Science.gov (United States)

    Zhang, Ni; Teti, Michele; Stanfield, Kellie; Campo, Shelly

    2017-07-01

    This exploratory qualitative study examines Chinese adolescents' health information sharing habits on social network sites. Ten focus group meetings with 76 adolescents, ages 12 to 17 years, were conducted at community-based organizations in Chicago's Chinatown. The research team transcribed the recording and analyzed the transcripts using ATLAS.ti. Chinese adolescents are using different social network sites for various topics of health information including food, physical activity, and so on. Adolescents would share useful and/or interesting health information. Many adolescents raised credibility concerns regarding health information and suggested evaluating the information based on self-experience or intuition, word-of-mouth, or information online. The findings shed lights on future intervention using social network sites to promote health among Chinese adolescents in the United States. Future interventions should provide adolescents with interesting and culturally sensitive health information and educate them to critically evaluate health information on social network sites.

  19. Evaluation from population registry data of health care expenditure during the 6 months after cancer diagnosis.

    Science.gov (United States)

    Schraub, S; Mougeot, M; Mercier, M; Bourgeois, P

    1991-08-01

    It is currently estimated in France, that the cost of cancer has risen to $3.8 billion, with an annual growth of 5-10%. This represents approximately 6% of all health expenditure. The data from the Registry of Tumors in the Doubs region have enabled us to make an evaluation of health expenditure, reimbursed by the French Securite Sociale (S.S.), and its distribution in relation to different activities (diagnosis, type of treatment, follow-up, transport), according to cancer site. In 1984, the average cost per patient within the first 6 months of the illness was evaluated at $4,000. The results show major differences for the cancer sites, care facilities and budget items. Diagnosis assessment represents 27% of all expenditure, surgery 37%, radiotherapy, chemotherapy and transport, 11% each. All kinds of expenses are fully reimbursed by the French S.S. and transportation, which at the beginning used to avoid hospitalization, is now used as a comfort system and represents a high cost to the S.S. The different costs for the same illness between private, general public and university hospitals do not reflect a difference in care, but rather different systems of calculating and functioning. Till now there has not been any logical evaluation of care in the French health system.

  20. The Health and Occupation Research Network: An Evolving Surveillance System

    Directory of Open Access Journals (Sweden)

    Melanie Carder

    2017-09-01

    Full Text Available Vital to the prevention of work-related ill-health (WRIH is the availability of good quality data regarding WRIH burden and risks. Physician-based surveillance systems such as The Health and Occupation Research (THOR network in the UK are often established in response to limitations of statutory, compensation-based systems for addressing certain epidemiological aspects of disease surveillance. However, to fulfil their purpose, THOR and others need to have methodologic rigor in capturing and ascertaining cases. This article describes how data collected by THOR and analogous systems can inform WRIH incidence, trends, and other determinants. An overview of the different strands of THOR research is provided, including methodologic advancements facilitated by increased data quantity/quality over time and the value of the research outputs for informing Government and other policy makers. In doing so, the utility of data collected by systems such as THOR to address a wide range of research questions, both in relation to WRIH and to wider issues of public and social health, is demonstrated.

  1. European Healthy Cities come to terms with health network governance.

    Science.gov (United States)

    de Leeuw, Evelyne; Kickbusch, Ilona; Palmer, Nicola; Spanswick, Lucy

    2015-06-01

    A focus on good governance in the WHO European network of Healthy Cities mirrors the WHO Region's strategic emphasis-its member states in the Health 2020 strategy espouse governance for health as key. Healthy Cities adopted governance as a key value and approach to delivering specific health programmes and policies. This article reviews the extent to which they actually introduce and align governance concepts and approaches with their local government commitments. Healthy Cities show that better participation, policy-making and intersectoral action result from an emphasis on governance. This happens across the designated cities and is not limited to a certain class (in terms of population or geographical location) or the time they have been designated. The support of WHO in driving the governance agenda seems important, but no data are available to show that European Healthy Cities are different from other urban environments. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  2. Consumers' online social network topologies and health behaviours.

    Science.gov (United States)

    Lau, Annie Y S; Dunn, Adam; Mortimer, Nathan; Proudfoot, Judith; Andrews, Annie; Liaw, Siaw-Teng; Crimmins, Jacinta; Arguel, Amaël; Coiera, Enrico

    2013-01-01

    Personally controlled health management systems (PCHMS) often consist of multiple design features. Yet, they currently lack empirical evidence on how consumers use and engage with a PCHMS. An online prospective study was designed to investigate how 709 consumers used a web-based PCHMS to manage their physical and emotional wellbeing over five months. The web-based PCHMS, Healthy.me, was developed at UNSW and incorporates an untethered personal health record, consumer care pathways, forums, polls, diaries, and messaging links with healthcare professionals. The two PCHMS features that consumers used most frequently, found most useful, and engaging were the social features, i.e. forum and poll. Compared to participants who did not use any PCHMS social feature, those who used either the poll or the forum were 12.3% more likely to visit a healthcare professional (P=0.001) during the study. Social network analysis of forums revealed a spectrum of social interaction patterns - from question-and-answer structures to community discussions. This study provides a basis for understanding how a PCHMS can be used as a socially-driven intervention to influence consumers' health behaviours.

  3. Diagnosis and management of malaria by rural community health providers in the Lao People's Democratic Republic (Laos).

    Science.gov (United States)

    Mayxay, Mayfong; Pongvongsa, Tiengkham; Phompida, Samlane; Phetsouvanh, Rattanaxay; White, Nicholas J; Newton, Paul N

    2007-04-01

    We assessed the knowledge of malaria diagnosis and management by community health providers in rural Vientiane and Savannakhet Provinces, Lao PDR. Sixty health providers (17 pharmacy owners/drug sellers and 43 village health volunteers) were interviewed. All diagnosed malaria using symptoms and signs only; 14% were aware of >2 criteria for the diagnosis of severe malaria. Although chloroquine and quinine, the then recommended Lao national policy for uncomplicated malaria treatment, were the most common antimalarials prescribed - 65% gave incorrect doses and 70% did not know the side effects. Although not recommended by the then national policy, 27% of the health providers used combinations of antimalarials as they considered monotherapy ineffective. This study strongly suggests that further training of Lao rural health providers in malaria diagnosis and management is needed to improve the quality of health services in areas remote from district hospitals.

  4. [An evaluation of diagnosis and treatment of acute sinusitis at three health care centers].

    Science.gov (United States)

    Oskarsson, Jón Pálmi; Halldórsson, Sigurdur

    2010-09-01

    The objective of this study was to evaluate the diagnosis and treatment of acute sinusitis at three health care centers in northern and eastern Iceland. Information on all those diagnosed with acute sinusitis (ICD 10 J01.0, J01.9) in the year 2004 at the communal health care centers in Akureyri, Husavik and Egilsstadir was obtained retrospectively from computerized clinical records. Key factors used for diagnosis and treatment were recorded. In order to obtain an equal distribution in population size only about one-third of the diagnoses made in Akureyri were included in the search (the first ten days of every month). The search yielded a total of 468 individuals. The average incidence of acute sinusitis was found to be 3.4 per 100 inhabitants per year. Adherence to clinical guidelines (albeit from other countries) regarding diagnosis of bacterial sinusitis was nearly nonexistent. There were considerable differences found between health care centers as to whether x-rays were used for diagnostic purposes. Blood tests were hardly used at all. The disease was diagnosed over the telephone in 28% of the cases (Husavik 38%, Akureyri 32%, Egilsstadir 10%). Over 90% of all individuals diagnosed with acute sinusitis received antibiotics, regardless of symptom duration. The antibiotics most often prescribed were Doxycyclin and Amoxicillin. The incidence of acute sinusitis in these three communities seems to be similar to other western countries. Acute bacterial sinusitis seems to be overdiagnosed and the use of antibiotics is in no context with clinical guidelines. Our results support the hypothesis that physicians tend to regard acute sinusitis as a bacterial disease, and treat it accordingly.

  5. Modules, networks and systems medicine for understanding disease and aiding diagnosis

    DEFF Research Database (Denmark)

    Gustafsson, Mika; Nestor, Colm E.; Zhang, Huan

    2014-01-01

    Many common diseases, such as asthma, diabetes or obesity, involve altered interactions between thousands of genes. High-throughput techniques (omics) allow identification of such genes and their products, but functional understanding is a formidable challenge. Network-based analyses of omics dat...

  6. Neural network analysis of lymphoma microarray data: prognosis and diagnosis near-perfect

    Directory of Open Access Journals (Sweden)

    Song Li

    2003-04-01

    Full Text Available Abstract Background Microarray chips are being rapidly deployed as a major tool in genomic research. To date most of the analysis of the enormous amount of information provided on these chips has relied on clustering techniques and other standard statistical procedures. These methods, particularly with regard to cancer patient prognosis, have generally been inadequate in providing the reduced gene subsets required for perfect classification. Results Networks trained on microarray data from DLBCL lymphoma patients have, for the first time, been able to predict the long-term survival of individual patients with 100% accuracy. Other networks were able to distinguish DLBCL lymphoma donors from other donors, including donors with other lymphomas, with 99% accuracy. Differentiating the trained network can narrow the gene profile to less than three dozen genes for each classification. Conclusions Here we show that artificial neural networks are a superior tool for digesting microarray data both with regard to making distinctions based on the data and with regard to providing very specific reference as to which genes were most important in making the correct distinction in each case.

  7. Delay in diagnosis of pulmonary tuberculosis at a primary health clinic in Vitoria, Brazil.

    Science.gov (United States)

    Maciel, E L N; Golub, J E; Peres, R L; Hadad, D J; Fávero, J L; Molino, L P; Bae, J W; Moreira, C M; Detoni, V do V; Vinhas, S A; Palaci, M; Dietze, R

    2010-11-01

    Primary health clinics in Vitoria, Espirito Santo, Brazil. To identify risk factors associated with patient and health care delays among patients seeking care at primary health clinics. A prospective study among tuberculosis (TB) patients diagnosed in Vitoria between 1 January 2003 and 30 December 2007. A questionnaire ascertained the date of onset and duration of TB symptoms and medical records were reviewed. Between-group distributions of delay were compared and multivariate logistic regression was performed. Of 304 patients, 296 (97%) reported at least one TB symptom presenting for the first time to a qualified health service; 244 (80%) reported cough > 3 weeks. Median health care delay was 30 days (range 5-68), and median total delay was 110 days (range 26-784). Multivariate analysis revealed any cough (OR(adj) 7.35, 95%CI 2.40-22.5) and weight at TB diagnosis < 60 kg (OR(adj) 5.92, 95%CI 1.83-19.1) to be associated with patient delay of ≥ 30 days. Factors increasing risk of prolonged delay (≥ 90 days) were age ≥ 30 years (OR(adj) 1.93, 95%CI 1.09-3.43) and chest pain (OR(adj) 2.42, 95%CI 1.29-4.53). Improving health care workers' education regarding TB symptoms and implementing active case finding in targeted populations may reduce delays.

  8. City leadership for health and sustainable development: the World Health Organization European Healthy Cities Network.

    Science.gov (United States)

    Tsouros, Agis

    2009-11-01

    This paper provides an overview of European Healthy Cities Network (EHCN) organized by the WHO Regional Office Europe. The focus is on the third of five phases covering the period 1998-2002. Fifty-six cities were members of the WHO-EHCN and over 1000 European cities were members of national networks. Association with WHO has given municipalities legitimacy to move into a domain often associated with health service. Equity and community participation are core values. City mayors provide political leadership. Intersectoral cooperation underpins a Healthy Cities approach. The WHO Regional Office for Europe supports WHO-EHCN, providing guidance and technical leadership. Cities' processes and structures are prerequisits for improvements in health and are central to the evaluation of Phase III of the WHO-EHCN.

  9. Public health educational comprehensiveness: The strategic rationale in establishing networks among schools of public health.

    Science.gov (United States)

    Otok, Robert; Czabanowska, Katarzyna; Foldspang, Anders

    2017-11-01

    The establishment and continuing development of a sufficient and competent public health workforce is fundamental for the planning, implementation, evaluation, effect and ethical validity of public health strategies and policies and, thus, for the development of the population's health and the cost-effectiveness of health and public health systems and interventions. Professional public health strategy-making demands a background of a comprehensive multi-disciplinary curriculum including mutually, dynamically coherent competences - not least, competences in sociology and other behavioural sciences and their interaction with, for example, epidemiology, biostatistics, qualitative methods and health promotion and disease prevention. The size of schools and university departments of public health varies, and smaller entities may run into problems if seeking to meet the comprehensive curriculum challenge entirely by use of in-house resources. This commentary discusses the relevance and strength of establishing comprehensive curriculum development networks between schools and university departments of public health, as one means to meet the comprehensiveness challenge. This commentary attempts to consider a two-stage strategy to develop complete curricula at the bachelor and master's as well as PhD levels.

  10. Energy Harvesting for Structural Health Monitoring Sensor Networks

    Energy Technology Data Exchange (ETDEWEB)

    Park, G.; Farrar, C. R.; Todd, M. D.; Hodgkiss, T.; Rosing, T.

    2007-02-26

    This report has been developed based on information exchanges at a 2.5-day workshop on energy harvesting for embedded structural health monitoring (SHM) sensing systems that was held June 28-30, 2005, at Los Alamos National Laboratory. The workshop was hosted by the LANL/UCSD Engineering Institute (EI). This Institute is an education- and research-focused collaboration between Los Alamos National Laboratory (LANL) and the University of California, San Diego (UCSD), Jacobs School of Engineering. A Statistical Pattern Recognition paradigm for SHM is first presented and the concept of energy harvesting for embedded sensing systems is addressed with respect to the data acquisition portion of this paradigm. Next, various existing and emerging sensing modalities used for SHM and their respective power requirements are summarized, followed by a discussion of SHM sensor network paradigms, power requirements for these networks and power optimization strategies. Various approaches to energy harvesting and energy storage are discussed and limitations associated with the current technology are addressed. This discussion also addresses current energy harvesting applications and system integration issues. The report concludes by defining some future research directions and possible technology demonstrations that are aimed at transitioning the concept of energy harvesting for embedded SHM sensing systems from laboratory research to field-deployed engineering prototypes.

  11. Automated plasmodia recognition in microscopic images for diagnosis of malaria using convolutional neural networks

    Science.gov (United States)

    Krappe, Sebastian; Benz, Michaela; Gryanik, Alexander; Tannich, Egbert; Wegner, Christine; Stamminger, Marc; Wittenberg, Thomas; Münzenmayer, Chrisitan

    2017-03-01

    Malaria is one of the world's most common and serious tropical diseases, caused by parasites of the genus plasmodia that are transmitted by Anopheles mosquitoes. Various parts of Asia and Latin America are affected but highest malaria incidence is found in Sub-Saharan Africa. Standard diagnosis of malaria comprises microscopic detection of parasites in stained thick and thin blood films. As the process of slide reading under the microscope is an error-prone and tedious issue we are developing computer-assisted microscopy systems to support detection and diagnosis of malaria. In this paper we focus on a deep learning (DL) approach for the detection of plasmodia and the evaluation of the proposed approach in comparison with two reference approaches. The proposed classification schemes have been evaluated with more than 180,000 automatically detected and manually classified plasmodia candidate objects from so-called thick smears. Automated solutions for the morphological analysis of malaria blood films could apply such a classifier to detect plasmodia in the highly complex image data of thick smears and thereby shortening the examination time. With such a system diagnosis of malaria infections should become a less tedious, more reliable and reproducible and thus a more objective process. Better quality assurance, improved documentation and global data availability are additional benefits.

  12. "Diagnosis of sleep apnea in network" respiratory polygraphy as a decentralization strategy.

    Science.gov (United States)

    Borsini, Eduardo; Blanco, Magali; Bosio, Martin; Fernando, Di Tullio; Ernst, Glenda; Salvado, Alejandro

    2016-01-01

    Obstructive sleep apnea syndrome (OSA) is diagnosed through polysomnography (PSG) or respiratory polygraphy (RP). Self-administered home-based RP using devices with data transmission could facilitate diagnosis in distant populations. The purpose of this work was to describe a telemedicine initiative using RP in four satellite outpatient care clinics (OCC) of Buenos Aires Hospital Británico Central (HBC). OCC technicians were trained both in the use of RP. Raw signals were sent to HBC via intranet software for scoring and final report. During a 24-month 499 RP were performed in 499 patients: 303 men (60.7%) with the following characteristics (mean and standard deviation): valid time for manual analysis: 392.8 min (±100.1), AHI: 17.05 (±16.49 and percentile 25-75 [Pt]: 5-23) ev/hour, ODI (criterion 3%): 18.05 (±16.48 and Pt 25-75: 6-25) ev/hour, and time below 90% (Tdisease). Physicians were able to diagnosis OSA by doing portable respiratory polygraphy at distance. The remote diagnosis strategy presented short delays, safe data transmission, and low rate of missing data.

  13. HealthTrust: a social network approach for retrieving online health videos.

    Science.gov (United States)

    Fernandez-Luque, Luis; Karlsen, Randi; Melton, Genevieve B

    2012-01-31

    significance with health consumers (r₇ = .65, P = .06) with videos on hemoglobinA(1c), but it did not perform as well with diabetic foot videos. The trust-based metric HealthTrust showed promising results when used to retrieve diabetes content from YouTube. Our research indicates that social network analysis may be used to identify trustworthy social media in health communities.

  14. Social Network Assessments and Interventions for Health Behavior Change: A Critical Review.

    Science.gov (United States)

    Latkin, Carl A; Knowlton, Amy R

    2015-01-01

    Social networks provide a powerful approach for health behavior change. This article documents how social network interventions have been successfully used for a range of health behaviors, including HIV risk practices, smoking, exercise, dieting, family planning, bullying, and mental health. We review the literature that suggests the relationship between health behaviors and social network attributes demonstrates a high degree of specificity. The article then examines hypothesized social influence mechanisms including social norms, modeling, and social rewards and the factors of social identity and social rewards that can be employed to sustain social network interventions. Areas of future research avenues are highlighted, including the need to examine and to adjust analytically for contamination and social diffusion, social influence versus differential affiliation, and network change. Use and integration of mhealth and face-to-face networks for promoting health behavior change are also critical research areas.

  15. Health implications of social networks for children living in public housing.

    Science.gov (United States)

    Kennedy-Hendricks, Alene; Schwartz, Heather L; Griffin, Beth Ann; Burkhauser, Susan; Green, Harold D; Kennedy, David P; Pollack, Craig Evan

    2015-11-01

    This study sought to examine whether: (1) the health composition of the social networks of children living in subsidized housing within market rate developments (among higher-income neighbors) differs from the social network composition of children living in public housing developments (among lower-income neighbors); and (2) children's social network composition is associated with children's own health. We found no significant differences in the health characteristics of the social networks of children living in these different types of public housing. However, social network composition was significantly associated with several aspects of children's own health, suggesting the potential importance of social networks for the health of vulnerable populations. Copyright © 2015 Elsevier Ltd. All rights reserved.

  16. Social Network Assessments and Interventions for Health Behavior Change: A Critical Review

    Science.gov (United States)

    Latkin, Carl A.; Knowlton, Amy R.

    2016-01-01

    Social networks provide a powerful approach for health behavior change. This article documents how social network interventions have been successfully utilized for a range of health behaviors including HIV risk practices, smoking, exercise, dieting, family planning, bullying, and mental health. We review the literature that suggests relationship between health behaviors and social network attributes demonstrate a high degree of specificity. The article then examines hypothesized social influence mechanisms including social norms, modeling, and social rewards and the factors of social identity and social rewards that can be employed to sustain social network interventions. Areas of future research avenues are highlighted, including the need to examine and analytically adjust for contamination and social diffusion, social influence versus differential affiliation, and network change. Use and integration of mhealth and face-to-face networks for promoting health behavior change are also critical research areas. PMID:26332926

  17. Diagnosis of Cognitive Impairment Compatible with Early Diagnosis of Alzheimer's Disease. A Bayesian Network Model based on the Analysis of Oral Definitions of Semantic Categories.

    Science.gov (United States)

    Guerrero, J M; Martínez-Tomás, R; Rincón, M; Peraita, H

    2016-01-01

    Early detection of Alzheimer's disease (AD) has become one of the principal focuses of research in medicine, particularly when the disease is incipient or even prodromic, because treatments are more effective in these stages. Lexical-semantic-conceptual deficit (LSCD) in the oral definitions of semantic categories for basic objects is an important early indicator in the evaluation of the cognitive state of patients. The objective of this research is to define an economic procedure for cognitive impairment (CI) diagnosis, which may be associated with early stages of AD, by analysing cognitive alterations affecting declarative semantic memory. Because of its low cost, it could be used for routine clinical evaluations or screenings, leading to more expensive and selective tests that confirm or rule out the disease accurately. It should necessarily be an explanatory procedure, which would allow us to study the evolution of the disease in relation to CI, the irregularities in different semantic categories, and other neurodegenerative diseases. On the basis of these requirements, we hypothesise that Bayesian networks (BNs) are the most appropriate tool for this purpose. We have developed a BN for CI diagnosis in mild and moderate AD patients by analysing the oral production of semantic features. The BN causal model represents LSCD in certain semantic categories, both of living things (dog, pine, and apple) and non-living things (chair, car, and trousers), as symptoms of CI. The model structure, the qualitative part of the model, uses domain knowledge obtained from psychology experts and epidemiological studies. Further, the model parameters, the quantitative part of the model, are learnt automatically from epidemiological studies and Peraita and Grasso's linguistic corpus of oral definitions. This corpus was prepared with an incidental sampling and included the analysis of the oral linguistic production of 81 participants (42 cognitively healthy elderly people and 39

  18. [Nurse care in primary health care: diagnosis and follow-up of health problems].

    Science.gov (United States)

    Granollers Mercader, S; Pont Ribas, A

    1993-02-01

    To evaluate the results of nursing staff putting into practice a number of health prevention and promotion schemes, with particular emphasis on the follow-up of the health problems detected. A crossover study. SITE. A care unit of the Sant Just Desvern Primary Care team. A total of 136 people were seen. 58% were men and 42% women. Their average age was 39.30 +/- 16. The risk factors found were: tobacco, alcohol, exercise, arterial tension, cholesterol, weight, dental and oral hygiene, gynecological check-ups, self-examination of breasts and anti-tetanus, German measles and flu vaccinations. Using the clinical records it was found that 42.64% were smokers; 1.58% were alcoholics and 20.63% consumed an excessive amount of alcohol; 47.58% were sedentary; 17.09% were diagnosed with dyslipaemia; and 1.58% with hypertension. After the intervention, 18.96% gave up smoking and 14.28% of excessive drinkers managed to stop. 76.47% of women advised to attend the gynaecologist did so. 89.61% of patients completed the series of anti-tetanus vaccinations. The favorable response of the population to a periodic health check-up should be emphasised. The changes in life-style brought about after the detection and subsequent follow-up of health problems was extremely positive.

  19. Semantic Web, Reusable Learning Objects, Personal Learning Networks in Health: Key Pieces for Digital Health Literacy.

    Science.gov (United States)

    Konstantinidis, Stathis Th; Wharrad, Heather; Windle, Richard; Bamidis, Panagiotis D

    2017-01-01

    The knowledge existing in the World Wide Web is exponentially expanding, while continuous advancements in health sciences contribute to the creation of new knowledge. There are a lot of efforts trying to identify how the social connectivity can endorse patients' empowerment, while other studies look at the identification and the quality of online materials. However, emphasis has not been put on the big picture of connecting the existing resources with the patients "new habits" of learning through their own Personal Learning Networks. In this paper we propose a framework for empowering patients' digital health literacy adjusted to patients' currents needs by utilizing the contemporary way of learning through Personal Learning Networks, existing high quality learning resources and semantics technologies for interconnecting knowledge pieces. The framework based on the concept of knowledge maps for health as defined in this paper. Health Digital Literacy needs definitely further enhancement and the use of the proposed concept might lead to useful tools which enable use of understandable health trusted resources tailored to each person needs.

  20. Comprehensive visual field test & diagnosis system in support of astronaut health and performance

    Science.gov (United States)

    Fink, Wolfgang; Clark, Jonathan B.; Reisman, Garrett E.; Tarbell, Mark A.

    Long duration spaceflight, permanent human presence on the Moon, and future human missions to Mars will require autonomous medical care to address both expected and unexpected risks. An integrated non-invasive visual field test & diagnosis system is presented for the identification, characterization, and automated classification of visual field defects caused by the spaceflight environment. This system will support the onboard medical provider and astronauts on space missions with an innovative, non-invasive, accurate, sensitive, and fast visual field test. It includes a database for examination data, and a software package for automated visual field analysis and diagnosis. The system will be used to detect and diagnose conditions affecting the visual field, while in space and on Earth, permitting the timely application of therapeutic countermeasures before astronaut health or performance are impaired. State-of-the-art perimetry devices are bulky, thereby precluding application in a spaceflight setting. In contrast, the visual field test & diagnosis system requires only a touchscreen-equipped computer or touchpad device, which may already be in use for other purposes (i.e., no additional payload), and custom software. The system has application in routine astronaut assessment (Clinical Status Exam), pre-, in-, and post-flight monitoring, and astronaut selection. It is deployable in operational space environments, such as aboard the International Space Station or during future missions to or permanent presence on the Moon and Mars.

  1. What is the potential for social networks and support to enhance future telehealth interventions for people with a diagnosis of schizophrenia: a critical interpretive synthesis.

    Science.gov (United States)

    Daker-White, Gavin; Rogers, Anne

    2013-11-01

    Digital technologies are increasingly directed at improved monitoring, management and treatment of mental health. However, their potential contribution to social networks and self-management support for people diagnosed with a serious mental illness has rarely been considered. This review and meta-synthesis aimed to examine the processes of engagement and perceived relevance and appropriateness of telehealth interventions for people with a diagnosis of schizophrenia. The review addresses three key questions. How is the use of digital communications technologies framed in the professional psychiatric literature? How might the recognised benefits of telehealth translate to people with a diagnosis of schizophrenia? What is the user perspective concerning Internet information and communication technologies? A critical interpretive synthesis (CIS) of published findings from quantitative and qualitative studies of telehealth interventions for people with a diagnosis of schizophrenia. Most studies were of an exploratory nature. The professional discourse about the use of different technologies was overlain by concerns with surveillance and control, focusing on the Internet as a potential site of risk and danger. The critical synthesis of findings showed that the key focus of the available studies was on the delivery of existing traditional approaches (e.g. improving medications adherence, provision of medical information about the condition, symptom monitoring and cognitive behavioural therapy). Even though it was clear that the Internet has considerable potential in terms of accessing and utilising lay support, the potential of communication technologies in mobilising of resources for personal self-management or peer support was a relatively absent or hidden a focus of the available studies. Based on an interpretive synthesis of available studies, people with a diagnosis of schizophrenia or psychosis use the Internet primarily for the purposes of disclosure and

  2. What is the potential for social networks and support to enhance future telehealth interventions for people with a diagnosis of schizophrenia: a critical interpretive synthesis

    Science.gov (United States)

    2013-01-01

    Background Digital technologies are increasingly directed at improved monitoring, management and treatment of mental health. However, their potential contribution to social networks and self-management support for people diagnosed with a serious mental illness has rarely been considered. This review and meta-synthesis aimed to examine the processes of engagement and perceived relevance and appropriateness of telehealth interventions for people with a diagnosis of schizophrenia. The review addresses three key questions. How is the use of digital communications technologies framed in the professional psychiatric literature? How might the recognised benefits of telehealth translate to people with a diagnosis of schizophrenia? What is the user perspective concerning Internet information and communication technologies? Methods A critical interpretive synthesis (CIS) of published findings from quantitative and qualitative studies of telehealth interventions for people with a diagnosis of schizophrenia. Results Most studies were of an exploratory nature. The professional discourse about the use of different technologies was overlain by concerns with surveillance and control, focusing on the Internet as a potential site of risk and danger. The critical synthesis of findings showed that the key focus of the available studies was on the delivery of existing traditional approaches (e.g. improving medications adherence, provision of medical information about the condition, symptom monitoring and cognitive behavioural therapy). Even though it was clear that the Internet has considerable potential in terms of accessing and utilising lay support, the potential of communication technologies in mobilising of resources for personal self-management or peer support was a relatively absent or hidden a focus of the available studies. Conclusions Based on an interpretive synthesis of available studies, people with a diagnosis of schizophrenia or psychosis use the Internet primarily for

  3. Artificial neural networks for diagnosis and survival prediction in colon cancer.

    Science.gov (United States)

    Ahmed, Farid E

    2005-08-06

    ANNs are nonlinear regression computational devices that have been used for over 45 years in classification and survival prediction in several biomedical systems, including colon cancer. Described in this article is the theory behind the three-layer free forward artificial neural networks with backpropagation error, which is widely used in biomedical fields, and a methodological approach to its application for cancer research, as exemplified by colon cancer. Review of the literature shows that applications of these networks have improved the accuracy of colon cancer classification and survival prediction when compared to other statistical or clinicopathological methods. Accuracy, however, must be exercised when designing, using and publishing biomedical results employing machine-learning devices such as ANNs in worldwide literature in order to enhance confidence in the quality and reliability of reported data.

  4. Artificial neural networks for diagnosis and survival prediction in colon cancer

    Directory of Open Access Journals (Sweden)

    Ahmed Farid E

    2005-08-01

    Full Text Available Abstract ANNs are nonlinear regression computational devices that have been used for over 45 years in classification and survival prediction in several biomedical systems, including colon cancer. Described in this article is the theory behind the three-layer free forward artificial neural networks with backpropagation error, which is widely used in biomedical fields, and a methodological approach to its application for cancer research, as exemplified by colon cancer. Review of the literature shows that applications of these networks have improved the accuracy of colon cancer classification and survival prediction when compared to other statistical or clinicopathological methods. Accuracy, however, must be exercised when designing, using and publishing biomedical results employing machine-learning devices such as ANNs in worldwide literature in order to enhance confidence in the quality and reliability of reported data.

  5. Artificial neural networks for diagnosis and survival prediction in colon cancer

    OpenAIRE

    Ahmed, Farid E

    2005-01-01

    Abstract ANNs are nonlinear regression computational devices that have been used for over 45 years in classification and survival prediction in several biomedical systems, including colon cancer. Described in this article is the theory behind the three-layer free forward artificial neural networks with backpropagation error, which is widely used in biomedical fields, and a methodological approach to its application for cancer research, as exemplified by colon cancer. Review of the literature ...

  6. European consensus statement on diagnosis and treatment of adult ADHD: The European Network Adult ADHD

    OpenAIRE

    Kooij Sandra JJ; Bejerot Susanne; Blackwell Andrew; Caci Herve; Casas-Brugué Miquel; Carpentier Pieter J; Edvinsson Dan; Fayyad John; Foeken Karin; Fitzgerald Michael; Gaillac Veronique; Ginsberg Ylva; Henry Chantal; Krause Johanna; Lensing Michael B

    2010-01-01

    Abstract Background Attention deficit hyperactivity disorder (ADHD) is among the most common psychiatric disorders of childhood that persists into adulthood in the majority of cases. The evidence on persistence poses several difficulties for adult psychiatry considering the lack of expertise for diagnostic assessment, limited treatment options and patient facilities across Europe. Methods The European Network Adult ADHD, founded in 2003, aims to increase awareness of this disorder and improve...

  7. Autism spectrum disorder in adults: diagnosis, management, and health services development

    Science.gov (United States)

    Murphy, Clodagh M; Wilson, C Ellie; Robertson, Dene M; Ecker, Christine; Daly, Eileen M; Hammond, Neil; Galanopoulos, Anastasios; Dud, Iulia; Murphy, Declan G; McAlonan, Grainne M

    2016-01-01

    Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by pervasive difficulties since early childhood across reciprocal social communication and restricted, repetitive interests and behaviors. Although early ASD research focused primarily on children, there is increasing recognition that ASD is a lifelong neurodevelopmental disorder. However, although health and education services for children with ASD are relatively well established, service provision for adults with ASD is in its infancy. There is a lack of health services research for adults with ASD, including identification of comorbid health difficulties, rigorous treatment trials (pharmacological and psychological), development of new pharmacotherapies, investigation of transition and aging across the lifespan, and consideration of sex differences and the views of people with ASD. This article reviews available evidence regarding the etiology, legislation, diagnosis, management, and service provision for adults with ASD and considers what is needed to support adults with ASD as they age. We conclude that health services research for adults with ASD is urgently warranted. In particular, research is required to better understand the needs of adults with ASD, including health, aging, service development, transition, treatment options across the lifespan, sex, and the views of people with ASD. Additionally, the outcomes of recent international legislative efforts to raise awareness of ASD and service provision for adults with ASD are to be determined. Future research is required to identify high-quality, evidence-based, and cost-effective models of care. Furthermore, future health services research is also required at the beginning and end of adulthood, including improved transition from youth to adult health care and increased understanding of aging and health in older adults with ASD. PMID:27462160

  8. Network analysis to support research management: evidence from the Fiocruz Observatory in Science, Technology and Innovation in Health

    Energy Technology Data Exchange (ETDEWEB)

    Fonseca, B.; Sampaio, R.B.; Silva, M.V.; Dos Santos, P.X.

    2016-07-01

    Brazil has been encouraging the establishment of research networks to address strategic health issues in response to social demands, creating an urgent need to develop indicators for their evaluation. The Oswaldo Cruz Foundation (Fiocruz), a national research, training and production institution, has initiated the development of an “Observatory in Science, Technology and Innovation in Health” to monitor and evaluate research and technological development for the formulation of institutional policies. In this context, we are proposing the use of social network analysis to map cooperation in strategic areas of research, identify prominent researchers and support internal research networks. In this preliminary study, coauthorship analysis was used to map the cooperative relations of Fiocruz in tuberculosis (TB) research, an important public health issue for which diagnosis and adequate treatment are still challenging. Our findings suggest that Brazilian research organizations acting in TB research are embedded in highly connected networks. The large number of international organizations present in the Brazilian network reflects the global increase in scientific collaboration and Brazil’s engagement in international collaborative research efforts. Fiocruz frequent cooperation with high-income countries demonstrates its concern in benefiting from the access to facilities, funding, equipment and networks that are often limited in its research setting. Collaboration with high burden countries has to be strengthened, as it could improve access to local knowledge and better understanding of the disease in different endemic contexts. Centrality analysis consolidated information on the importance of Fiocruz in connecting TB research institutions in Brazil. Fiocruz Observatory intends to advance this analysis by looking into the mechanisms of collaboration, identifying priority themes and assessing comparative advantages of the network members, an important contribution

  9. Human african trypanosomiasis diagnosis in first-line health services of endemic countries, a systematic review.

    Directory of Open Access Journals (Sweden)

    Patrick Mitashi

    Full Text Available While the incidence of Human African Trypanosomiasis (HAT is decreasing, the control approach is shifting from active population screening by mobile teams to passive case detection in primary care centers. We conducted a systematic review of the literature between 1970 and 2011 to assess which diagnostic tools are most suitable for use in first-line health facilities in endemic countries. Our search retrieved 16 different screening and confirmation tests for HAT. The thermostable format of the Card Agglutination Test for Trypanosomiasis (CATT test was the most appropriate screening test. Lateral flow antibody detection tests could become alternative screening tests in the near future. Confirmation of HAT diagnosis still depends on visualizing the parasite in direct microscopy. All other currently available confirmation tests are either technically too demanding and/or lack sensitivity and thus rather inappropriate for use at health center level. Novel applications of molecular tests may have potential for use at district hospital level.

  10. Health professional networks as a vector for improving healthcare quality and safety: a systematic review.

    Science.gov (United States)

    Cunningham, Frances C; Ranmuthugala, Geetha; Plumb, Jennifer; Georgiou, Andrew; Westbrook, Johanna I; Braithwaite, Jeffrey

    2012-03-01

    While there is a considerable corpus of theoretical and empirical literature on networks within and outside of the health sector, multiple research questions are yet to be answered. To conduct a systematic review of studies of professionals' network structures, identifying factors associated with network effectiveness and sustainability, particularly in relation to quality of care and patient safety. The authors searched MEDLINE, CINAHL, EMBASE, Web of Science and Business Source Premier from January 1995 to December 2009. A majority of the 26 unique studies identified used social network analysis to examine structural relationships in networks: structural relationships within and between networks, health professionals and their social context, health collaboratives and partnerships, and knowledge sharing networks. Key aspects of networks explored were administrative and clinical exchanges, network performance, integration, stability and influences on the quality of healthcare. More recent studies show that cohesive and collaborative health professional networks can facilitate the coordination of care and contribute to improving quality and safety of care. Structural network vulnerabilities include cliques, professional and gender homophily, and over-reliance on central agencies or individuals. Effective professional networks employ natural structural network features (eg, bridges, brokers, density, centrality, degrees of separation, social capital, trust) in producing collaboratively oriented healthcare. This requires efficient transmission of information and social and professional interaction within and across networks. For those using networks to improve care, recurring success factors are understanding your network's characteristics, attending to its functioning and investing time in facilitating its improvement. Despite this, there is no guarantee that time spent on networks will necessarily improve patient care.

  11. [Research Networks in Public Health: Requirements for Sustainability and Effectiveness - a Sociological Perspective].

    Science.gov (United States)

    Pfaff, Holger; Ohlmeier, Silke

    2017-11-01

    The Public Health White Paper draws up a vision of public health as a living, decentralized network that can help improve the health of the population in a sustained fashion. However, the central question remains open as to which prerequisites public health networks should fulfill in order to be effective in the long term. The aim of this paper is to provide a sociological view of the issue and offer some discussion ideas. Parsons' structural functionalism leads to the thesis that science networks in public health require structures that ensure that the 4 basic functions of viable social networks - (1) adaptation, (2) goal attainment, (3) integration and (4) latent pattern maintenance - are fulfilled. On this theoretical basis, suggestions are made to establish functional formal structures in public health networks. © Georg Thieme Verlag KG Stuttgart · New York.

  12. Models of logistic regression analysis, support vector machine, and back-propagation neural network based on serum tumor markers in colorectal cancer diagnosis.

    Science.gov (United States)

    Zhang, B; Liang, X L; Gao, H Y; Ye, L S; Wang, Y G

    2016-05-13

    We evaluated the application of three machine learning algorithms, including logistic regression, support vector machine and back-propagation neural network, for diagnosing congenital heart disease and colorectal cancer. By inspecting related serum tumor marker levels in colorectal cancer patients and healthy subjects, early diagnosis models for colorectal cancer were built using three machine learning algorithms to assess their corresponding diagnostic values. Except for serum alpha-fetoprotein, the levels of 11 other serum markers of patients in the colorectal cancer group were higher than those in the benign colorectal cancer group (P model and back-propagation, a neural network diagnosis model was built with diagnostic accuracies of 82 and 75%, sensitivities of 85 and 80%, and specificities of 80 and 70%, respectively. Colorectal cancer diagnosis models based on the three machine learning algorithms showed high diagnostic value and can help obtain evidence for the early diagnosis of colorectal cancer.

  13. Distributed Prognostics and Health Management with a Wireless Network Architecture

    Science.gov (United States)

    Goebel, Kai; Saha, Sankalita; Sha, Bhaskar

    2013-01-01

    A heterogeneous set of system components monitored by a varied suite of sensors and a particle-filtering (PF) framework, with the power and the flexibility to adapt to the different diagnostic and prognostic needs, has been developed. Both the diagnostic and prognostic tasks are formulated as a particle-filtering problem in order to explicitly represent and manage uncertainties in state estimation and remaining life estimation. Current state-of-the-art prognostic health management (PHM) systems are mostly centralized in nature, where all the processing is reliant on a single processor. This can lead to a loss in functionality in case of a crash of the central processor or monitor. Furthermore, with increases in the volume of sensor data as well as the complexity of algorithms, traditional centralized systems become for a number of reasons somewhat ungainly for successful deployment, and efficient distributed architectures can be more beneficial. The distributed health management architecture is comprised of a network of smart sensor devices. These devices monitor the health of various subsystems or modules. They perform diagnostics operations and trigger prognostics operations based on user-defined thresholds and rules. The sensor devices, called computing elements (CEs), consist of a sensor, or set of sensors, and a communication device (i.e., a wireless transceiver beside an embedded processing element). The CE runs in either a diagnostic or prognostic operating mode. The diagnostic mode is the default mode where a CE monitors a given subsystem or component through a low-weight diagnostic algorithm. If a CE detects a critical condition during monitoring, it raises a flag. Depending on availability of resources, a networked local cluster of CEs is formed that then carries out prognostics and fault mitigation by efficient distribution of the tasks. It should be noted that the CEs are expected not to suspend their previous tasks in the prognostic mode. When the

  14. Analysing Health Professionals' Learning Interactions in an Online Social Network: A Longitudinal Study.

    Science.gov (United States)

    Li, Xin; Verspoor, Karin; Gray, Kathleen; Barnett, Stephen

    2016-01-01

    This paper summarises a longitudinal analysis of learning interactions occurring over three years among health professionals in an online social network. The study employs the techniques of Social Network Analysis (SNA) and statistical modeling to identify the changes in patterns of interaction over time and test associated structural network effects. SNA results indicate overall low participation in the network, although some participants became active over time and even led discussions. In particular, the analysis has shown that a change of lead contributor results in a change in learning interaction and network structure. The analysis of structural network effects demonstrates that the interaction dynamics slow down over time, indicating that interactions in the network are more stable. The health professionals may be reluctant to share knowledge and collaborate in groups but were interested in building personal learning networks or simply seeking information.

  15. Social networks and health-related quality of life among Chinese older adults with vision impairment.

    Science.gov (United States)

    Wang, Chong-Wen; Chan, Cecilia L W; Ho, Andy H Y; Xiong, Zhifan

    2008-10-01

    This study examines the associations between social networks and vision-specific health-related quality of life (HRQOL) among Chinese older adults. Urban older adults with visual problems (N = 167) were interviewed using a structured questionnaire to assess self-reported visual functioning, general health, social networks, and vision-specific HRQOL. Objective visual function was clinically measured by ophthalmologists in terms of distance visual acuity. Findings indicate that age-related vision loss is significantly associated with older adults' social networks. Multiple regression analyses show that social networks are mildly related to vision-specific HRQOL even after controlling for other variables, and that friendship network was a significant predictor, independent of family network, of vision-specific HRQOL. Previous studies have emphasized the importance of family network, whereas this study finds that friendship network correlates more strongly with HRQOL measures among Chinese visually impaired older adults. This suggests a need to expand intervention beyond the family system.

  16. Neural network-based robust actuator fault diagnosis for a non-linear multi-tank system.

    Science.gov (United States)

    Mrugalski, Marcin; Luzar, Marcel; Pazera, Marcin; Witczak, Marcin; Aubrun, Christophe

    2016-03-01

    The paper is devoted to the problem of the robust actuator fault diagnosis of the dynamic non-linear systems. In the proposed method, it is assumed that the diagnosed system can be modelled by the recurrent neural network, which can be transformed into the linear parameter varying form. Such a system description allows developing the designing scheme of the robust unknown input observer within H∞ framework for a class of non-linear systems. The proposed approach is designed in such a way that a prescribed disturbance attenuation level is achieved with respect to the actuator fault estimation error, while guaranteeing the convergence of the observer. The application of the robust unknown input observer enables actuator fault estimation, which allows applying the developed approach to the fault tolerant control tasks. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  17. Screening for and Diagnosis of Depression Among Adolescents in a Large Health Maintenance Organization.

    Science.gov (United States)

    Lewandowski, R Eric; O'Connor, Briannon; Bertagnolli, Andrew; Beck, Arne; Tinoco, Aldo; Gardner, William P; Jelinek-Berents, Christine X; Newton, Douglas A; Wain, Kris F; Boggs, Jennifer M; Brace, Nancy E; deSa, Patricia; Scholle, Sarah Hudson; Hoagwood, Kimberly; Horwitz, Sarah McCue

    2016-06-01

    The aim of this analysis was to determine changes in patterns of depression screening and diagnosis over three years in primary and specialty mental health care in a large health maintenance organization (HMO) as part of a project to develop quality measures for adolescent depression treatment. Two series of aggregate data (2010-2012) were gathered from the electronic health records of the HMO for 44,342 unique adolescents (ages 12 to 21) who had visits in primary and mental health care. Chi square tests assessed the significance of changes in frequency and departmental location of Patient Health Questionnaire-9 (PHQ-9) administration, incidence of depression symptoms, and depression diagnoses. There was a significant increase in PHQ-9 use, predominantly in primary care, consistent with internally generated organizational recommendations to increase screening with the PHQ-9. The increase in PHQ-9 use led to an increase in depression diagnoses in primary care and a shift in the location of some diagnoses from specialty mental health care to primary care. The increase in PHQ-9 use was also linked to a decrease in the proportion of positive PHQ-9 results that led to formal depression diagnoses. The rate of depression screening in primary care increased over the study period. This increase corresponded to an increase in the number of depression diagnoses made in primary care and a shift in the location in which depression diagnoses were made, from the mental health department to primary care. The frequency of positive PHQ-9 administrations not associated with depression diagnoses also increased.

  18. The Role of Social Support and Social Networks in Health Information Seeking Behavior among Korean Americans

    Science.gov (United States)

    Kim, Wonsun

    2013-01-01

    Access to health information appears to be a crucial piece of the racial and ethnic health disparities puzzle among immigrants. There are a growing number of scholars who are investigating the role of social networks that have shown that the number and even types of social networks among minorities and lower income groups differ (Chatman, 1991;…

  19. Attitudes toward non-invasive prenatal diagnosis among pregnant women and health professionals in Japan.

    Science.gov (United States)

    Yotsumoto, Junko; Sekizawa, Akihiko; Koide, Keiko; Purwosunu, Yuditiya; Ichizuka, Kiyotake; Matsuoka, Ryu; Kawame, Hiroshi; Okai, Takashi

    2012-07-01

    This study aims to assess the attitudes toward non-invasive prenatal diagnosis (NIPD) and NIPD problems in clinical practice in Japan. A mail-in survey using a self-reported questionnaire was conducted among pregnant women and health professionals. The questionnaire enquired about attitudes, concerns, and expectations regarding NIPD. The responses from 252 respondents revealed that pregnant women have more positive attitudes toward NIPD than health professionals. In addition, there were wide discrepancies in concerns and expectations about NIPD, between medical professionals and pregnant women. The respondents with less NIPD knowledge had a more positive attitude toward the clinical application of NIPD. There was concern expressed by clinical geneticists whether an NIPD test should be performed or not when there is a lack of knowledge about the NIPD. All of the health professionals emphasized the importance of providing genetic counseling prior to and after the testing. Pregnant women place importance on the safety and non-invasiveness of the NIPD tests, whereas medical professionals consider the diagnostic accuracy and reliability of the test to be the most important. Health professionals pointed out that the tests might be frequently performed without the pregnant women having adequate knowledge or counseling. © 2012 John Wiley & Sons, Ltd.

  20. Diagnosis of Alzheimer’s Disease Using Dual-Tree Complex Wavelet Transform, PCA, and Feed-Forward Neural Network

    Directory of Open Access Journals (Sweden)

    Debesh Jha

    2017-01-01

    Full Text Available Background. Error-free diagnosis of Alzheimer’s disease (AD from healthy control (HC patients at an early stage of the disease is a major concern, because information about the condition’s severity and developmental risks present allows AD sufferer to take precautionary measures before irreversible brain damage occurs. Recently, there has been great interest in computer-aided diagnosis in magnetic resonance image (MRI classification. However, distinguishing between Alzheimer’s brain data and healthy brain data in older adults (age > 60 is challenging because of their highly similar brain patterns and image intensities. Recently, cutting-edge feature extraction technologies have found extensive application in numerous fields, including medical image analysis. Here, we propose a dual-tree complex wavelet transform (DTCWT for extracting features from an image. The dimensionality of feature vector is reduced by using principal component analysis (PCA. The reduced feature vector is sent to feed-forward neural network (FNN to distinguish AD and HC from the input MR images. These proposed and implemented pipelines, which demonstrate improvements in classification output when compared to that of recent studies, resulted in high and reproducible accuracy rates of 90.06 ± 0.01% with a sensitivity of 92.00 ± 0.04%, a specificity of 87.78 ± 0.04%, and a precision of 89.6 ± 0.03% with 10-fold cross-validation.

  1. Error-correction learning for artificial neural networks using the Bayesian paradigm. Application to automated medical diagnosis.

    Science.gov (United States)

    Belciug, Smaranda; Gorunescu, Florin

    2014-12-01

    Automated medical diagnosis models are now ubiquitous, and research for developing new ones is constantly growing. They play an important role in medical decision-making, helping physicians to provide a fast and accurate diagnosis. Due to their adaptive learning and nonlinear mapping properties, the artificial neural networks are widely used to support the human decision capabilities, avoiding variability in practice and errors based on lack of experience. Among the most common learning approaches, one can mention either the classical back-propagation algorithm based on the partial derivatives of the error function with respect to the weights, or the Bayesian learning method based on posterior probability distribution of weights, given training data. This paper proposes a novel training technique gathering together the error-correction learning, the posterior probability distribution of weights given the error function, and the Goodman-Kruskal Gamma rank correlation to assembly them in a Bayesian learning strategy. This study had two main purposes; firstly, to develop anovel learning technique based on both the Bayesian paradigm and the error back-propagation, and secondly,to assess its effectiveness. The proposed model performance is compared with those obtained by traditional machine learning algorithms using real-life breast and lung cancer, diabetes, and heart attack medical databases. Overall, the statistical comparison results indicate that thenovellearning approach outperforms the conventional techniques in almost all respects. Copyright © 2014 Elsevier Inc. All rights reserved.

  2. On the Use of Local Search in the Evolution of Neural Networks for the Diagnosis of Breast Cancer

    Directory of Open Access Journals (Sweden)

    Agam Gupta

    2015-07-01

    Full Text Available With the advancement in the field of Artificial Intelligence, there have been considerable efforts to develop technologies for pattern recognition related to medical diagnosis. Artificial Neural Networks (ANNs, a significant piece of Artificial Intelligence forms the base for most of the marvels in the former field. However, ANNs face the problem of premature convergence at a local minimum and inability to set hyper-parameters (like the number of neurons, learning rate, etc. while using Back Propagation Algorithm (BPA. In this paper, we have used the Genetic Algorithm (GA for the evolution of the ANN, which overcomes the limitations of the BPA. Since GA alone cannot fit for a high-dimensional, complex and multi-modal optimization landscape of the ANN, BPA is used as a local search algorithm to aid the evolution. The contributions of GA and BPA in the resultant approach are adjudged to determine the magnitude of local search necessary for optimization, striking a clear balance between exploration and exploitation in the evolution. The algorithm was applied to deal with the problem of Breast Cancer diagnosis. Results showed that under optimal settings, hybrid algorithm performs better than BPA or GA alone.

  3. Milling tool wear diagnosis by feed motor current signal using an artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Khajavi, Mehrdad Nouri; Nasernia, Ebrahim; Rostaghi, Mostafa [Dept. of Mechanical Engineering, Shahid Rajaee Teacher Training University, Tehran (Iran, Islamic Republic of)

    2016-11-15

    In this paper, a Multi-layer perceptron (MLP) neural network was used to predict tool wear in face milling. For this purpose, a series of experiments was conducted using a milling machine on a CK45 work piece. Tool wear was measured by an optical microscope. To improve the accuracy and reliability of the monitoring system, tool wear state was classified into five groups, namely, no wear, slight wear, normal wear, severe wear and broken tool. Experiments were conducted with the aforementioned tool wear states, and different machining conditions and data were extracted. An increase in current amplitude was observed as the tool wear increased. Furthermore, effects of parameters such as tool wear, feed, and cut depth on motor current consumption were analyzed. Considering the complexity of the wear state classification, a multi-layer neural network was used. The root mean square of motor current, feed, cut depth, and tool rpm were chosen as the input and amount of flank wear as the output of MLP. Results showed good performance of the designed tool wear monitoring system.

  4. The role of health literacy and social networks in arthritis patients' health information-seeking behavior: a qualitative study.

    Science.gov (United States)

    Ellis, Janette; Mullan, Judy; Worsley, Anthony; Pai, Nagesh

    2012-01-01

    Background. Patients engage in health information-seeking behaviour to maintain their wellbeing and to manage chronic diseases such as arthritis. Health literacy allows patients to understand available treatments and to critically appraise information they obtain from a wide range of sources. Aims. To explore how arthritis patients' health literacy affects engagement in arthritis-focused health information-seeking behaviour and the selection of sources of health information available through their informal social network. Methods. An exploratory, qualitative study consisting of one-on-one semi-structured interviews. Twenty participants with arthritis were recruited from community organizations. The interviews were designed to elicit participants' understanding about their arthritis and arthritis medication and to determine how the participants' health literacy informed selection of where they found information about their arthritis and pain medication. Results. Participants with low health literacy were less likely to be engaged with health information-seeking behaviour. Participants with intermediate health literacy were more likely to source arthritis-focused health information from newspapers, television, and within their informal social network. Those with high health literacy sourced information from the internet and specialist health sources and were providers of information within their informal social network. Conclusion. Health professionals need to be aware that levels of engagement in health information-seeking behaviour and sources of arthritis-focused health information may be related to their patients' health literacy.

  5. Health care costs in US patients with and without a diagnosis of osteoarthritis

    Directory of Open Access Journals (Sweden)

    Le TK

    2012-02-01

    Full Text Available T Kim Le1, Leslie B Montejano2, Zhun Cao2, Yang Zhao1, Dennis Ang31Eli Lilly and Company, Indianapolis, IN, 2Thomson Reuters, Washington, DC, 3Indiana University School of Medicine, Indianapolis, IN, USABackground: Osteoarthritis is a chronic and costly condition affecting 14% of adults in the US, and has a significant impact on patient quality of life. This retrospective cohort study compared direct health care utilization and costs between patients with osteoarthritis and a matched control group without osteoarthritis.Methods: MarketScan® databases were used to identify adult patients with an osteoarthritis claim (ICD-9-CM, 715.xx in 2007, and the date of first diagnosis served as the index. Patients were excluded if they did not have 12 months of continuous health care benefit prior to and following the index date, were aged <18 years, or lacked a second diagnosis code for osteoarthritis between 15 and 365 days pre-index or post-index. Osteoarthritis patients were matched 1:1 to patients without osteoarthritis for age group, gender, geographic region, health plan type, and Medicare eligibility. Multivariate analyses were conducted to assess for differences in utilization and costs, controlling for differences between cohorts.Results: The study sample included 258,237 patients with osteoarthritis and 258,237 matched controls without osteoarthritis. Most patients were women and over 55 years of age. Patients with osteoarthritis had significantly higher pre-index rates of comorbidity than controls. Mean total adjusted direct costs for osteoarthritis patients were more than double those for the control group at US$18,435 (95% confidence interval [CI]: US$18,318–US$18,560 versus US$7494 (95% CI: US$7425–US$7557. Osteoarthritis patients incurred significantly higher inpatient costs at US$6668 (95% CI: US$6587–US$6744 versus US$1756 (95% CI: US$1717–US$1794, outpatient costs at US$7840 (95% CI: US$7786–US$7902 versus US$3675 (95% CI: US

  6. The role of Indonesian patients’ health behaviors in delaying the diagnosis of nasopharyngeal carcinoma

    Directory of Open Access Journals (Sweden)

    R . Fles

    2017-05-01

    Full Text Available Abstract Background With an estimated 13,000 newly diagnosed patients per year, nasopharyngeal carcinoma (NPC is one of the most common types of cancer in males in Indonesia. Moreover, most patients are diagnosed at an advanced stage of the disease. This study aimed to explore the health behaviors of patients diagnosed with NPC and the possible causes of patient delay in NPC diagnosis. Methods A qualitative research method was used to gain better insight into patient behaviors. Twelve patients were interviewed using semi-structured interview guidelines. All interviews were recorded, transcribed verbatim and analyzed according to a standard content analysis framework. Results Most patients had limited knowledge regarding NPC and its causes. Fifty percent of the patients had a delay of six months from the onset of symptoms to diagnosis. The main reason for this delay was the lack of awareness among the patients, which was influenced by their environment, economic status, family, culture, and religion. The perceived barriers to seeking medical help included direct non-medical costs not covered by health insurance, complex and time-consuming insurance and referral systems, and negative experiences in the past. Health insurance did motivate people to seek medical help. Conclusion This study provides additional insight into patients’ motivations to delay seeking medical help and can facilitate the design of NPC education programs. To improve awareness of the abovementioned causes for delay, community-based education programs are highly warranted and should focus on the recognition of NPC symptoms and possible solutions to overcome the main barriers at an earlier disease stage.

  7. Randomised primary health center based interventions to improve the diagnosis and treatment of undifferentiated fever and dengue in Vietnam

    NARCIS (Netherlands)

    Phuong, H.L.; Nga, T.T.T.; Giao, P.T.; Hung, L.Q.; Binh, T.Q.; Nam, N.V.; Nagelkerke, N.; de Vries, P.J.

    2010-01-01

    ABSTRACT: BACKGROUND: Fever is a common reason for attending primary health facilities in Vietnam. Response of health care providers to patients with fever commonly consists of making a presumptive diagnosis and proposing corresponding treatment. In Vietnam, where malaria was brought under control,

  8. Artificial neural network classifier for the diagnosis of Parkinson's disease using [99mTc]TRODAT-1 and SPECT

    Science.gov (United States)

    Acton, Paul D.; Newberg, Andrew

    2006-06-01

    Imaging the dopaminergic neurotransmitter system with positron emission tomography (PET) or single photon emission tomography (SPECT) is a powerful tool for the diagnosis of Parkinson's disease (PD). Previous studies have indicated that human observers have a diagnostic accuracy similar to conventional ROI analysis of SPECT imaging data. Consequently, it has been hypothesized that an artificial neural network (ANN), which can mimic the pattern recognition skills of human observers, may provide similar results. A set of patients with PD, and normal healthy control subjects, were studied using the dopamine transporter tracer [99mTc]TRODAT-1 and SPECT. The sample was comprised of 81 patients (mean age ± SD: 63.4 ± 10.4 years; age range: 39.0-84.2 years) and 94 healthy controls (mean age ± SD: 61.8 ± 11.0 years; age range: 40.9-83.3 years). The images were processed to extract the striatum and the striatal pixel values were used as inputs to a three-layer ANN. The same set of data was used to both train and test the ANN, in a 'leave one out' procedure. The diagnostic accuracy of the ANN was higher than any previous analysis method applied to the same data (94.4% total accuracy, 97.5% specificity and 91.4% sensitivity). However, it should be stressed that, as with all applications of an ANN, it was difficult to interpret precisely what triggers in the images were being detected by the network.

  9. A Fault Diagnosis Method for Oil Well Pump Using Radial Basis Function Neural Network Combined with Modified Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Deliang Yu

    2017-01-01

    Full Text Available This paper presents a new method to diagnose oil well pump faults using a modified radial basis function neural network. With the development of submersible linear motor technology, rodless pumping units have been widely used in oil exploration. However, the ground indicator diagram method cannot be used to diagnose the working conditions of rodless pumping units because it is based on the load change of the polished rod suspension point and its displacement. To solve this problem, this paper presents a new method that is applicable to rodless oil pumps. The advantage of this new method is its use of a simple feature extraction method and advanced genetic algorithm to optimize the threshold and weight of the RBF neural network. In this paper, we extract the characteristic value from the operation parameters of the submersible linear motor and oil wellhead as the input vector of the fault diagnosis model. Through experimental analysis, the proposed method is proven to have good convergence performance, high accuracy, and high reliability.

  10. The Relationship of Policymaking and Networking Characteristics among Leaders of Large Urban Health Departments.

    Science.gov (United States)

    Leider, Jonathon P; Castrucci, Brian C; Harris, Jenine K; Hearne, Shelley

    2015-08-06

    The relationship between policy networks and policy development among local health departments (LHDs) is a growing area of interest to public health practitioners and researchers alike. In this study, we examine policy activity and ties between public health leadership across large urban health departments. This study uses data from a national profile of local health departments as well as responses from a survey sent to three staff members (local health official, chief of policy, chief science officer) in each of 16 urban health departments in the United States. Network questions related to frequency of contact with health department personnel in other cities. Using exponential random graph models, network density and centrality were examined, as were patterns of communication among those working on several policy areas using exponential random graph models. All 16 LHDs were active in communicating about chronic disease as well as about use of alcohol, tobacco, and other drugs (ATOD). Connectedness was highest among local health officials (density = .55), and slightly lower for chief science officers (d = .33) and chiefs of policy (d = .29). After accounting for organizational characteristics, policy homophily (i.e., when two network members match on a single characteristic) and tenure were the most significant predictors of formation of network ties. Networking across health departments has the potential for accelerating the adoption of public health policies. This study suggests similar policy interests and formation of connections among senior leadership can potentially drive greater connectedness among other staff.

  11. The Relationship of Policymaking and Networking Characteristics among Leaders of Large Urban Health Departments

    Directory of Open Access Journals (Sweden)

    Jonathon P. Leider

    2015-08-01

    Full Text Available Background: The relationship between policy networks and policy development among local health departments (LHDs is a growing area of interest to public health practitioners and researchers alike. In this study, we examine policy activity and ties between public health leadership across large urban health departments. Methods: This study uses data from a national profile of local health departments as well as responses from a survey sent to three staff members (local health official, chief of policy, chief science officer in each of 16 urban health departments in the United States. Network questions related to frequency of contact with health department personnel in other cities. Using exponential random graph models, network density and centrality were examined, as were patterns of communication among those working on several policy areas using exponential random graph models. Results: All 16 LHDs were active in communicating about chronic disease as well as about use of alcohol, tobacco, and other drugs (ATOD. Connectedness was highest among local health officials (density = .55, and slightly lower for chief science officers (d = .33 and chiefs of policy (d = .29. After accounting for organizational characteristics, policy homophily (i.e., when two network members match on a single characteristic and tenure were the most significant predictors of formation of network ties. Conclusion: Networking across health departments has the potential for accelerating the adoption of public health policies. This study suggests similar policy interests and formation of connections among senior leadership can potentially drive greater connectedness among other staff.

  12. Social network types among older Korean adults: Associations with subjective health.

    Science.gov (United States)

    Sohn, Sung Yun; Joo, Won-Tak; Kim, Woo Jung; Kim, Se Joo; Youm, Yoosik; Kim, Hyeon Chang; Park, Yeong-Ran; Lee, Eun

    2017-01-01

    With population aging now a global phenomenon, the health of older adults is becoming an increasingly important issue. Because the Korean population is aging at an unprecedented rate, preparing for public health problems associated with old age is particularly salient in this country. As the physical and mental health of older adults is related to their social relationships, investigating the social networks of older adults and their relationship to health status is important for establishing public health policies. The aims of this study were to identify social network types among older adults in South Korea and to examine the relationship of these social network types with self-rated health and depression. Data from the Korean Social Life, Health, and Aging Project were analyzed. Model-based clustering using finite normal mixture modeling was conducted to identify the social network types based on ten criterion variables of social relationships and activities: marital status, number of children, number of close relatives, number of friends, frequency of attendance at religious services, attendance at organized group meetings, in-degree centrality, out-degree centrality, closeness centrality, and betweenness centrality. Multivariate regression analysis was conducted to examine associations between the identified social network types and self-rated health and depression. The model-based clustering analysis revealed that social networks clustered into five types: diverse, family, congregant, congregant-restricted, and restricted. Diverse or family social network types were significantly associated with more favorable subjective mental health, whereas the restricted network type was significantly associated with poorer ratings of mental and physical health. In addition, our analysis identified unique social network types related to religious activities. In summary, we developed a comprehensive social network typology for older Korean adults. Copyright © 2016

  13. Failure Diagnosis and Prognosis of Rolling - Element Bearings using Artificial Neural Networks: A Critical Overview

    Science.gov (United States)

    Rao, B. K. N.; Srinivasa Pai, P.; Nagabhushana, T. N.

    2012-05-01

    Rolling - Element Bearings are extensively used in almost all global industries. Any critical failures in these vitally important components would not only affect the overall systems performance but also its reliability, safety, availability and cost-effectiveness. Proactive strategies do exist to minimise impending failures in real time and at a minimum cost. Continuous innovative developments are taking place in the field of Artificial Neural Networks (ANNs) technology. Significant research and development are taking place in many universities, private and public organizations and a wealth of published literature is available highlighting the potential benefits of employing ANNs in intelligently monitoring, diagnosing, prognosing and managing rolling-element bearing failures. This paper attempts to critically review the recent trends in this topical area of interest.

  14. Validation of diabetes mellitus and hypertension diagnosis in computerized medical records in primary health care

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    Abánades-Herranz Juan C

    2011-10-01

    Full Text Available Abstract Background Computerized Clinical Records, which are incorporated in primary health care practice, have great potential for research. In order to use this information, data quality and reliability must be assessed to prevent compromising the validity of the results. The aim of this study is to validate the diagnosis of hypertension and diabetes mellitus in the computerized clinical records of primary health care, taking the diagnosis criteria established in the most prominently used clinical guidelines as the gold standard against which what measure the sensitivity, specificity, and determine the predictive values. The gold standard for diabetes mellitus was the diagnostic criteria established in 2003 American Diabetes Association Consensus Statement for diabetic subjects. The gold standard for hypertension was the diagnostic criteria established in the Joint National Committee published in 2003. Methods A cross-sectional multicentre validation study of diabetes mellitus and hypertension diagnoses in computerized clinical records of primary health care was carried out. Diagnostic criteria from the most prominently clinical practice guidelines were considered for standard reference. Sensitivity, specificity, positive and negative predictive values, and global agreement (with kappa index, were calculated. Results were shown overall and stratified by sex and age groups. Results The agreement for diabetes mellitus with the reference standard as determined by the guideline was almost perfect (κ = 0.990, with a sensitivity of 99.53%, a specificity of 99.49%, a positive predictive value of 91.23% and a negative predictive value of 99.98%. Hypertension diagnosis showed substantial agreement with the reference standard as determined by the guideline (κ = 0.778, the sensitivity was 85.22%, the specificity 96.95%, the positive predictive value 85.24%, and the negative predictive value was 96.95%. Sensitivity results were worse in patients who

  15. Social Networking Addiction among Health Sciences Students in Oman

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    Ken Masters

    2015-08-01

    Full Text Available Objectives: Addiction to social networking sites (SNSs is an international issue with numerous methods of measurement. The impact of such addictions among health science students is of particular concern. This study aimed to measure SNS addiction rates among health sciences students at Sultan Qaboos University (SQU in Muscat, Oman. Methods: In April 2014, an anonymous English-language six-item electronic self-reporting survey based on the Bergen Facebook Addiction Scale was administered to a non-random cohort of 141 medical and laboratory science students at SQU. The survey was used to measure usage of three SNSs: Facebook (Facebook Inc., Menlo Park, California, USA, YouTube (YouTube, San Bruno, California, USA and Twitter (Twitter Inc., San Francisco, California, USA. Two sets of criteria were used to calculate addiction rates (a score of 3 on at least four survey items or a score of 3 on all six items. Work-related SNS usage was also measured. Results: A total of 81 students completed the survey (response rate: 57.4%. Of the three SNSs, YouTube was most commonly used (100%, followed by Facebook (91.4% and Twitter (70.4%. Usage and addiction rates varied significantly across the three SNSs. Addiction rates to Facebook, YouTube and Twitter, respectively, varied according to the criteria used (14.2%, 47.2% and 33.3% versus 6.3%, 13.8% and 12.8%. However, addiction rates decreased when workrelated activity was taken into account. Conclusion: Rates of SNS addiction among this cohort indicate a need for intervention. Additionally, the results suggest that addiction to individual SNSs should be measured and that workrelated activities should be taken into account during measurement.

  16. Social Networking Addiction among Health Sciences Students in Oman.

    Science.gov (United States)

    Masters, Ken

    2015-08-01

    Addiction to social networking sites (SNSs) is an international issue with numerous methods of measurement. The impact of such addictions among health science students is of particular concern. This study aimed to measure SNS addiction rates among health sciences students at Sultan Qaboos University (SQU) in Muscat, Oman. In April 2014, an anonymous English-language six-item electronic self-reporting survey based on the Bergen Facebook Addiction Scale was administered to a non-random cohort of 141 medical and laboratory science students at SQU. The survey was used to measure usage of three SNSs: Facebook (Facebook Inc., Menlo Park, California, USA), YouTube (YouTube, San Bruno, California, USA) and Twitter (Twitter Inc., San Francisco, California, USA). Two sets of criteria were used to calculate addiction rates (a score of 3 on at least four survey items or a score of 3 on all six items). Work-related SNS usage was also measured. A total of 81 students completed the survey (response rate: 57.4%). Of the three SNSs, YouTube was most commonly used (100%), followed by Facebook (91.4%) and Twitter (70.4%). Usage and addiction rates varied significantly across the three SNSs. Addiction rates to Facebook, YouTube and Twitter, respectively, varied according to the criteria used (14.2%, 47.2% and 33.3% versus 6.3%, 13.8% and 12.8%). However, addiction rates decreased when work-related activity was taken into account. Rates of SNS addiction among this cohort indicate a need for intervention. Additionally, the results suggest that addiction to individual SNSs should be measured and that work-related activities should be taken into account during measurement.

  17. Social networks, health promoting-behavior, and health-related quality of life in older Korean adults.

    Science.gov (United States)

    Hong, Minjoo; De Gagne, Jennie C; Shin, Hyewon

    2017-11-27

    In this cross-sectional, descriptive study, we compared the sociodemographic characteristics, social networks, health-promoting behavior, and the health-related quality of life of older Korean adults living in South Korea to those of older Korean adult immigrants living in the USA. A total of 354 older adults, aged 65 years or older, participated. Data were collected through self-directed questionnaires, and analyzed using a two way analysis of variance, t-tests, χ2 -tests, and Pearson's correlation coefficient. The association between four sociodemographic characteristics and health-related quality of life was significantly different between the two groups. For the older Korean adults living in South Korea, positive correlations existed between a measure of their social networks and both health-promoting behavior and health-related quality of life. For the older Korean immigrants, the findings revealed a positive correlation only between social networks and health-promoting behavior. The study findings support the important association social networks can have with health-related quality of life, and their possible relationship to health-promoting behaviors of older Korean adults. We suggest that health policy-makers and healthcare providers develop comprehensive programs that are designed to improve older adults' social networks. © 2017 John Wiley & Sons Australia, Ltd.

  18. Health-related quality of life of adolescents with childhood diagnosis of specific language impairment.

    Science.gov (United States)

    Arkkila, E; Räsänen, P; Roine, R P; Sintonen, H; Saar, V; Vilkman, E

    2009-09-01

    To evaluate the health-related quality of life (HRQoL) of adolescents with a diagnosis of specific language impairment (SLI). A clinical sample of 67 subjects with a childhood diagnosis of SLI, now aged 12-16, were asked to fill out the generic 16D HRQoL questionnaire. The comparison group comprised 235 typically developing peers. Another questionnaire gathered information about school and rehabilitation. Of the surveyed 73% answered; 77% were male. Total HRQoL score between subjects and controls did not differ. The group profiles had some differences. The SLI group experienced more problems in the dimension of mental functioning (p=0.001), whereas the control group was worse off on the dimension vitality (p=0.003). In the SLI group, low vitality was related to low verbal IQ in childhood, and own perception of literacy problems. Long-term speech therapy was associated with problems in the dimension of speech. The overall HRQoL of adolescents with SLI was at age-level, but language-related problems seemed to lead to increased problems in mental functioning. Low vitality was more of a problem for the controls, but also for those SLI children who had inferior language performance. Adolescents' own perceptions of their life quality are of clinical importance, and 16D seems a usable tool to capture them.

  19. Energy Harvesting Based Body Area Networks for Smart Health.

    Science.gov (United States)

    Hao, Yixue; Peng, Limei; Lu, Huimin; Hassan, Mohammad Mehedi; Alamri, Atif

    2017-07-10

    Body area networks (BANs) are configured with a great number of ultra-low power consumption wearable devices, which constantly monitor physiological signals of the human body and thus realize intelligent monitoring. However, the collection and transfer of human body signals consume energy, and considering the comfort demand of wearable devices, both the size and the capacity of a wearable device's battery are limited. Thus, minimizing the energy consumption of wearable devices and optimizing the BAN energy efficiency is still a challenging problem. Therefore, in this paper, we propose an energy harvesting-based BAN for smart health and discuss an optimal resource allocation scheme to improve BAN energy efficiency. Specifically, firstly, considering energy harvesting in a BAN and the time limits of human body signal transfer, we formulate the energy efficiency optimization problem of time division for wireless energy transfer and wireless information transfer. Secondly, we convert the optimization problem into a convex optimization problem under a linear constraint and propose a closed-form solution to the problem. Finally, simulation results proved that when the size of data acquired by the wearable devices is small, the proportion of energy consumed by the circuit and signal acquisition of the wearable devices is big, and when the size of data acquired by the wearable devices is big, the energy consumed by the signal transfer of the wearable device is decisive.

  20. Health care communication networks: disseminating employee information for hospital security.

    Science.gov (United States)

    Sumner, Jennifer; Liberman, Aaron; Rotarius, Timothy; Wan, Thomas T H; Eaglin, Ronald

    2009-01-01

    Health care in the United States is a system that, organizationally speaking, is fragmented. Each hospital facility is independently operated and is responsible for the hiring of its own employees. Corrupt individuals can take advantage of this fragmentation and move from hospital to hospital, gaining employment while hiding previous employment history. However, the need to exchange pertinent information regarding employees will become necessary as hospitals seek to fill positions throughout their organizations. One way to promote this information exchange is to develop trusted information sharing networks among hospital units. This study examined the problems surrounding organizational information sharing and the cultural factors necessary to enhance the exchange of employee information. Surveys were disseminated to 2,603 hospital chief executive officers and chief information officers throughout the nation. A sample of 154 respondents provided data into their current hiring practices and on their willingness to engage in the sharing of employee information. Findings indicated that, although fear of defamation and privacy violations do hinder the exchange of information between hospitals during the hiring process, by increasing external trust, linking the sharing process with the organizational goals of the hospital, and developing a "sharing culture" among hospitals, the exchange of employee information could be enhanced.

  1. Energy Harvesting Based Body Area Networks for Smart Health

    Directory of Open Access Journals (Sweden)

    Yixue Hao

    2017-07-01

    Full Text Available Body area networks (BANs are configured with a great number of ultra-low power consumption wearable devices, which constantly monitor physiological signals of the human body and thus realize intelligent monitoring. However, the collection and transfer of human body signals consume energy, and considering the comfort demand of wearable devices, both the size and the capacity of a wearable device’s battery are limited. Thus, minimizing the energy consumption of wearable devices and optimizing the BAN energy efficiency is still a challenging problem. Therefore, in this paper, we propose an energy harvesting-based BAN for smart health and discuss an optimal resource allocation scheme to improve BAN energy efficiency. Specifically, firstly, considering energy harvesting in a BAN and the time limits of human body signal transfer, we formulate the energy efficiency optimization problem of time division for wireless energy transfer and wireless information transfer. Secondly, we convert the optimization problem into a convex optimization problem under a linear constraint and propose a closed-form solution to the problem. Finally, simulation results proved that when the size of data acquired by the wearable devices is small, the proportion of energy consumed by the circuit and signal acquisition of the wearable devices is big, and when the size of data acquired by the wearable devices is big, the energy consumed by the signal transfer of the wearable device is decisive.

  2. Assessment of Nutrition Information System Using Health Metrics Network Framework

    Directory of Open Access Journals (Sweden)

    Mochamad Iqbal Nurmansyah

    2015-08-01

    Sistem informasi gizi (Sigizi dikembangkan oleh Direktorat Bina Gizi Kementerian Kesehatan sejak 2011. Data Sigizi mencakup data penimbangan balita di posyandu, kasus gizi buruk, cakupan pemberian tablet Fe pada ibu hamil, konsumsi garam beryodium, pemberian vitamin A, dan ASI eksklusif. Penelitian ini bertujuan untuk mengukur kinerja pengelolaan Sigizi di Dinas Kesehatan Kota Tangerang Selatan menggunakan kerangka Health Metrics Network yang dikeluarkan oleh WHO tahun 2008. Sigizi merupakan sistem informasi yang diaplikasikan pada tingkat nasional dengan mekanisme pelaporan berjenjang, dari 508 kabupaten/kota menuju 34 provinsi dan bermuara di tingkat nasional. Di Provinsi Banten, terdapat delapan kabupaten/kota yang menjalankan Sigizi. Informan penelitian berjumlah enam orang, yaitu seksi gizi, seksi sumber daya kesehatan dan sistem informasi kesehatan, dua tenaga pelaksana gizi, dan dua kader posyandu. Pengumpulan data dilakukan Januari – April 2013 menggunakan pedoman wawancara, observasi, dan telaah dokumen. Analisis interpretasi digunakan dalam menganalisis data. Hasil penelitian menunjukan belum ada kebijakan serta pelatihan mengenai pengawasan gizi. Kegiatan pemantauan telah dilakukan. Sarana dinilai cukup, namun terdapat kekurangan dalam upaya perawatannya. Terdapat enam indikator dalam pembinaan gizi yang mengacu pada MDGs. Terdapat pengelompokan dan kamus data. Pelaporan data dilakukan setiap bulan. Grafik dan peta digunakan untuk menyajikan data. Data yang tersedia digunakan untuk pemonitoran dan pengambilan keputusan dalam kegiatan pembinaan gizi, baik di tingkat posyandu, puskesmas maupun dinkes. Secara umum, pelaksanaan Sigizi di Dinas Kesehatan Kota Tangerang Selatan telah memadai.

  3. Fault diagnosis of sensor networked structures with multiple faults using a virtual beam based approach

    Science.gov (United States)

    Wang, H.; Jing, X. J.

    2017-07-01

    This paper presents a virtual beam based approach suitable for conducting diagnosis of multiple faults in complex structures with limited prior knowledge of the faults involved. The ;virtual beam;, a recently-proposed concept for fault detection in complex structures, is applied, which consists of a chain of sensors representing a vibration energy transmission path embedded in the complex structure. Statistical tests and adaptive threshold are particularly adopted for fault detection due to limited prior knowledge of normal operational conditions and fault conditions. To isolate the multiple faults within a specific structure or substructure of a more complex one, a 'biased running' strategy is developed and embedded within the bacterial-based optimization method to construct effective virtual beams and thus to improve the accuracy of localization. The proposed method is easy and efficient to implement for multiple fault localization with limited prior knowledge of normal conditions and faults. With extensive experimental results, it is validated that the proposed method can localize both single fault and multiple faults more effectively than the classical trust index subtract on negative add on positive (TI-SNAP) method.

  4. Effective Interpersonal Health Communication for Linkage to Care After HIV Diagnosis in South Africa.

    Science.gov (United States)

    Mabuto, Tonderai; Charalambous, Salome; Hoffmann, Christopher J

    2017-01-01

    Early in the global response to HIV, health communication was focused toward HIV prevention. More recently, the role of health communication along the entire HIV care continuum has been highlighted. We sought to describe how a strategy of interpersonal communication allows for precision health communication to influence behavior regarding care engagement. We analyzed 1 to 5 transcripts from clients participating in longitudinal counseling sessions from a communication strategy arm of a randomized trial to accelerate entry into care in South Africa. The counseling arm was selected because it increased verified entry into care by 40% compared with the standard of care. We used thematic analysis to identify key aspects of communication directed specifically toward a client's goals or concerns. Of the participants, 18 of 28 were female and 21 entered HIV care within 90 days of diagnosis. Initiating a communication around client-perceived consequences of HIV was at times effective. However, counselors also probed around general topics of life disruption-such as potential for child bearing-as a technique to direct the conversation toward the participant's needs. Once individual concerns and needs were identified, counselors tried to introduce clinical care seeking and collaboratively discuss potential barriers and approaches to overcome to accessing that care. Through the use of interpersonal communication messages were focused on immediate needs and concerns of the client. When effectively delivered, it may be an important communication approach to improve care engagement.

  5. [Organic food and educational actions in schools: diagnosis for health and nutrition education].

    Science.gov (United States)

    da Cunha, Elisângela; de Sousa, Anete Araújo; Machado, Neila Maria Viçosa

    2010-01-01

    This research involved a diagnosis of the educational actions and organic food of the Taste and Awareness Project (Projeto Sabor e Saber, PSS) in a state school in Florianopolis, Brazil. Based on a qualitative approach, a semi-structured interview, documentation analysis and focal groups were used for data collection. The participants were managers of School Meals; a school head and a group of students and teachers representing the school. The results indicated that the PSS has advanced in its objectives, combining the introduction of organic foods with educational actions involving food, health, nutrition and the environment but with no evaluations of this process; organic food is present in school meals, although there is no record of educational actions; food is a subject on the Science course; the themes of food, health and nutrition in the school environment come up without planning; the evaluation of students regarding the food is positive, but no reference was made to organic foods. It was concluded that the use of organic food, is still not an element of the pedagogical project. However, the research contributed to the teachers, on the need to develop educational actions in health, organic foods and nutrition, within the school community.

  6. [Evaluation of Germany's sixth national health target entitled "Depressive illnesses - prevention, early diagnosis, sustainable treatment"].

    Science.gov (United States)

    Bermejo, I; Klärs, G; Böhm, K; Hundertmark-Mayser, J; Lampert, T; Maschewsky-Schneider, U; Riedel-Heller, S; Härter, M

    2009-10-01

    In 2006, Germany's sixth national health target entitled "Depressive illnesses - prevention, early diagnosis, sustainable treatment" was developed by an interdisciplinary group of experts. A total of six areas of activity and proposals for action with potential for improvement were defined. Subsequently, a group of experts was entrusted with designing evaluation strategies, defining indicators of progress, and examining the accessibility of data sources for evaluation. For the primary start-up activities set out in the health targets, specific progress indicators were deduced, and routine data available for evaluation were identified. As a next step, the limitations of these data sources were analyzed and necessary improvements described. Relevant indicators of progress for specific areas of activity have been described, the availability and usability of different existing data sources examined, and further supplements or additional specifications with respect to the indicators described. Due to inadequate data sources, additional systematic surveys are required to evaluate the health target and its implementation. Existing German surveys should be extended by questions concerning relevant measures and progress indicators; various progress indicators should be analyzed on a general basis.

  7. Quantifying and Valuing Community Health Worker Time in Improving Access to Malaria Diagnosis and Treatment.

    Science.gov (United States)

    Castellani, Joëlle; Mihaylova, Borislava; Ajayi, IkeOluwapo O; Siribié, Mohamadou; Nsungwa-Sabiiti, Jesca; Afonne, Chinenye; Sermé, Luc; Balyeku, Andrew; Kabarungi, Vanessa; Kyaligonza, Josephine; Evers, Silvia M A A; Paulus, Aggie T G; Petzold, Max; Singlovic, Jan; Gomes, Melba

    2016-12-15

     Community health workers (CHWs) are members of a community who are chosen by their communities as first-line, volunteer health workers. The time they spend providing healthcare and the value of this time are often not evaluated. Our aim was to quantify the time CHWs spent on providing healthcare before and during the implementation of an integrated program of diagnosis and treatment of febrile illness in 3 African countries.  In Burkina Faso, Nigeria, and Uganda, CHWs were trained to assess and manage febrile patients in keeping with Integrated Management of Childhood Illness recommendations to use rapid diagnostic tests, artemisinin-based combination therapy, and rectal artesunate for malaria treatment. All CHWs provided healthcare only to young children usually earnings of persons with similar experience.  During the high malaria season of the intervention, CHWs spent nearly 50 minutes more in daily healthcare provision (average daily time, 30.2 minutes before the intervention vs 79.5 minutes during the intervention; test for difference in means P quality services.  ISRCTN13858170. © 2016 World Health Organization; licensee Oxford Journals.

  8. Time from pre-eclampsia diagnosis to delivery affects future health prospects of children.

    Science.gov (United States)

    Hollegaard, Birgitte; Lykke, Jacob A; Boomsma, Jacobus J

    2017-01-01

    Background and objectives: Pre-eclampsia often has detrimental health effects for pregnant women and their fetuses, but whether exposure in the womb has long-term health-consequences for children as they grow up remains poorly understood. We assessed overall morbidity of children following exposure to either mild or severe pre-eclampsia up to 30 years after birth and related disease risks to duration of exposure, i.e. the time from diagnosis to delivery. Methodology: We did a registry-based retrospective cohort study in Denmark covering the years 1979-2009, using the separate diagnoses of mild and severe pre-eclampsia and the duration of exposure as predictor variables for specific and overall risks of later disease. We analysed 3 537 525 diagnoses for 14 disease groups, accumulated by 758 524 singleton children, after subdividing deliveries in six gestational age categories, partialing out effects of eight potentially confounding factors. Results: Exposure to mild pre-eclampsia appeared to have consistent negative effects on health later in life, although only a few specific disease cases remained significant after corrections for multiple testing. Morbidity risks associated with mild pre-eclampsia were of similar magnitude as those associated with severe pre-eclampsia. Apart from this overall trend in number of diagnoses incurred across disease groups, hazard ratios for several disorders also increased with the duration of exposure, including disorders related to the metabolic syndrome. Conclusions and implications: Maternal pre-eclampsia has lasting effects on offspring health and differences between exposure to severe and mild pre-eclampsia appear to be less than previously assumed. Our results suggest that it would be prudent to include the long-term health prospects of children in the complex clinical management of mild pre-eclampsia.

  9. Mental health network governance and coordination: comparative analysis across Canadian regions

    Directory of Open Access Journals (Sweden)

    Mary E. Wiktorowicz

    2010-10-01

    Full Text Available Objective: Modes of governance were compared in ten local mental health networks in diverse contexts (rural/urban and regionalized/non-regionalized to clarify the governance processes that foster inter-organizational collaboration and the conditions that support them.Methods: Case studies of ten local mental health networks were developed using qualitative methods of document review, semi-structured interviews and focus groups that incorporated provincial policy, network and organizational levels of analysis.Results: Mental health networks adopted either a corporate structure, mutual adjustment or an alliance governance model. A corporate structure supported by regionalization offered the most direct means for local governance to attain inter-organizational collaboration. The likelihood that networks with an alliance model developed coordination processes depended on the presence of the following conditions: a moderate number of organizations, goal consensus and trust among the organizations, and network-level competencies. In the small and mid-sized urban networks where these conditions were met their alliance realized the inter-organizational collaboration sought. In the large urban and rural networks where these conditions were not met, externally brokered forms of network governance were required to support alliance based models.Discussion: In metropolitan and rural networks with such shared forms of network governance as an alliance or voluntary mutual adjustment, external mediation by a regional or provincial authority was an important lever to foster inter-organizational collaboration.

  10. State of health battery estimator enabling degradation diagnosis: Model and algorithm description

    Science.gov (United States)

    Dubarry, Matthieu; Berecibar, M.; Devie, A.; Anseán, D.; Omar, N.; Villarreal, I.

    2017-08-01

    This paper presents a novel approach for automated state of health estimation that offers similar advantages to the adaptive methods without being computation intensive. The onboard diagnosis uses a look-up table compiling the evolution of selected features of interest under any possible degradation paths. The look-up table is built from simulations of the impact of degradation on the cell electrochemical behavior. This multi-step method only requires intensive calculations prior to deployment. This approach is validated by experimental data from cells that underwent normal aging as well as plating and overcharge. Additional validation via modeling showed that the method is able to diagnose cells undergoing any degradation scenario automatically in close to 90% of cases.

  11. Methods for inferring health-related social networks among coworkers from online communication patterns.

    Directory of Open Access Journals (Sweden)

    Luke J Matthews

    Full Text Available Studies of social networks, mapped using self-reported contacts, have demonstrated the strong influence of social connections on the propensity for individuals to adopt or maintain healthy behaviors and on their likelihood to adopt health risks such as obesity. Social network analysis may prove useful for businesses and organizations that wish to improve the health of their populations by identifying key network positions. Health traits have been shown to correlate across friendship ties, but evaluating network effects in large coworker populations presents the challenge of obtaining sufficiently comprehensive network data. The purpose of this study was to evaluate methods for using online communication data to generate comprehensive network maps that reproduce the health-associated properties of an offline social network. In this study, we examined three techniques for inferring social relationships from email traffic data in an employee population using thresholds based on: (1 the absolute number of emails exchanged, (2 logistic regression probability of an offline relationship, and (3 the highest ranked email exchange partners. As a model of the offline social network in the same population, a network map was created using social ties reported in a survey instrument. The email networks were evaluated based on the proportion of survey ties captured, comparisons of common network metrics, and autocorrelation of body mass index (BMI across social ties. Results demonstrated that logistic regression predicted the greatest proportion of offline social ties, thresholding on number of emails exchanged produced the best match to offline network metrics, and ranked email partners demonstrated the strongest autocorrelation of BMI. Since each method had unique strengths, researchers should choose a method based on the aspects of offline behavior of interest. Ranked email partners may be particularly useful for purposes related to health traits in a

  12. Methods for Inferring Health-Related Social Networks among Coworkers from Online Communication Patterns

    Science.gov (United States)

    Matthews, Luke J.; DeWan, Peter; Rula, Elizabeth Y.

    2013-01-01

    Studies of social networks, mapped using self-reported contacts, have demonstrated the strong influence of social connections on the propensity for individuals to adopt or maintain healthy behaviors and on their likelihood to adopt health risks such as obesity. Social network analysis may prove useful for businesses and organizations that wish to improve the health of their populations by identifying key network positions. Health traits have been shown to correlate across friendship ties, but evaluating network effects in large coworker populations presents the challenge of obtaining sufficiently comprehensive network data. The purpose of this study was to evaluate methods for using online communication data to generate comprehensive network maps that reproduce the health-associated properties of an offline social network. In this study, we examined three techniques for inferring social relationships from email traffic data in an employee population using thresholds based on: (1) the absolute number of emails exchanged, (2) logistic regression probability of an offline relationship, and (3) the highest ranked email exchange partners. As a model of the offline social network in the same population, a network map was created using social ties reported in a survey instrument. The email networks were evaluated based on the proportion of survey ties captured, comparisons of common network metrics, and autocorrelation of body mass index (BMI) across social ties. Results demonstrated that logistic regression predicted the greatest proportion of offline social ties, thresholding on number of emails exchanged produced the best match to offline network metrics, and ranked email partners demonstrated the strongest autocorrelation of BMI. Since each method had unique strengths, researchers should choose a method based on the aspects of offline behavior of interest. Ranked email partners may be particularly useful for purposes related to health traits in a social network. PMID

  13. Methods for inferring health-related social networks among coworkers from online communication patterns.

    Science.gov (United States)

    Matthews, Luke J; DeWan, Peter; Rula, Elizabeth Y

    2013-01-01

    Studies of social networks, mapped using self-reported contacts, have demonstrated the strong influence of social connections on the propensity for individuals to adopt or maintain healthy behaviors and on their likelihood to adopt health risks such as obesity. Social network analysis may prove useful for businesses and organizations that wish to improve the health of their populations by identifying key network positions. Health traits have been shown to correlate across friendship ties, but evaluating network effects in large coworker populations presents the challenge of obtaining sufficiently comprehensive network data. The purpose of this study was to evaluate methods for using online communication data to generate comprehensive network maps that reproduce the health-associated properties of an offline social network. In this study, we examined three techniques for inferring social relationships from email traffic data in an employee population using thresholds based on: (1) the absolute number of emails exchanged, (2) logistic regression probability of an offline relationship, and (3) the highest ranked email exchange partners. As a model of the offline social network in the same population, a network map was created using social ties reported in a survey instrument. The email networks were evaluated based on the proportion of survey ties captured, comparisons of common network metrics, and autocorrelation of body mass index (BMI) across social ties. Results demonstrated that logistic regression predicted the greatest proportion of offline social ties, thresholding on number of emails exchanged produced the best match to offline network metrics, and ranked email partners demonstrated the strongest autocorrelation of BMI. Since each method had unique strengths, researchers should choose a method based on the aspects of offline behavior of interest. Ranked email partners may be particularly useful for purposes related to health traits in a social network.

  14. [Health system sustainability from a network perspective: a proposal to optimize healthy habits and social support].

    Science.gov (United States)

    Marqués Sánchez, Pilar; Fernández Peña, Rosario; Cabrera León, Andrés; Muñoz Doyague, María F; Llopis Cañameras, Jaime; Arias Ramos, Natalia

    2013-01-01

    The search of new health management formulas focused to give wide services is one of the priorities of our present health policies. Those formulas examine the optimization of the links between the main actors involved in public health, ie, users, professionals, local socio-political and corporate agents. This paper is aimed to introduce the Social Network Analysis as a method for analyzing, measuring and interpreting those connections. The knowledge of people's relationships (what is called social networks) in the field of public health is becoming increasingly important at an international level. In fact, countries such as UK, Netherlands, Italy, Australia and U.S. are looking formulas to apply this knowledge to their health departments. With this work we show the utility of the ARS on topics related to sustainability of the health system, particularly those related with health habits and social support, topics included in the 2020 health strategies that underline the importance of the collaborative aspects in networks.

  15. An Appraisal of Social Network Theory and Analysis as Applied to Public Health: Challenges and Opportunities.

    Science.gov (United States)

    Valente, Thomas W; Pitts, Stephanie R

    2017-03-20

    The use of social network theory and analysis methods as applied to public health has expanded greatly in the past decade, yielding a significant academic literature that spans almost every conceivable health issue. This review identifies several important theoretical challenges that confront the field but also provides opportunities for new research. These challenges include (a) measuring network influences, (b) identifying appropriate influence mechanisms, (c) the impact of social media and computerized communications, (d) the role of networks in evaluating public health interventions, and (e) ethics. Next steps for the field are outlined and the need for funding is emphasized. Recently developed network analysis techniques, technological innovations in communication, and changes in theoretical perspectives to include a focus on social and environmental behavioral influences have created opportunities for new theory and ever broader application of social networks to public health topics.

  16. Evolutionary programming technique for reducing complexity of artifical neural networks for breast cancer diagnosis

    Science.gov (United States)

    Lo, Joseph Y.; Land, Walker H., Jr.; Morrison, Clayton T.

    2000-06-01

    An evolutionary programming (EP) technique was investigated to reduce the complexity of artificial neural network (ANN) models that predict the outcome of mammography-induced breast biopsy. By combining input variables consisting of mammography lesion descriptors and patient history data, the ANN predicted whether the lesion was benign or malignant, which may aide in reducing the number of unnecessary benign biopsies and thus the cost of mammography screening of breast cancer. The EP has the ability to optimize the ANN both structurally and parametrically. An EP was partially optimized using a data set of 882 biopsy-proven cases from Duke University Medical Center. Although many different architectures were evolved, the best were often perceptrons with no hidden nodes. A rank ordering of the inputs was performed using twenty independent EP runs. This confirmed the predictive value of the mass margin and patient age variables, and revealed the unexpected usefulness of the history of previous breast cancer. Further work is required to improve the performance of the EP over all cases in general and calcification cases in particular.

  17. Identifying socio-ecological networks in rural-urban gradients: Diagnosis of a changing cultural landscape.

    Science.gov (United States)

    Arnaiz-Schmitz, C; Schmitz, M F; Herrero-Jáuregui, C; Gutiérrez-Angonese, J; Pineda, F D; Montes, C

    2018-01-15

    Socio-ecological systems maintain reciprocal interactions between biophysical and socioeconomic structures. As a result of these interactions key essential services for society emerge. Urban expansion is a direct driver of land change and cause serious shifts in socio-ecological relationships and the associated lifestyles. The framework of rural-urban gradients has proved to be a powerful tool for ecological research about urban influences on ecosystems and on sociological issues related to social welfare. However, to date there has not been an attempt to achieve a classification of municipalities in rural-urban gradients based on socio-ecological interactions. In this paper, we developed a methodological approach that allows identifying and classifying a set of socio-ecological network configurations in the Region of Madrid, a highly dynamic cultural landscape considered one of the European hotspots in urban development. According to their socio-ecological links, the integrated model detects four groups of municipalities, ordered along a rural-urban gradient, characterized by their degree of biophysical and socioeconomic coupling and different indicators of landscape structure and social welfare. We propose the developed model as a useful tool to improve environmental management schemes and land planning from a socio-ecological perspective, especially in territories subject to intense urban transformations and loss of rurality. Copyright © 2017 Elsevier B.V. All rights reserved.

  18. Network evaluation: principles, structures and outcomes of the German working group of Health Promoting Universities.

    Science.gov (United States)

    Stock, Christiane; Milz, Simone; Meier, Sabine

    2010-03-01

    With more than 60 participating universities, the German working group of Health Promoting Universities (German HPU network) is the largest and most active network of universities as healthy settings. This study aims at evaluating processes and effects of the German HPU network and at supporting the future development of the network. The evaluation was based on the multi faceted network assessment instrument developed by Broesskamp-Stone (7). We used a document analysis, two expert interviews and a survey among members (n = 33) to collect relevant data for the assessment. The analysis showed that the visions of the network can be regarded as fulfilled in most aspects. The members of the network received network support through trustful and mutual relationships. The network ranked high on general network principles like implementation of mutual relationships, sharing of information, risks and resources, equal access to resources, responsibility and consensus orientation. However, a high degree of centralization was found as a negative indicator. Other critical aspects of the network's structures and processes have been the regional predominance of universities from the northern and middle part of Germany, the low representation of students in the network, and the low proportion of members that could successfully implement health promotion into the guiding principles of their university. Overall, the evaluation has shown that the network has worked effectively, has developed meaningful processes and structures and has formulated practical guidelines. Since its 12 years of existence the German HPU network has been able to adapt and to adequately respond to changing contextual conditions regarding health promotion at universities in Germany. The network should develop strategies to counteract the critical aspects and detected imbalances in order to further increase its impact on universities as healthy settings.

  19. Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network

    Science.gov (United States)

    Lim, Woohyung; Kim, Myoung Shin; Na, Jung Im; Park, Ilwoo

    2018-01-01

    Although there have been reports of the successful diagnosis of skin disorders using deep learning, unrealistically large clinical image datasets are required for artificial intelligence (AI) training. We created datasets of standardized nail images using a region-based convolutional neural network (R-CNN) trained to distinguish the nail from the background. We used R-CNN to generate training datasets of 49,567 images, which we then used to fine-tune the ResNet-152 and VGG-19 models. The validation datasets comprised 100 and 194 images from Inje University (B1 and B2 datasets, respectively), 125 images from Hallym University (C dataset), and 939 images from Seoul National University (D dataset). The AI (ensemble model; ResNet-152 + VGG-19 + feedforward neural networks) results showed test sensitivity/specificity/ area under the curve values of (96.0 / 94.7 / 0.98), (82.7 / 96.7 / 0.95), (92.3 / 79.3 / 0.93), (87.7 / 69.3 / 0.82) for the B1, B2, C, and D datasets. With a combination of the B1 and C datasets, the AI Youden index was significantly (p = 0.01) higher than that of 42 dermatologists doing the same assessment manually. For B1+C and B2+ D dataset combinations, almost none of the dermatologists performed as well as the AI. By training with a dataset comprising 49,567 images, we achieved a diagnostic accuracy for onychomycosis using deep learning that was superior to that of most of the dermatologists who participated in this study. PMID:29352285

  20. Insights Into Collaborative Networks Of Nonprofit, Private, And Public Organizations That Address Complex Health Issues.

    Science.gov (United States)

    Hogg, Rachel A; Varda, Danielle

    2016-11-01

    Community networks that include nonprofit, public, and private organizations have formed around many health issues, such as chronic disease management and healthy living and eating. Despite the increases in the numbers of and funding for cross-sector networks, and the growing literature about them, there are limited data and methods that can be used to assess their effectiveness and analyze their designs. We addressed this gap in knowledge by analyzing the characteristics of 260 cross-sector community health networks that collectively consisted of 7,816 organizations during the period 2008-15. We found that nonprofit organizations were more prevalent than private firms or government agencies in these networks. Traditional types of partners in community health networks such as hospitals, community health centers, and public health agencies were the most trusted and valued by other members of their networks. However, nontraditional partners, such as employer or business groups and colleges or universities, reported contributing relatively high numbers of resources to their networks. Further evidence is needed to inform collaborative management processes and policies as a mechanism for building what the Robert Wood Johnson Foundation describes as a culture of health. Project HOPE—The People-to-People Health Foundation, Inc.

  1. Social networks, social participation, and health among youth living in extreme poverty in rural Malawi.

    Science.gov (United States)

    Rock, Amelia; Barrington, Clare; Abdoulayi, Sara; Tsoka, Maxton; Mvula, Peter; Handa, Sudhanshu

    2016-12-01

    Extensive research documents that social network characteristics affect health, but knowledge of peer networks of youth in Malawi and sub-Saharan Africa is limited. We examine the networks and social participation of youth living in extreme poverty in rural Malawi, using in-depth interviews with 32 youth and caregivers. We describe youth's peer networks and assess how gender and the context of extreme poverty influence their networks and participation, and how their networks influence health. In-school youth had larger, more interactive, and more supportive networks than out-of-school youth, and girls described less social participation and more isolation than boys. Youth exchanged social support and influence within their networks that helped cope with poverty-induced stress and sadness, and encouraged protective sexual health practices. However, poverty hampered their involvement in school, religious schools, and community organizations, directly by denying them required material means, and indirectly by reducing time and emotional resources and creating shame and stigma. Poverty alleviation policy holds promise for improving youth's social wellbeing and mental and physical health by increasing their opportunities to form networks, receive social support, and experience positive influence. Copyright © 2016 Elsevier Ltd. All rights reserved.

  2. [Networks of experiences on community health as an information system in health promotion: lessons learned in Aragon (Spain)].

    Science.gov (United States)

    Gállego-Diéguez, Javier; Aliaga Traín, Pilar; Benedé Azagra, Carmen Belén; Bueno Franco, Manuel; Ferrer Gracia, Elisa; Ipiéns Sarrate, José Ramón; Muñoz Nadal, Pilar; Plumed Parrilla, Manuela; Vilches Urrutia, Begoña

    2016-11-01

    Networks of community health experiences promote interaction and knowledge management in health promotion among their participants. These networks integrate both professionals and social agents who work directly on the ground in small environments, with defined objectives and inclusion criteria and voluntary participation. In this article, networks in Aragon (Spain) are reviewed in order to analyse their role as an information system. The Health Promotion Projects Network of Aragon (Red Aragonesa de Proyectos de Promoción de la Salud, RAPPS) was launched in 1996 and currently includes 73 projects. The average duration of projects is 12.7 years. RAPPS interdisciplinary teams involve 701 people, of which 89.6% are professionals and 10.6% are social agents. The Aragon Health Promoting Schools Network (Red Aragonesa de Escuelas Promotoras de Salud, RAEPS) integrates 134 schools (24.9% of Aragon). The schools teams involve 829 teachers and members of the school community, students (35.2%), families (26.2%) and primary care health professionals (9.8%). Experiences Networks boost citizen participation, have an influence in changing social determinants and contribute to the formulation of plans and regional strategies. Networks can provide indicators for a health promotion information and monitoring system on: capacity building services in the territory, identifying assets and models of good practice, cross-sectoral and equity initiatives. Experiences Networks represent an opportunity to create a health promotion information system, systematising available information and establishing quality criteria for initiatives. Copyright © 2016 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

  3. The effect of artificial neural network model combined with six tumor markers in auxiliary diagnosis of lung cancer.

    Science.gov (United States)

    Feng, Feifei; Wu, Yiming; Wu, Yongjun; Nie, Guangjin; Ni, Ran

    2012-10-01

    To evaluate the diagnosis potential of artificial neural network (ANN) model combined with six tumor markers in auxiliary diagnosis of lung cancer, to differentiate lung cancer from lung benign disease, normal control, and gastrointestinal cancers. Serum carcino-embryonic antigen (CEA), gastrin, neurone specific enolase (NSE), sialic acid (SA), Cu/Zn, Ca were measured through different experimental procedures in 117 lung cancer patients, 93 lung benign disease patients, 111 normal control, 47 gastric cancer patients, 50 patients with colon cancer and 50 esophagus cancer patients, 19 parameters of basic information were surveyed among lung cancer, lung benign disease and normal control, then developed and evaluated ANN models to distinguish lung cancer. Using the ANN model with the six serum tumor markers and 19 parameters to distinguish lung cancer from benign lung disease and healthy people, the sensitivity was 98.3%, the specificity was 99.5% and the accuracy was 96.9%. Another three ANN models with the six serum tumor markers were employed to differentiate lung cancer from three gastrointestinal cancers, the sensitivity, specificity and accuracy of distinguishing lung cancer from gastric cancer by the ANN model of lung cancer-gastric cancer were 100%, 83.3% and 93.5%, respectively; The sensitivity, specificity and accuracy of discriminating lung cancer by lung cancer-colon cancer ANN model were 90.0%, 90.0%, and 90.0%; And which were 86.7%, 84.6%, and 86.0%, respectively, by lung cancer-esophagus cancer ANN model. ANN model built with the six serum tumor markers could distinguish lung cancer, not only from lung benign disease and normal people, but also from three common gastrointestinal cancers. And our evidence indicates the ANN model maybe is an excellent and intelligent system to discriminate lung cancer.

  4. Clinician practice and the National Healthcare Safety Network definition for the diagnosis of catheter-associated urinary tract infection.

    Science.gov (United States)

    Al-Qas Hanna, Fadi; Sambirska, Oksana; Iyer, Sugantha; Szpunar, Susanna; Fakih, Mohamad G

    2013-12-01

    The National Healthcare Safety Network (NHSN) definition for catheter-associated urinary tract infection (CAUTI) is used to evaluate improvements in CAUTI prevention efforts. We assessed whether clinician practice was reflective of the NHSN definition. We evaluated all adult inpatients hospitalized between July 2010 and June 2011, with a first positive urine culture > 48 hours of admission obtained while catheterized or within 48 hours of catheter discontinuation. Data comprised patients' signs, symptoms, and diagnostic tests; clinician's diagnosis; and the impression of the infectious diseases (ID) consultant. The clinician's practice was compared with the NHSN definition and the ID consultant's impression. Antibiotics were initiated by clinicians to treat CAUTI in 216 of 387 (55.8%) cases, with 119 of 387 (30.7%) fitting the NHSN CAUTI definition, and 63 of 211 (29.9%) considered by ID to have a CAUTI. The sensitivity, specificity, and positive and negative predictive values of a clinician diagnosis of CAUTI were 62.2%, 47%, 34.3%, and 73.7% when compared with NHSN CAUTI definition (n = 387) and 100%, 57.4%, 50%, and 100% when compared with the ID consultant evaluation (n = 211), respectively. The positive predictive value of the NHSN CAUTI definition was 35.1% when compared with the ID consultant's impression (n = 211). NHSN CAUTI definition did not reflect clinician or ID consultant practices. Our findings reflect the differences between surveillance definitions and clinical practice. Copyright © 2013 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Mosby, Inc. All rights reserved.

  5. Implementing multiple intervention strategies in Dutch public health-related policy networks.

    Science.gov (United States)

    Harting, Janneke; Peters, Dorothee; Grêaux, Kimberly; van Assema, Patricia; Verweij, Stefan; Stronks, Karien; Klijn, Erik-Hans

    2017-10-13

    Improving public health requires multiple intervention strategies. Implementing such an intervention mix is supposed to require a multisectoral policy network. As evidence to support this assumption is scarce, we examined under which conditions public health-related policy networks were able to implement an intervention mix. Data were collected (2009-14) from 29 Dutch public health policy networks. Surveys were used to identify the number of policy sectors, participation of actors, level of trust, networking by the project leader, and intervention strategies implemented. Conditions sufficient for an intervention mix (≥3 of 4 non-educational strategies present) were determined in a fuzzy-set qualitative comparative analysis. A multisectoral policy network (≥7 of 14 sectors present) was neither a necessary nor a sufficient condition. In multisectoral networks, additionally required was either the active participation of network actors (≥50% actively involved) or active networking by the project leader (≥monthly contacts with network actors). In policy networks that included few sectors, a high level of trust (positive perceptions of each other's intentions) was needed-in the absence though of any of the other conditions. If the network actors were also actively involved, an extra requirement was active networking by the project leader. We conclude that the multisectoral composition of policy networks can contribute to the implementation of a variety of intervention strategies, but not without additional efforts. However, policy networks that include only few sectors are also able to implement an intervention mix. Here, trust seems to be the most important condition. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  6. Toward Predicting Social Support Needs in Online Health Social Networks.

    Science.gov (United States)

    Choi, Min-Je; Kim, Sung-Hee; Lee, Sukwon; Kwon, Bum Chul; Yi, Ji Soo; Choo, Jaegul; Huh, Jina

    2017-08-02

    While online health social networks (OHSNs) serve as an effective platform for patients to fulfill their various social support needs, predicting the needs of users and providing tailored information remains a challenge. The objective of this study was to discriminate important features for identifying users' social support needs based on knowledge gathered from survey data. This study also provides guidelines for a technical framework, which can be used to predict users' social support needs based on raw data collected from OHSNs. We initially conducted a Web-based survey with 184 OHSN users. From this survey data, we extracted 34 features based on 5 categories: (1) demographics, (2) reading behavior, (3) posting behavior, (4) perceived roles in OHSNs, and (5) values sought in OHSNs. Features from the first 4 categories were used as variables for binary classification. For the prediction outcomes, we used features from the last category: the needs for emotional support, experience-based information, unconventional information, and medical facts. We compared 5 binary classifier algorithms: gradient boosting tree, random forest, decision tree, support vector machines, and logistic regression. We then calculated the scores of the area under the receiver operating characteristic (ROC) curve (AUC) to understand the comparative effectiveness of the used features. The best performance was AUC scores of 0.89 for predicting users seeking emotional support, 0.86 for experience-based information, 0.80 for unconventional information, and 0.83 for medical facts. With the gradient boosting tree as our best performing model, we analyzed the strength of individual features in predicting one's social support need. Among other discoveries, we found that users seeking emotional support tend to post more in OHSNs compared with others. We developed an initial framework for automatically predicting social support needs in OHSNs using survey data. Future work should involve nonsurvey

  7. [Initial evaluation, diagnosis, staging, treatment, and follow-up of patients with primary cutaneous malignant melanoma. Consensus statement of the Network of Catalan and Balearic Melanoma Centers].

    Science.gov (United States)

    Mangas, C; Paradelo, C; Puig, S; Gallardo, F; Marcoval, J; Azon, A; Bartralot, R; Bel, S; Bigatà, X; Curcó, N; Dalmau, J; del Pozo, L J; Ferrándiz, C; Formigón, M; González, A; Just, M; Llambrich, A; Llistosella, E; Malvehy, J; Martí, R M; Nogués, M E; Pedragosa, R; Rocamora, V; Sàbat, M; Salleras, M

    2010-03-01

    The consensus statement on the management of primary cutaneous melanoma that we present here was based on selection, discussion, review, and comparison of recent literature (including national and international guidelines). The protocols for the diagnosis, treatment, and follow-up used in the hospital centers throughout Catalonia and the Balearic Isles belonging to the Network of Catalan and Balearic Melanoma Centers were also considered. The main objective of this statement was to present the overall management of melanoma patients typically used in our region at the present time. As such, the statement was not designed to be an obligatory protocol for health professionals caring for this group of patients, and neither can it nor should it be used for this purpose. Professionals reading the statement should not therefore consider it binding on their practice, and in no case can this text be used to guarantee or seek responsibility for a given medical opinion. The group of dermatologists who have signed this statement was created 3 years ago with the aim of making our authorities aware of the importance of this complex tumor, which, in comparison with other types of cancer, we believe does not receive sufficient attention in Spain. In addition, the regular meetings of the group have produced interesting proposals for collaboration in various epidemiological, clinical, and basic applied research projects on the subject of malignant melanoma in our society.

  8. [Benefit of network education to college students' knowledge about sexual and reproductive health in Ningbo city].

    Science.gov (United States)

    Wang, Guo-yao; Ji, Yun-xin; Ding, Hui-qing; Gui, Zhong-bao; Liang, Xiao-ming; Fu, Jian-fei; Cheng, Yue

    2015-12-01

    To investigate how network education can improve college students' knowledge on sexual and reproductive health in Ningbo city. From December 2012 to June 2013, we conducted a questionnaire investigation among college students in Ningbo city about the effects of network education on their knowledge about sexual psychology, sexual physiology, sexual ethics, and reproductive health. A total of 7 362 college students accomplished the investigation, of whom 2 483 (42.1% males and 57.9% females) received network education, while the other 4 879 (24.1% males and 75.9% females) did not. Approximately 47.1% of the male and 28.0% of the female students acquired sexual and reproductive knowledge via network education. Reproductive health-related network education significantly enriched the students' knowledge about the reproductive system and sex, pubertal development, sexual physiology, conception and embryonic development, methods of contraception, sexual psychology, sexually transmitted diseases and their prevention, pregnancy care and eugenics, and environment- and occupation-related reproductive health (P reproductive health knowledge (P reproductive health-related network education showed a significantly higher rate of masturbation (P reproductive health education among college students and improve their sexual experience and health.

  9. Expanding delivery system research in public health settings: lessons from practice-based research networks.

    Science.gov (United States)

    Mays, Glen P; Hogg, Rachel A

    2012-11-01

    Delivery system research to identify how best to organize, finance, and implement health improvement strategies has focused heavily on clinical practice settings, with relatively little attention paid to public health settings-where research is made more difficult by wide heterogeneity in settings and limited sources of existing data and measures. This study examines the approaches used by public health practice-based research networks (PBRNs) to expand delivery system research and evidence-based practice in public health settings. Practice-based research networks employ quasi-experimental research designs, natural experiments, and mixed-method analytic techniques to evaluate how community partnerships, economic shocks, and policy changes impact delivery processes in public health settings. In addition, network analysis methods are used to assess patterns of interaction between practitioners and researchers within PBRNs to produce and apply research findings. Findings from individual PBRN studies elucidate the roles of information exchange, community resources, and leadership and decision-making structures in shaping implementation outcomes in public health delivery. Network analysis of PBRNs reveals broad engagement of both practitioners and researchers in scientific inquiry, with practitioners in the periphery of these networks reporting particularly large benefits from research participation. Public health PBRNs provide effective mechanisms for implementing delivery system research and engaging practitioners in the process. These networks also hold promise for accelerating the translation and application of research findings into public health settings.

  10. The challenge of social networking in the field of environment and health

    Science.gov (United States)

    2012-01-01

    Background The fields of environment and health are both interdisciplinary and trans-disciplinary, and until recently had little engagement in social networking designed to cross disciplinary boundaries. The EU FP6 project HENVINET aimed to establish integrated social network and networking facilities for multiple stakeholders in environment and health. The underlying assumption is that increased social networking across disciplines and sectors will enhance the quality of both problem knowledge and problem solving, by facilitating interactions. Inter- and trans-disciplinary networks are considered useful for this purpose. This does not mean that such networks are easily organized, as openness to such cooperation and exchange is often difficult to ascertain. Methods Different methods may enhance network building. Using a mixed method approach, a diversity of actions were used in order to investigate the main research question: which kind of social networking activities and structures can best support the objective of enhanced inter- and trans-disciplinary cooperation and exchange in the fields of environment and health. HENVINET applied interviews, a role playing session, a personal response system, a stakeholder workshop and a social networking portal as part of the process of building an interdisciplinary and trans-disciplinary network. Results The interviews provided support for the specification of requirements for an interdisciplinary and trans-disciplinary network. The role playing session, the personal response system and the stakeholder workshop were assessed as useful tools in forming such network, by increasing the awareness by different disciplines of other’s positions. The social networking portal was particularly useful in delivering knowledge, but the role of the scientist in social networking is not yet clear. Conclusions The main challenge in the field of environment and health is not so much a lack of scientific problem knowledge, but rather the

  11. The challenge of social networking in the field of environment and health.

    Science.gov (United States)

    van den Hazel, Peter; Keune, Hans; Randall, Scott; Yang, Aileen; Ludlow, David; Bartonova, Alena

    2012-06-28

    The fields of environment and health are both interdisciplinary and trans-disciplinary, and until recently had little engagement in social networking designed to cross disciplinary boundaries. The EU FP6 project HENVINET aimed to establish integrated social network and networking facilities for multiple stakeholders in environment and health. The underlying assumption is that increased social networking across disciplines and sectors will enhance the quality of both problem knowledge and problem solving, by facilitating interactions. Inter- and trans-disciplinary networks are considered useful for this purpose. This does not mean that such networks are easily organized, as openness to such cooperation and exchange is often difficult to ascertain. Different methods may enhance network building. Using a mixed method approach, a diversity of actions were used in order to investigate the main research question: which kind of social networking activities and structures can best support the objective of enhanced inter- and trans-disciplinary cooperation and exchange in the fields of environment and health. HENVINET applied interviews, a role playing session, a personal response system, a stakeholder workshop and a social networking portal as part of the process of building an interdisciplinary and trans-disciplinary network. The interviews provided support for the specification of requirements for an interdisciplinary and trans-disciplinary network. The role playing session, the personal response system and the stakeholder workshop were assessed as useful tools in forming such network, by increasing the awareness by different disciplines of other's positions. The social networking portal was particularly useful in delivering knowledge, but the role of the scientist in social networking is not yet clear. The main challenge in the field of environment and health is not so much a lack of scientific problem knowledge, but rather the ability to effectively communicate, share

  12. Independent review of social and population variation in mental health could improve diagnosis in DSM revisions.

    Science.gov (United States)

    Hansen, Helena B; Donaldson, Zoe; Link, Bruce G; Bearman, Peter S; Hopper, Kim; Bates, Lisa M; Cheslack-Postava, Keely; Harper, Kristin; Holmes, Seth M; Lovasi, Gina; Springer, Kristen W; Teitler, Julien O

    2013-05-01

    At stake in the May 2013 publication of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), are billions of dollars in insurance payments and government resources, as well as the diagnoses and treatment of millions of patients. We argue that the most recent revision process has missed social determinants of mental health disorders and their diagnosis: environmental factors triggering biological responses that manifest themselves in behavior; differing cultural perceptions about what is normal and what is abnormal behavior; and institutional pressures related to such matters as insurance reimbursements, disability benefits, and pharmaceutical marketing. In addition, the experts charged with revising the DSM lack a systematic way to take population-level variations in diagnoses into account. To address these problems, we propose the creation of an independent research review body that would monitor variations in diagnostic patterns, inform future DSM revisions, identify needed changes in mental health policy and practice, and recommend new avenues of research. Drawing on the best available knowledge, the review body would make possible more precise and equitable psychiatric diagnoses and interventions.

  13. New Delhi Metallo-β-Lactamase-Mediated Carbapenem Resistance: Origin, Diagnosis, Treatment and Public Health Concern.

    Science.gov (United States)

    Wei, Wen-Juan; Yang, Hai-Fei; Ye, Ying; Li, Jia-Bin

    2015-07-20

    To review the origin, diagnosis, treatment and public health concern of New Delhi metallo-β-lactamase (NDM)-producing bacteria. We searched database for studies published in English. The database of PubMed from 2007 to 2015 was used to conduct a search using the keyword term "NDM and Acinetobacter or Enterobacteriaceae or Pseudomonas aeruginosa." We collected data including the relevant articles on international transmission, testing methods and treatment strategies of NDM-positive bacteria. Worldwide NDM cases were reviewed based on 22 case reports. The first documented case of infection caused by bacteria producing NDM-1 occurred in India, in 2008. Since then, 13 blaNDM variants have been reported. The rise of NDM is not only due to its high rate of genetic transfer among unrelated bacterial species, but also to human factors such as travel, sanitation and food production and preparation. With limited treatment options, scientists try to improve available therapies and create new ones. In order to slow down the spread of these NDM-positive bacteria, a series of measures must be implemented. The creation and transmission of blaNDM are potentially global health issues, which are not issues for one country or one medical community, but for global priorities in general and for individual wound care practitioners specifically.

  14. New Delhi Metallo-β-Lactamase-Mediated Carbapenem Resistance: Origin, Diagnosis, Treatment and Public Health Concern

    Directory of Open Access Journals (Sweden)

    Wen-Juan Wei

    2015-01-01

    Full Text Available Objective: To review the origin, diagnosis, treatment and public health concern of New Delhi metallo-β-lactamase (NDM-producing bacteria. Data Sources: We searched database for studies published in English. The database of PubMed from 2007 to 2015 was used to conduct a search using the keyword term "NDM and Acinetobacter or Enterobacteriaceae or Pseudomonas aeruginosa." Study Selection: We collected data including the relevant articles on international transmission, testing methods and treatment strategies of NDM-positive bacteria. Worldwide NDM cases were reviewed based on 22 case reports. Results: The first documented case of infection caused by bacteria producing NDM-1 occurred in India, in 2008. Since then, 13 blaNDM variants have been reported. The rise of NDM is not only due to its high rate of genetic transfer among unrelated bacterial species, but also to human factors such as travel, sanitation and food production and preparation. With limited treatment options, scientists try to improve available therapies and create new ones. Conclusions: In order to slow down the spread of these NDM-positive bacteria, a series of measures must be implemented. The creation and transmission of blaNDM are potentially global health issues, which are not issues for one country or one medical community, but for global priorities in general and for individual wound care practitioners specifically.

  15. [Health diagnosis and risk perception: key elements of a proposed intervention for indigenous communities in Mexico].

    Science.gov (United States)

    Terán-Hernández, Mónica; Díaz-Barriga, Fernando; Cubillas-Tejeda, Ana Cristina

    2016-02-01

    Objective To carry out a diagnosis of children's environmental health and an analysis of risk perception in indigenous communities of the Huasteca Sur region of San Luis Potosí, Mexico, in order to design an intervention strategy in line with their needs. Methods The study used mixed methods research, carried out in two phases. It was conducted in three indigenous communities of Tancanhuitz municipality from January 2005 to June 2006. In the adult population, risk perception was analyzed through focus groups, in-depth interviews, and questionnaires. In the child population, analysis of children's drawings was used to study perception. An assessment of health risks was carried out through biological monitoring and environmental monitoring of water and soil. Results The three communities face critical problems that reveal their vulnerability. When the results were triangulated and integrated, it was found that the principal problems relate to exposure to pathogenic microorganisms in water and soil, exposure to indoor wood smoke, exposure to smoke from the burning of refuse, use of insecticides, exposure to lead from the use of glazed ceramics, and alcoholism. Conclusions To ensure that the intervention strategy is adapted to the target population, it is essential to incorporate risk perception analysis and to promote the participation of community members. The proposed intervention strategy to address the detected problems is based on the principles of risk communication, community participation, and interinstitutional linkage.

  16. Practical recommendations for strengthening national and regional laboratory networks in Africa in the Global Health Security era.

    Science.gov (United States)

    Best, Michele; Sakande, Jean

    2016-01-01

    The role of national health laboratories in support of public health response has expanded beyond laboratory testing to include a number of other core functions such as emergency response, training and outreach, communications, laboratory-based surveillance and data management. These functions can only be accomplished by an efficient and resilient national laboratory network that includes public health, reference, clinical and other laboratories. It is a primary responsibility of the national health laboratory in the Ministry of Health to develop and maintain the national laboratory network in the country. In this article, we present practical recommendations based on 17 years of network development experience for the development of effective national laboratory networks. These recommendations and examples of current laboratory networks, are provided to facilitate laboratory network development in other states. The development of resilient, integrated laboratory networks will enhance each state's public health system and is critical to the development of a robust national laboratory response network to meet global health security threats.

  17. Loneliness, Social Networks, and Health: A Cross-Sectional Study in Three Countries.

    Directory of Open Access Journals (Sweden)

    Laura Alejandra Rico-Uribe

    Full Text Available It is widely recognized that social networks and loneliness have effects on health. The present study assesses the differential association that the components of the social network and the subjective perception of loneliness have with health, and analyzes whether this association is different across different countries.A total of 10 800 adults were interviewed in Finland, Poland and Spain. Loneliness was assessed by means of the 3-item UCLA Loneliness Scale. Individuals' social networks were measured by asking about the number of members in the network, how often they had contacts with these members, and whether they had a close relationship. The differential association of loneliness and the components of the social network with health was assessed by means of hierarchical linear regression models, controlling for relevant covariates.In all three countries, loneliness was the variable most strongly correlated with health after controlling for depression, age, and other covariates. Loneliness contributed more strongly to health than any component of the social network. The relationship between loneliness and health was stronger in Finland (|β| = 0.25 than in Poland (|β| = 0.16 and Spain (|β| = 0.18. Frequency of contact was the only component of the social network that was moderately correlated with health.Loneliness has a stronger association with health than the components of the social network. This association is similar in three different European countries with different socio-economic and health characteristics and welfare systems. The importance of evaluating and screening feelings of loneliness in individuals with health problems should be taken into account. Further studies are needed in order to be able to confirm the associations found in the present study and infer causality.

  18. Loneliness, Social Networks, and Health: A Cross-Sectional Study in Three Countries.

    Science.gov (United States)

    Rico-Uribe, Laura Alejandra; Caballero, Francisco Félix; Olaya, Beatriz; Tobiasz-Adamczyk, Beata; Koskinen, Seppo; Leonardi, Matilde; Haro, Josep Maria; Chatterji, Somnath; Ayuso-Mateos, José Luis; Miret, Marta

    2016-01-01

    It is widely recognized that social networks and loneliness have effects on health. The present study assesses the differential association that the components of the social network and the subjective perception of loneliness have with health, and analyzes whether this association is different across different countries. A total of 10 800 adults were interviewed in Finland, Poland and Spain. Loneliness was assessed by means of the 3-item UCLA Loneliness Scale. Individuals' social networks were measured by asking about the number of members in the network, how often they had contacts with these members, and whether they had a close relationship. The differential association of loneliness and the components of the social network with health was assessed by means of hierarchical linear regression models, controlling for relevant covariates. In all three countries, loneliness was the variable most strongly correlated with health after controlling for depression, age, and other covariates. Loneliness contributed more strongly to health than any component of the social network. The relationship between loneliness and health was stronger in Finland (|β| = 0.25) than in Poland (|β| = 0.16) and Spain (|β| = 0.18). Frequency of contact was the only component of the social network that was moderately correlated with health. Loneliness has a stronger association with health than the components of the social network. This association is similar in three different European countries with different socio-economic and health characteristics and welfare systems. The importance of evaluating and screening feelings of loneliness in individuals with health problems should be taken into account. Further studies are needed in order to be able to confirm the associations found in the present study and infer causality.

  19. Social Networks and Health Among Older Adults in Lebanon: The Mediating Role of Support and Trust

    Science.gov (United States)

    Antonucci, Toni C.; Ajrouch, Kristine J.; Abdulrahim, Sawsan

    2015-01-01

    Objectives. Despite a growing body of literature documenting the influence of social networks on health, less is known in other parts of the world. The current study investigates this link by clustering characteristics of network members nominated by older adults in Lebanon. We then identify the degree to which various types of people exist within the networks. This study further examines how network composition as measured by the proportion of each type (i.e., type proportions) is related to health; and the mediating role of positive support and trust in this process. Method. Data are from the Family Ties and Aging Study (2009). Respondents aged ≥60 were selected (N = 195) for analysis. Results. Three types of people within the networks were identified: Geographically Distant Male Youth, Geographically Close/Emotionally Distant Family, and Close Family. Having more Geographically Distant Male Youth in one’s network was associated with health limitations, whereas more Close Family was associated with no health limitations. Positive support mediated the link between type proportions and health limitations, whereas trust mediated the link between type proportions and depressive symptoms. Discussion. Results document links between the social networks and health of older adults in Lebanon within the context of ongoing demographic transitions. PMID:25324295

  20. Social networks and health among older adults in Lebanon: the mediating role of support and trust.

    Science.gov (United States)

    Webster, Noah J; Antonucci, Toni C; Ajrouch, Kristine J; Abdulrahim, Sawsan

    2015-01-01

    Despite a growing body of literature documenting the influence of social networks on health, less is known in other parts of the world. The current study investigates this link by clustering characteristics of network members nominated by older adults in Lebanon. We then identify the degree to which various types of people exist within the networks. This study further examines how network composition as measured by the proportion of each type (i.e., type proportions) is related to health; and the mediating role of positive support and trust in this process. Data are from the Family Ties and Aging Study (2009). Respondents aged ≥60 were selected (N = 195) for analysis. Three types of people within the networks were identified: Geographically Distant Male Youth, Geographically Close/Emotionally Distant Family, and Close Family. Having more Geographically Distant Male Youth in one's network was associated with health limitations, whereas more Close Family was associated with no health limitations. Positive support mediated the link between type proportions and health limitations, whereas trust mediated the link between type proportions and depressive symptoms. Results document links between the social networks and health of older adults in Lebanon within the context of ongoing demographic transitions. © The Author 2014. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  1. Intergenerational Social Networks and Health Behaviors Among Children Living in Public Housing.

    Science.gov (United States)

    Kennedy-Hendricks, Alene; Schwartz, Heather; Thornton, Rachel Johnson; Griffin, Beth Ann; Green, Harold D; Kennedy, David P; Burkhauser, Susan; Pollack, Craig Evan

    2015-11-01

    In a survey of families living in public housing, we investigated whether caretakers' social networks are linked with children's health status. In 2011, 209 children and their caretakers living in public housing in suburban Montgomery County, Maryland, were surveyed regarding their health and social networks. We used logistic regression models to examine the associations between the perceived health composition of caretaker social networks and corresponding child health characteristics (e.g., exercise, diet). With each 10% increase in the proportion of the caretaker's social network that exercised regularly, the child's odds of exercising increased by 34% (adjusted odds ratio = 1.34; 95% confidence interval = 1.07, 1.69) after the caretaker's own exercise behavior and the composition of the child's peer network had been taken into account. Although children's overweight or obese status was associated with caretakers' social networks, the results were no longer significant after adjustment for caretakers' own weight status. We found that caretaker social networks are independently associated with certain aspects of child health, suggesting the importance of the broader social environment for low-income children's health.

  2. [Do the structure and functioning of the elderly's social network influence functional health: a preliminary study].

    Science.gov (United States)

    Masse, Marie; Swine, Christian

    2015-06-01

    We examined structural and functional characteristics of social networks related to health and well-being among community-dwelling older adults. A survey was performed in Brussels, using an original name-generating network inventory, to explore the structure and types of social ties (e.g. children, friends, neighbors) which forms the elderly's network. Different kinds of support (instrumental, emotional, social) were assessed due to the multiple contents of social exchanges between the elderly and their network's members. Our results highlighted some important social network resources. Especially, social participation, contacts with friends of the same age and reciprocity of social relationships are likely to promote functional health and well-being in later life. We discuss our findings in relation to major social network's typologies referring to older adults.

  3. Cost-effectiveness analysis of introducing RDTs for malaria diagnosis as compared to microscopy and presumptive diagnosis in central and peripheral public health facilities in Ghana

    DEFF Research Database (Denmark)

    Ansah, Evelyn K; Epokor, Michael; Whitty, Christopher J M

    2013-01-01

    Cost-effectiveness information on where malaria rapid diagnostic tests (RDTs) should be introduced is limited. We developed incremental cost-effectiveness analyses with data from rural health facilities in Ghana with and without microscopy. In the latter, where diagnosis had been presumptive......, the introduction of RDTs increased the proportion of patients who were correctly treated in relation to treatment with antimalarials, from 42% to 65% at an incremental societal cost of Ghana cedis (GHS)12.2 (US$8.3) per additional correctly treated patients. In the "microscopy setting" there was no advantage...

  4. Health care index score and risk of death following tuberculosis diagnosis in HIV-positive patients.

    Science.gov (United States)

    Podlekareva, D N; Grint, D; Post, F A; Mocroft, A; Panteleev, A M; Miller, R F; Miro, J M; Bruyand, M; Furrer, H; Riekstina, V; Girardi, E; Losso, M H; Caylá, J A; Malashenkov, E A; Obel, N; Skrahina, A M; Lundgren, J D; Kirk, O

    2013-02-01

    To assess health care utilisation for patients co-infected with TB and HIV (TB-HIV), and to develop a weighted health care index (HCI) score based on commonly used interventions and compare it with patient outcome. A total of 1061 HIV patients diagnosed with TB in four regions, Central/Northern, Southern and Eastern Europe and Argentina, between January 2004 and December 2006 were enrolled in the TB-HIV study. A weighted HCI score (range 0-5), based on independent prognostic factors identified in multivariable Cox models and the final score, included performance of TB drug susceptibility testing (DST), an initial TB regimen containing a rifamycin, isoniazid and pyrazinamide, and start of combination antiretroviral treatment (cART). The mean HCI score was highest in Central/Northern Europe (3.2, 95%CI 3.1-3.3) and lowest in Eastern Europe (1.6, 95%CI 1.5-1.7). The cumulative probability of death 1 year after TB diagnosis decreased from 39% (95%CI 31-48) among patients with an HCI score of 0, to 9% (95%CI 6-13) among those with a score of ≥4. In an adjusted Cox model, a 1-unit increase in the HCI score was associated with 27% reduced mortality (relative hazard 0.73, 95%CI 0.64-0.84). Our results suggest that DST, standard anti-tuberculosis treatment and early cART may improve outcome for TB-HIV patients. The proposed HCI score provides a tool for future research and monitoring of the management of TB-HIV patients. The highest HCI score may serve as a benchmark to assess TB-HIV management, encouraging continuous health care improvement.

  5. Developing an inter-organizational community-based health network: an Australian investigation.

    Science.gov (United States)

    Short, Alison; Phillips, Rebecca; Nugus, Peter; Dugdale, Paul; Greenfield, David

    2015-12-01

    Networks in health care typically involve services delivered by a defined set of organizations. However, networked associations between the healthcare system and consumers or consumer organizations tend to be open, fragmented and are fraught with difficulties. Understanding the role and activities of consumers and consumer groups in a formally initiated inter-organizational health network, and the impacts of the network, is a timely endeavour. This study addresses this aim in three ways. First, the Unbounded Network Inter-organizational Collaborative Impact Model, a purpose-designed framework developed from existing literature, is used to investigate the process and products of inter-organizational network development. Second, the impact of a network artefact is explored. Third, the lessons learned in inter-organizational network development are considered. Data collection methods were: 16 h of ethnographic observation; 10 h of document analysis; six interviews with key informants and a survey (n = 60). Findings suggested that in developing the network, members used common aims, inter-professional collaboration, the power and trust engendered by their participation, and their leadership and management structures in a positive manner. These elements and activities underpinned the inter-organizational network to collaboratively produce the Health Expo network artefact. This event brought together healthcare providers, community groups and consumers to share information. The Health Expo demonstrated and reinforced inter-organizational working and community outreach, providing consumers with community-based information and linkages. Support and resources need to be offered for developing community inter-organizational networks, thereby building consumer capacity for self-management in the community. © The Author (2014). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  6. Neighborhoods and adolescent health-risk behavior: an ecological network approach.

    Science.gov (United States)

    Browning, Christopher R; Soller, Brian; Jackson, Aubrey L

    2015-01-01

    This study integrates insights from social network analysis, activity space perspectives, and theories of urban and spatial processes to present an novel approach to neighborhood effects on health-risk behavior among youth. We suggest spatial patterns of neighborhood residents' non-home routines may be conceptualized as ecological, or "eco"-networks, which are two-mode networks that indirectly link residents through socio-spatial overlap in routine activities. We further argue structural configurations of eco-networks are consequential for youth's behavioral health. In this study we focus on a key structural feature of eco-networks--the neighborhood-level extent to which household dyads share two or more activity locations, or eco-network reinforcement--and its association with two dimensions of health-risk behavior, substance use and delinquency/sexual activity. Using geographic data on non-home routine activity locations among respondents from the Los Angeles Family and Neighborhood Survey (L.A.FANS), we constructed neighborhood-specific eco-networks by connecting sampled households to "activity clusters," which are sets of spatially-proximate activity locations. We then measured eco-network reinforcement and examined its association with dimensions of adolescent health risk behavior employing a sample of 830 youth ages 12-17 nested in 65 census tracts. We also examined whether neighborhood-level social processes (collective efficacy and intergenerational closure) mediate the association between eco-network reinforcement and the outcomes considered. Results indicated eco-network reinforcement exhibits robust negative associations with both substance use and delinquency/sexual activity scales. Eco-network reinforcement effects were not explained by potential mediating variables. In addition to introducing a novel theoretical and empirical approach to neighborhood effects on youth, our findings highlight the importance of intersecting conventional routines for

  7. A Simple Network to Remove Interference in Surface EMG Signal from Single Gene Affected Phenylketonuria Patients for Proper Diagnosis

    Science.gov (United States)

    Mohanty, Madhusmita; Basu, Mousumi; Pattanayak, Deba Narayan; Mohapatra, Sumant Kumar

    2018-01-01

    Recently Autosomal Recessive Single Gene (ARSG) diseases are highly effective to the children within the age of 5-10 years. One of the most ARSG disease is a Phenylketonuria (PKU). This single gene disease is associated with mutations in the gene that encodes the enzyme phenylalanine hydroxylase (PAH, Gene 612349). Through this mutation process, PAH of the gene affected patient can not properly manufacture PAH as a result the patients suffer from decreased muscle tone which shows abnormality in EMG signal. Here the extraction of the quality of the PKU affected EMG (PKU-EMG) signal is a keen interest, so it is highly necessary to remove the added ECG signal as well as the biological and instrumental noises. In the Present paper we proposed a method for detection and classification of the PKU affected EMG signal. Here Discrete Wavelet Transformation is implemented for extraction of the features of the PKU affected EMG signal. Adaptive Neuro-Fuzzy Inference System (ANFIS) network is used for the classification of the signal. Modified Particle Swarm Optimization (MPSO) and Modified Genetic Algorithm (MGA) are used to train the ANFIS network. Simulation result shows that the proposed method gives better performance as compared to existing approaches. Also it gives better accuracy of 98.02% for the detection of PKU-EMG signal. The advantages of the proposed model is to use MGA and MPSO to train the parameters of ANFIS network for classification of ECG and EMG signal of PKU affected patients. The proposed method obtained the high SNR (18.13 ± 0.36 dB), SNR (0.52 ± 1.62 dB), RE (0.02 ± 0.32), MSE (0.64 ± 2.01), CC (0.99 ± 0.02), RMSE (0.75 ± 0.35) and MFRE (0.01 ± 0.02), RMSE (0.75 ± 0.35) and MFRE (0.01 ± 0.02). From authors knowledge, this is the first time a composite method is used for diagnosis of PKU affected patients. The accuracy (98.02%), sensitivity (100%) and specificity (98.59%) helps for proper clinical treatment. It can help for readers

  8. Real-Time Monitoring and Fault Diagnosis of a Low Power Hub Motor Using Feedforward Neural Network

    Science.gov (United States)

    Şimşir, Mehmet; Bayır, Raif; Uyaroğlu, Yılmaz

    2016-01-01

    Low power hub motors are widely used in electromechanical systems such as electrical bicycles and solar vehicles due to their robustness and compact structure. Such systems driven by hub motors (in wheel motors) encounter previously defined and undefined faults under operation. It may inevitably lead to the interruption of the electromechanical system operation; hence, economic losses take place at certain times. Therefore, in order to maintain system operation sustainability, the motor should be precisely monitored and the faults are diagnosed considering various significant motor parameters. In this study, the artificial feedforward backpropagation neural network approach is proposed to real-time monitor and diagnose the faults of the hub motor by measuring seven main system parameters. So as to construct a necessary model, we trained the model, using a data set consisting of 4160 samples where each has 7 parameters, by the MATLAB environment until the best model is obtained. The results are encouraging and meaningful for the specific motor and the developed model may be applicable to other types of hub motors. The prosperous model of the whole system was embedded into Arduino Due microcontroller card and the mobile real-time monitoring and fault diagnosis system prototype for hub motor was designed and manufactured. PMID:26819590

  9. Computer-aided diagnosis of mammography using an artificial neural network: predicting the invasiveness of breast cancers from image features

    Science.gov (United States)

    Lo, Joseph Y.; Kim, Jeffrey; Baker, Jay A.; Floyd, Carey E., Jr.

    1996-04-01

    The study aim is to develop an artificial neural network (ANN) for computer-aided diagnosis of mammography. Using 9 mammographic image features and patient age, the ANN predicted whether breast lesions were benign, invasive malignant, or noninvasive malignant. Given only 97 malignant patients, the 3-layer backpropagation ANN successfully predicted the invasiveness of those breast cancers, performing with Az of 0.88 plus or minus 0.03. To determine more generalized clinical performance, a different ANN was developed using 266 consecutive patients (97 malignant, 169 benign). This ANN predicted whether those patients were benign or noninvasive malignant vs. invasive malignant with Az of 0.86 plus or minus 0.03. This study is unique because it is the first to predict the invasiveness of breast cancers using mammographic features and age. This knowledge, which was previously available only through surgical biopsy, may assist in the planning of surgical procedures for patients with breast lesions, and may help reduce the cost and morbidity associated with unnecessary surgical biopsies.

  10. Real-Time Monitoring and Fault Diagnosis of a Low Power Hub Motor Using Feedforward Neural Network

    Directory of Open Access Journals (Sweden)

    Mehmet Şimşir

    2016-01-01

    Full Text Available Low power hub motors are widely used in electromechanical systems such as electrical bicycles and solar vehicles due to their robustness and compact structure. Such systems driven by hub motors (in wheel motors encounter previously defined and undefined faults under operation. It may inevitably lead to the interruption of the electromechanical system operation; hence, economic losses take place at certain times. Therefore, in order to maintain system operation sustainability, the motor should be precisely monitored and the faults are diagnosed considering various significant motor parameters. In this study, the artificial feedforward backpropagation neural network approach is proposed to real-time monitor and diagnose the faults of the hub motor by measuring seven main system parameters. So as to construct a necessary model, we trained the model, using a data set consisting of 4160 samples where each has 7 parameters, by the MATLAB environment until the best model is obtained. The results are encouraging and meaningful for the specific motor and the developed model may be applicable to other types of hub motors. The prosperous model of the whole system was embedded into Arduino Due microcontroller card and the mobile real-time monitoring and fault diagnosis system prototype for hub motor was designed and manufactured.

  11. Detection and diagnosis of a natural gas dehydration plant by absorption with triethylene glycol, employing a artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2004-07-01

    The natural gas dehydration is a great importance operation in the gas and petroleum industry, It avoids operational problems associated with the water content, which appear frequently in the industrial facilities that use the natural gas as raw material or as work tool. Due to the presence of undesirable pollutants which may enter the plant with the wet natural gas current (lubricating, corrosion inhibitors, salts, and others), the equipment that constitutes the dehydration plants are capable to suffering operational faults as the heat exchangers fouling, foam formation in the absorber, glycol losses for dragging; trays, packings, valves and filters fouling; glycol degradation, inadequate temperatures of regeneration and others. The above mentioned faults often cannot be detected by the operators and engineers but up to the moment when a catastrophic damage occurs or when products are obtained out of specification, which causes big economic and time losses. By means of the application of artificial neural networks, there was achieved the detection and the effective diagnosis of faults, still in incipient state, in a gas dehydration plant. (author)

  12. Fractality and a wavelet-chaos-neural network methodology for EEG-based diagnosis of autistic spectrum disorder.

    Science.gov (United States)

    Ahmadlou, Mehran; Adeli, Hojjat; Adeli, Amir

    2010-10-01

    A method is presented for investigation of EEG of children with autistic spectrum disorder using complexity and chaos theory with the goal of discovering a nonlinear feature space. Fractal Dimension is proposed for investigation of complexity and dynamical changes in autistic spectrum disorder in brain. Two methods are investigated for computation of fractal dimension: Higuchi's Fractal Dimension and Katz's Fractal Dimension. A wavelet-chaos-neural network methodology is presented for automated EEG-based diagnosis of autistic spectrum disorder. The model is tested on a database of eyes-closed EEG data obtained from two groups: nine autistic spectrum disorder children, 6 to 13 years old, and eight non-autistic spectrum disorder children, 7 to 13 years old. Using a radial basis function classifier, an accuracy of 90% was achieved based on the most significant features discovered via analysis of variation statistical test, which are three Katz's Fractal Dimensions in delta (of loci Fp2 and C3) and gamma (of locus T6) EEG sub-bands with P < 0.001.

  13. Real-Time Monitoring and Fault Diagnosis of a Low Power Hub Motor Using Feedforward Neural Network.

    Science.gov (United States)

    Şimşir, Mehmet; Bayır, Raif; Uyaroğlu, Yılmaz

    2016-01-01

    Low power hub motors are widely used in electromechanical systems such as electrical bicycles and solar vehicles due to their robustness and compact structure. Such systems driven by hub motors (in wheel motors) encounter previously defined and undefined faults under operation. It may inevitably lead to the interruption of the electromechanical system operation; hence, economic losses take place at certain times. Therefore, in order to maintain system operation sustainability, the motor should be precisely monitored and the faults are diagnosed considering various significant motor parameters. In this study, the artificial feedforward backpropagation neural network approach is proposed to real-time monitor and diagnose the faults of the hub motor by measuring seven main system parameters. So as to construct a necessary model, we trained the model, using a data set consisting of 4160 samples where each has 7 parameters, by the MATLAB environment until the best model is obtained. The results are encouraging and meaningful for the specific motor and the developed model may be applicable to other types of hub motors. The prosperous model of the whole system was embedded into Arduino Due microcontroller card and the mobile real-time monitoring and fault diagnosis system prototype for hub motor was designed and manufactured.

  14. College Health Surveillance Network: Epidemiology and Health Care Utilization of College Students at US 4-Year Universities

    Science.gov (United States)

    Turner, James C.; Keller, Adrienne

    2015-01-01

    Objective: This description of the College Health Surveillance Network (CHSN) includes methodology, demography, epidemiology, and health care utilization. Participants: Twenty-three universities representing approximately 730,000 enrolled students contributed data from January 1, 2011, through May 31, 2014. Methods: Participating schools uploaded…

  15. Socioeconomic Factors, Health Behavior, and Late-Stage Diagnosis of Breast Cancer: Considering the Impact of Delay in Diagnosis.

    Science.gov (United States)

    Dianatinasab, Mostafa; Mohammadianpanah, Mohammad; Daneshi, Nima; Zare-Bandamiri, Mohammad; Rezaeianzadeh, Abbas; Fararouei, Mohammad

    2017-09-19

    Stage of cancer at diagnosis is one of the most important factors in patient prognosis. By controlling for diagnostic delay, this study aimed to identify factors associated with late-stage breast cancer (BC). From November 2014 to January 2017, required information on 497 patients who were newly diagnosed with BC was obtained from patients' medical records. Logistic regression was used to measure the association between cancer stage and study variables. Only 18.3% of patients were diagnosed at stage I. The rest were diagnosed at stage II (45.5%) or higher (36.2%). Among those with ≤ 3 months' diagnostic delay, age (odds ratio [OR] = 0.96; 95% confidence interval [CI], 0.93-0.99), place of residence (OR urban/rural = 1.72; 95% CI, 1.42-1.93), income (OR high/low = 0.27; 95% CI, 0.10-0.72), performing breast self-examination (OR yes/no = 0.51; 95% CI, 0.0.26 -0.98), smoking (OR yes/no = 2.23; 95% CI, 1.37-3.62), history of chest X-ray (OR yes/no = 1.40; 95% CI, 1.16-1.98), presence of chronic diseases (OR yes/no = 1.73; 95% CI, 1.36-5.48), and, for those with a delay of > 3 months, marriage age (OR = 0.83; 95% CI, 0.73-0.94), income (OR high/low = 0.07; 95% CI, 0.008-0.63), family history of BC (OR = 3.82; 95% CI, 1.05-5.05), daily exercise (OR < 10/10-20 = 0.10; 95% CI, 0.01-0.67), and presence of chronic diseases (OR yes/no = 1.77; 95% CI, 1.73-5.07), were associated with late-stage of cancer. Shortening the diagnostic delay can help patients receive medical treatment at an earlier disease stage, resulting in better prognosis. Smokers, younger women, and those with chronic conditions or a family history of BC should take extra caution, as they may have worse prognosis if diagnosed with cancer. Copyright © 2017 Elsevier Inc. All rights reserved.

  16. Diabetes prevalence and diagnosis in US states: analysis of health surveys

    Directory of Open Access Journals (Sweden)

    Oza Shefali

    2009-09-01

    Full Text Available Abstract Background Current US surveillance data provide estimates of diabetes using laboratory tests at the national level as well as self-reported data at the state level. Self-reported diabetes prevalence may be biased because respondents may not be aware of their risk status. Our objective was to estimate the prevalence of diagnosed and undiagnosed diabetes by state. Methods We estimated undiagnosed diabetes prevalence as a function of a set of health system and sociodemographic variables using a logistic regression in the National Health and Nutrition Examination Survey (2003-2006. We applied this relationship to identical variables from the Behavioral Risk Factor Surveillance System (2003-2007 to estimate state-level prevalence of undiagnosed diabetes by age group and sex. We assumed that those who report being diagnosed with diabetes in both surveys are truly diabetic. Results The prevalence of diabetes in the U.S. was 13.7% among men and 11.7% among women ≥ 30 years. Age-standardized diabetes prevalence was highest in Mississippi, West Virginia, Louisiana, Texas, South Carolina, Alabama, and Georgia (15.8 to 16.6% for men and 12.4 to 14.8% for women. Vermont, Minnesota, Montana, and Colorado had the lowest prevalence (11.0 to 12.2% for men and 7.3 to 8.4% for women. Men in all states had higher diabetes prevalence than women. The absolute prevalence of undiagnosed diabetes, as a percent of total population, was highest in New Mexico, Texas, Florida, and California (3.5 to 3.7 percentage points and lowest in Montana, Oklahoma, Oregon, Alaska, Vermont, Utah, Washington, and Hawaii (2.1 to 3 percentage points. Among those with no established diabetes diagnosis, being obese, being Hispanic, not having insurance and being ≥ 60 years old were significantly associated with a higher risk of having undiagnosed diabetes. Conclusion Diabetes prevalence is highest in the Southern and Appalachian states and lowest in the Midwest and the Northeast

  17. Sexual Health Promotion on Social Networking Sites: A Process Evaluation of the FaceSpace Project

    National Research Council Canada - National Science Library

    Nguyen, Phuong; Gold, Judy; Pedrana, Alisa; Chang, Shanton; Howard, Steve; Ilic, Olivia; Hellard, Margaret; Stoove, Mark

    2013-01-01

    Purpose: This article reports findings from an evaluation of reach and engagement of The FaceSpace Project, a novel sexual health promotion project delivered through social networking sites that targeted...

  18. Client satisfaction in a faith-based health network: findings from a ...

    African Journals Online (AJOL)

    Client satisfaction in a faith-based health network: findings from a survey in Uganda. Constance Sibongile Shumba, Kenneth Kabali, Jonathan Miyonga, Jairus Mugadu, Luke Lakidi, Patrick Kerchan, Tonny Tumwesigye ...

  19. Social network as a determinant of pathway to mental health service ...

    African Journals Online (AJOL)

    14,15] and the perceived or actual reactions of ... of perceived support. Both network and support can act as coping resources.[20,21] While some ..... disability and service utilisation. Overview of the. Australian National Mental Health Survey.

  20. Open for business: private networks create a marketplace for health information exchange.

    Science.gov (United States)

    Dimick, Chris

    2012-05-01

    Large health systems and their IT vendors are creating private information exchange networks at a time when federally funded state operations are gearing up for launch, Is there room for private and public offerings in the new HIE marketplace?

  1. A conceptual review on systems biology in health and diseases: from biological networks to modern therapeutics.

    Science.gov (United States)

    Somvanshi, Pramod Rajaram; Venkatesh, K V

    2014-03-01

    Human physiology is an ensemble of various biological processes spanning from intracellular molecular interactions to the whole body phenotypic response. Systems biology endures to decipher these multi-scale biological networks and bridge the link between genotype to phenotype. The structure and dynamic properties of these networks are responsible for controlling and deciding the phenotypic state of a cell. Several cells and various tissues coordinate together to generate an organ level response which further regulates the ultimate physiological state. The overall network embeds a hierarchical regulatory structure, which when unusually perturbed can lead to undesirable physiological state termed as disease. Here, we treat a disease diagnosis problem analogous to a fault diagnosis problem in engineering systems. Accordingly we review the application of engineering methodologies to address human diseases from systems biological perspective. The review highlights potential networks and modeling approaches used for analyzing human diseases. The application of such analysis is illustrated in the case of cancer and diabetes. We put forth a concept of cell-to-human framework comprising of five modules (data mining, networking, modeling, experimental and validation) for addressing human physiology and diseases based on a paradigm of system level analysis. The review overtly emphasizes on the importance of multi-scale biological networks and subsequent modeling and analysis for drug target identification and designing efficient therapies.

  2. Identifying public health policymakers' sources of information: comparing survey and network analyses.

    Science.gov (United States)

    Oliver, Kathryn A; de Vocht, Frank; Money, Annemarie; Everett, Martin

    2017-05-01

    Research suggests that policymakers often use personal contacts to find information and advice. However, the main sources of information for public health policymakers are not known. This study aims to describe policymakers' sources of information. A questionnaire survey of public health policymakers across Greater Manchester (GM) was carried out (response rate 48%). All policy actors above Director level involved in public health policy (finding, analyzing or producing information, producing or implementing policy) in GM were included in the sampling frame. Respondents were provided with a list of sources of information and asked which they used (categorical data) and to name specific individuals who acted as sources of information (network data). Data were analyzed using frequencies and network analysis. The most frequently chosen sources of information from the categorical data were NICE, government websites and Directors of Public Health. However, the network data showed that the main sources of information in the network were actually mid-level managers in the NHS, who had no direct expertise in public health. Academics and researchers did not feature in the network. Both survey and network analyses provide useful insights into how policymakers access information. Network analysis offers practical and theoretical contributions to the evidence-based policy debate. Identifying individuals who act as key users and producers of evidence allows academics to target actors likely to use and disseminate their work.

  3. Utilizing social networking sites to promote adolescents' health: a pragmatic review of the literature.

    Science.gov (United States)

    Francomano, Jesse A; Harpin, Scott B

    2015-01-01

    Social networking site use has exploded among youth in the last few years and is being adapted as an important tool for healthcare interventions and serving as a platform for adolescents to gain access to health information. The aim of this study was to examine the strengths, weaknesses, and best practices of utilizing Facebook in adolescent health promotion and research via pragmatic literature review. We also examine how sites can facilitate ethically sound healthcare for adolescents, particularly at-risk youth. We conducted a literature review of health and social sciences literature from the past 5 years related to adolescent health and social network site use. Publications were grouped by shared content then categorized by themes. Five themes emerged: access to healthcare information, peer support and networking, risk and benefits of social network site use in care delivery, overcoming technological barriers, and social network site interventions. More research is needed to better understand how such Web sites can be better utilized to provide access to adolescents seeking healthcare. Given the broad reach of social network sites, all health information must be closely monitored for accurate, safe distribution. Finally, consent and privacy issues are omnipresent in social network sites, which calls for standards of ethical use.

  4. Gel network shampoo formulation and hair health benefits.

    Science.gov (United States)

    Marsh, J M; Brown, M A; Felts, T J; Hutton, H D; Vatter, M L; Whitaker, S; Wireko, F C; Styczynski, P B; Li, C; Henry, I D

    2017-10-01

    The objective of this work was to create a shampoo formula that contains a stable ordered gel network structure that delivers fatty alcohols inside hair. X-ray diffraction (SAXS and WAXS), SEM and DSC have been used to confirm formation of the ordered Lβ gel network with fatty alcohol (cetyl and stearyl alcohols) and an anionic surfactant (SLE1S). Micro-autoradiography and extraction methods using GC-MS were used to confirm penetration of fatty alcohols into hair, and cyclic fatigue testing was used to measure hair strength. In this work, evidence of a stable Lβ ordered gel network structure created from cetyl and stearyl alcohols and anionic surfactant (SLE1S) is presented, and this is confirmed via scanning electron microscopy images showing lamella layers and differential scanning calorimetry (DSC) showing new melting peaks vs the starting fatty alcohols. Hair washed for 16 repeat cycles with this shampoo showed penetration of fatty alcohols from the gel network into hair as confirmed by a differential extraction method with GC-MS and by radiolabelling of stearyl alcohol and showing its presence inside hair cross-sections. The gel network role in delivering fatty alcohol inside hair is demonstrated by comparing with a shampoo with added fatty alcohol not in an ordered gel network structure. The hair containing fatty alcohol was measured via the Dia-stron cyclic fatigue instrument and showed a significantly higher number of cycles to break vs control. The formation of a stable gel network was confirmed in the formulated shampoo, and it was demonstrated that this gel network is important to deliver cetyl and stearyl alcohols into hair. The presence of fatty alcohol inside hair was shown to deliver a hair strength benefit via cyclic fatigue testing. © 2017 Society of Cosmetic Scientists and the Société Française de Cosmétologie.

  5. Social Networks and Social Support in Health Promotion Programmes

    OpenAIRE

    Donev, Doncho; Pavlekovic, Gordana; Zaletel Kragelj, Lijana

    2008-01-01

    Social networks and social support are general terms to describe different aspects of social relationships, including those mechanisms, which may protect the individual from the negative effects of stress. The social support is offered by the part of the social network, the people around us, that are ready to help us, and on whose help we can always count. Those enjoying strong social ties appear to be at low risk of psychosocial and physical impairment, whereas a lack of social support has b...

  6. Using Patient Pathway Analysis to Design Patient-centered Referral Networks for Diagnosis and Treatment of Tuberculosis: The Case of the Philippines.

    Science.gov (United States)

    Garfin, Celine; Mantala, Mariquita; Yadav, Rajendra; Hanson, Christy L; Osberg, Mike; Hymoff, Aaron; Makayova, Julia

    2017-11-06

    Tuberculosis (TB) is the 8th leading cause of death in the Philippines. A recent prevalence survey found that there were nearly 70% more cases of tuberculosis than previously estimated. Given these new data, the National TB Program (NTP), operating through a decentralized health system, identified about 58% of the estimated new drug-sensitive (DS) TB patients in 2016. However, the NTP only identified and commenced treatment for around 17% of estimated new drug-resistant patients. In order to reach the remaining 42% of drug-sensitive patients and 83% of drug-resistant patients, it is necessary to develop a better understanding of where patients seek care. National and regional patient pathway analyses (PPAs) were undertaken using existing national survey and NTP data. The PPA assessed the alignment between patient care seeking and the availability of TB diagnostic and treatment services. Systemic referral networks from the community-level Barangay Health Stations (BHSs) to diagnostic facilities have enabled more efficient detection of drug-sensitive tuberculosis in the public sector. Approximately 36% of patients initiated care in the private sector, where there is limited coverage of appropriate diagnostic technologies. Important differences in the alignment between care seeking patterns and diagnostic and treatment availability were found between regions. The PPA identified opportunities for strengthening access to care for all forms of tuberculosis and for accelerating the time to diagnosis by aligning services to where patients initiate care. Geographic variations in care seeking may guide prioritization of some regions for intensified engagement with the private sector.

  7. Mental Health Service and Drug Treatment Utilization: Adolescents with Substance Use/Mental Disorders and Dual Diagnosis

    Science.gov (United States)

    Cheng, Tyrone C.; Lo, Celia C.

    2010-01-01

    This research is a secondary data analysis of the impact of adolescents' mental/substance-use disorders and dual diagnosis on their utilization of drug treatment and mental health services. By analyzing the same teenagers who participated in the NIMH Methods for the Epidemiology of Child and Adolescent Mental Disorders (MECA) study, logistic…

  8. Mental Health: Knowledge, Attitudes and Training of Professionals on Dual Diagnosis of Intellectual Disability and Psychiatric Disorder

    Science.gov (United States)

    Werner, S.; Stawski, M.

    2012-01-01

    Background: Dual diagnosis (DD) refers to the coexistence of intellectual disability and psychiatric disorder. In order to provide individuals with DD with adequate care, it is essential for mental health workers to have adequate knowledge and positive attitudes. These may be achieved through proper training. Aims: To summarise the available…

  9. Newly diagnosed psychogenic nonepileptic seizures: health care demand prior to and following diagnosis at a first seizure clinic.

    Science.gov (United States)

    Razvi, Saif; Mulhern, Sharon; Duncan, Roderick

    2012-01-01

    Patients with psychogenic nonepileptic seizures (PNES) are heavy users of emergency and nonemergency health care. We performed a 1-year prospective audit of use of a group of PNES-related health care items in patients with newly diagnosed (mean duration: 7.3 months) PNES from PNES onset to diagnosis and from diagnosis to 6 months postdiagnosis. Twenty-eight patients (20 women, age: 34±16 years) were responsible for 14 general practitioner home visits, 31 ambulance calls, 34 emergency department visits, 21 hospital admissions (66 inpatient days), 8 MRI scans, 24 CT scans, 2 standard EEGs, 28 short video EEG recordings, and 5 ambulatory EEG recordings. In the 6 months following diagnosis, there were 2 emergency department visits (94.1% reduction), no hospital admissions (100% reduction), 2 ambulance calls, no general practitioner visits, 1 MRI scan, and no CT scans or EEGs. The immediacy of this marked health care demand reduction suggests that the relationship between presentation of diagnosis and health care demand reduction is causal. Copyright © 2011 Elsevier Inc. All rights reserved.

  10. [Communication management of collaborative networks of science, technology and innovation in health].

    Science.gov (United States)

    Martins, Wagner de Jesus; Artmann, Elizabeth; Rivera, Francisco Javier Uribe

    2012-12-01

    The objective of the article was to propose a model of communication management of networks for the Health Innovation System in Brazil. The health production complex and its relationship with the nation's development are addressed and some suggestions for operationalization of the proposed model are also presented. The discussion is based on Habermas' theory and similar cases from other countries. Communication strategies and approaches to commitment dialogue for concerted actions and consensus-building based on critical reasoning may help strengthen democratic networks.

  11. Making "social" safer: are Facebook and other online networks becoming less hazardous for health professionals?

    Science.gov (United States)

    George, Daniel R

    2012-01-01

    Major concerns about privacy have limited health professionals' usage of popular social networking sites such as Facebook. However, the landscape of social media is changing in favor of more sophisticated privacy controls that enable users to more carefully manage public and private information. This evolution in technology makes it potentially less hazardous for health professionals to consider accepting colleagues and patients into their online networks, and invites medicine to think constructively about how social media may add value to contemporary healthcare.

  12. Health risk to non-ionizing radiation by the telecommunications networks in Peru

    OpenAIRE

    Cruz, Víctor M.; Salud ambiental; Radiación no ionizante; Red de telecomunicaciones, Teléfono celular, Perú.

    2009-01-01

    We review the various studies on the potential impact of telecommunications networks on health, these studies relate to the possible health effects due to thermal effect of non-ionizing radiation produced greater increases in body temperature at 1 ºC. f urthermore, the studies were reviewed for assessment of exposure to non-ionizing radiation of telecommunications networks in Peru from 2000 to 2006 that include the measurement of more than 500 locations. The highest average levels of exposure...

  13. A Network Based Theory of Health Systems and Cycles of Well-being.

    Science.gov (United States)

    Rhodes, Michael Grant

    2013-06-01

    There are two dominant approaches to describe and understand the anatomy of complete health and well-being systems internationally. Yet, neither approach has been able to either predict or explain occasional but dramatic crises in health and well-being systems around the world and in developed emerging market or developing country contexts. As the impacts of such events can be measured not simply in terms of their social and economic consequences but also public health crises, there is a clear need to look for and formulate an alternative approach. This paper examines multi-disciplinary theoretical evidence to suggest that health systems exhibit natural and observable systemic and long cycle characteristics that can be modelled. A health and well-being system model of two slowly evolving anthropological network sub-systems is defined. The first network sub-system consists of organised professional networks of exclusive suppliers of health and well-being services. The second network sub-system consists of communities organising themselves to resource those exclusive services. Together these two network sub-systems interact to form the specific (sovereign) health and well-being systems we know today. But the core of a truly 'complex adaptive system' can also be identified and a simplified two sub-system model of recurring Lotka-Volterra predator-prey cycles is specified. The implications of such an adaptive and evolving model of system anatomy for effective public health, social security insurance and well-being systems governance could be considerable.

  14. Optimizing network connectivity for mobile health technologies in sub-Saharan Africa.

    Science.gov (United States)

    Siedner, Mark J; Lankowski, Alexander; Musinga, Derrick; Jackson, Jonathon; Muzoora, Conrad; Hunt, Peter W; Martin, Jeffrey N; Bangsberg, David R; Haberer, Jessica E

    2012-01-01

    Mobile health (mHealth) technologies hold incredible promise to improve healthcare delivery in resource-limited settings. Network reliability across large catchment areas can be a major challenge. We performed an analysis of network failure frequency as part of a study of real-time adherence monitoring in rural Uganda. We hypothesized that the addition of short messaging service (SMS+GPRS) to the standard cellular network modality (GPRS) would reduce network disruptions and improve transmission of data. Participants were enrolled in a study of real-time adherence monitoring in southwest Uganda. In June 2011, we began using Wisepill devices that transmit data each time the pill bottle is opened. We defined network failures as medication interruptions of >48 hours duration that were transmitted when network connectivity was re-established. During the course of the study, we upgraded devices from GPRS to GPRS+SMS compatibility. We compared network failure rates between GPRS and GPRS+SMS periods and created geospatial maps to graphically demonstrate patterns of connectivity. One hundred fifty-seven participants met inclusion criteria of seven days of SMS and seven days of SMS+GPRS observation time. Seventy-three percent were female, median age was 40 years (IQR 33-46), 39% reported >1-hour travel time to clinic and 17% had home electricity. One hundred one had GPS coordinates recorded and were included in the geospatial maps. The median number of network failures per person-month for the GPRS and GPRS+SMS modalities were 1.5 (IQR 1.0-2.2) and 0.3 (IQR 0-0.9) respectively, (mean difference 1.2, 95%CI 1.0-1.3, p-valueGPRS cellular network connectivity can significantly reduce network connection failures for mobile health applications in remote areas. Projects depending on mobile health data in resource-limited settings should consider this upgrade to optimize mHealth applications.

  15. Wearable sensors network for health monitoring using e-Health platform

    Directory of Open Access Journals (Sweden)

    I. Orha

    2014-06-01

    Full Text Available In this paper we have proposed to present a wearable system for automatic recording of the main physiological parameters of the human body: body temperature, galvanic skin response, respiration rate, blood pressure, pulse, blood oxygen content, blood glucose content, electrocardiogram (ECG, electromyography(EMG, and patient position. To realize this system, we have developed a program that can read and automatically save in a file, the data from specialized sensors. The results can be later interpreted, by comparing them with known normal values and thus offering the possibility for a primary health status diagnosis by specialized personnel. The data received from the wearable sensors is taken by an interface circuit, provided with signal conditioning (filtering, amplification, etc. A microcontroller controls the data acquisition. In this applications we used an Arduino Uno standard development platform. The data are transferred to a PC, using serial communication port of Arduino platform and a communications shield. The whole process of health assessment is commissioned by a program developed by us in the Python programming language. The program provides automatic recording of the aforementioned parameters in a predetermined sequence, or only certain parameters are registered.

  16. Identifying emergent social networks at a federally qualified health center-based farmers' market.

    Science.gov (United States)

    Alia, Kassandra A; Freedman, Darcy A; Brandt, Heather M; Browne, Teri

    2014-06-01

    Identifying potential mechanisms connecting farmers' market interventions with health, economic, and community outcomes could inform strategies for addressing health disparities. The present study used social network theory to guide the in-depth examination of naturally occurring social interactions at a farmers' market located at a federally qualified health center located in a rural, low-income community. Trained observers recorded 61 observation logs at the market over 18 weeks. Thematic analysis revealed a range of actors and nonhuman facilitators instrumental to the farmers' market context. These actors connected with one another for communication and relationship development, economic and financial exchange, education, resource sharing, community ownership of the farmers' market, and conflict resolution. These interactions provided opportunities for social networks to develop among attendees, which may have facilitated the acquisition of social supports related to improved health, economic and community outcomes. Results provide insight into the role social networks may play in mediating the relationship between a farmers' market intervention and individual benefits. Findings also contribute to defining the typology of social networks, which may further disentangle the complex relationships between social networks and health outcomes. Future research should identify strategies for purposefully targeting social networks as a way to reduce diet-related health disparities.

  17. Father Absence, Social Networks, and Maternal Ratings of Child Health: Evidence from the 2013 Social Networks and Health Information Survey in Mexico.

    Science.gov (United States)

    Edelblute, Heather B; Altman, Claire E

    2018-01-19

    Objectives To bridge the literature on the effect of father absence, international migration, and social networks on child health, we assess the association between father absence and maternal ratings of child poor health (MCPH). Next we test whether social networks of immediate and extended kin mediate the relationship between fathers' absence and MCPH. Methods Nested logistic regression models predicting MCPH are estimated using the 2013 Social Networks and Health Information Survey, collected in a migrant-sending community in Guanajuato, Mexico. These unique data distinguish among father absence due to migration versus other reasons and between immediate and extended kin ties. Results Descriptive results indicate that 25% of children with migrant fathers are assessed as having poor health, more often than children with present (15.5%) or otherwise absent fathers (17.5%). In the multivariate models, fathers' absence is not predictive of MCPH. However, the presence of extended kin ties for the mother was associated with approximately a 50% reduction in the odds of MCPH. Additionally, mother's poor self-assessed health was associated with increased odds of MCPH while the presence of a co-resident adult lowered the odds of MCPH. In sensitivity analysis among children with migrant fathers, the receipt of paternal remittances lowered the odds of MCPH. Conclusions for Practice Social networks have a direct and positive association with MCPH rather than mediating the father absence-MCPH relationship. The presence of extended kin ties in the local community is salient for more favorable child health and should be considered in public health interventions aimed at improving child health.

  18. The accuracy of burn diagnosis codes in health administrative data: A validation study.

    Science.gov (United States)

    Mason, Stephanie A; Nathens, Avery B; Byrne, James P; Fowler, Rob; Gonzalez, Alejandro; Karanicolas, Paul J; Moineddin, Rahim; Jeschke, Marc G

    2017-03-01

    Health administrative databases may provide rich sources of data for the study of outcomes following burn. We aimed to determine the accuracy of International Classification of Diseases diagnoses codes for burn in a population-based administrative database. Data from a regional burn center's clinical registry of patients admitted between 2006-2013 were linked to administrative databases. Burn total body surface area (TBSA), depth, mechanism, and inhalation injury were compared between the registry and administrative records. The sensitivity, specificity, and positive and negative predictive values were determined, and coding agreement was assessed with the kappa statistic. 1215 burn center patients were linked to administrative records. TBSA codes were highly sensitive and specific for ≥10 and ≥20% TBSA (89/93% sensitive and 95/97% specific), with excellent agreement (κ, 0.85/κ, 0.88). Codes were weakly sensitive (68%) in identifying ≥10% TBSA full-thickness burn, though highly specific (86%) with moderate agreement (κ, 0.46). Codes for inhalation injury had limited sensitivity (43%) but high specificity (99%) with moderate agreement (κ, 0.54). Burn mechanism had excellent coding agreement (κ, 0.84). Administrative data diagnosis codes accurately identify burn by burn size and mechanism, while identification of inhalation injury or full-thickness burns is less sensitive but highly specific. Copyright © 2016 Elsevier Ltd and ISBI. All rights reserved.

  19. [psychenet - Hamburg Network for Mental Health: Results of the Process Evaluation].

    Science.gov (United States)

    Makowski, Anna C; Mnich, Eva E; Kofahl, Christopher; von dem Knesebeck, Olaf

    2015-07-01

    Aspects of implementation, functionality, acceptability and sustainability of the network psychenet - Hamburg network for mental health were examined.In March 2012 and September 2013, 19 expert interviews were carried out with leaders of subprojects and representatives of insurances, public authorities and patients.The complexity of the network was hindering. Positive aspects were communication, clear hierarchies and qualified staff.The implementation of a complex network requires shared goals. The establishment of a steering committee has proved as crucial. © Georg Thieme Verlag KG Stuttgart · New York.

  20. How universal is coverage and access to diagnosis and treatment for Chagas disease in Colombia? A health systems analysis.

    Science.gov (United States)

    Cucunubá, Zulma M; Manne-Goehler, Jennifer M; Díaz, Diana; Nouvellet, Pierre; Bernal, Oscar; Marchiol, Andrea; Basáñez, María-Gloria; Conteh, Lesong

    2017-02-01

    Limited access to Chagas disease diagnosis and treatment is a major obstacle to reaching the 2020 World Health Organization milestones of delivering care to all infected and ill patients. Colombia has been identified as a health system in transition, reporting one of the highest levels of health insurance coverage in Latin America. We explore if and how this high level of coverage extends to those with Chagas disease, a traditionally marginalised population. Using a mixed methods approach, we calculate coverage for screening, diagnosis and treatment of Chagas. We then identify supply-side constraints both quantitatively and qualitatively. A review of official registries of tests and treatments for Chagas disease delivered between 2008 and 2014 is compared to estimates of infected people. Using the Flagship Framework, we explore barriers limiting access to care. Screening coverage is estimated at 1.2% of the population at risk. Aetiological treatment with either benznidazol or nifurtimox covered 0.3-0.4% of the infected population. Barriers to accessing screening, diagnosis and treatment are identified for each of the Flagship Framework's five dimensions of interest: financing, payment, regulation, organization and persuasion. The main challenges identified were: a lack of clarity in terms of financial responsibilities in a segmented health system, claims of limited resources for undertaking activities particularly in primary care, non-inclusion of confirmatory test(s) in the basic package of diagnosis and care, poor logistics in the distribution and supply chain of medicines, and lack of awareness of medical personnel. Very low screening coverage emerges as a key obstacle hindering access to care for Chagas disease. Findings suggest serious shortcomings in this health system for Chagas disease, despite the success of universal health insurance scale-up in Colombia. Whether these shortcomings exist in relation to other neglected tropical diseases needs investigating

  1. Social networks, mental health problems, and mental health service utilization in OEF/OIF National Guard veterans.

    Science.gov (United States)

    Sripada, Rebecca K; Bohnert, Amy S B; Teo, Alan R; Levine, Debra S; Pfeiffer, Paul N; Bowersox, Nicholas W; Mizruchi, Mark S; Chermack, Stephen T; Ganoczy, Dara; Walters, Heather; Valenstein, Marcia

    2015-09-01

    Low social support and small social network size have been associated with a variety of negative mental health outcomes, while their impact on mental health services use is less clear. To date, few studies have examined these associations in National Guard service members, where frequency of mental health problems is high, social support may come from military as well as other sources, and services use may be suboptimal. Surveys were administered to 1448 recently returned National Guard members. Multivariable regression models assessed the associations between social support characteristics, probable mental health conditions, and service utilization. In bivariate analyses, large social network size, high social network diversity, high perceived social support, and high military unit support were each associated with lower likelihood of having a probable mental health condition (p social support (OR .90, CI .88-.92) and high unit support (OR .96, CI .94-.97) continued to be significantly associated with lower likelihood of mental health conditions. Two social support measures were associated with lower likelihood of receiving mental health services in bivariate analyses, but were not significant in adjusted models. General social support and military-specific support were robustly associated with reduced mental health symptoms in National Guard members. Policy makers, military leaders, and clinicians should attend to service members' level of support from both the community and their units and continue efforts to bolster these supports. Other strategies, such as focused outreach, may be needed to bring National Guard members with need into mental health care.

  2. The Implementation, Promotion and Evaluation of the International Health Communication Hotline as a Tool for Interdisciplinary Networking and Disciplinary Advocacy.

    Science.gov (United States)

    Assante, Leonard E.; Schrader, Stuart M.

    The International Health Communication Hotline (InHealth) represents an attempt to firmly establish, develop and promote a new Communication Studies subdiscipline in the academic and health care arenas via computer networking. If successful, the project will demonstrate the power of computer networking as an agent of change.