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Sample records for statins data-mining study

  1. The study on privacy preserving data mining for information security

    Science.gov (United States)

    Li, Xiaohui

    2012-04-01

    Privacy preserving data mining have a rapid development in a short year. But it still faces many challenges in the future. Firstly, the level of privacy has different definitions in different filed. Therefore, the measure of privacy preserving data mining technology protecting private information is not the same. So, it's an urgent issue to present a unified privacy definition and measure. Secondly, the most of research in privacy preserving data mining is presently confined to the theory study.

  2. Statin-associated muscular and renal adverse events: data mining of the public version of the FDA adverse event reporting system.

    Directory of Open Access Journals (Sweden)

    Toshiyuki Sakaeda

    Full Text Available OBJECTIVE: Adverse event reports (AERs submitted to the US Food and Drug Administration (FDA were reviewed to assess the muscular and renal adverse events induced by the administration of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA reductase inhibitors (statins and to attempt to determine the rank-order of the association. METHODS: After a revision of arbitrary drug names and the deletion of duplicated submissions, AERs involving pravastatin, simvastatin, atorvastatin, or rosuvastatin were analyzed. Authorized pharmacovigilance tools were used for quantitative detection of signals, i.e., drug-associated adverse events, including the proportional reporting ratio, the reporting odds ratio, the information component given by a Bayesian confidence propagation neural network, and the empirical Bayes geometric mean. Myalgia, rhabdomyolysis and an increase in creatine phosphokinase level were focused on as the muscular adverse events, and acute renal failure, non-acute renal failure, and an increase in blood creatinine level as the renal adverse events. RESULTS: Based on 1,644,220 AERs from 2004 to 2009, signals were detected for 4 statins with respect to myalgia, rhabdomyolysis, and an increase in creatine phosphokinase level, but these signals were stronger for rosuvastatin than pravastatin and atorvastatin. Signals were also detected for acute renal failure, though in the case of atorvastatin, the association was marginal, and furthermore, a signal was not detected for non-acute renal failure or for an increase in blood creatinine level. CONCLUSIONS: Data mining of the FDA's adverse event reporting system, AERS, is useful for examining statin-associated muscular and renal adverse events. The data strongly suggest the necessity of well-organized clinical studies with respect to statin-associated adverse events.

  3. Statin-associated muscular and renal adverse events: data mining of the public version of the FDA adverse event reporting system.

    Science.gov (United States)

    Sakaeda, Toshiyuki; Kadoyama, Kaori; Okuno, Yasushi

    2011-01-01

    Adverse event reports (AERs) submitted to the US Food and Drug Administration (FDA) were reviewed to assess the muscular and renal adverse events induced by the administration of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors (statins) and to attempt to determine the rank-order of the association. After a revision of arbitrary drug names and the deletion of duplicated submissions, AERs involving pravastatin, simvastatin, atorvastatin, or rosuvastatin were analyzed. Authorized pharmacovigilance tools were used for quantitative detection of signals, i.e., drug-associated adverse events, including the proportional reporting ratio, the reporting odds ratio, the information component given by a Bayesian confidence propagation neural network, and the empirical Bayes geometric mean. Myalgia, rhabdomyolysis and an increase in creatine phosphokinase level were focused on as the muscular adverse events, and acute renal failure, non-acute renal failure, and an increase in blood creatinine level as the renal adverse events. Based on 1,644,220 AERs from 2004 to 2009, signals were detected for 4 statins with respect to myalgia, rhabdomyolysis, and an increase in creatine phosphokinase level, but these signals were stronger for rosuvastatin than pravastatin and atorvastatin. Signals were also detected for acute renal failure, though in the case of atorvastatin, the association was marginal, and furthermore, a signal was not detected for non-acute renal failure or for an increase in blood creatinine level. Data mining of the FDA's adverse event reporting system, AERS, is useful for examining statin-associated muscular and renal adverse events. The data strongly suggest the necessity of well-organized clinical studies with respect to statin-associated adverse events.

  4. Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining

    OpenAIRE

    Chen, D

    2012-01-01

    Many small online retailers and new entrants to the online retail sector are keen to practice data mining and consumer-centric marketing in their businesses yet technically lack the necessary knowledge and expertise to do so. In this article a case study of using data mining techniques in customer-centric business intelligence for an online retailer is presented. The main purpose of this analysis is to help the business better understand its customers and therefore conduct customer-centric ma...

  5. DATA MINING IN HIGHER EDUCATION : UNIVERSITY STUDENT DROPOUT CASE STUDY

    OpenAIRE

    Ghadeer S. Abu-Oda; Alaa M. El-Halees

    2015-01-01

    In this paper, we apply different data mining approaches for the purpose of examining and predicting students’ dropouts through their university programs. For the subject of the study we select a total of 1290 records of computer science students Graduated from ALAQSA University between 2005 and 2011. The collected data included student study history and transcript for courses taught in the first two years of computer science major in addition to student GPA , high school average ...

  6. Educational data mining: a sample of review and study case

    Directory of Open Access Journals (Sweden)

    Alejandro Pena, Rafael Domínguez, Jose de Jesus Medel

    2009-12-01

    Full Text Available The aim of this work is to encourage the research in a novel merged field: Educational data mining (EDM. Thereby, twosubjects are outlined: The first one corresponds to a review of data mining (DM methods and EDM applications. Thesecond topic represents an EDM study case. As a result of the application of DM in Web-based Education Systems (WBES,stratified groups of students were found during a trial. Such groups reveal key attributes of volunteers that deserted orremained during a WBES experiment. This kind of discovered knowledge inspires the statement of correlational hypothesisto set relations between attributes and behavioral patterns of WBES users. We concluded that: When EDM findings aretaken into account for designing and managing WBES, the learning objectives are improved

  7. Using data mining techniques to characterize participation in observational studies.

    Science.gov (United States)

    Linden, Ariel; Yarnold, Paul R

    2016-12-01

    Data mining techniques are gaining in popularity among health researchers for an array of purposes, such as improving diagnostic accuracy, identifying high-risk patients and extracting concepts from unstructured data. In this paper, we describe how these techniques can be applied to another area in the health research domain: identifying characteristics of individuals who do and do not choose to participate in observational studies. In contrast to randomized studies where individuals have no control over their treatment assignment, participants in observational studies self-select into the treatment arm and therefore have the potential to differ in their characteristics from those who elect not to participate. These differences may explain part, or all, of the difference in the observed outcome, making it crucial to assess whether there is differential participation based on observed characteristics. As compared to traditional approaches to this assessment, data mining offers a more precise understanding of these differences. To describe and illustrate the application of data mining in this domain, we use data from a primary care-based medical home pilot programme and compare the performance of commonly used classification approaches - logistic regression, support vector machines, random forests and classification tree analysis (CTA) - in correctly classifying participants and non-participants. We find that CTA is substantially more accurate than the other models. Moreover, unlike the other models, CTA offers transparency in its computational approach, ease of interpretation via the decision rules produced and provides statistical results familiar to health researchers. Beyond their application to research, data mining techniques could help administrators to identify new candidates for participation who may most benefit from the intervention. © 2016 John Wiley & Sons, Ltd.

  8. Data Mining and Complex Problems: Case Study in Composite Materials

    Science.gov (United States)

    Rabelo, Luis; Marin, Mario

    2009-01-01

    Data mining is defined as the discovery of useful, possibly unexpected, patterns and relationships in data using statistical and non-statistical techniques in order to develop schemes for decision and policy making. Data mining can be used to discover the sources and causes of problems in complex systems. In addition, data mining can support simulation strategies by finding the different constants and parameters to be used in the development of simulation models. This paper introduces a framework for data mining and its application to complex problems. To further explain some of the concepts outlined in this paper, the potential application to the NASA Shuttle Reinforced Carbon-Carbon structures and genetic programming is used as an illustration.

  9. A Case Study for Student Performance Analysis based on Educational Data Mining (EDM)

    OpenAIRE

    Daxa Kundariya; Prof. Vaseem Ghada

    2016-01-01

    Educational Data Mining (EDM) is a study methodology and an application of data mining techniques related to student’s data from academic database. Like other domain, educational domain also produce vast amount of studying data. To enhance the quality of education system student performance analysis plays an important role for decision support. This paper elaborates a study on various Educational data mining technique and how they could be used to educational system to analysis student perfor...

  10. Data mining in the study of nuclear fuel cells

    International Nuclear Information System (INIS)

    Medina P, J. A.; Ortiz S, J. J.; Castillo, A.; Montes T, J. L.; Perusquia, R.

    2015-09-01

    In this paper is presented a study of data mining application in the analysis of fuel cells and their performance within a nuclear boiling water reactor. A decision tree was used to fulfill questions of the type If (condition) and Then (conclusion) to classify if the fuel cells will have good performance. The performance is measured by compliance or not of the cold shutdown margin, the rate of linear heat generation and the average heat generation in a plane of the reactor. It is assumed that the fuel cells are simulated in the reactor under a fuel reload and rod control patterns pre designed. 18125 fuel cells were simulated according to a steady-state calculation. The decision tree works on a target variable which is one of the three mentioned before. To analyze this objective, the decision tree works with a set of attribute variables. In this case, the attributes are characteristics of the cell as number of gadolinium rods, rods number with certain uranium enrichment mixed with a concentration of gadolinium, etc. The found model was able to predict the execution or not of the shutdown margin with a precision of around 95%. However, the other two variables showed lower percentages due to few learning cases of the model in which these variables were or were not achieved. Even with this inconvenience, the model is quite reliable and can be used in way coupled in optimization systems of fuel cells. (Author)

  11. Data warehousing and data mining: A case study

    Directory of Open Access Journals (Sweden)

    Suknović Milija

    2005-01-01

    Full Text Available This paper shows design and implementation of data warehouse as well as the use of data mining algorithms for the purpose of knowledge discovery as the basic resource of adequate business decision making process. The project is realized for the needs of Student's Service Department of the Faculty of Organizational Sciences (FOS, University of Belgrade, Serbia and Montenegro. This system represents a good base for analysis and predictions in the following time period for the purpose of quality business decision-making by top management. Thus, the first part of the paper shows the steps in designing and development of data warehouse of the mentioned business system. The second part of the paper shows the implementation of data mining algorithms for the purpose of deducting rules, patterns and knowledge as a resource for support in the process of decision making.

  12. Data Mining in Course Management Systems: Moodle Case Study and Tutorial

    Science.gov (United States)

    Romero, Cristobal; Ventura, Sebastian; Garcia, Enrique

    2008-01-01

    Educational data mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. This work is a survey of the specific application of data mining in learning management systems and a case study tutorial with the Moodle system. Our objective is to introduce it both…

  13. Information Extraction for Clinical Data Mining: A Mammography Case Study.

    Science.gov (United States)

    Nassif, Houssam; Woods, Ryan; Burnside, Elizabeth; Ayvaci, Mehmet; Shavlik, Jude; Page, David

    2009-01-01

    Breast cancer is the leading cause of cancer mortality in women between the ages of 15 and 54. During mammography screening, radiologists use a strict lexicon (BI-RADS) to describe and report their findings. Mammography records are then stored in a well-defined database format (NMD). Lately, researchers have applied data mining and machine learning techniques to these databases. They successfully built breast cancer classifiers that can help in early detection of malignancy. However, the validity of these models depends on the quality of the underlying databases. Unfortunately, most databases suffer from inconsistencies, missing data, inter-observer variability and inappropriate term usage. In addition, many databases are not compliant with the NMD format and/or solely consist of text reports. BI-RADS feature extraction from free text and consistency checks between recorded predictive variables and text reports are crucial to addressing this problem. We describe a general scheme for concept information retrieval from free text given a lexicon, and present a BI-RADS features extraction algorithm for clinical data mining. It consists of a syntax analyzer, a concept finder and a negation detector. The syntax analyzer preprocesses the input into individual sentences. The concept finder uses a semantic grammar based on the BI-RADS lexicon and the experts' input. It parses sentences detecting BI-RADS concepts. Once a concept is located, a lexical scanner checks for negation. Our method can handle multiple latent concepts within the text, filtering out ultrasound concepts. On our dataset, our algorithm achieves 97.7% precision, 95.5% recall and an F 1 -score of 0.97. It outperforms manual feature extraction at the 5% statistical significance level.

  14. A Data Mining and Survey Study on Diseases Associated with Paraesophageal Hernia

    OpenAIRE

    Yang, Jianji; Logan, Judith

    2006-01-01

    Paraesophageal hernia is a severe form of hiatal hernia, characterized by the upward dislocation of the gastric fundus into the thoracic cavity. In this study, the 1999 National Inpatient Sample dataset of the Healthcare Cost and Utilization Project was analyzed using data mining techniques to explore disorders associated with paraesophageal hernia. The result of this data mining process was compared with a subsequent expert knowledge survey of 97 gastrointestinal tract surgeons. This two-ste...

  15. Data mining in radiology

    International Nuclear Information System (INIS)

    Kharat, Amit T; Singh, Amarjit; Kulkarni, Vilas M; Shah, Digish

    2014-01-01

    Data mining facilitates the study of radiology data in various dimensions. It converts large patient image and text datasets into useful information that helps in improving patient care and provides informative reports. Data mining technology analyzes data within the Radiology Information System and Hospital Information System using specialized software which assesses relationships and agreement in available information. By using similar data analysis tools, radiologists can make informed decisions and predict the future outcome of a particular imaging finding. Data, information and knowledge are the components of data mining. Classes, Clusters, Associations, Sequential patterns, Classification, Prediction and Decision tree are the various types of data mining. Data mining has the potential to make delivery of health care affordable and ensure that the best imaging practices are followed. It is a tool for academic research. Data mining is considered to be ethically neutral, however concerns regarding privacy and legality exists which need to be addressed to ensure success of data mining

  16. Studies of MHD stability using data mining technique in helical plasmas

    International Nuclear Information System (INIS)

    Yamamoto, Satoshi; Pretty, David; Blackwell, Boyd

    2010-01-01

    Data mining techniques, which automatically extract useful knowledge from large datasets, are applied to multichannel magnetic probe signals of several helical plasmas in order to identify and classify MHD instabilities in helical plasmas. This method is useful to find new MHD instabilities as well as previously identified ones. Moreover, registering the results obtained from data mining in a database allows us to investigate the characteristics of MHD instabilities with parameter studies. We introduce the data mining technique consisted of pre-processing, clustering and visualizations using results from helical plasmas in H-1 and Heliotron J. We were successfully able to classify the MHD instabilities using the criterion of phase differences of each magnetic probe and identify them as energetic-ion-driven MHD instabilities using parameter study in Heliotron J plasmas. (author)

  17. A Comparative Study to Predict Student’s Performance Using Educational Data Mining Techniques

    Science.gov (United States)

    Uswatun Khasanah, Annisa; Harwati

    2017-06-01

    Student’s performance prediction is essential to be conducted for a university to prevent student fail. Number of student drop out is one of parameter that can be used to measure student performance and one important point that must be evaluated in Indonesia university accreditation. Data Mining has been widely used to predict student’s performance, and data mining that applied in this field usually called as Educational Data Mining. This study conducted Feature Selection to select high influence attributes with student performance in Department of Industrial Engineering Universitas Islam Indonesia. Then, two popular classification algorithm, Bayesian Network and Decision Tree, were implemented and compared to know the best prediction result. The outcome showed that student’s attendance and GPA in the first semester were in the top rank from all Feature Selection methods, and Bayesian Network is outperforming Decision Tree since it has higher accuracy rate.

  18. A Study of Children's Musical Preference: A Data Mining Approach

    Science.gov (United States)

    Yim, Hoi Yin Bonnie; Boo, Yee Ling; Ebbeck, Marjory

    2014-01-01

    Musical preference has long been a research interest in the field of music education, and studies consistently confirm the importance of musical preference in one's musical learning experiences. However, only a limited number of studies have been focussed on the field of early childhood education (e.g., Hargreaves, North, & Tarrant, 2006;…

  19. Generative Topic Modeling in Image Data Mining and Bioinformatics Studies

    Science.gov (United States)

    Chen, Xin

    2012-01-01

    Probabilistic topic models have been developed for applications in various domains such as text mining, information retrieval and computer vision and bioinformatics domain. In this thesis, we focus on developing novel probabilistic topic models for image mining and bioinformatics studies. Specifically, a probabilistic topic-connection (PTC) model…

  20. Data mining

    CERN Document Server

    Gorunescu, Florin

    2011-01-01

    The knowledge discovery process is as old as Homo sapiens. Until some time ago, this process was solely based on the 'natural personal' computer provided by Mother Nature. Fortunately, in recent decades the problem has begun to be solved based on the development of the Data mining technology, aided by the huge computational power of the 'artificial' computers. Digging intelligently in different large databases, data mining aims to extract implicit, previously unknown and potentially useful information from data, since 'knowledge is power'. The goal of this book is to provide, in a friendly way

  1. [Broader indication for treatment with statins; the 'heart protection study'

    NARCIS (Netherlands)

    Stalenhoef, A.F.H.; Stuyt, P.M.J.

    2002-01-01

    The introduction of statins has been a breakthrough in the treatment of hypercholesterolaemia. Statins are safe and effective in reducing the risk of coronary heart disease in the general population. The 'Heart protection study' has provided evidence for the benefit of statin treatment in much

  2. Applied data mining

    CERN Document Server

    Xu, Guandong

    2013-01-01

    Data mining has witnessed substantial advances in recent decades. New research questions and practical challenges have arisen from emerging areas and applications within the various fields closely related to human daily life, e.g. social media and social networking. This book aims to bridge the gap between traditional data mining and the latest advances in newly emerging information services. It explores the extension of well-studied algorithms and approaches into these new research arenas.

  3. Statin Effects on Aggression: Results from the UCSD Statin Study, a Randomized Control Trial

    Science.gov (United States)

    Golomb, Beatrice A.; Dimsdale, Joel E.; Koslik, Hayley J.; Evans, Marcella A.; Lu, Xun; Rossi, Steven; Mills, Paul J.; Criqui, Michael H.

    2015-01-01

    Background Low/ered cholesterol is linked to aggression in some study designs. Cases/series have reported reproducible aggression increases on statins, but statins also bear mechanisms that could reduce aggression. Usual statin effects on aggression have not been characterized. Methods 1016 adults (692 men, 324 postmenopausal women) underwent double-blind sex-stratified randomization to placebo, simvastatin 20mg, or pravastatin 40mg (6 months). The Overt-Aggression-Scale-Modified–Aggression-Subscale (OASMa) assessed behavioral aggression. A significant sex-statin interaction was deemed to dictate sex-stratified analysis. Exploratory analyses assessed the influence of baseline-aggression, testosterone-change (men), sleep and age. Results The sex-statin interaction was significant (P=0.008). In men, statins tended to decrease aggression, significantly so on pravastatin: difference=-1.0(SE=0.49)P=0.038. Three marked outliers (OASMa-change ≥40 points) offset otherwise strong significance-vs-placebo: statins:-1.3(SE=0.38)P=0.0007; simvastatin:-1.4(SE=0.43)P=0.0011; pravastatin:-1.2(SE=0.45)P=0.0083. Age≤40 predicted greater aggression-decline on statins: difference=-1.4(SE=0.64)P=0.026. Aggression-protection was emphasized in those with low baseline aggression: ageaggression (N=40) statin-difference-vs-placebo=-2.4(SE=0.71)P=0.0016. Statins (especially simvastatin) lowered testosterone, and increased sleep problems. Testosterone-drop on statins predicted aggression-decline: β=0.64(SE=0.30)P=0.034, particularly on simvastatin: β=1.29(SE=0.49)P=0.009. Sleep-worsening on statins significantly predicted aggression-increase: β=2.2(SE=0.55)Paggression-increase on statins became significant with exclusion of one younger, surgically-menopausal woman (N=310) β=0.70(SE=0.34)P=0.039. The increase was significant, without exclusions, for women of more typical postmenopausal age (≥45): (N=304) β=0.68(SE=0.34)P=0.048 – retaining significance with modified age

  4. Model Design for Personnel Selection with Data Mining Approach (Case Study: A Commerce Bank of Iran

    Directory of Open Access Journals (Sweden)

    Adel Azar

    2010-03-01

    Full Text Available The success or failure of an organization has a direct relationship with how its human resources are employed and retained. It is the case that organizations keep large amounts of information and data on entrance evaluations and processes. This information, however, is often left unutilized. Data mining is considered a solution for analyzing these data. This paper is investigating educated and objective methods of data analysis. It follows statistical rules, data mining techniques, and the relationship between entrance evaluation scores and personal and professional variables. These factors are studied in order to determine the assignment and rank of potential employees. The database and personnel information of the a Commerce Bank of Iran (in years of 2005 and 2006 is studied and analyzed as a case study in order to identify the labor factors which are considered effective in job performance. The data mining technique that is used in this project serves as the decision-tree. Rules Derivation has been accomplished by the QUEST, CHAID, C5.0 and CART algorithms. The objective and the appropriate algorithms are determined based on seemingly “irrelevant” components, which the Commerce Bank Human Resources management experts described. Results indicated not taking into account the “performance assessment” variable as the objective. Also this project has identified the following from 26 variables have been investigated, five variables as the effective factors in employee promotion: examination score, interview score, degree, years of experience, and job location. The paper's results led in knowledge that can be practical.

  5. GWA study data mining and independent replication identify cardiomyopathy-associated 5 (CMYA5) as a risk gene for schizophrenia

    NARCIS (Netherlands)

    Chen, X.; Lee, G.; Maher, B. S.; Fanous, A. H.; Chen, J.; Zhao, Z.; Guo, A.; van den Oord, E.; Sullivan, P. F.; Shi, J.; Levinson, D. F.; Gejman, P. V.; Sanders, A.; Duan, J.; Owen, M. J.; Craddock, N. J.; O'Donovan, M. C.; Blackman, J.; Lewis, D.; Kirov, G. K.; Qin, W.; Schwab, S.; Wildenauer, D.; Chowdari, K.; Nimgaonkar, V.; Straub, R. E.; Weinberger, D. R.; O'Neill, F. A.; Walsh, D.; Bronstein, M.; Darvasi, A.; Lencz, T.; Malhotra, A. K.; Rujescu, D.; Giegling, I.; Werge, T.; Hansen, T.; Ingason, A.; Nöethen, M. M.; Rietschel, M.; Cichon, S.; Djurovic, S.; Andreassen, O. A.; Cantor, R. M.; Ophoff, R.; Corvin, A.; Morris, D. W.; Gill, M.; Pato, C. N.; Pato, M. T.; Macedo, A.; Gurling, H. M. D.; McQuillin, A.; Pimm, J.; Hultman, C.; Lichtenstein, P.; Sklar, P.; Purcell, S. M.; Scolnick, E.; St Clair, D.; Blackwood, D. H. R.; Kendler, K. S.; Kahn, René S.; Linszen, Don H.; van Os, Jim; Wiersma, Durk; Bruggeman, Richard; Cahn, Wiepke; de Haan, Lieuwe; Krabbendam, Lydia; Myin-Germeys, Inez; O'Donovan, Michael C.; Kirov, George K.; Craddock, Nick J.; Holmans, Peter A.; Williams, Nigel M.; Georgieva, Lyudmila; Nikolov, Ivan; Norton, N.; Williams, H.; Toncheva, Draga; Milanova, Vihra; Owen, Michael J.; Hultman, Christina M.; Lichtenstein, Paul; Thelander, Emma F.; Sullivan, Patrick; Morris, Derek W.; O'Dushlaine, Colm T.; Kenny, Elaine; Quinn, Emma M.; Gill, Michael; Corvin, Aiden; McQuillin, Andrew; Choudhury, Khalid; Datta, Susmita; Pimm, Jonathan; Thirumalai, Srinivasa; Puri, Vinay; Krasucki, Robert; Lawrence, Jacob; Quested, Digby; Bass, Nicholas; Gurling, Hugh; Crombie, Caroline; Fraser, Gillian; Kuan, Soh Leh; Walker, Nicholas; St Clair, David; Blackwood, Douglas H. R.; Muir, Walter J.; McGhee, Kevin A.; Pickard, Ben; Malloy, Pat; Maclean, Alan W.; van Beck, Margaret; Wray, Naomi R.; Macgregor, Stuart; Visscher, Peter M.; Pato, Michele T.; Medeiros, Helena; Middleton, Frank; Carvalho, Celia; Morley, Christopher; Fanous, Ayman; Conti, David; Knowles, James A.; Ferreira, Carlos Paz; Macedo, Antonio; Azevedo, M. Helena; Pato, Carlos N.; Stone, Jennifer L.; Ruderfer, Douglas M.; Kirby, Andrew N.; Ferreira, Manuel A. R.; Daly, Mark J.; Purcell, Shaun M.; Sklar, Pamela; Chambert, Kimberly; Kuruvilla, Finny; Gabriel, Stacey B.; Ardlie, Kristin; Moran, Jennifer L.; Scolnick, Edward M.

    2011-01-01

    We conducted data-mining analyses using the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) and molecular genetics of schizophrenia genome-wide association study supported by the genetic association information network (MGS-GAIN) schizophrenia data sets and performed

  6. Case study: how to apply data mining techniques in a healthcare data warehouse.

    Science.gov (United States)

    Silver, M; Sakata, T; Su, H C; Herman, C; Dolins, S B; O'Shea, M J

    2001-01-01

    Healthcare provider organizations are faced with a rising number of financial pressures. Both administrators and physicians need help analyzing large numbers of clinical and financial data when making decisions. To assist them, Rush-Presbyterian-St. Luke's Medical Center and Hitachi America, Ltd. (HAL), Inc., have partnered to build an enterprise data warehouse and perform a series of case study analyses. This article focuses on one analysis, which was performed by a team of physicians and computer science researchers, using a commercially available on-line analytical processing (OLAP) tool in conjunction with proprietary data mining techniques developed by HAL researchers. The initial objective of the analysis was to discover how to use data mining techniques to make business decisions that can influence cost, revenue, and operational efficiency while maintaining a high level of care. Another objective was to understand how to apply these techniques appropriately and to find a repeatable method for analyzing data and finding business insights. The process used to identify opportunities and effect changes is described.

  7. Applying Data-mining techniques to study drought periods in Spain

    Science.gov (United States)

    Belda, F.; Penades, M. C.

    2010-09-01

    Data-mining is a technique that it can be used to interact with large databases and to help in the discovery relations between parameters by extracting information from massive and multiple data archives. Drought affects many economic and social sectors, from agricultural to transportation, going through urban water deficit and the development of modern industries. With these problems and drought geographical and temporal distribution it's difficult to find a single definition of drought. Improving the understanding of the knowledge of climatic index is necessary to reduce the impacts of drought and to facilitate quick decisions regarding this problem. The main objective is to analyze drought periods from 1950 to 2009 in Spain. We use several kinds of information, different formats, sources and transmission mode. We use satellite-based Vegetation Index, dryness index for several temporal periods. We use daily and monthly precipitation and temperature data and soil moisture data from numerical weather model. We calculate mainly Standardized Precipitation Index (SPI) that it has been used amply in the bibliography. We use OLAP-Mining techniques to discovery of association rules between remote-sensing, numerical weather model and climatic index. Time series Data- Mining techniques organize data as a sequence of events, with each event having a time of recurrence, to cluster the data into groups of records or cluster with similar characteristics. Prior climatological classification is necessary if we want to study drought periods over all Spain.

  8. Implementasi Data Warehouse dan Data Mining: Studi Kasus Analisis Peminatan Studi Siswa

    Directory of Open Access Journals (Sweden)

    Eka Miranda

    2011-06-01

    Full Text Available This paper discusses the implementation of data mining and their role in helping decision-making related to students’ specialization program selection. Currently, the university uses a database to store records of transactions which can not directly be used to assist analysis and decision making. Based on these issues then made the data warehouse design used to store large amounts of data and also has the potential to gain new data distribution perspectives and allows to answer the ad hoc question as well as to perform data analysis. The method used consists of: record analysis related to students’ academic achievement, designing data warehouse and data mining. The paper’s results are in a form of data warehouse and data mining design and its implementation with the classification techniques and association rules. From these results can be seen the students’ tendency and pattern background in choosing the specialization, to help them make decisions. 

  9. Data Mining Aplications in Livestock

    Directory of Open Access Journals (Sweden)

    Feyza ALEV ÇETİN

    2016-03-01

    Full Text Available Data mining provides discovering the required and applicable knowledge from very large amounts of information collected in one centre. Data mining has been used in the information industry and society. Although many methods of data mining has been used, these techniques has been remarkable in animal husbandry in recent years. For the solution of complex problems in animal husbandry many methods were discussed and developed. Brief information on data mining techniques such as k-means approach, k-nearest neighbor approach, multivariate adaptive regression function (MARS, naive Bayesian classifiers (NBC, artificial neural networks (ANN, support vector machines (SVM, decision trees are given in the study. Some data mining methods are presented and examples of the application of data mining in the field of animal husbandry in the world are provided with this study.

  10. [Statin associated myopathy in clinical practice. Results of DAMA study].

    Science.gov (United States)

    Millán, Jesús; Pedro-Botet, Juan; Climent, Elisenda; Millán, Joaquín; Rius, Joan

    Muscle symptoms, with or without elevation of creatin kinase are one of the main adverse effects of statin therapy, a fact that sometimes limits their use. The aim of this study was to evaluate the clinical characteristics of patients treated with statins who have complained muscle symptoms and to identify possible predictive factors. A cross-sectional one-visit, non-interventional, national multicenter study including patients of both sexes over 18 years of age referred for past or present muscle symptoms associated with statin therapy was conducted. 3,845 patients were recruited from a one-day record from 2,001 physicians. Myalgia was present in 78.2% of patients included in the study, myositis in 19.3%, and rhabdomyolysis in 2.5%. Patients reported muscle pain in 77.5% of statin-treated individuals, general weakness 42.7%, and cramps 28.1%. Kidney failure, intense physical exercise, alcohol consumption (>30g/d in men and 20g/d in women) and abdominal obesity were the clinical situations associated with statin myopathy. Myalgia followed by myositis are the most frequent statin-related side effects. It should be recommended control environmental factors such as intense exercise and alcohol intake as well as abdominal obesity and renal function of the patient treated with statins. Copyright © 2016 Sociedad Española de Arteriosclerosis. Publicado por Elsevier España, S.L.U. All rights reserved.

  11. Statin use and peripheral sensory perception: a pilot study.

    Science.gov (United States)

    West, Brenton; Williams, Cylie M; Jilbert, Elise; James, Alicia M; Haines, Terry P

    2014-06-01

    Peripheral sensory neuropathy is a neurological deficit resulting in decreased detection of sensation through the peripheral nervous system. Peripheral sensory neuropathy is commonly diagnosed with the use of a monofilament and either a tuning fork or neurothesiometer. Statins are a widely used medication and there has been some debate of association with their use and peripheral sensory neuropathy. This pilot study aimed to test the sensory perception of participants with long-term statin use and compare these results to their peers who were not taking statins. Thirty participants were recruited and equally divided into a statin and non-statin group. Healthy participants were screened by their medical and medication history, Australian Type 2 Diabetes Risk assessment, and random blood glucose level. An assessor who was blinded to the participant group conducted sensory assessments using a 10 g monofilament and neurothesiometer. There was no difference in monofilament testing results between the groups. The statin group was less sensate at the styloid process (p = 0.031) and medial malleolus (p = 0.003) than the control group. Results at the hallux were not statistically significant (p = 0.183). This result is suggestive of a potential association between long-term statin use and a decrease in peripheral sensory perception. This may be because of peripheral sensory neuropathy. Limitations such as consideration of participant height, participant numbers, and inability to analyze results against statin groups are reported. As statins are a life-saving medication, careful consideration should be applied to these results and further research be conducted to determine if these results are applicable to larger populations.

  12. Pilot study on the use of data mining to identify cochlear implant candidates.

    Science.gov (United States)

    Grisel, Jedidiah J; Schafer, Erin; Lam, Anne; Griffin, Terry

    2018-05-01

    The goal of this pilot study was to determine the clinical utility of data-mining software that screens for cochlear implant (CI) candidacy. The Auditory Implant Initiative developed a software module that screens for CI candidates via integration with a software system (Noah 4) that serves as a depository for hearing test data. To identify candidates, patient audiograms from one practice were exported into the screening module. Candidates were tracked to determine if any eventually underwent implantation. After loading 4836 audiograms from the Noah 4 system, the screening module identified 558 potential CI candidates. After reviewing the data for the potential candidates, 117 were targeted and invited to an educational event. Following the event, a total of six candidates were evaluated, and two were implanted. This objective approach to identifying candidates has the potential to address the gross underutilization of CIs by removing any bias or lack of knowledge regarding the management of severe to profound sensorineural hearing loss with CIs. The screening module was an effective tool for identifying potential CI candidates at one ENT practice. On a larger scale, the screening module has the potential to impact thousands of CI candidates worldwide.

  13. Expanding the Role of Institutional Research at Small Private Universities: A Case Study in Enrollment Management Using Data Mining

    Science.gov (United States)

    Antons, Christopher M.; Maltz, Elliot N.

    2006-01-01

    This case study documents a successful application of data-mining techniques in enrollment management through a partnership between the admissions office, a business administration master's-degree program, and the institutional research office at Willamette University (Salem, Oregon). (Contains 1 table and 3 figures.)

  14. Population Validity for Educational Data Mining Models: A Case Study in Affect Detection

    Science.gov (United States)

    Ocumpaugh, Jaclyn; Baker, Ryan; Gowda, Sujith; Heffernan, Neil; Heffernan, Cristina

    2014-01-01

    Information and communication technology (ICT)-enhanced research methods such as educational data mining (EDM) have allowed researchers to effectively model a broad range of constructs pertaining to the student, moving from traditional assessments of knowledge to assessment of engagement, meta-cognition, strategy and affect. The automated…

  15. Comparison analysis for classification algorithm in data mining and the study of model use

    Science.gov (United States)

    Chen, Junde; Zhang, Defu

    2018-04-01

    As a key technique in data mining, classification algorithm was received extensive attention. Through an experiment of classification algorithm in UCI data set, we gave a comparison analysis method for the different algorithms and the statistical test was used here. Than that, an adaptive diagnosis model for preventive electricity stealing and leakage was given as a specific case in the paper.

  16. Data Mining for CRM

    Science.gov (United States)

    Thearling, Kurt

    Data Mining technology allows marketing organizations to better understand their customers and respond to their needs. This chapter describes how Data Mining can be combined with customer relationship management to help drive improved interactions with customers. An example showing how to use Data Mining to drive customer acquisition activities is presented.

  17. Pre-stroke use of statins on stroke outcome : a meta-analysis of observational studies

    NARCIS (Netherlands)

    Cordenier, Ann; De Smedt, Ann; Brouns, Raf; Uyttenboogaart, Maarten; De Raedt, Sylvie; Luijckx, Gert-Jan; De Keyser, Jacques

    2011-01-01

    Background: Animal pre-clinical studies suggest that statins may have neuroprotective effects in acute ischaemic stroke. Statins might also increase the risk of developing haemorrhagic transformation after thrombolytic treatment. Methods: We performed a systematic review and included studies that

  18. Identification of functionally related genes using data mining and data integration: a breast cancer case study

    Directory of Open Access Journals (Sweden)

    Zucchi Ileana

    2009-10-01

    . Two genes among the predicted putative partners of TBX3 (GLI3 and GATA3 were confirmed by in vivo binding assays (crosslinking immunoprecipitation, X-ChIP in which the putative DNA enhancer sequence sites of GATA3 and GLI3 were found to be bound by the Tbx3 protein. Conclusion The presented strategy is demonstrated to be an effective approach to identify genes that establish functional relationships. The methodology identifies and characterises genes with a similar expression profile, through data mining and integrating data from publicly available resources, to contribute to a better understanding of gene regulation and cell function. The prediction of the TBX3 target genes GLI3 and GATA3 was experimentally confirmed.

  19. Using data mining on student behavior and cognitive style data for improving e-learning systems: a case study

    Directory of Open Access Journals (Sweden)

    Milos Jovanovic

    2012-06-01

    Full Text Available In this research we applied classification models for prediction of studentsarsquo; performance, and cluster models for grouping students based on their cognitive styles in e-learning environment. Classification models described in this paper should help: teachers, students and business people, for early engaging with students who are likely to become excellent on a selected topic. Clustering students based on cognitive styles and their overall performance should enable better adaption of the learning materials with respect to their learning styles. The approach is tested using well-established data mining algorithms, and evaluated by several evaluation measures. Model building process included data preprocessing, parameter optimization and attribute selection steps, which enhanced the overall performance. Additionally we propose a Moodle module that allows automatic extraction of data needed for educational data mining analysis and deploys models developed in this study.

  20. A Study on the application of Data Mining Methods in the analysis of Transcripts

    Directory of Open Access Journals (Sweden)

    Luis Raunheitte

    2012-06-01

    Full Text Available Schools always had an essential role in the formation of students' intellect; however, the constant incorporation of knowledge to improve techniques and technologies used in the production of goods and services has caused a major demand for highly qualified professionals and, in order to meet that need, the teaching process must understand and adapt to the profile of the students. The transcript is the most used document to measure the performance of a student. Its digital storage combined with data mining methodologies can contribute not only to the analysis of performances, but also to the identification of significant information about student

  1. Perancangan Dan Pembuatan Modul Data Mining Market Basket Analysis Pada Odoo Dengan Studi Kasus Supermarket X

    OpenAIRE

    Hendratha, Stefani Natalia; Yulia, Yulia; Budhi, Gregorius Satia

    2016-01-01

    Odoo Enterprise Resource Planning (ERP) system storing company's transaction data. However, Odoo doesn't have a module for managing data. It takes a module for managing data into useful information.Based on the above problems, a module for data mining Market Basket Analysis is being designed. This module uses FP-Growth algorithm by utilizing the sales transaction data.For the testing, this module using data from X Supermarket. The final result of this module is an association rule from data m...

  2. Collaborative Data Mining

    Science.gov (United States)

    Moyle, Steve

    Collaborative Data Mining is a setting where the Data Mining effort is distributed to multiple collaborating agents - human or software. The objective of the collaborative Data Mining effort is to produce solutions to the tackled Data Mining problem which are considered better by some metric, with respect to those solutions that would have been achieved by individual, non-collaborating agents. The solutions require evaluation, comparison, and approaches for combination. Collaboration requires communication, and implies some form of community. The human form of collaboration is a social task. Organizing communities in an effective manner is non-trivial and often requires well defined roles and processes. Data Mining, too, benefits from a standard process. This chapter explores the standard Data Mining process CRISP-DM utilized in a collaborative setting.

  3. Study and application of data mining and data warehouse in CIMS

    Science.gov (United States)

    Zhou, Lijuan; Liu, Chi; Liu, Daxin

    2003-03-01

    The interest in analyzing data has grown tremendously in recent years. To analyze data, a multitude of technologies is need, namely technologies from the fields of Data Warehouse, Data Mining, On-line Analytical Processing (OLAP). This paper gives a new architecture of data warehouse in CIMS according to CRGC-CIMS application engineering. The data source of this architecture comes from database of CRGC-CIMS system. The data is put in global data set by extracting, filtrating and integrating, and then the data is translated to data warehouse according information request. We have addressed two advantages of the new model in CRGC-CIMS application. In addition, a Data Warehouse contains lots of materialized views over the data provided by the distributed heterogeneous databases for the purpose of efficiently implementing decision-support, OLAP queries or data mining. It is important to select the right view to materialize that answer a given set of queries. In this paper, we also have designed algorithms for selecting a set of views to be materialized in a data warehouse in order to answer the most queries under the constraint of given space. First, we give a cost model for selecting materialized views. Then we give the algorithms that adopt gradually recursive method from bottom to top. We give description and realization of algorithms. Finally, we discuss the advantage and shortcoming of our approach and future work.

  4. Contrast data mining concepts, algorithms, and applications

    CERN Document Server

    Dong, Guozhu

    2012-01-01

    A Fruitful Field for Researching Data Mining Methodology and for Solving Real-Life Problems Contrast Data Mining: Concepts, Algorithms, and Applications collects recent results from this specialized area of data mining that have previously been scattered in the literature, making them more accessible to researchers and developers in data mining and other fields. The book not only presents concepts and techniques for contrast data mining, but also explores the use of contrast mining to solve challenging problems in various scientific, medical, and business domains. Learn from Real Case Studies

  5. Statin treatment and risk of recurrent venous thromboembolism: a nationwide cohort study

    NARCIS (Netherlands)

    Nguyen, Cu Dinh; Andersson, Charlotte; Jensen, Thomas Bo; Gjesing, Anne; Schjerning Olsen, Anne-Marie; Malta Hansen, Carolina; Büller, Harry; Torp-Pedersen, Christian; Gislason, Gunnar H.

    2013-01-01

    Statins may decrease the risk of primary venous thromboembolism (VTE), that is, deep vein thrombosis (DVT) and pulmonary embolism (PE) but the effect of statins in preventing recurrent VTE is less clear. The aim of this study was therefore to investigate the association between statin therapy and

  6. Data mining for materials design: A computational study of single molecule magnet

    Energy Technology Data Exchange (ETDEWEB)

    Dam, Hieu Chi [Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292 (Japan); Faculty of Physics, Vietnam National University, 334 Nguyen Trai, Hanoi (Viet Nam); Pham, Tien Lam; Ho, Tu Bao [Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292 (Japan); Nguyen, Anh Tuan [Faculty of Physics, Vietnam National University, 334 Nguyen Trai, Hanoi (Viet Nam); Nguyen, Viet Cuong [HPC Systems, Inc., 3-9-15 Kaigan, Minato-ku, Tokyo 108-0022 (Japan)

    2014-01-28

    We develop a method that combines data mining and first principles calculation to guide the designing of distorted cubane Mn{sup 4+} Mn {sub 3}{sup 3+} single molecule magnets. The essential idea of the method is a process consisting of sparse regressions and cross-validation for analyzing calculated data of the materials. The method allows us to demonstrate that the exchange coupling between Mn{sup 4+} and Mn{sup 3+} ions can be predicted from the electronegativities of constituent ligands and the structural features of the molecule by a linear regression model with high accuracy. The relations between the structural features and magnetic properties of the materials are quantitatively and consistently evaluated and presented by a graph. We also discuss the properties of the materials and guide the material design basing on the obtained results.

  7. Data mining for service

    CERN Document Server

    2014-01-01

    Virtually all nontrivial and modern service related problems and systems involve data volumes and types that clearly fall into what is presently meant as "big data", that is, are huge, heterogeneous, complex, distributed, etc. Data mining is a series of processes which include collecting and accumulating data, modeling phenomena, and discovering new information, and it is one of the most important steps to scientific analysis of the processes of services.  Data mining application in services requires a thorough understanding of the characteristics of each service and knowledge of the compatibility of data mining technology within each particular service, rather than knowledge only in calculation speed and prediction accuracy. Varied examples of services provided in this book will help readers understand the relation between services and data mining technology. This book is intended to stimulate interest among researchers and practitioners in the relation between data mining technology and its application to ...

  8. Data mining in agriculture

    CERN Document Server

    Mucherino, Antonio; Pardalos, Panos M

    2009-01-01

    Data Mining in Agriculture represents a comprehensive effort to provide graduate students and researchers with an analytical text on data mining techniques applied to agriculture and environmental related fields. This book presents both theoretical and practical insights with a focus on presenting the context of each data mining technique rather intuitively with ample concrete examples represented graphically and with algorithms written in MATLAB®. Examples and exercises with solutions are provided at the end of each chapter to facilitate the comprehension of the material. For each data mining technique described in the book variants and improvements of the basic algorithm are also given. Also by P.J. Papajorgji and P.M. Pardalos: Advances in Modeling Agricultural Systems, 'Springer Optimization and its Applications' vol. 25, ©2009.

  9. Ensemble Data Mining Methods

    Data.gov (United States)

    National Aeronautics and Space Administration — Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve...

  10. Applied data mining for business and industry

    CERN Document Server

    Giudici, Paolo

    2009-01-01

    The increasing availability of data in our current, information overloaded society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract knowledge from such data. This book provides an accessible introduction to data mining methods in a consistent and application oriented statistical framework, using case studies drawn from real industry projects and highlighting the use of data mining methods in a variety of business applications. Introduces data mining methods and applications.Covers classical and Bayesian multivariate statistical methodology as well as machine learning and computational data mining methods.Includes many recent developments such as association and sequence rules, graphical Markov models, lifetime value modelling, credit risk, operational risk and web mining.Features detailed case studies based on applied projects within industry.Incorporates discussion of data mining software, with case studies a...

  11. Continued Statin Prescriptions After Adverse Reactions and Patient Outcomes: A Cohort Study.

    Science.gov (United States)

    Zhang, Huabing; Plutzky, Jorge; Shubina, Maria; Turchin, Alexander

    2017-08-15

    Many patients discontinue statin treatment, often after having a possible adverse reaction. The risks and benefits of continued statin therapy after an adverse reaction are not known. To examine the relationship between continuation of statin therapy (any prescription within 12 months after an adverse reaction) and clinical outcomes. Retrospective cohort study. Primary care practices affiliated with 2 academic medical centers. Patients with a presumed adverse reaction to a statin between 2000 and 2011. Information on adverse reactions to statins was obtained from structured electronic medical record data or natural-language processing of narrative provider notes. The primary composite outcome was time to a cardiovascular event (myocardial infarction or stroke) or death. Most (81%) of the adverse reactions to statins were identified from the text of electronic provider notes. Among 28 266 study patients, 19 989 (70.7%) continued receiving statin prescriptions after the adverse reaction. Four years after the presumed adverse event, the cumulative incidence of the composite primary outcome was 12.2% for patients with continued statin prescriptions, compared with 13.9% for those without them (difference, 1.7% [95% CI, 0.8% to 2.7%]; P statin was prescribed after the adverse reaction, 2014 (26.5%) had a documented adverse reaction to the second statin, but 1696 (84.2%) of those patients continued receiving statin prescriptions. The risk for recurrent adverse reactions to statins could not be established for the entire sample. It was also not possible to determine whether patients actually took the statins. Continued statin prescriptions after an adverse reaction were associated with a lower incidence of death and cardiovascular events. Chinese National Key Program of Clinical Science, National Natural Science Foundation of China, and Young Scientific Research Fund of Peking Union Medical College Hospital.

  12. Data mining in pharma sector: benefits.

    Science.gov (United States)

    Ranjan, Jayanthi

    2009-01-01

    The amount of data getting generated in any sector at present is enormous. The information flow in the pharma industry is huge. Pharma firms are progressing into increased technology-enabled products and services. Data mining, which is knowledge discovery from large sets of data, helps pharma firms to discover patterns in improving the quality of drug discovery and delivery methods. The paper aims to present how data mining is useful in the pharma industry, how its techniques can yield good results in pharma sector, and to show how data mining can really enhance in making decisions using pharmaceutical data. This conceptual paper is written based on secondary study, research and observations from magazines, reports and notes. The author has listed the types of patterns that can be discovered using data mining in pharma data. The paper shows how data mining is useful in the pharma industry and how its techniques can yield good results in pharma sector. Although much work can be produced for discovering knowledge in pharma data using data mining, the paper is limited to conceptualizing the ideas and view points at this stage; future work may include applying data mining techniques to pharma data based on primary research using the available, famous significant data mining tools. Research papers and conceptual papers related to data mining in Pharma industry are rare; this is the motivation for the paper.

  13. Statins and morbidity and mortality in COPD in the COMIC study: a prospective COPD cohort study.

    Science.gov (United States)

    Citgez, Emanuel; van der Palen, Job; Koehorst-Ter Huurne, Kirsten; Movig, Kris; van der Valk, Paul; Brusse-Keizer, Marjolein

    2016-01-01

    Both chronic inflammation and cardiovascular comorbidity play an important role in the morbidity and mortality of patients with chronic obstructive pulmonary disease (COPD). Statins could be a potential adjunct therapy. The additional effects of statins in COPD are, however, still under discussion. The aim of this study is to further investigate the association of statin use with clinical outcomes in a well-described COPD cohort. 795 patients of the Cohort of Mortality and Inflammation in COPD (COMIC) study were divided into statin users or not. Statin use was defined as having a statin for at least 90 consecutive days after inclusion. Outcome parameters were 3-year survival, based on all-cause mortality, time until first hospitalisation for an acute exacerbation of COPD (AECOPD) and time until first community-acquired pneumonia (CAP). A sensitivity analysis was performed without patients who started a statin 3 months or more after inclusion to exclude immortal time bias. Statin use resulted in a better overall survival (corrected HR 0.70 (95% CI 0.51 to 0.96) in multivariate analysis), but in the sensitivity analysis this association disappeared. Statin use was not associated with time until first hospitalisation for an AECOPD (cHR 0.95, 95% CI 0.74 to 1.22) or time until first CAP (cHR 1.1, 95% CI 0.83 to 1.47). In the COMIC study, statin use is not associated with a reduced risk of all-cause mortality, time until first hospitalisation for an AECOPD or time until first CAP in patients with COPD.

  14. Data mining methods

    CERN Document Server

    Chattamvelli, Rajan

    2015-01-01

    DATA MINING METHODS, Second Edition discusses both theoretical foundation and practical applications of datamining in a web field including banking, e-commerce, medicine, engineering and management. This book starts byintroducing data and information, basic data type, data category and applications of data mining. The second chapterbriefly reviews data visualization technology and importance in data mining. Fundamentals of probability and statisticsare discussed in chapter 3, and novel algorithm for sample covariants are derived. The next two chapters give an indepthand useful discussion of data warehousing and OLAP. Decision trees are clearly explained and a new tabularmethod for decision tree building is discussed. The chapter on association rules discusses popular algorithms andcompares various algorithms in summary table form. An interesting application of genetic algorithm is introduced inthe next chapter. Foundations of neural networks are built from scratch and the back propagation algorithm is derived...

  15. An extended data mining method for identifying differentially expressed assay-specific signatures in functional genomic studies

    Directory of Open Access Journals (Sweden)

    Rollins Derrick K

    2010-12-01

    Full Text Available Abstract Background Microarray data sets provide relative expression levels for thousands of genes for a small number, in comparison, of different experimental conditions called assays. Data mining techniques are used to extract specific information of genes as they relate to the assays. The multivariate statistical technique of principal component analysis (PCA has proven useful in providing effective data mining methods. This article extends the PCA approach of Rollins et al. to the development of ranking genes of microarray data sets that express most differently between two biologically different grouping of assays. This method is evaluated on real and simulated data and compared to a current approach on the basis of false discovery rate (FDR and statistical power (SP which is the ability to correctly identify important genes. Results This work developed and evaluated two new test statistics based on PCA and compared them to a popular method that is not PCA based. Both test statistics were found to be effective as evaluated in three case studies: (i exposing E. coli cells to two different ethanol levels; (ii application of myostatin to two groups of mice; and (iii a simulated data study derived from the properties of (ii. The proposed method (PM effectively identified critical genes in these studies based on comparison with the current method (CM. The simulation study supports higher identification accuracy for PM over CM for both proposed test statistics when the gene variance is constant and for one of the test statistics when the gene variance is non-constant. Conclusions PM compares quite favorably to CM in terms of lower FDR and much higher SP. Thus, PM can be quite effective in producing accurate signatures from large microarray data sets for differential expression between assays groups identified in a preliminary step of the PCA procedure and is, therefore, recommended for use in these applications.

  16. Data mining for dummies

    CERN Document Server

    Brown, Meta S

    2014-01-01

    Delve into your data for the key to success Data mining is quickly becoming integral to creating value and business momentum. The ability to detect unseen patterns hidden in the numbers exhaustively generated by day-to-day operations allows savvy decision-makers to exploit every tool at their disposal in the pursuit of better business. By creating models and testing whether patterns hold up, it is possible to discover new intelligence that could change your business''s entire paradigm for a more successful outcome. Data Mining for Dummies shows you why it doesn''t take a data scientist to gain

  17. Data mining mobile devices

    CERN Document Server

    Mena, Jesus

    2013-01-01

    With today's consumers spending more time on their mobiles than on their PCs, new methods of empirical stochastic modeling have emerged that can provide marketers with detailed information about the products, content, and services their customers desire.Data Mining Mobile Devices defines the collection of machine-sensed environmental data pertaining to human social behavior. It explains how the integration of data mining and machine learning can enable the modeling of conversation context, proximity sensing, and geospatial location throughout large communities of mobile users

  18. GWA study data mining and independent replication identify cardiomyopathy-associated 5 (CMYA5) as a risk gene for schizophrenia

    DEFF Research Database (Denmark)

    Chen, X; Lee, G; Maher, B S

    2011-01-01

    We conducted data-mining analyses using the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) and molecular genetics of schizophrenia genome-wide association study supported by the genetic association information network (MGS-GAIN) schizophrenia data sets and performed...... bioinformatic prioritization for all the markers with P-values ¿0.05 in both data sets. In this process, we found that in the CMYA5 gene, there were two non-synonymous markers, rs3828611 and rs10043986, showing nominal significance in both the CATIE and MGS-GAIN samples. In a combined analysis of both the CATIE...... in our Irish samples and was dropped out without further investigation. The other two markers were verified in 23 other independent data sets. In a meta-analysis of all 23 replication samples (family samples, 912 families with 4160 subjects; case-control samples, 11¿380 cases and 15¿021 controls), we...

  19. Switching statins in Norway after new reimbursement policy – a nationwide prescription study

    Science.gov (United States)

    Sakshaug, Solveig; Furu, Kari; Karlstad, Øystein; Rønning, Marit; Skurtveit, Svetlana

    2007-01-01

    What is already known about this subject Use of statins is growing worldwide and costs represent a burden to public budgets. The introduction of simvastatin generics, generic substitution and price regulations have contributed to price reductions and resulted in overall cost reductions of statin use in Norway. What this study adds New reimbursement regulations for statins in Norway in June 2005, making simvastatin the drug of choice, had a great impact on physicians' prescribing of statins. Nearly 40% of the atorvastatin users switched to simvastatin during the 13-month period after implementation of the new regulations. Among the new users of statins the proportion receiving simvastatin increased from 48% in May 2005 to 92% in June 2006. The new regulations have reduced costs of statins, even though the prevalence of statin use has increased. Aims To assess the changes in prescribing of statins in Norway after implementation of the new reimbursement regulations for statins in June 2005. Methods Data were retrieved from the Norwegian Prescription Database covering the total population in Norway (4.6 million). Outcome measures were the proportion of atorvastatin users switching to simvastatin and changes in the proportion of new statin users receiving simvastatin. Based on retail costs for all statin prescriptions dispensed in Norway, expenditure was measured in Norwegian currency. Results One-year prevalences of statin use increased from 6.3 to 6.8% for women and from 7.5 to 8.1% for men from the year before to the year after the new statin regulations. Of atorvastatin users (N = 131 222), 39% switched to simvastatin during the 13-month period after the implementation. The proportion of switching was higher in women (41%) than in men (36%). In May 2005, 48% of the new statin users received simvastatin. The proportion of new users receiving simvastatin increased rapidly after implementation of the new regulations to 68% in June 2005 and reached 92% in June 2006

  20. Meta-analysis of genome-wide association studies of HDL cholesterol response to statins

    DEFF Research Database (Denmark)

    Postmus, Iris; Warren, Helen R; Trompet, Stella

    2016-01-01

    BACKGROUND: In addition to lowering low density lipoprotein cholesterol (LDL-C), statin therapy also raises high density lipoprotein cholesterol (HDL-C) levels. Inter-individual variation in HDL-C response to statins may be partially explained by genetic variation. METHODS AND RESULTS: We performed...... a meta-analysis of genome-wide association studies (GWAS) to identify variants with an effect on statin-induced high density lipoprotein cholesterol (HDL-C) changes. The 123 most promising signals with p

  1. Social big data mining

    CERN Document Server

    Ishikawa, Hiroshi

    2015-01-01

    Social Media. Big Data and Social Data. Hypotheses in the Era of Big Data. Social Big Data Applications. Basic Concepts in Data Mining. Association Rule Mining. Clustering. Classification. Prediction. Web Structure Mining. Web Content Mining. Web Access Log Mining, Information Extraction and Deep Web Mining. Media Mining. Scalability and Outlier Detection.

  2. Biomedical Data Mining

    NARCIS (Netherlands)

    Peek, N.; Combi, C.; Tucker, A.

    2009-01-01

    Objective: To introduce the special topic of Methods of Information in Medicine on data mining in biomedicine, with selected papers from two workshops on Intelligent Data Analysis in bioMedicine (IDAMAP) held in Verona (2006) and Amsterdam (2007). Methods: Defining the field of biomedical data

  3. Clinical Profile of Statin Intolerance in the Phase 3 GAUSS-2 Study.

    Science.gov (United States)

    Cho, Leslie; Rocco, Michael; Colquhoun, David; Sullivan, David; Rosenson, Robert S; Dent, Ricardo; Xue, Allen; Scott, Rob; Wasserman, Scott M; Stroes, Erik

    2016-06-01

    Recent evidence suggests that statin intolerance may be more common than reported in randomized trials. However, the statin-intolerant population is not well characterized. The goal of this report is to characterize the population enrolled in the phase 3 Goal Achievement after Utilizing an anti-PCSK9 antibody in Statin Intolerant Subjects Study (GAUSS-2; NCT 01763905). GAUSS-2 compared evolocumab, a fully human monoclonal antibody to proprotein convertase subtilisin/kexin type 9 (PCSK9) to ezetimibe in hypercholesterolemic patients who discontinued statin therapy due to statin-associated muscle symptoms (SAMS). GAUSS-2 was a 12-week, double-blind, placebo-controlled, randomized study that enrolled patients with elevated LDL-C who were either not on a statin or able to tolerate only a low-dose due to SAMS. Patients had received ≥2 statins and were unable to tolerate any statin dose or increase in dose above a specified weekly dose due to SAMS. Three hundred seven patients (mean [SD] age, 62 [10] years; 54 % males) were randomized 2:1 (evolocumab:ezetimibe). Mean (SD) LDL-C was 4.99 (1.51) mmol/L. Patients had used ≥2 (100 %), ≥3 (55 %), or ≥4 (21 %) statins. Coronary artery disease was present in 29 % of patients. Statin-intolerant symptoms were myalgia in 80 % of patients, weakness in 39 %, and more serious complications in 20 %. In 98 % of patients, SAMS interfered with normal daily activity; in 52 %, symptoms precluded moderate exertion. Evaluation of the GAUSS-2 trial population of statin-intolerant patients demonstrates that most patients were high risk with severely elevated LDL-C and many had statin-associated muscle symptoms that interfered with their quality of life.

  4. Security Measures in Data Mining

    OpenAIRE

    Anish Gupta; Vimal Bibhu; Rashid Hussain

    2012-01-01

    Data mining is a technique to dig the data from the large databases for analysis and executive decision making. Security aspect is one of the measure requirement for data mining applications. In this paper we present security requirement measures for the data mining. We summarize the requirements of security for data mining in tabular format. The summarization is performed by the requirements with different aspects of security measure of data mining. The performances and outcomes are determin...

  5. Reduced Risk of Barrett's Esophagus in Statin Users: Case-Control Study and Meta-Analysis.

    Science.gov (United States)

    Beales, Ian L P; Dearman, Leanne; Vardi, Inna; Loke, Yoon

    2016-01-01

    Use of statins has been associated with a reduced incidence of esophageal adenocarcinoma in population-based studies. However there are few studies examining statin use and the development of Barrett's esophagus. The purpose of this study was to examine the association between statin use and the presence of Barrett's esophagus in patients having their first gastroscopy. We have performed a case-control study comparing statin use between patients with, and without, an incident diagnosis of non-dysplastic Barrett's esophagus. Male Barrett's cases (134) were compared to 268 male age-matched controls in each of two control groups (erosive gastro-esophageal reflux and dyspepsia without significant upper gastrointestinal disease). Risk factor and drug exposure were established using standardised interviews. Logistic regression was used to compare statin exposure and correct for confounding factors. We performed a meta-analysis pooling our results with three other case-control studies. Regular statin use was associated with a significantly lower incidence of Barrett's esophagus compared to the combined control groups [adjusted OR 0.62 (95 % confidence intervals 0.37-0.93)]. This effect was more marked in combined statin plus aspirin users [adjusted OR 0.43 (95 % CI 0.21-0.89)]. The inverse association between statin or statin plus aspirin use and risk of Barrett's was significantly greater with longer duration of use. Meta-analysis of pooled data (1098 Barrett's, 2085 controls) showed that statin use was significantly associated with a reduced risk of Barrett's esophagus [pooled adjusted OR 0.63 (95 % CI 0.51-0.77)]. Statin use is associated with a reduced incidence of a new diagnosis of Barrett's esophagus.

  6. Gestão do conhecimento usando data mining: estudo de caso na Universidade Federal de Lavras Knowledge management using data mining: a case study of the Federal University of Lavras

    Directory of Open Access Journals (Sweden)

    Olinda Nogueira Paes Cardoso

    2008-06-01

    Full Text Available A gestão do conhecimento abrange toda a forma de gerar, armazenar, distribuir e utilizar o conhecimento, tornando necessária a utilização de tecnologias de informação para facilitar esse processo, devido ao grande aumento no volume de dados. A descoberta de conhecimento em banco de dados é uma metodologia que tenta solucionar esse problema e o data mining é uma técnica que faz parte dessa metodologia. Este artigo desenvolve, aplica e analisa uma ferramenta de data mining, para extrair conhecimento referente à produção científica das pessoas envolvidas com a pesquisa na Universidade Federal de Lavras. A metodologia utilizada envolveu a pesquisa bibliográfica, a pesquisa documental e o método do estudo de caso. As limitações encontradas na análise dos resultados indicam que ainda é preciso padronizar o modo do preenchimento dos currículos Lattes para refinar as análises e, com isso, estabelecer indicadores. A contribuição foi gerar um banco de dados estruturado, que faz parte de um processo maior de desenvolvimento de indicadores de ciência e tecnologia, para auxiliar na elaboração de novas políticas de gestão científica e tecnológica e aperfeiçoamento do sistema de ensino superior brasileiro.The management of knowledge embraces every form of production, storage, distribution and use of the knowledge, making necessary the use of information technologies to facilitate the process, due to the great increase in the volume of data. An emergent methodology that tries to solve the problem of the analysis of great amounts of data is the knowledge discovery in database (KDD and data mining, a technique that is part of this methodology. This article aims to develop, apply and analyze a tool of data mining, to extract knowledge regarding people's scientific production involved with the research at the Federal University of Lavras (Ufla. The methodology used involved bibliographical research, documental research, and method of

  7. Statin use after acute myocardial infarction: a nationwide study in Denmark

    DEFF Research Database (Denmark)

    Rasmussen, Jeppe Nørgaard; Gislason, Gunnar H; Abildstrom, Steen Z

    2005-01-01

    AIMS: To study outpatient statin use after first acute myocardial infarction (AMI) in Denmark between 1995 and 2002 and to determine the predictors of statin use. METHODS: This is a nationwide population-based study using administrative registries. Patients with first AMI between 1995 and 2002 ol...

  8. Data mining and gap analysis for weather responsive traffic management studies.

    Science.gov (United States)

    2010-12-01

    Weather causes a variety of impacts on the transportation system. An Oak Ridge National Laboratory study estimated the : delay experienced by American drivers due to snow, ice, and fog in 1999 at 46 million hours. While severe winter storms, : hurric...

  9. Data mining in Cloud Computing

    Directory of Open Access Journals (Sweden)

    Ruxandra-Ştefania PETRE

    2012-10-01

    Full Text Available This paper describes how data mining is used in cloud computing. Data Mining is used for extracting potentially useful information from raw data. The integration of data mining techniques into normal day-to-day activities has become common place. Every day people are confronted with targeted advertising, and data mining techniques help businesses to become more efficient by reducing costs.Data mining techniques and applications are very much needed in the cloud computing paradigm. The implementation of data mining techniques through Cloud computing will allow the users to retrieve meaningful information from virtually integrated data warehouse that reduces the costs of infrastructure and storage.

  10. Data Mining and Analysis

    Science.gov (United States)

    Samms, Kevin O.

    2015-01-01

    The Data Mining project seeks to bring the capability of data visualization to NASA anomaly and problem reporting systems for the purpose of improving data trending, evaluations, and analyses. Currently NASA systems are tailored to meet the specific needs of its organizations. This tailoring has led to a variety of nomenclatures and levels of annotation for procedures, parts, and anomalies making difficult the realization of the common causes for anomalies. Making significant observations and realizing the connection between these causes without a common way to view large data sets is difficult to impossible. In the first phase of the Data Mining project a portal was created to present a common visualization of normalized sensitive data to customers with the appropriate security access. The tool of the visualization itself was also developed and fine-tuned. In the second phase of the project we took on the difficult task of searching and analyzing the target data set for common causes between anomalies. In the final part of the second phase we have learned more about how much of the analysis work will be the job of the Data Mining team, how to perform that work, and how that work may be used by different customers in different ways. In this paper I detail how our perspective has changed after gaining more insight into how the customers wish to interact with the output and how that has changed the product.

  11. Organizational Data Mining

    Science.gov (United States)

    Nemati, Hamid R.; Barko, Christopher D.

    Many organizations today possess substantial quantities of business information but have very little real business knowledge. A recent survey of 450 business executives reported that managerial intuition and instinct are more prevalent than hard facts in driving organizational decisions. To reverse this trend, businesses of all sizes would be well advised to adopt Organizational Data Mining (ODM). ODM is defined as leveraging Data Mining tools and technologies to enhance the decision-making process by transforming data into valuable and actionable knowledge to gain a competitive advantage. ODM has helped many organizations optimize internal resource allocations while better understanding and responding to the needs of their customers. The fundamental aspects of ODM can be categorized into Artificial Intelligence (AI), Information Technology (IT), and Organizational Theory (OT), with OT being the key distinction between ODM and Data Mining. In this chapter, we introduce ODM, explain its unique characteristics, and report on the current status of ODM research. Next we illustrate how several leading organizations have adopted ODM and are benefiting from it. Then we examine the evolution of ODM to the present day and conclude our chapter by contemplating ODM's challenging yet opportunistic future.

  12. DATA MINING AND STATISTICS METHODS USAGE FOR ADVANCED TRAINING COURSES QUALITY MEASUREMENT: CASE STUDY

    Directory of Open Access Journals (Sweden)

    Maxim I. Galchenko

    2014-01-01

    Full Text Available In the article we consider a case of the analysis of the data connected with educational statistics, namely – result of professional development courses students survey with specialized software usage. Need for expanded statistical results processing, the scheme of carrying out the analysis is shown. Conclusions on a studied case are presented. 

  13. A novel data mining method to identify assay-specific signatures in functional genomic studies

    Directory of Open Access Journals (Sweden)

    Guidarelli Jack W

    2006-08-01

    Full Text Available Abstract Background: The highly dimensional data produced by functional genomic (FG studies makes it difficult to visualize relationships between gene products and experimental conditions (i.e., assays. Although dimensionality reduction methods such as principal component analysis (PCA have been very useful, their application to identify assay-specific signatures has been limited by the lack of appropriate methodologies. This article proposes a new and powerful PCA-based method for the identification of assay-specific gene signatures in FG studies. Results: The proposed method (PM is unique for several reasons. First, it is the only one, to our knowledge, that uses gene contribution, a product of the loading and expression level, to obtain assay signatures. The PM develops and exploits two types of assay-specific contribution plots, which are new to the application of PCA in the FG area. The first type plots the assay-specific gene contribution against the given order of the genes and reveals variations in distribution between assay-specific gene signatures as well as outliers within assay groups indicating the degree of importance of the most dominant genes. The second type plots the contribution of each gene in ascending or descending order against a constantly increasing index. This type of plots reveals assay-specific gene signatures defined by the inflection points in the curve. In addition, sharp regions within the signature define the genes that contribute the most to the signature. We proposed and used the curvature as an appropriate metric to characterize these sharp regions, thus identifying the subset of genes contributing the most to the signature. Finally, the PM uses the full dataset to determine the final gene signature, thus eliminating the chance of gene exclusion by poor screening in earlier steps. The strengths of the PM are demonstrated using a simulation study, and two studies of real DNA microarray data – a study of

  14. Statin Use and Self-Reported Hindering Muscle Complaints in Older Persons: A Population Based Study.

    Directory of Open Access Journals (Sweden)

    Milly A van der Ploeg

    Full Text Available Statins are widely used by older persons in primary and secondary prevention of cardiovascular disease. Although serious adverse events are rare, many statin users report mild muscle pain and/or muscle weakness. It's unclear what impact statins exert on a patient's daily life. Research on statin related side effects in older persons is relatively scarce. We therefore investigated the relation between statin use and self-reported hindering muscle complaints in older persons in the general population.The present research was performed within the Integrated Systematic Care for Older Persons (ISCOPE study in the Netherlands (Netherlands trial register, NTR1946. All registered adults aged ≥ 75 years from 59 participating practices (n = 12,066 were targeted. Information about the medical history and statin use at baseline and after 9 months was available for 4355 participants from the Electronic Patient Records of the general practitioners. In the screening questionnaire at baseline we asked participants: 'At the moment, which health complaints limit you the most in your day-to-day life?' Answers indicating muscle or musculoskeletal complaints were coded as such. No specific questions about muscle complaints were asked.The participants had a median age of 80.3 (IQR 77.6-84.4 years, 60.8% were female and 28.5% had a history of CVD. At baseline 29% used a statin. At follow-up, no difference was found in the prevalence of self-reported hindering muscle complaints in statin users compared to non-statin users (3.3% vs. 2.5%, OR 1.39, 95% CI 0.94-2.05; P = 0.98. Discontinuation of statin use during follow-up was independent of self-reported hindering muscle complaints.Based on the present findings, prevalent statin use in this community-dwelling older population is not associated with self-reported hindering muscle complaints; however, the results might be different for incident users.

  15. Statin Use and Self-Reported Hindering Muscle Complaints in Older Persons: A Population Based Study.

    Science.gov (United States)

    van der Ploeg, Milly A; Poortvliet, Rosalinde K E; van Blijswijk, Sophie C E; den Elzen, Wendy P J; van Peet, Petra G; de Ruijter, Wouter; Blom, Jeanet W; Gussekloo, Jacobijn

    2016-01-01

    Statins are widely used by older persons in primary and secondary prevention of cardiovascular disease. Although serious adverse events are rare, many statin users report mild muscle pain and/or muscle weakness. It's unclear what impact statins exert on a patient's daily life. Research on statin related side effects in older persons is relatively scarce. We therefore investigated the relation between statin use and self-reported hindering muscle complaints in older persons in the general population. The present research was performed within the Integrated Systematic Care for Older Persons (ISCOPE) study in the Netherlands (Netherlands trial register, NTR1946). All registered adults aged ≥ 75 years from 59 participating practices (n = 12,066) were targeted. Information about the medical history and statin use at baseline and after 9 months was available for 4355 participants from the Electronic Patient Records of the general practitioners. In the screening questionnaire at baseline we asked participants: 'At the moment, which health complaints limit you the most in your day-to-day life?' Answers indicating muscle or musculoskeletal complaints were coded as such. No specific questions about muscle complaints were asked. The participants had a median age of 80.3 (IQR 77.6-84.4) years, 60.8% were female and 28.5% had a history of CVD. At baseline 29% used a statin. At follow-up, no difference was found in the prevalence of self-reported hindering muscle complaints in statin users compared to non-statin users (3.3% vs. 2.5%, OR 1.39, 95% CI 0.94-2.05; P = 0.98). Discontinuation of statin use during follow-up was independent of self-reported hindering muscle complaints. Based on the present findings, prevalent statin use in this community-dwelling older population is not associated with self-reported hindering muscle complaints; however, the results might be different for incident users.

  16. [A comparative study of maintenance services using the data-mining technique].

    Science.gov (United States)

    Cruz, Antonio M; Aguilera-Huertas, Wilmer A; Días-Mora, Darío A

    2009-08-01

    The main goal in this research was comparing two hospitals' maintenance service quality. One of them had a contract service; the other one had an in-house maintenance service. The authors followed the next stages when conducting this research: domain understanding, data characterisation and sample reduction, insight characterisation and building the TAT predictor. Multiple linear regression and clustering techniques were used for improving the efficiency of corrective maintenance tasks in a clinical engineering department (CED). The indicator being studied was turnaround time (TAT). The institution having an in-house maintenance service had better quality indicators than the contract maintenance service. There was lineal dependence between availability and service productivity.

  17. Study of Railway Track Irregularity Standard Deviation Time Series Based on Data Mining and Linear Model

    Directory of Open Access Journals (Sweden)

    Jia Chaolong

    2013-01-01

    Full Text Available Good track geometry state ensures the safe operation of the railway passenger service and freight service. Railway transportation plays an important role in the Chinese economic and social development. This paper studies track irregularity standard deviation time series data and focuses on the characteristics and trend changes of track state by applying clustering analysis. Linear recursive model and linear-ARMA model based on wavelet decomposition reconstruction are proposed, and all they offer supports for the safe management of railway transportation.

  18. Association between acute statin therapy, survival, and improved functional outcome after ischemic stroke: the North Dublin Population Stroke Study.

    LENUS (Irish Health Repository)

    2011-04-01

    Statins improve infarct volume and neurological outcome in animal stroke models. We investigated the relationship between statin therapy and ischemic stroke outcome in the North Dublin Population Stroke Study.

  19. Prediction by data mining, of suicide attempts in Korean adolescents: a national study

    Directory of Open Access Journals (Sweden)

    Bae SM

    2015-09-01

    Full Text Available Sung Man Bae,1 Seung A Lee,2 Seung-Hwan Lee2,3 1Department of Counseling Psychology, The Cyber University of Korea, Seoul, South Korea; 2Clinical Emotion and Cognition Research Laboratory, Goyang, South Korea; 3Department of Psychiatry, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, South Korea Objective: This study aimed to develop a prediction model for suicide attempts in Korean adolescents.Methods: We conducted a decision tree analysis of 2,754 middle and high school students nationwide. We fixed suicide attempt as the dependent variable and eleven sociodemographic, intrapersonal, and extrapersonal variables as independent variables.Results: The rate of suicide attempts of the total sample was 9.5%, and severity of depression was the strongest variable to predict suicide attempt. The rates of suicide attempts in the depression and potential depression groups were 5.4 and 2.8 times higher than that of the non-depression group. In the depression group, the most powerful factor to predict a suicide attempt was delinquency, and the rate of suicide attempts in those in the depression group with higher delinquency was two times higher than in those in the depression group with lower delinquency. Of special note, the rate of suicide attempts in the depressed females with higher delinquency was the highest. Interestingly, in the potential depression group, the most impactful factor to predict a suicide attempt was intimacy with family, and the rate of suicide attempts of those in the potential depression group with lower intimacy with family was 2.4 times higher than that of those in the potential depression group with higher intimacy with family. And, among the potential depression group, middle school students with lower intimacy with family had a 2.5-times higher rate of suicide attempts than high school students with lower intimacy with family. Finally, in the non-depression group, stress level was the most powerful factor to

  20. Unsupervised Data Mining in nanoscale X-ray Spectro-Microscopic Study of NdFeB Magnet.

    Science.gov (United States)

    Duan, Xiaoyue; Yang, Feifei; Antono, Erin; Yang, Wenge; Pianetta, Piero; Ermon, Stefano; Mehta, Apurva; Liu, Yijin

    2016-09-29

    Novel developments in X-ray based spectro-microscopic characterization techniques have increased the rate of acquisition of spatially resolved spectroscopic data by several orders of magnitude over what was possible a few years ago. This accelerated data acquisition, with high spatial resolution at nanoscale and sensitivity to subtle differences in chemistry and atomic structure, provides a unique opportunity to investigate hierarchically complex and structurally heterogeneous systems found in functional devices and materials systems. However, handling and analyzing the large volume data generated poses significant challenges. Here we apply an unsupervised data-mining algorithm known as DBSCAN to study a rare-earth element based permanent magnet material, Nd 2 Fe 14 B. We are able to reduce a large spectro-microscopic dataset of over 300,000 spectra to 3, preserving much of the underlying information. Scientists can easily and quickly analyze in detail three characteristic spectra. Our approach can rapidly provide a concise representation of a large and complex dataset to materials scientists and chemists. For example, it shows that the surface of common Nd 2 Fe 14 B magnet is chemically and structurally very different from the bulk, suggesting a possible surface alteration effect possibly due to the corrosion, which could affect the material's overall properties.

  1. Unsupervised Data Mining in nanoscale X-ray Spectro-Microscopic Study of NdFeB Magnet

    Science.gov (United States)

    Duan, Xiaoyue; Yang, Feifei; Antono, Erin; Yang, Wenge; Pianetta, Piero; Ermon, Stefano; Mehta, Apurva; Liu, Yijin

    2016-09-01

    Novel developments in X-ray based spectro-microscopic characterization techniques have increased the rate of acquisition of spatially resolved spectroscopic data by several orders of magnitude over what was possible a few years ago. This accelerated data acquisition, with high spatial resolution at nanoscale and sensitivity to subtle differences in chemistry and atomic structure, provides a unique opportunity to investigate hierarchically complex and structurally heterogeneous systems found in functional devices and materials systems. However, handling and analyzing the large volume data generated poses significant challenges. Here we apply an unsupervised data-mining algorithm known as DBSCAN to study a rare-earth element based permanent magnet material, Nd2Fe14B. We are able to reduce a large spectro-microscopic dataset of over 300,000 spectra to 3, preserving much of the underlying information. Scientists can easily and quickly analyze in detail three characteristic spectra. Our approach can rapidly provide a concise representation of a large and complex dataset to materials scientists and chemists. For example, it shows that the surface of common Nd2Fe14B magnet is chemically and structurally very different from the bulk, suggesting a possible surface alteration effect possibly due to the corrosion, which could affect the material’s overall properties.

  2. Data mining in the study of nuclear fuel cells; Mineria de datos en el estudio de celdas de combustible nuclear

    Energy Technology Data Exchange (ETDEWEB)

    Medina P, J. A. [Universidad Autonoma de Campeche, Av. Agustin Melgar s/n, Col. Buenavista, 24039 San Francisco de Campeche, Campeche (Mexico); Ortiz S, J. J.; Castillo, A.; Montes T, J. L.; Perusquia, R., E-mail: j.angel.mp@hotmail.com [ININ, Departamento de Sistemas Nucleares, Carretera Mexico-Toluca s/n, 52750 Ocoyoacac, Estado de Mexico (Mexico)

    2015-09-15

    In this paper is presented a study of data mining application in the analysis of fuel cells and their performance within a nuclear boiling water reactor. A decision tree was used to fulfill questions of the type If (condition) and Then (conclusion) to classify if the fuel cells will have good performance. The performance is measured by compliance or not of the cold shutdown margin, the rate of linear heat generation and the average heat generation in a plane of the reactor. It is assumed that the fuel cells are simulated in the reactor under a fuel reload and rod control patterns pre designed. 18125 fuel cells were simulated according to a steady-state calculation. The decision tree works on a target variable which is one of the three mentioned before. To analyze this objective, the decision tree works with a set of attribute variables. In this case, the attributes are characteristics of the cell as number of gadolinium rods, rods number with certain uranium enrichment mixed with a concentration of gadolinium, etc. The found model was able to predict the execution or not of the shutdown margin with a precision of around 95%. However, the other two variables showed lower percentages due to few learning cases of the model in which these variables were or were not achieved. Even with this inconvenience, the model is quite reliable and can be used in way coupled in optimization systems of fuel cells. (Author)

  3. Statin therapy and mortality in HIV-infected individuals; a Danish nationwide population-based cohort study

    DEFF Research Database (Denmark)

    Rasmussen, Line; Kronborg, Gitte; Larsen, Carsten S

    2013-01-01

    Recent studies have suggested that statins possess diverse immune modulatory and anti-inflammatory properties. As statins might attenuate inflammation, statin therapy has been hypothesized to reduce mortality in HIV-infected individuals. We therefore used a Danish nationwide cohort of HIV......-infected individuals to estimate the impact of statin use on mortality before and after a diagnosis of cardiovascular disease, chronic kidney disease or diabetes....

  4. Vitamin D status modifies the association between statin use and musculoskeletal pain: a population based study.

    Science.gov (United States)

    Morioka, Travis Y; Lee, Alice J; Bertisch, Suzanne; Buettner, Catherine

    2015-01-01

    Past studies examining the effect of vitamin D on statin myalgia have been variable; however, these studies were done in limited samples not representative of the general population. We aimed to evaluate whether vitamin D status modifies the association between statin use and musculoskeletal pain in a sample representative of the general population. We conducted a cross-sectional study using the National Health and Nutrition Examination Survey 2001-2004. Musculoskeletal symptoms and statin use were self-reported. Vitamin D status was assessed using serum 25 hydroxyvitamin D (25[OH]D), categorized as statin use and prevalent musculoskeletal pain, we performed multivariable-adjusted logistic regression models stratified by 25(OH)D status. Among 5907 participants ≥40 years old, mean serum 25(OH)D was 23.6 ng/mL (95% CI, 22.9-24.3). In stratified multivariable-adjusted logistic regression models, individuals with 25(OH)D statin had a significantly higher odds of musculoskeletal pain compared to those not using a statin (adjusted odds ratio [aOR], 1.90; 95% CI, 1.18-3.05). Among those with 25(OH)D ≥15 ng/mL, we found no significant association between statin use and musculoskeletal pain (aOR, 0.91; 95% CI, 0.71-1.16). Among adults ≥ 40 years old with 25(OH)D statin users had nearly 2 times greater odds of reporting musculoskeletal pain compared to non-statin users. Our findings support the hypothesis that vitamin D deficiency modifies the risk of musculoskeletal symptoms experienced with statin use. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  5. Safety of statins

    Directory of Open Access Journals (Sweden)

    Debasish Maji

    2013-01-01

    Full Text Available Statins are an established class of drugs with proven efficacy in cardiovascular risk reduction. The concern over statin safety was first raised with the revelation of myopathy and rhabdomyolysis with the use of now withdrawn cerivastatin. Enhanced understanding of the mechanisms behind adverse effects of statins including an insight into the pharmacokinetic properties have minimised fear of statin use among clinicians. Studies reveal that occurrence of myopathy and rhabdomyolysis are rare 1/100000 patient-years. The risk of myopathy/rhabdomyolysis varies between statins due to varying pharmacokinetic profiles. This explains the differing abilities of statins to adverse effects and drug interaction potentials that precipitate adverse effects. Higher dose of rosuvastatin (80 mg/day was associated with proteinuria and hematuria while lower doses were devoid of such effects. Awareness of drugs interacting with statins and knowledge of certain combinations such as statin and fibrates together with monitoring of altered creatine kinase activity may greatly minimise associated adverse effects. Statins also asymptomatically raise levels of hepatic transaminases but are not correlated with hepatotoxicity. Statins are safe and well tolerated including more recent potent statins such as, rosuvastatin. The benefits of intensive statin use in cardiovascular risk reduction greatly outweigh risks. The present review discusses underlying causes of statin-associated adverse effects including management in high risk groups.

  6. A text-based data mining and toxicity prediction modeling system for a clinical decision support in radiation oncology: A preliminary study

    Science.gov (United States)

    Kim, Kwang Hyeon; Lee, Suk; Shim, Jang Bo; Chang, Kyung Hwan; Yang, Dae Sik; Yoon, Won Sup; Park, Young Je; Kim, Chul Yong; Cao, Yuan Jie

    2017-08-01

    The aim of this study is an integrated research for text-based data mining and toxicity prediction modeling system for clinical decision support system based on big data in radiation oncology as a preliminary research. The structured and unstructured data were prepared by treatment plans and the unstructured data were extracted by dose-volume data image pattern recognition of prostate cancer for research articles crawling through the internet. We modeled an artificial neural network to build a predictor model system for toxicity prediction of organs at risk. We used a text-based data mining approach to build the artificial neural network model for bladder and rectum complication predictions. The pattern recognition method was used to mine the unstructured toxicity data for dose-volume at the detection accuracy of 97.9%. The confusion matrix and training model of the neural network were achieved with 50 modeled plans (n = 50) for validation. The toxicity level was analyzed and the risk factors for 25% bladder, 50% bladder, 20% rectum, and 50% rectum were calculated by the artificial neural network algorithm. As a result, 32 plans could cause complication but 18 plans were designed as non-complication among 50 modeled plans. We integrated data mining and a toxicity modeling method for toxicity prediction using prostate cancer cases. It is shown that a preprocessing analysis using text-based data mining and prediction modeling can be expanded to personalized patient treatment decision support based on big data.

  7. Data mining and education.

    Science.gov (United States)

    Koedinger, Kenneth R; D'Mello, Sidney; McLaughlin, Elizabeth A; Pardos, Zachary A; Rosé, Carolyn P

    2015-01-01

    An emerging field of educational data mining (EDM) is building on and contributing to a wide variety of disciplines through analysis of data coming from various educational technologies. EDM researchers are addressing questions of cognition, metacognition, motivation, affect, language, social discourse, etc. using data from intelligent tutoring systems, massive open online courses, educational games and simulations, and discussion forums. The data include detailed action and timing logs of student interactions in user interfaces such as graded responses to questions or essays, steps in rich problem solving environments, games or simulations, discussion forum posts, or chat dialogs. They might also include external sensors such as eye tracking, facial expression, body movement, etc. We review how EDM has addressed the research questions that surround the psychology of learning with an emphasis on assessment, transfer of learning and model discovery, the role of affect, motivation and metacognition on learning, and analysis of language data and collaborative learning. For example, we discuss (1) how different statistical assessment methods were used in a data mining competition to improve prediction of student responses to intelligent tutor tasks, (2) how better cognitive models can be discovered from data and used to improve instruction, (3) how data-driven models of student affect can be used to focus discussion in a dialog-based tutoring system, and (4) how machine learning techniques applied to discussion data can be used to produce automated agents that support student learning as they collaborate in a chat room or a discussion board. © 2015 John Wiley & Sons, Ltd.

  8. Factores de éxito de un emprendimiento: Un estudio exploratorio con base en técnicas de data mining (Entrepreneurial success factors: An exploratory study based on Data Mining Techniques

    Directory of Open Access Journals (Sweden)

    María Messina

    2015-04-01

    Full Text Available El Centro de Emprendedurismo CCEEmprende de- sarrolla, desde 2007, un programa de apoyo a emprende- dores. Para mejorar su gestión, resulta de gran importancia analizar, en forma preliminar, los emprendimientos en una de dos categorías: éxito o fracaso. En este artículo se identifican los principales factores asociados al éxito de un emprendimiento y cómo se vincu- lan para anticipar el futuro del emprendimiento. Se presenta un caso de estudio con base en los datos de una encuesta realizada a emprendedores participantes del programa, aplicando técnicas de clasificación. Las dos técnicas utilizadas de data mining son árbol de decisión y regresión logística, en ambas se obtuvieron resultados coincidentes. Los hallazgos muestran que los dos elementos más relevantes para anticipar el éxito de un emprendimiento son contar con financiamiento y que, anteriormente, la situa- ción laboral del emprendedor sea trabajador independiente. Estos primeros resultados obtenidos en el estudio de caso revelan información útil acerca de las mejores formas de apoyo al emprendedor, cómo generar incentivos al em- prendedor y la definición de herramientas o actividades que incidan favorablemente en el éxito de los emprendimientos. Si bien desde la teoría o para otras realidades existe infor- mación sobre los factores que colaboran en la determina- ción del éxito, para la realidad del Uruguay no se identifican estudios similares.   Abstract  Since 2007, the CCEE Entrepreneurship Centre has developed a supporting program for entrepreneurs. A preliminary analysis to determine if the venture was successful or a failure is made to improve the program’s management . In this article, the authors identify the main factors associated with entrepreneurship’s success, and how they can anticipate entrepreneurship’s performance. The case study is based on a survey data applied to the Entrepreneurship Program participants. The two data mining

  9. Genetically Guided Statin Therapy on Statin Perceptions, Adherence, and Cholesterol Lowering: A Pilot Implementation Study in Primary Care Patients

    Directory of Open Access Journals (Sweden)

    Josephine H. Li

    2014-03-01

    Full Text Available Statin adherence is often limited by side effects. The SLCO1B1*5 variant is a risk factor for statin side effects and exhibits statin-specific effects: highest with simvastatin/atorvastatin and lowest with pravastatin/rosuvastatin. The effects of SLCO1B1*5 genotype guided statin therapy (GGST are unknown. Primary care patients (n = 58 who were nonadherent to statins and their providers received SLCO1B1*5 genotyping and guided recommendations via the electronic medical record (EMR. The primary outcome was the change in Beliefs about Medications Questionnaire, which measured patients’ perceived needs for statins and concerns about adverse effects, measured before and after SLCO1B1*5 results. Concurrent controls (n = 59 were identified through the EMR to compare secondary outcomes: new statin prescriptions, statin utilization, and change in LDL-cholesterol (LDL-c. GGST patients had trends (p = 0.2 towards improved statin necessity and concerns. The largest changes were the “need for statin to prevent sickness” (p < 0.001 and “concern for statin to disrupt life” (p = 0.006. GGST patients had more statin prescriptions (p < 0.001, higher statin use (p < 0.001, and greater decrease in LDL-c (p = 0.059 during follow-up. EMR delivery of SLCO1B1*5 results and recommendations is feasible in the primary care setting. This novel intervention may improve patients’ perceptions of statins and physician behaviors that promote higher statin adherence and lower LDL-c.

  10. On data mining in context : cases, fusion and evaluation

    NARCIS (Netherlands)

    Putten, Petrus Wilhelmus Henricus van der

    2010-01-01

    Data mining can be seen as a process, with modeling as the core step. However, other steps such as planning, data preparation, evaluation and deployment are of key importance for applications. This thesis studies data mining in the context of these other steps with the goal of improving data mining

  11. Statin use and breast cancer risk in the Nurses’ Health Study

    Science.gov (United States)

    Borgquist, Signe; Tamimi, Rulla M.; Chen, Wendy Y.; Garber, Judy E.; Eliassen, A. Heather; Ahern, Thomas P.

    2016-01-01

    Pre-clinical studies support an anti-cancer effect of statin drugs, yet epidemiological evidence remains inconsistent regarding their role in breast cancer primary prevention. Here we report an updated analysis of the association between statin use and breast cancer incidence in the Nurses’ Health Study cohort. Post-menopausal Nurses’ Health Study participants without a cancer history were followed from 2000 until 2012 (n=79,518). Data on statin use were retrieved from biennial questionnaires. We fit Cox regression models to estimate associations between longitudinal statin use and breast cancer incidence. Over 823,086 person-years of follow-up, 3,055 cases of invasive breast cancer occurred. Compared with never users, both former and current statin users had similar rates of invasive breast cancer incidence (former users: HRadj=0.96, 95% CI: 0.82, 1.1; current users: HRadj=1.1, 95% CI: 0.92, 1.3). Associations did not differ by estrogen receptor status or histology (ductal vs. lobular carcinoma). Statin use was not associated with risk of invasive breast cancer, irrespective of histological subtype and ER status. Statin drugs do not appear to modify processes involved in breast cancer initiation. PMID:26762806

  12. Statin Safety in Chinese: A Population-Based Study of Older Adults.

    Science.gov (United States)

    Li, Daniel Q; Kim, Richard B; McArthur, Eric; Fleet, Jamie L; Hegele, Robert A; Shah, Baiju R; Weir, Matthew A; Molnar, Amber O; Dixon, Stephanie; Tu, Jack V; Anand, Sonia; Garg, Amit X

    2016-01-01

    Compared to Caucasians, Chinese achieve a higher blood concentration of statin for a given dose. It remains unknown whether this translates to increased risk of serious statin-associated adverse events amongst Chinese patients. We conducted a population-based retrospective cohort study of older adults (mean age, 74 years) newly prescribed a statin in Ontario, Canada between 2002 and 2013, where 19,033 Chinese (assessed through a validated surname algorithm) were matched (1:3) by propensity score to 57,099 non-Chinese. This study used linked healthcare databases. The follow-up observation period (mean 1.1, maximum 10.8 years) was similar between groups, as were the reasons for censoring the observation period (end of follow-up, death, or statin discontinuation). Forty-seven percent (47%) of Chinese were initiated on a higher than recommended statin dose. Compared to non-Chinese, Chinese ethnicity did not associate with any of the four serious statin-associated adverse events assessed in this study [rhabdomyolysis hazard ratio (HR) 0.61 (95% CI 0.28 to 1.34), incident diabetes HR 1.02 (95% CI 0.80 to 1.30), acute kidney injury HR 0.90 (95% CI 0.72 to 1.13), or all-cause mortality HR 0.88 (95% CI 0.74 to 1.05)]. Similar results were observed in subgroups defined by statin type and dose. We observed no higher risk of serious statin toxicity in Chinese than matched non-Chinese older adults with similar indicators of baseline health. Regulatory agencies should review available data, including findings from our study, to decide if a change in their statin dosing recommendations for people of Chinese ethnicity is warranted.

  13. Ensemble Data Mining Methods

    Science.gov (United States)

    Oza, Nikunj C.

    2004-01-01

    Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve better prediction accuracy than any of the individual models could on their own. The basic goal when designing an ensemble is the same as when establishing a committee of people: each member of the committee should be as competent as possible, but the members should be complementary to one another. If the members are not complementary, Le., if they always agree, then the committee is unnecessary---any one member is sufficient. If the members are complementary, then when one or a few members make an error, the probability is high that the remaining members can correct this error. Research in ensemble methods has largely revolved around designing ensembles consisting of competent yet complementary models.

  14. Data Mining SIAM Presentation

    Science.gov (United States)

    Srivastava, Ashok; McIntosh, Dawn; Castle, Pat; Pontikakis, Manos; Diev, Vesselin; Zane-Ulman, Brett; Turkov, Eugene; Akella, Ram; Xu, Zuobing; Kumaresan, Sakthi Preethi

    2006-01-01

    This viewgraph document describes the data mining system developed at NASA Ames. Many NASA programs have large numbers (and types) of problem reports.These free text reports are written by a number of different people, thus the emphasis and wording vary considerably With so much data to sift through, analysts (subject experts) need help identifying any possible safety issues or concerns and help them confirm that they haven't missed important problems. Unsupervised clustering is the initial step to accomplish this; We think we can go much farther, specifically, identify possible recurring anomalies. Recurring anomalies may be indicators of larger systemic problems. The requirement to identify these anomalies has led to the development of Recurring Anomaly Discovery System (ReADS).

  15. Data mining goes multidimensional.

    Science.gov (United States)

    Hettler, M

    1997-03-01

    The success of a healthcare organization depends on its ability to acquire, store, analyze and compare data across many parts of the enterprise, by many individuals. While relational databases have been around since the 1970s, their two-dimensional structure has limited--or made impossible--the kind of cross-dimensional trend analysis so necessary to healthcare today. Enter online analytical processing (OLAP), in which servers store data in multiple dimensions, opening a world of opportunity for data-mining across the enterprise. In this issue of HEALTHCARE INFORMATICS, we feature our first report from the National Software Testing Laboratories (NSTL) about technologies that will change the way healthcare does business. A division of The McGraw-Hill Companies, NSTL is an independent software and hardware testing lab offering services that include compatibility testing, bug testing, comparison testing, documentation evaluation and usability.

  16. Patient beliefs and attitudes to taking statins: systematic review of qualitative studies.

    Science.gov (United States)

    Ju, Angela; Hanson, Camilla S; Banks, Emily; Korda, Rosemary; Craig, Jonathan C; Usherwood, Tim; MacDonald, Peter; Tong, Allison

    2018-06-01

    Statins are effective in preventing cardiovascular disease (CVD) events and are recommended for at-risk individuals but estimated adherence rates are low. To describe patients' perspectives, experiences, and attitudes towards taking statins. Systematic review of qualitative studies reporting perspectives of patients on statins. PsycINFO, CINAHL, Embase, MEDLINE, and PhD dissertations from inception to 6 October 2016 were searched for qualitative studies on adult patients' perspectives on statins. All text and participant quotations were extracted from each article and analysed by thematic synthesis. Thirty-two studies involving 888 participants aged 22-93 years across eight countries were included. Seven themes were identified: confidence in prevention (trust in efficacy, minimising long-term catastrophic CVD, taking control, easing anxiety about high cholesterol); routinising into daily life; questioning utility (imperceptible benefits, uncertainties about pharmacological mechanisms); medical distrust (scepticism about overprescribing, pressure to start therapy); threatening health (competing priorities and risks, debilitating side effects, toxicity to body); signifying sickness (fear of perpetual dependence, losing the battle); and financial strain. An expectation that statins could prevent CVD and being able to integrate the statin regimen in daily life facilitated acceptance of statins among patients. However, avoiding the 'sick' identity and prolonged dependence on medications, uncertainties about the pharmacological mechanisms, risks to health, side effects, costs, and scepticism about clinicians' motives for prescribing statins were barriers to uptake. Shared decision making that addresses the risks, reasons for prescribing, patient priorities, and implementing strategies to minimise lifestyle intrusion and manage side effects may improve patient satisfaction and continuation of statins. © British Journal of General Practice 2018.

  17. Data mining for bioinformatics applications

    CERN Document Server

    Zengyou, He

    2015-01-01

    Data Mining for Bioinformatics Applications provides valuable information on the data mining methods have been widely used for solving real bioinformatics problems, including problem definition, data collection, data preprocessing, modeling, and validation. The text uses an example-based method to illustrate how to apply data mining techniques to solve real bioinformatics problems, containing 45 bioinformatics problems that have been investigated in recent research. For each example, the entire data mining process is described, ranging from data preprocessing to modeling and result validation. Provides valuable information on the data mining methods have been widely used for solving real bioinformatics problems Uses an example-based method to illustrate how to apply data mining techniques to solve real bioinformatics problems Contains 45 bioinformatics problems that have been investigated in recent research.

  18. GWA study data mining and independent replication identify cardiomyopathy-associated 5 (CMYA5) as a risk gene for schizophrenia.

    LENUS (Irish Health Repository)

    Chen, X

    2011-11-01

    We conducted data-mining analyses using the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) and molecular genetics of schizophrenia genome-wide association study supported by the genetic association information network (MGS-GAIN) schizophrenia data sets and performed bioinformatic prioritization for all the markers with P-values ≤0.05 in both data sets. In this process, we found that in the CMYA5 gene, there were two non-synonymous markers, rs3828611 and rs10043986, showing nominal significance in both the CATIE and MGS-GAIN samples. In a combined analysis of both the CATIE and MGS-GAIN samples, rs4704591 was identified as the most significant marker in the gene. Linkage disequilibrium analyses indicated that these markers were in low LD (3 828 611-rs10043986, r(2)=0.008; rs10043986-rs4704591, r(2)=0.204). In addition, CMYA5 was reported to be physically interacting with the DTNBP1 gene, a promising candidate for schizophrenia, suggesting that CMYA5 may be involved in the same biological pathway and process. On the basis of this information, we performed replication studies for these three single-nucleotide polymorphisms. The rs3828611 was found to have conflicting results in our Irish samples and was dropped out without further investigation. The other two markers were verified in 23 other independent data sets. In a meta-analysis of all 23 replication samples (family samples, 912 families with 4160 subjects; case-control samples, 11 380 cases and 15 021 controls), we found that both markers are significantly associated with schizophrenia (rs10043986, odds ratio (OR)=1.11, 95% confidence interval (CI)=1.04-1.18, P=8.2 × 10(-4) and rs4704591, OR=1.07, 95% CI=1.03-1.11, P=3.0 × 10(-4)). The results were also significant for the 22 Caucasian replication samples (rs10043986, OR=1.11, 95% CI=1.03-1.17, P=0.0026 and rs4704591, OR=1.07, 95% CI=1.02-1.11, P=0.0015). Furthermore, haplotype conditioned analyses indicated that the association

  19. Data mining applications in healthcare.

    Science.gov (United States)

    Koh, Hian Chye; Tan, Gerald

    2005-01-01

    Data mining has been used intensively and extensively by many organizations. In healthcare, data mining is becoming increasingly popular, if not increasingly essential. Data mining applications can greatly benefit all parties involved in the healthcare industry. For example, data mining can help healthcare insurers detect fraud and abuse, healthcare organizations make customer relationship management decisions, physicians identify effective treatments and best practices, and patients receive better and more affordable healthcare services. The huge amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analyzed by traditional methods. Data mining provides the methodology and technology to transform these mounds of data into useful information for decision making. This article explores data mining applications in healthcare. In particular, it discusses data mining and its applications within healthcare in major areas such as the evaluation of treatment effectiveness, management of healthcare, customer relationship management, and the detection of fraud and abuse. It also gives an illustrative example of a healthcare data mining application involving the identification of risk factors associated with the onset of diabetes. Finally, the article highlights the limitations of data mining and discusses some future directions.

  20. Expanding the Evidence Base: Comparing Randomized Controlled Trials and Observational Studies of Statins.

    Science.gov (United States)

    Atar, Dan; Ong, Seleen; Lansberg, Peter J

    2015-01-01

    It is widely accepted that randomized controlled trials (RCTs) are the gold standard for demonstrating the efficacy of a given therapy (results under ideal conditions). Observational studies, on the other hand, can complement this by demonstrating effectiveness (results under real-world conditions). To examine the role that observational studies can play in complementing data from RCTs, we reviewed published studies for statins, a class of drugs that have been widely used to reduce the risk of cardiovascular (CV) events by lowering low-density lipoprotein cholesterol levels. RCTs have consistently demonstrated the benefits of statin treatment in terms of CV risk reduction and have demonstrated that more intensive statin therapy has incremental benefits over less intensive treatment. Observational studies of statin use in 'real-world' populations have served to augment the evidence base generated from statin RCTs in preselected populations of patients who are often at high CV risk and have led to similar safety and efficacy findings. They have also raised questions about factors affecting medication adherence, under-treatment, switching between statins, and failure to reach low-density lipoprotein cholesterol target levels, questions for which the answers could lead to improved patient care.

  1. Statin adherence and the risk of Parkinson's disease: A population-based cohort study.

    Science.gov (United States)

    Rozani, Violetta; Giladi, Nir; El-Ad, Baruch; Gurevich, Tanya; Tsamir, Judith; Hemo, Beatriz; Peretz, Chava

    2017-01-01

    While experimental data provided some compelling evidence on the benefits of statins on dopaminergic neurons, observational studies reported conflicting results regarding the potential of statins to effect the risk of Parkinson's disease (PD). To evaluate the association between changes in statin adherence over time and PD risk. A population-based cohort of new statin users (ages 40-79, years 1999-2012) was derived from a large Israeli healthcare services organization. Data included history of statin purchases and low density lipoprotein cholesterol (LDL-C) levels. Personal statin adherence was measured annually by the proportion of days covered (PDC). PD was detected employing a drug-tracer approach. Stratified (by sex, LDL-C levels at baseline and age) Cox proportional hazards models with time-dependent covariates were used to compute adjusted Hazard Ratio (HR) with 95%CI. The cohort included 232,877 individuals, 49.3% men. Mean age at first statin purchase was 56.5 (±9.8) years for men and 58.7 (±9.2) years for women. PDC distribution for the whole follow up period differed between men and women: medians 58.3% and 54.1% respectively. During a mean follow up of 7.6 (±3.4) years, 2,550 (1.1%) PD cases were identified. In a 1-year lagged analysis, we found no association between annual statin adherence and PD risk in all age-groups regardless of statin type and potency. Age-pooled HR (95%CI) for men and women with LDL-C levels at baseline ≤160mg/dL were: 0.99 (0.99-1.01), 1.01 (1.00-1.02); and for men and women with LDL-C >160mg/dL levels: 0.99 (0.98-1.01), 0.97 (0.98-1.01). Our findings suggest that statin adherence over time does not affect PD risk. Future studies should use large-scale cohorts and refining assessments of long-term profiles in statin adherence.

  2. Statin dose reduction with complementary diet therapy: A pilot study of personalized medicine

    Directory of Open Access Journals (Sweden)

    Bianca Scolaro

    2018-05-01

    Full Text Available Objective: Statin intolerance, whether real or perceived, is a growing issue in clinical practice. Our aim was to evaluate the effects of reduced-dose statin therapy complemented with nutraceuticals. Methods: First phase: Initially, 53 type 2 diabetic statin-treated patients received a supplementation with fish oil (1.7 g EPA + DHA/day, chocolate containing plant sterols (2.2 g/day, and green tea (two sachets/day for 6 weeks. Second phase: “Good responders” to supplementation were identified after multivariate analysis (n = 10, and recruited for a pilot protocol of statin dose reduction. “Good responders” were then provided with supplementation for 12 weeks: standard statin therapy was kept during the first 6 weeks and reduced by 50% from weeks 6–12. Results: First phase: After 6 weeks of supplementation, plasma LDL-C (−13.7% ± 3.7, P = .002 and C-reactive protein (−35.5% ± 5.9, P = .03 were reduced. Analysis of lathosterol and campesterol in plasma suggested that intensity of LDL-C reduction was influenced by cholesterol absorption rate rather than its synthesis. Second phase: no difference was observed for plasma lipids, inflammation, cholesterol efflux capacity, or HDL particles after statin dose reduction when compared to standard therapy. Conclusions: Although limited by the small sample size, our study demonstrates the potential for a new therapeutic approach combining lower statin dose and specific dietary compounds. Further studies should elucidate “good responders” profile as a tool for personalized medicine. This may be particularly helpful in the many patients with or at risk for CVD who cannot tolerate high dose statin therapy. Trial registration: ClinicalTrials.gov, NCT02732223. Keywords: Atherosclerosis, Omega-3 fatty acids, Plant sterols, Polyphenols, Responders

  3. Finding Gold in Data Mining

    Science.gov (United States)

    Flaherty, Bill

    2013-01-01

    Data-mining systems provide a variety of opportunities for school district personnel to streamline operations and focus on student achievement. This article describes the value of data mining for school personnel, finance departments, teacher evaluations, and in the classroom. It suggests that much could be learned about district practices if one…

  4. Data Mining for Intrusion Detection

    Science.gov (United States)

    Singhal, Anoop; Jajodia, Sushil

    Data Mining Techniques have been successfully applied in many different fields including marketing, manufacturing, fraud detection and network management. Over the past years there is a lot of interest in security technologies such as intrusion detection, cryptography, authentication and firewalls. This chapter discusses the application of Data Mining techniques to computer security. Conclusions are drawn and directions for future research are suggested.

  5. Analysis Of Data Mining For Car Sales Sparepart Using Apriori Algorithm (Case Study: PT. IDK 1 FIELD

    Directory of Open Access Journals (Sweden)

    Khairul Ummi

    2016-10-01

    Full Text Available PT. IDK 1 is one of the branch offices honda car dealership that sells various types of variants honda matic or manual car and motorcycle parts. Any sales or goods sold will be performed by inputting the database directly connected directly to the central office. But PT. IDK 1 do not know a couple items frequently purchased parts simultaneously. When the stock of spare parts which amount is low, the office is only asking them to send the stock of spare parts from the central office without knowing that the other parts if the parts were purchased then the other parts were also purchased. It was considered difficult when restocking of goods because of the many types of auto parts. Data mining techniques have been widely used to solve the existing problems with the implementation of the algorithm one A-Priori to obtain information about the association between the product of a database transaction. Sales transaction data honda car parts at PT. IDK 1 can be reprocessed using data mining applications resulting association rules is a strong link between itemset sales of spare parts so that it can provide recommendations and facilitate restocking items in the arrangement or placement of goods related to a strong interdependence.

  6. Application of data mining to the analysis of meteorological data for air quality prediction: A case study in Shenyang

    Science.gov (United States)

    Zhao, Chang; Song, Guojun

    2017-08-01

    Air pollution is one of the important reasons for restricting the current economic development. PM2.5 which is a vital factor in the measurement of air pollution is defined as a kind of suspended particulate matter with its equivalent diameter less than 25μm, which may enter the alveoli and therefore make a great impact on the human body. Meteorological factors are also one of the main factors affecting the production of PM2.5, therefore, it is essential to establish the model between meteorological factors and PM2.5 for the prediction. Data mining is a promising approach to model PM2.5 change, Shenyang which is one of the most important industrial city in Northeast China with severe air pollutions is set as the case city. Meteorological data (wind direction, wind speed, temperature, humidity, rainfall, etc.) from 2013 to 2015 and PM2.5 concentration data are used for this prediction. As to the requirements of the World Health Organization (WHO), three data mining models, whereby the predictions of PM2.5 are directly generated by the meteorological data. After assessment, the random forest model is appeared to offer better prediction performance than the other two. At last, the accuracy of the generated models are analysed.

  7. The host response in critically ill sepsis patients on statin therapy: a prospective observational study.

    Science.gov (United States)

    Wiewel, Maryse A; Scicluna, Brendon P; van Vught, Lonneke A; Hoogendijk, Arie J; Zwinderman, Aeilko H; Lutter, René; Horn, Janneke; Cremer, Olaf L; Bonten, Marc J; Schultz, Marcus J; van der Poll, Tom

    2018-01-18

    Statins can exert pleiotropic anti-inflammatory, vascular protective and anticoagulant effects, which in theory could improve the dysregulated host response during sepsis. We aimed to determine the association between prior statin use and host response characteristics in critically ill patients with sepsis. We performed a prospective observational study in 1060 patients admitted with sepsis to the mixed intensive care units (ICUs) of two hospitals in the Netherlands between January 2011 and July 2013. Of these, 351 patients (33%) were on statin therapy before admission. The host response was evaluated by measuring 23 biomarkers providing insight into key pathways implicated in sepsis pathogenesis and by analyzing whole-blood leukocyte transcriptomes in samples obtained within 24 h after ICU admission. To account for indication bias, a propensity score-matched cohort was created (N = 194 in both groups for protein biomarkers and N = 95 in both groups for gene expression analysis). Prior statin use was not associated with an altered mortality up to 90 days after admission (38.0 vs. 39.7% in the non-statin users in the propensity-matched analysis). Statin use did not modify systemic inflammatory responses, activation of the vascular endothelium or the coagulation system. The blood leukocyte genomic response, characterized by over-expression of genes involved in inflammatory and innate immune signaling pathways as well as under-expression of genes associated to T cell function, was not different between patients with and without prior statin use. Statin therapy is not associated with a modified host response in sepsis patients on admission to the ICU.

  8. Real world data mining applications

    CERN Document Server

    Abou-Nasr, Mahmoud; Stahlbock, Robert; Weiss, Gary M

    2014-01-01

    Data mining applications range from commercial to social domains, with novel applications appearing swiftly; for example, within the context of social networks. The expanding application sphere and social reach of advanced data mining raise pertinent issues of privacy and security. Present-day data mining is a progressive multidisciplinary endeavor. This inter- and multidisciplinary approach is well reflected within the field of information systems. The information systems research addresses software and hardware requirements for supporting computationally and data-intensive applications. Furthermore, it encompasses analyzing system and data aspects, and all manual or automated activities. In that respect, research at the interface of information systems and data mining has significant potential to produce actionable knowledge vital for corporate decision-making. The aim of the proposed volume is to provide a balanced treatment of the latest advances and developments in data mining; in particular, exploring s...

  9. Do statin users adhere to a healthy diet and lifestyle? The Australian Diabetes, Obesity and Lifestyle Study.

    Science.gov (United States)

    Johal, Simran; Jamsen, Kris M; Bell, J Simon; Mc Namara, Kevin P; Magliano, Dianna J; Liew, Danny; Ryan-Atwood, Taliesin E; Anderson, Claire; Ilomäki, Jenni

    2017-04-01

    Background Lifestyle and dietary advice typically precedes or accompanies the prescription of statin medications. However, evidence for adherence to this advice is sparse. The objective was to compare saturated fat intake, exercise, alcohol consumption and smoking between statin users and non-users in Australia. Methods Data were analysed for 4614 participants aged ≥37 years in the Australian Diabetes, Obesity and Lifestyle study in 2011-2012. Statin use, smoking status and physical activity were self-reported. Saturated fat and alcohol intake were measured via a food frequency questionnaire. Multinomial logistic regression was used to compute adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for the associations between statin use and the four lifestyle factors. All models were adjusted for age, sex, education, number of general practitioner visits, body mass index, hypertension, diabetes and prior cardiovascular diseases. Results In total 1108 (24%) participants used a statin. Statin users were 29% less likely to be within the highest quartile versus the lowest quartile of daily saturated fat intake compared to non-users (OR 0.71, 95% CI 0.54-0.94). There were no statistically significant associations between statin use and smoking, physical activity or alcohol consumption. Conclusions Smoking status, alcohol consumption and exercise level did not differ between users and non-users of statins. However, statin users were less likely to consume high levels of saturated fat than non-users. We found no evidence that people took statins to compensate for a poor diet or lifestyle.

  10. Visual cues for data mining

    Science.gov (United States)

    Rogowitz, Bernice E.; Rabenhorst, David A.; Gerth, John A.; Kalin, Edward B.

    1996-04-01

    This paper describes a set of visual techniques, based on principles of human perception and cognition, which can help users analyze and develop intuitions about tabular data. Collections of tabular data are widely available, including, for example, multivariate time series data, customer satisfaction data, stock market performance data, multivariate profiles of companies and individuals, and scientific measurements. In our approach, we show how visual cues can help users perform a number of data mining tasks, including identifying correlations and interaction effects, finding clusters and understanding the semantics of cluster membership, identifying anomalies and outliers, and discovering multivariate relationships among variables. These cues are derived from psychological studies on perceptual organization, visual search, perceptual scaling, and color perception. These visual techniques are presented as a complement to the statistical and algorithmic methods more commonly associated with these tasks, and provide an interactive interface for the human analyst.

  11. Statin-associated muscle symptoms: impact on statin therapy

    DEFF Research Database (Denmark)

    Stroes, Erik S; Thompson, Paul D; Corsini, Alberto

    2015-01-01

    degradation, thereby providing a potential link between statins and muscle symptoms; controlled mechanistic and genetic studies in humans are necessary to further understanding. The Panel proposes to identify SAMS by symptoms typical of statin myalgia (i.e. muscle pain or aching) and their temporal......Statin-associated muscle symptoms (SAMS) are one of the principal reasons for statin non-adherence and/or discontinuation, contributing to adverse cardiovascular outcomes. This European Atherosclerosis Society (EAS) Consensus Panel overviews current understanding of the pathophysiology of statin......-associated myopathy, and provides guidance for diagnosis and management of SAMS. Statin-associated myopathy, with significant elevation of serum creatine kinase (CK), is a rare but serious side effect of statins, affecting 1 per 1000 to 1 per 10 000 people on standard statin doses. Statin-associated muscle symptoms...

  12. Eligibility for Statin Treatment in Korean Subjects with Reduced Renal Function: An Observational Study

    Directory of Open Access Journals (Sweden)

    Byung Sub Moon

    2016-09-01

    Full Text Available BackgroundThe purpose of this study was to investigate the relationship between statin eligibility and the degree of renal dysfunction using the Adult Treatment Panel (ATP III and the American College of Cardiology (ACC/American Heart Association (AHA guidelines in Korean adults.MethodsRenal function was assessed in 18,746 participants of the Kangbuk Samsung Health Study from January 2011 to December 2012. Subjects were divided into three groups according to estimated glomerular filtration rate (eGFR: stage 1, eGFR ≥90 mL/min/1.73 m2; stage 2, eGFR 60 to 89 mL/min/1.73 m2; and stages 3 to 5, eGFR <60 mL/min/1.73 m2. Statin eligibility in these groups was determined using the ATP III and ACC/AHA guidelines, and the risk for 10-year atherosclerotic cardiovascular disease (ASCVD was calculated using the Framingham Risk Score (FRS and Pooled Cohort Equation (PCE.ResultsThere were 3,546 (18.9% and 4,048 (21.5% statin-eligible subjects according to ATP III and ACC/AHA guidelines, respectively. The proportion of statin-eligible subjects increased as renal function deteriorated. Statin eligibility by the ACC/AHA guidelines showed better agreement with the Kidney Disease Improving Global Outcomes (KDIGO recommendations compared to the ATP III guidelines in subjects with stage 3 to 5 chronic kidney disease (CKD (κ value, 0.689 vs. 0.531. When the 10-year ASCVD risk was assessed using the FRS and PCE, the mean risk calculated by both equations significantly increased as renal function declined.ConclusionsThe proportion of statin-eligible subjects significantly increased according to worsening renal function in this Korean cohort. ACC/AHA guideline showed better agreement for statin eligibility with that recommended by KDIGO guideline compared to ATP III in subjects with CKD.

  13. Data preprocessing in data mining

    CERN Document Server

    García, Salvador; Herrera, Francisco

    2015-01-01

    Data Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to analyze it. Thanks to data preprocessing, it is possible to convert the impossible into possible, adapting the data to fulfill the input demands of each data mining algorithm. Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data. This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process. A comprehensive look from a practical point of view, including basic concepts and surveying t...

  14. Privacy Preserving Distributed Data Mining

    Data.gov (United States)

    National Aeronautics and Space Administration — Distributed data mining from privacy-sensitive multi-party data is likely to play an important role in the next generation of integrated vehicle health monitoring...

  15. The handbook of data mining

    CERN Document Server

    Ye, Nong

    2003-01-01

    This bk is the 1st comprehensive one to feature systematic coverage of the concepts, techniques, examples, issues, software tools and future advancements of data mining. The demand for DM apps are increasing in indus, gov, & academia.

  16. Learning data mining with R

    CERN Document Server

    Makhabel, Bater

    2015-01-01

    This book is intended for the budding data scientist or quantitative analyst with only a basic exposure to R and statistics. This book assumes familiarity with only the very basics of R, such as the main data types, simple functions, and how to move data around. No prior experience with data mining packages is necessary; however, you should have a basic understanding of data mining concepts and processes.

  17. Data mining for social network data

    CERN Document Server

    Memon, Nasrullah; Hicks, David L; Chen, Hsinchun

    2010-01-01

    Driven by counter-terrorism efforts, marketing analysis and an explosion in online social networking in recent years, data mining has moved to the forefront of information science. This proposed Special Issue on ""Data Mining for Social Network Data"" will present a broad range of recent studies in social networking analysis. It will focus on emerging trends and needs in discovery and analysis of communities, solitary and social activities, and activities in open fora, and commercial sites as well. It will also look at network modeling, infrastructure construction, dynamic growth and evolution

  18. Statin use and risk of prostate cancer: a Danish population-based case-control study, 1997-2010.

    Science.gov (United States)

    Jespersen, Christina G; Nørgaard, Mette; Friis, Søren; Skriver, Charlotte; Borre, Michael

    2014-02-01

    Conflicting evidence has suggested that statins possess chemopreventive properties against prostate cancer (PCa). Therefore, we examined the association between statin use and risk of PCa in a Denmark-based case-control study. We identified 42,480 patients diagnosed with incident PCa during 1997-2010 from a national cancer registry. Five age-matched population controls (n=212,400) were selected for each case using risk-set sampling. Statin use from 1996 to the index date was obtained from the National Prescription Registry. Odds ratios (ORs) adjusted for age, comorbidity, non-steroidal anti-inflammatory drug use, and educational level for PCa associated with statin use, were computed using conditional logistic regression. Analyses were stratified by duration of statin use (0-1, 2-4, 5-9, or ≥10 years), stage of PCa (localized or advanced), and type of statin used (lipophilic or hydrophilic). In total, 7915 patients (19%) and 39,384 controls (19%) redeemed statin prescriptions prior to the index date. Overall, statin users had a 6% lower risk of PCa compared with non-users [adjusted OR (ORa), 0.94; 95% confidence interval (CI), 0.91-0.97]. Risk estimates did not differ substantially by duration or type of statin used. Slightly larger statin use-associated risk reductions were observed for advanced PCa (ORa, 0.90; 95% CI, 0.85-0.96) and with statin use ≥10 years (ORa, 0.78; 95% CI, 0.65-0.95). Statin use was associated with a risk reduction overall (6%) and, specifically with advanced PCa (10%). Differences in diagnostic measures and residual confounding by socioeconomic parameters may have influenced our results. Copyright © 2013 Elsevier Ltd. All rights reserved.

  19. Implications of Emerging Data Mining

    Science.gov (United States)

    Kulathuramaiyer, Narayanan; Maurer, Hermann

    Data Mining describes a technology that discovers non-trivial hidden patterns in a large collection of data. Although this technology has a tremendous impact on our lives, the invaluable contributions of this invisible technology often go unnoticed. This paper discusses advances in data mining while focusing on the emerging data mining capability. Such data mining applications perform multidimensional mining on a wide variety of heterogeneous data sources, providing solutions to many unresolved problems. This paper also highlights the advantages and disadvantages arising from the ever-expanding scope of data mining. Data Mining augments human intelligence by equipping us with a wealth of knowledge and by empowering us to perform our daily tasks better. As the mining scope and capacity increases, users and organizations become more willing to compromise privacy. The huge data stores of the ‚master miners` allow them to gain deep insights into individual lifestyles and their social and behavioural patterns. Data integration and analysis capability of combining business and financial trends together with the ability to deterministically track market changes will drastically affect our lives.

  20. Effect of an increased dosage of statins on spinal degenerative joint disease: a retrospective cohort study.

    Science.gov (United States)

    Cheng, Yuan-Yang; Kao, Chung-Lan; Lin, Shih-Yi; Chang, Shin-Tsu; Wei, Tz-Shiang; Chang, Shih-Ni; Lin, Ching-Heng

    2018-02-08

    It has been proven that statin can protect synovial joints from developing osteoarthritis through its anti-inflammatory effects. However, studies on the effect of statins on spinal degenerative joint diseases are few and limited to in vitro studies. Therefore, we investigated the relationship between the statin dosage and the development of spinal degenerative joint diseases. A retrospective cohort study. Patients registered in Taiwan National Health Insurance Research Database. Patients aged 40-65 years old from 2001 to 2010 were included. Those who received statin treatment before 2001, were diagnosed with spinal degenerative joint diseases or received any spinal surgery before 2004 or had any spinal trauma before 2011 were excluded. A total of 7238 statin users and 164 454 non-users were identified and followed up for the next 7 years to trace the development of spinal degenerative joint disease. The incident rate of spinal degenerative joint diseases and HRs among the groups treated with different statin dosages. A higher dosage of statins was associated with a significantly lower risk of developing spinal degenerative joint disease in patients with hypercholesterolaemia. Compared with the group receiving less than 5400 mg of a statin, the HR of the 11 900-28 000 mg group was 0.83 (95% CI 0.70 to 0.99), and that of the group receiving more than 28 000 mg was 0.81 (95% CI 0.68 to 0.97). Results of subgroup analysis showed a significantly lower risk in men, those aged 50-59 years and those with a monthly income less than US$600. Our study's findings clearly indicated that a higher dosage of statins can reduce the incidence of spinal degenerative joint disease in patients with hypercholesterolaemia, and it can be beneficial for people with a higher risk of spine degeneration. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise

  1. A Data mining and Meta-Analysis of Geodiversity and Geological Preservation Studies: Pedodiversity, the Other Side of the Coin

    Science.gov (United States)

    Ibáñez, Juanjo; Brevik, Eric C.; Cerdà, Artemi

    2017-04-01

    Land degradation processes are complex and diverse (Yan and Cai, 2015; Álvarez-Martínez et al., 2016; Zhang et al., 2016). To understand the processes of degradation there is the need to understand a wide range of factors, including the sociological, biological, hydrological and geological components of the ecosystems (de Araujo et al., 2015; Li et al., 2016; Muñoz Rojas et al., 2016; Rodrigo Comino et al., 2016). Although the most widely accepted definitions of geodiversity include geology, landforms and soils, in practice published studies on the preservation of geodiversity typically ignore the soil resource and pay more attention to other items, while many others do not include soils in such definitions. Soils are a key component of the Earth system as they regulate the hydrological, erosional, biological and geochemical Earth cycles (Keesstra et al., 2012; Brevik et al., 2015), offer resources, goods and services to human societies (Mol and Keesstra, 2012), and are a key issue in the United Nations goals to achieve sustainability (Keesstra et al., 2016). The preservation of pedodiversity is practically ignored by governments. The same can be said in the acceptance of geoparks by UNESCO, when soils are ignored. A few researchers have paid attention to geodiversity (Ibáñez et al., 2016; Stavi et al., 2016). In this study a data mining and metanalysis of this topic has been carried out using four different sources; Google, Google Scholar, Scopus, and the contents of Geoheritage journal. The results obtained show the same trends in all four studied sources. Soil resources are neglected in geodiversity studies as well as in the preservation of geological heritage against the scientific rationality inherent in the definition of geodiversity. Furthermore, pedodiversity studies have been carried out by a small set of interested pedologists following the same conceptual frames and mathematical tools reaching interesting universal patterns and a common language

  2. Using methods from the data mining and machine learning literature for disease classification and prediction: A case study examining classification of heart failure sub-types

    Science.gov (United States)

    Austin, Peter C.; Tu, Jack V.; Ho, Jennifer E.; Levy, Daniel; Lee, Douglas S.

    2014-01-01

    Objective Physicians classify patients into those with or without a specific disease. Furthermore, there is often interest in classifying patients according to disease etiology or subtype. Classification trees are frequently used to classify patients according to the presence or absence of a disease. However, classification trees can suffer from limited accuracy. In the data-mining and machine learning literature, alternate classification schemes have been developed. These include bootstrap aggregation (bagging), boosting, random forests, and support vector machines. Study design and Setting We compared the performance of these classification methods with those of conventional classification trees to classify patients with heart failure according to the following sub-types: heart failure with preserved ejection fraction (HFPEF) vs. heart failure with reduced ejection fraction (HFREF). We also compared the ability of these methods to predict the probability of the presence of HFPEF with that of conventional logistic regression. Results We found that modern, flexible tree-based methods from the data mining literature offer substantial improvement in prediction and classification of heart failure sub-type compared to conventional classification and regression trees. However, conventional logistic regression had superior performance for predicting the probability of the presence of HFPEF compared to the methods proposed in the data mining literature. Conclusion The use of tree-based methods offers superior performance over conventional classification and regression trees for predicting and classifying heart failure subtypes in a population-based sample of patients from Ontario. However, these methods do not offer substantial improvements over logistic regression for predicting the presence of HFPEF. PMID:23384592

  3. Statins and Hip Fracture Prevention – A Population Based Cohort Study in Women

    Science.gov (United States)

    Helin-Salmivaara, Arja; Korhonen, Maarit J.; Lehenkari, Petri; Junnila, Seppo Y. T.; Neuvonen, Pertti J.; Ruokoniemi, Päivi; Huupponen, Risto

    2012-01-01

    Objective To study the association of long-term statin use and the risk of low-energy hip fractures in middle-aged and elderly women. Design A register-based cohort study. Setting Finland. Participants Women aged 45–75 years initiating statin therapy between 1996 and 2001 with adherence to statins ≥80% during the subsequent five years (n = 40 254), a respective cohort initiating hypertension drugs (n = 41 610), and women randomly selected from the population (n = 62 585). Main Outcome Measures Incidence rate of and hazard ratio (HR) for low-energy hip fracture during the follow-up extending up to 7 years after the 5-year exposure period. Results Altogether 199 low-energy hip fractures occurred during the 135 330 person-years (py) of follow-up in the statin cohort, giving an incidence rate of 1.5 hip fractures per 1000 py. In the hypertension and the population cohorts, the rates were 2.0 per 1000 py (312 fractures per 157 090 py) and 1.0 per 1000 py (212 fractures per 216 329 py), respectively. Adjusting for a propensity score and individual variables strongly predicting the outcome, good adherence to statins for five years was associated with a 29% decreased risk (HR 0.71; 95% CI 0.58–0.86) of a low-energy hip fracture in comparison with adherent use of hypertension drugs. The association was of the same magnitude when comparing the statin users with the population cohort, the HR being 0.69 (0.55–0.87). When women with poor (statins were compared to those with good adherence to hypertension drugs (≥80%) or to the population cohort, the protective effect associated with statin use attenuated with the decreasing level of adherence. Conclusions 5-year exposure to statins is associated with a reduced risk of low-energy hip fracture in women aged 50–80 years without prior hospitalizations for fractures. PMID:23144731

  4. DATA MINING AND APPLICATION OF IT TO CAPITAL MARKETS

    Directory of Open Access Journals (Sweden)

    Cenk AKKAYA

    2011-07-01

    Full Text Available Nowadays with the development of technology importance given to knowledge increases gradually. Data mining enables to form forecasts and models regarding future by making use of past data. Any method which helps to discover data can be used as a data mining method. Enterprises gain important competitive advantage by data mining methods. Data mining is used in different fields. In finance field it is a specially used in financial performance applications, guessing the enterprise bankruptcies and failures, determining transaction manipulation, determining financial risk management, determining customer profile and depth management. It can be costly, risky and time consuming for enterprises to gain knowledge. Thus today enterprises use data mining as an innovative competitive mean. The aim of the study is to determine the importance of data mining applications to capital markets.

  5. A statin a day keeps the doctor away: comparative proverb assessment modelling study

    Science.gov (United States)

    Mizdrak, Anja; Scarborough, Peter

    2013-01-01

    Objective To model the effect on UK vascular mortality of all adults over 50 years old being prescribed either a statin or an apple a day. Design Comparative proverb assessment modelling study. Setting United Kingdom. Population Adults aged over 50 years. Intervention Either a statin a day for people not already taking a statin or an apple a day for everyone, assuming 70% compliance and no change in calorie consumption. The modelling used routinely available UK population datasets; parameters describing the relations between statins, apples, and health were derived from meta-analyses. Main outcome measure Mortality due to vascular disease. Results The estimated annual reduction in deaths from vascular disease of a statin a day, assuming 70% compliance and a reduction in vascular mortality of 12% (95% confidence interval 9% to 16%) per 1.0 mmol/L reduction in low density lipoprotein cholesterol, is 9400 (7000 to 12 500). The equivalent reduction from an apple a day, modelled using the PRIME model (assuming an apple weighs 100 g and that overall calorie consumption remains constant) is 8500 (95% credible interval 6200 to 10 800). Conclusions Both nutritional and pharmaceutical approaches to the prevention of vascular disease may have the potential to reduce UK mortality significantly. With similar reductions in mortality, a 150 year old health promotion message is able to match modern medicine and is likely to have fewer side effects.

  6. Educational Data Mining Acceptance among Undergraduate Students

    Science.gov (United States)

    Wook, Muslihah; Yusof, Zawiyah M.; Nazri, Mohd Zakree Ahmad

    2017-01-01

    The acceptance of Educational Data Mining (EDM) technology is on the rise due to, its ability to extract new knowledge from large amounts of students' data. This knowledge is important for educational stakeholders, such as policy makers, educators, and students themselves to enhance efficiency and achievements. However, previous studies on EDM…

  7. Risk of Intracranial Hemorrhage From Statin Use in Asians: A Nationwide Cohort Study.

    Science.gov (United States)

    Chang, Chia-Hsuin; Lin, Chin-Hsien; Caffrey, James L; Lee, Yen-Chieh; Liu, Ying-Chun; Lin, Jou-Wei; Lai, Mei-Shu

    2015-06-09

    Reports of statin usage and increased risk of intracranial hemorrhage (ICH) have been inconsistent. This study examined potential associations between statin usage and the risk of ICH in subjects without a previous history of stroke. Patients initiating statin therapy between 2005 and 2009 without a previous history of ischemic or hemorrhagic stroke were identified from Taiwan's National Health Insurance database. Participants were stratified by advanced age (≥70 years), sex, and diagnosed hypertension. The outcome of interest was hospital admission for ICH (International Classification of Diseases, Ninth Revision, Clinical Modification codes 430, 431, 432). Cox regression models were applied to estimate the hazard ratio of ICH. The cumulative statin dosage stratified by quartile and adjusted for baseline disease risk score served as the primary variable using the lowest quartile of cumulative dosage as a reference. There were 1 096 547 statin initiators with an average follow-up of 3.3 years. The adjusted hazard ratio for ICH between the highest and the lowest quartile was nonsignificant at 1.06 with a 95% confidence interval spanning 1.00 (0.94-1.19). Similar nonsignificant results were found in sensitivity analyses using different outcome definitions or model adjustments, reinforcing the robustness of the study findings. Subgroup analysis identified an excess of ICH frequency in patients without diagnosed hypertension (adjusted hazard ratio 1.36 [1.11-1.67]). In general, no association was observed between cumulative statin use and the risk of ICH among subjects without a previous history of stroke. An increased risk was identified among the nonhypertensive cohort, but this finding should be interpreted with caution. © 2015 American Heart Association, Inc.

  8. Data mining methods and applications

    CERN Document Server

    Lawrence, Kenneth D; Klimberg, Ronald K

    2007-01-01

    With today's information explosion, many organizations are now able to access a wealth of valuable data. Unfortunately, most of these organizations find they are ill-equipped to organize this information, let alone put it to work for them. Gain a Competitive Advantage Employ data mining in research and forecasting Build models with data management tools and methodology optimization Gain sophisticated breakdowns and complex analysis through multivariate, evolutionary, and neural net methodsLearn how to classify data and maintain qualityTransform Data into Business Acumen Data Mining Methods and

  9. PROGRAMS WITH DATA MINING CAPABILITIES

    Directory of Open Access Journals (Sweden)

    Ciobanu Dumitru

    2012-03-01

    Full Text Available The fact that the Internet has become a commodity in the world has created a framework for anew economy. Traditional businesses migrate to this new environment that offers many features and options atrelatively low prices. However competitiveness is fierce and successful Internet business is tied to rigorous use of allavailable information. The information is often hidden in data and for their retrieval is necessary to use softwarecapable of applying data mining algorithms and techniques. In this paper we want to review some of the programswith data mining capabilities currently available in this area.We also propose some classifications of this softwareto assist those who wish to use such software.

  10. Integrating Data Mining Techniques into Telemedicine Systems

    Directory of Open Access Journals (Sweden)

    Mihaela GHEORGHE

    2014-01-01

    Full Text Available The medical system is facing a wide range of challenges nowadays due to changes that are taking place in the global healthcare systems. These challenges are represented mostly by economic constraints (spiraling costs, financial issues, but also, by the increased emphasis on accountability and transparency, changes that were made in the education field, the fact that the biomedical research keeps growing in what concerns the complexities of the specific studies etc. Also the new partnerships that were made in medical care systems and the great advances in IT industry suggest that a predominant paradigm shift is occurring. This needs a focus on interaction, collaboration and increased sharing of information and knowledge, all of these may is in turn be leading healthcare organizations to embrace the techniques of data mining in order to create and sustain optimal healthcare outcomes. Data mining is a domain of great importance nowadays as it provides advanced data analysis techniques for extracting the knowledge from the huge volumes of data collected and stored by every system of a daily basis. In the healthcare organizations data mining can provide valuable information for patient's diagnosis and treatment planning, customer relationship management, organization resources management or fraud detection. In this article we focus on describing the importance of data mining techniques and systems for healthcare organizations with a focus on developing and implementing telemedicine solution in order to improve the healthcare services provided to the patients. We provide architecture for integrating data mining techniques into telemedicine systems and also offer an overview on understanding and improving the implemented solution by using Business Process Management methods.

  11. Mastering SQL Server 2014 data mining

    CERN Document Server

    Bassan, Amarpreet Singh

    2014-01-01

    If you are a developer who is working on data mining for large companies and would like to enhance your knowledge of SQL Server Data Mining Suite, this book is for you. Whether you are brand new to data mining or are a seasoned expert, you will be able to master the skills needed to build a data mining solution.

  12. A Tools-Based Approach to Teaching Data Mining Methods

    Science.gov (United States)

    Jafar, Musa J.

    2010-01-01

    Data mining is an emerging field of study in Information Systems programs. Although the course content has been streamlined, the underlying technology is still in a state of flux. The purpose of this paper is to describe how we utilized Microsoft Excel's data mining add-ins as a front-end to Microsoft's Cloud Computing and SQL Server 2008 Business…

  13. Data Mining Tools in Science Education

    OpenAIRE

    Premysl Zaskodny

    2012-01-01

    The main principle of paper is Data Mining in Science Education (DMSE) as Problem Solving. The main goal of paper is consisting in Delimitation of Complex Data Mining Tool and Partial Data Mining Tool of DMSE. The procedure of paper is consisting of Data Preprocessing in Science Education, Data Processing in Science Education, Description of Curricular Process as Complex Data Mining Tool (CP-DMSE), Description of Analytical Synthetic Modeling as Partial Data Mining Tool (ASM-DMSE) and finally...

  14. Early Statin Use and the Progression of Alzheimer Disease: A Total Population-Based Case-Control Study.

    Science.gov (United States)

    Lin, Feng-Cheng; Chuang, Yun-Shiuan; Hsieh, Hui-Min; Lee, Tzu-Chi; Chiu, Kuei-Fen; Liu, Ching-Kuan; Wu, Ming-Tsang

    2015-11-01

    The protective effect of statin on Alzheimer disease (AD) is still controversial, probably due to the debate about when to start the use of statin and the lack of any large-scale randomized evidence that actually supports the hypothesis. The purpose of this study was to examine the protective effect of early statin use on mild-to-moderate AD in the total Taiwanese population.This was a total population-based case-control study, using the total population of Taiwanese citizens seen in general medical practice; therefore, the findings can be applied to the general population. The study patients were those with newly diagnosed dementia (ICD-9 290.x) and prescribed any acetylcholinesterase inhibitors (AChEI) from the Taiwan National Health Insurance dataset in 1997 to 2008. The newly diagnosed eligible mild-to-moderate AD patients were traced from the dates of their index dates, which was defined as the first day to receive any AChEI treatment, back to 1 year (exposure period) to categorize them into AD with early statin use and without early statin use. Early statin use was defined as patients using statin before AChEI treatment. Alzheimer disease patients with early statin use were those receiving any statin treatment during the exposure period. Then, we used propensity-score-matched strategy to match these 2 groups as 1:1. The matched study patients were followed-up from their index dates. The primary outcome was the discontinuation of AChEI treatment, indicating AD progression.There were 719 mild-to-moderate AD-paired patients with early statin use and without early statin use for analyses. Alzheimer disease progression was statistically lower in AD patients with early statin use than those without (P = 0.00054). After adjusting for other covariates, mild-to-moderate AD patients with early stain use exhibited a 0.85-risk (95% CI = 0.76-0.95, P = 0.0066) to have AD progression than those without.Early statin use was significantly associated with a reduction in AD

  15. Application and Exploration of Big Data Mining in Clinical Medicine

    Science.gov (United States)

    Zhang, Yue; Guo, Shu-Li; Han, Li-Na; Li, Tie-Ling

    2016-01-01

    Objective: To review theories and technologies of big data mining and their application in clinical medicine. Data Sources: Literatures published in English or Chinese regarding theories and technologies of big data mining and the concrete applications of data mining technology in clinical medicine were obtained from PubMed and Chinese Hospital Knowledge Database from 1975 to 2015. Study Selection: Original articles regarding big data mining theory/technology and big data mining's application in the medical field were selected. Results: This review characterized the basic theories and technologies of big data mining including fuzzy theory, rough set theory, cloud theory, Dempster–Shafer theory, artificial neural network, genetic algorithm, inductive learning theory, Bayesian network, decision tree, pattern recognition, high-performance computing, and statistical analysis. The application of big data mining in clinical medicine was analyzed in the fields of disease risk assessment, clinical decision support, prediction of disease development, guidance of rational use of drugs, medical management, and evidence-based medicine. Conclusion: Big data mining has the potential to play an important role in clinical medicine. PMID:26960378

  16. A Data Mining Classification Approach for Behavioral Malware Detection

    Directory of Open Access Journals (Sweden)

    Monire Norouzi

    2016-01-01

    Full Text Available Data mining techniques have numerous applications in malware detection. Classification method is one of the most popular data mining techniques. In this paper we present a data mining classification approach to detect malware behavior. We proposed different classification methods in order to detect malware based on the feature and behavior of each malware. A dynamic analysis method has been presented for identifying the malware features. A suggested program has been presented for converting a malware behavior executive history XML file to a suitable WEKA tool input. To illustrate the performance efficiency as well as training data and test, we apply the proposed approaches to a real case study data set using WEKA tool. The evaluation results demonstrated the availability of the proposed data mining approach. Also our proposed data mining approach is more efficient for detecting malware and behavioral classification of malware can be useful to detect malware in a behavioral antivirus.

  17. Research on Customer Value Based on Extension Data Mining

    Science.gov (United States)

    Chun-Yan, Yang; Wei-Hua, Li

    Extenics is a new discipline for dealing with contradiction problems with formulize model. Extension data mining (EDM) is a product combining Extenics with data mining. It explores to acquire the knowledge based on extension transformations, which is called extension knowledge (EK), taking advantage of extension methods and data mining technology. EK includes extensible classification knowledge, conductive knowledge and so on. Extension data mining technology (EDMT) is a new data mining technology that mining EK in databases or data warehouse. Customer value (CV) can weigh the essentiality of customer relationship for an enterprise according to an enterprise as a subject of tasting value and customers as objects of tasting value at the same time. CV varies continually. Mining the changing knowledge of CV in databases using EDMT, including quantitative change knowledge and qualitative change knowledge, can provide a foundation for that an enterprise decides the strategy of customer relationship management (CRM). It can also provide a new idea for studying CV.

  18. Statin Utilization and Recommendations Among HIV- and HCV-infected Veterans: A Cohort Study.

    Science.gov (United States)

    Clement, Meredith E; Park, Lawrence P; Navar, Ann Marie; Okeke, Nwora Lance; Pencina, Michael J; Douglas, Pamela S; Naggie, Susanna

    2016-08-01

    Human immunodeficiency virus (HIV) and hepatitis C virus (HCV) infections are associated with increased risk of cardiovascular disease (CVD). The potential impact of recently updated cholesterol guidelines on treatment of HIV- and HCV-infected veterans is unknown. We performed a retrospective cohort study to assess statin use and recommendations among 13 579 HIV-infected, 169 767 HCV-infected, and 6628 HIV/HCV-coinfected male veterans aged 40-75 years. Prior 2004 Adult Treatment Panel (ATP-III) guidelines were compared with current 2013 American College of Cardiology/American Heart Association (ACC/AHA) cholesterol guidelines and 2014 US Department of Veterans Affairs (VA)/US Department of Defense (DoD) joint clinical practice guidelines using laboratory, medication, and comorbidity data from the VA Clinical Case Registry from 2008 through 2010. Using risk criteria delineated by the ATP-III guidelines, 50.6% of HIV-infected, 45.9% of HCV-infected, and 33.8% of HIV/HCV-coinfected veterans had an indication for statin therapy. However, among those eligible, 22.7%, 30.5%, and 31.5%, respectively, were not receiving ATP-III recommended statin therapy. When current cholesterol guidelines were applied by VA/DoD and ACC/AHA criteria, increases in recommendations for statins were found in all groups (57.3% and 66.1% of HIV-infected, 64.4% and 73.7% of HCV-infected, 49.1% and 58.5% of HIV/HCV-coinfected veterans recommended). Statins were underutilized among veterans infected with HIV, HCV, and HIV/HCV according to previous ATP-III guidelines. Current VA/DoD and ACC/AHA guidelines substantially expand statin recommendations and widen the gap of statin underutilization in all groups. These gaps in care present an opportunity to improve CVD prevention efforts in these at-risk populations. © The Author 2016. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail journals.permissions@oup.com.

  19. Commercial Online Social Network Data and Statin Side-Effect Surveillance: A Pilot Observational Study of Aggregate Mentions on Facebook.

    Science.gov (United States)

    Huesch, Marco D

    2017-12-01

    Surveillance of the safety of prescribed drugs after marketing approval has been secured remains fraught with complications. Formal ascertainment by providers and reporting to adverse-event registries, formal surveys by manufacturers, and mining of electronic medical records are all well-known approaches with varying degrees of difficulty, cost, and success. Novel approaches may be a useful adjunct, especially approaches that mine or sample internet-based methods such as online social networks. A novel commercial software-as-a-service data-mining product supplied by Sysomos from Datasift/Facebook was used to mine all mentions on Facebook of statins and stain-related side effects in the US in the 1-month period 9 January 2017 through 8 February 2017. A total of 4.3% of all 25,700 mentions of statins also mentioned typical stain-related side effects. Multiple methodological weaknesses stymie interpretation of this percentage, which is however not inconsistent with estimates that 5-20% of patients taking statins will experience typical side effects at some time. Future work on pharmacovigilance may be informed by this novel commercial tool, but the inability to mine the full text of a posting poses serious challenges to content categorization.

  20. Statin prescribing for people with severe mental illnesses: a staggered cohort study of 'real-world' impacts.

    Science.gov (United States)

    Blackburn, R; Osborn, D; Walters, K; Falcaro, M; Nazareth, I; Petersen, I

    2017-03-07

    To estimate the 'real-world effectiveness of statins for primary prevention of cardiovascular disease (CVD) and for lipid modification in people with severe mental illnesses (SMI), including schizophrenia and bipolar disorder. Series of staggered cohorts. We estimated the effect of statin prescribing on CVD outcomes using a multivariable Poisson regression model or linear regression for cholesterol outcomes. 587 general practice (GP) surgeries across the UK reporting data to The Health Improvement Network. All permanently registered GP patients aged 40-84 years between 2002 and 2012 who had a diagnosis of SMI. Exclusion criteria were pre-existing CVD, statin-contraindicating conditions or a statin prescription within the 24 months prior to the study start. One or more statin prescriptions during a 24-month 'baseline' period (vs no statin prescription during the same period). The primary outcome was combined first myocardial infarction and stroke. All-cause mortality and total cholesterol concentration were secondary outcomes. We identified 2944 statin users and 42 886 statin non-users across the staggered cohorts. Statin prescribing was not associated with significant reduction in CVD events (incident rate ratio 0.89; 95% CI 0.68 to 1.15) or all-cause mortality (0.89; 95% CI 0.78 to 1.02). Statin prescribing was, however, associated with statistically significant reductions in total cholesterol of 1.2 mmol/L (95% CI 1.1 to 1.3) for up to 2 years after adjusting for differences in baseline characteristics. On average, total cholesterol decreased from 6.3 to 4.6 in statin users and 5.4 to 5.3 mmol/L in non-users. We found that statin prescribing to people with SMI in UK primary care was effective for lipid modification but not CVD events. The latter finding may reflect insufficient power to detect a smaller effect size than that observed in randomised controlled trials of statins in people without SMI. Published by the BMJ Publishing Group Limited. For

  1. Data mining concepts and techniques

    CERN Document Server

    Han, Jiawei

    2005-01-01

    Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge.Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and app...

  2. Data Mining in Institutional Economics Tasks

    Science.gov (United States)

    Kirilyuk, Igor; Kuznetsova, Anna; Senko, Oleg

    2018-02-01

    The paper discusses problems associated with the use of data mining tools to study discrepancies between countries with different types of institutional matrices by variety of potential explanatory variables: climate, economic or infrastructure indicators. An approach is presented which is based on the search of statistically valid regularities describing the dependence of the institutional type on a single variable or a pair of variables. Examples of regularities are given.

  3. Data mining and visualization techniques

    Science.gov (United States)

    Wong, Pak Chung [Richland, WA; Whitney, Paul [Richland, WA; Thomas, Jim [Richland, WA

    2004-03-23

    Disclosed are association rule identification and visualization methods, systems, and apparatus. An association rule in data mining is an implication of the form X.fwdarw.Y where X is a set of antecedent items and Y is the consequent item. A unique visualization technique that provides multiple antecedent, consequent, confidence, and support information is disclosed to facilitate better presentation of large quantities of complex association rules.

  4. Data preprocessing for data mining

    OpenAIRE

    Ren, Yifei

    2013-01-01

    People have increasing amounts data in the current prosperous information age. In order to improve competitive power and work efficiency, discovering knowledge from data is becoming more and more important. Data mining, as an emerging interdisciplinary applications field, plays a significant role in various trades’ and industries' decision making. However, it is known that original data is always dirty and not suitable for further analysis which have become a major obstacle of finding knowled...

  5. Open data mining for Taiwan's dengue epidemic.

    Science.gov (United States)

    Wu, ChienHsing; Kao, Shu-Chen; Shih, Chia-Hung; Kan, Meng-Hsuan

    2018-07-01

    By using a quantitative approach, this study examines the applicability of data mining technique to discover knowledge from open data related to Taiwan's dengue epidemic. We compare results when Google trend data are included or excluded. Data sources are government open data, climate data, and Google trend data. Research findings from analysis of 70,914 cases are obtained. Location and time (month) in open data show the highest classification power followed by climate variables (temperature and humidity), whereas gender and age show the lowest values. Both prediction accuracy and simplicity decrease when Google trends are considered (respectively 0.94 and 0.37, compared to 0.96 and 0.46). The article demonstrates the value of open data mining in the context of public health care. Copyright © 2018 Elsevier B.V. All rights reserved.

  6. Data Mining Smart Energy Time Series

    Directory of Open Access Journals (Sweden)

    Janina POPEANGA

    2015-07-01

    Full Text Available With the advent of smart metering technology the amount of energy data will increase significantly and utilities industry will have to face another big challenge - to find relationships within time-series data and even more - to analyze such huge numbers of time series to find useful patterns and trends with fast or even real-time response. This study makes a small review of the literature in the field, trying to demonstrate how essential is the application of data mining techniques in the time series to make the best use of this large quantity of data, despite all the difficulties. Also, the most important Time Series Data Mining techniques are presented, highlighting their applicability in the energy domain.

  7. Statins and New-Onset Diabetes Mellitus and Diabetic Complications: A Retrospective Cohort Study of US Healthy Adults.

    Science.gov (United States)

    Mansi, Ishak; Frei, Christopher R; Wang, Chen-Pin; Mortensen, Eric M

    2015-11-01

    Statin use is associated with increased incidence of diabetes and possibly with increased body weight and reduced exercise capacity. Data on the long-term effects of these associations in healthy adults, however, are very limited. In addition, the relationship between these effects and diabetic complications has not been adequately studied. To examine the association between statin use and new-onset diabetes, diabetic complications, and overweight/obesity in a cohort of healthy adults. This was a retrospective cohort study. Subjects were Tricare beneficiaries who were evaluated between October 1, 2003 and March 1, 2012. Patients were divided into statin users and nonusers. We excluded patients who, at baseline, had a preexisting disease indicative of cardiovascular diseases, any positive element of the Charlson comorbidity index (including diabetes mellitus), or life-limiting chronic diseases. Using 42 baseline characteristics, we generated a propensity score to match statin users and nonusers. Outcomes assessed included new-onset diabetes, diabetic complications, and overweight/obesity. A total of 25,970 patients (3982 statin users and 21,988 nonusers) were identified as healthy adults at baseline. Of these, 3351 statins users and 3351 nonusers were propensity score-matched. Statin users had higher odds of new-onset diabetes (odds ratio [OR] 1.87; 95 % confidence interval [95 % CI] 1.67-2.01), diabetes with complications (OR 2.50; 95 % CI 1.88-3.32), and overweight/obesity (OR 1.14; 95 % CI 1.04-1.25). Secondary and sensitivity analyses demonstrated similar findings. Diabetes, diabetic complications, and overweight/obesity were more commonly diagnosed among statin-users than similar nonusers in a healthy cohort of adults. This study demonstrates that short-term clinical trials might not fully describe the risk/benefit of long-term statin use for primary prevention.

  8. Population-Based Case-Control Study Assessing the Association between Statins Use and Pulmonary Tuberculosis in Taiwan

    Directory of Open Access Journals (Sweden)

    Kuan-Fu Liao

    2017-08-01

    Full Text Available Background and Objectives: Little evidence is available about the relationship between statins use and pulmonary tuberculosis in Taiwan. The aim of the study was to explore this issue.Methods: Using the database of the Taiwan National Health Insurance Program, we conducted a population-based case-control study to identify 8,236 subjects aged 20 years and older with newly diagnosed pulmonary tuberculosis from 2000 to 2013 as the cases. We randomly selected 8,236 sex-matched and age-matched subjects without pulmonary tuberculosis as the controls. Subjects who had at least one prescription of statins before the index date were defined as “ever use.” Subjects who never had one prescription of statins before the index date were defined as “never use.” The odds ratio (OR and 95% confidence interval (CI for pulmonary tuberculosis associated with statins use was estimated by a multivariable logistic regression model.Results: After adjustment for co-variables, the adjusted OR of pulmonary tuberculosis was 0.67 for subjects with ever use of statins (95% CI 0.59, 0.75. In a sub-analysis, the adjusted ORs of pulmonary tuberculosis were 0.87 (95% CI 0.69, 1.10 for subjects with cumulative duration of statins use <3 months, 0.77 (95% CI 0.58, 1.03 for 3–6 months, and 0.59 (95% CI 0.51, 0.68 for ≥6 months, compared with subjects with never use of statins.Conclusions: Statins use correlates with a small but statistically significant risk reduction of pulmonary tuberculosis. The protective effect is stronger for longer duration of statins use. Due to a case-control design, a causal-relationship cannot be established in our study. A prospective cohort design is needed to confirm our findings.

  9. A population-based case-control study on statin exposure and risk of acute diverticular disease.

    Science.gov (United States)

    Sköldberg, Filip; Svensson, Tobias; Olén, Ola; Hjern, Fredrik; Schmidt, Peter T; Ljung, Rickard

    2016-01-01

    A reduced risk of perforated diverticular disease among individuals with current statin exposure has been reported. The aim of the present study was to investigate whether statins reduce the risk of acute diverticular disease. A nation-wide population-based case-control study was performed, including 13,127 cases hospitalised during 2006-2010 with a first-time diagnosis of colonic diverticular disease, and 128,442 control subjects (matched for sex, age, county of residence and calendar year). Emergency surgery, assumed to be a proxy for complicated diverticulitis, was performed on 906 of the cases during the index admission, with 8818 matched controls. Statin exposure was classified as "current" or "former" if a statin prescription was last dispensed ≤ 125 days or >125 days before index date, respectively. The association between statin exposure and acute diverticular disease was investigated by conditional logistic regression, including models adjusting for country of birth, educational level, marital status, comorbidities, nonsteroidal anti-inflammatory drug/steroid exposure and healthcare utilisation. A total of 1959 cases (14.9%) and 16,456 controls (12.8%) were current statin users (crude OR 1.23 [95% CI 1.17-1.30]; fully adjusted OR 1.00 [0.94-1.06]). One hundred and thirty-two of the cases subjected to surgery (14.6%), and 1441 of the corresponding controls (16.3%) were current statin users (crude OR 0.89 [95% CI 0.73-1.08]; fully adjusted OR 0.70 [0.55-0.89]). The results do not indicate that statins affect the development of symptomatic diverticular disease in general. However, current statin use was associated with a reduced risk of emergency surgery for diverticular disease.

  10. Genome-wide association of lipid-lowering response to statins in combined study populations.

    Directory of Open Access Journals (Sweden)

    Mathew J Barber

    2010-03-01

    Full Text Available Statins effectively lower total and plasma LDL-cholesterol, but the magnitude of decrease varies among individuals. To identify single nucleotide polymorphisms (SNPs contributing to this variation, we performed a combined analysis of genome-wide association (GWA results from three trials of statin efficacy.Bayesian and standard frequentist association analyses were performed on untreated and statin-mediated changes in LDL-cholesterol, total cholesterol, HDL-cholesterol, and triglyceride on a total of 3932 subjects using data from three studies: Cholesterol and Pharmacogenetics (40 mg/day simvastatin, 6 weeks, Pravastatin/Inflammation CRP Evaluation (40 mg/day pravastatin, 24 weeks, and Treating to New Targets (10 mg/day atorvastatin, 8 weeks. Genotype imputation was used to maximize genomic coverage and to combine information across studies. Phenotypes were normalized within each study to account for systematic differences among studies, and fixed-effects combined analysis of the combined sample were performed to detect consistent effects across studies. Two SNP associations were assessed as having posterior probability greater than 50%, indicating that they were more likely than not to be genuinely associated with statin-mediated lipid response. SNP rs8014194, located within the CLMN gene on chromosome 14, was strongly associated with statin-mediated change in total cholesterol with an 84% probability by Bayesian analysis, and a p-value exceeding conventional levels of genome-wide significance by frequentist analysis (P = 1.8 x 10(-8. This SNP was less significantly associated with change in LDL-cholesterol (posterior probability = 0.16, P = 4.0 x 10(-6. Bayesian analysis also assigned a 51% probability that rs4420638, located in APOC1 and near APOE, was associated with change in LDL-cholesterol.Using combined GWA analysis from three clinical trials involving nearly 4,000 individuals treated with simvastatin, pravastatin, or atorvastatin, we

  11. Spatiotemporal Data Mining: A Computational Perspective

    Directory of Open Access Journals (Sweden)

    Shashi Shekhar

    2015-10-01

    Full Text Available Explosive growth in geospatial and temporal data as well as the emergence of new technologies emphasize the need for automated discovery of spatiotemporal knowledge. Spatiotemporal data mining studies the process of discovering interesting and previously unknown, but potentially useful patterns from large spatiotemporal databases. It has broad application domains including ecology and environmental management, public safety, transportation, earth science, epidemiology, and climatology. The complexity of spatiotemporal data and intrinsic relationships limits the usefulness of conventional data science techniques for extracting spatiotemporal patterns. In this survey, we review recent computational techniques and tools in spatiotemporal data mining, focusing on several major pattern families: spatiotemporal outlier, spatiotemporal coupling and tele-coupling, spatiotemporal prediction, spatiotemporal partitioning and summarization, spatiotemporal hotspots, and change detection. Compared with other surveys in the literature, this paper emphasizes the statistical foundations of spatiotemporal data mining and provides comprehensive coverage of computational approaches for various pattern families. ISPRS Int. J. Geo-Inf. 2015, 4 2307 We also list popular software tools for spatiotemporal data analysis. The survey concludes with a look at future research needs.

  12. Application of data mining in performance measures

    Science.gov (United States)

    Chan, Michael F. S.; Chung, Walter W.; Wong, Tai Sun

    2001-10-01

    This paper proposes a structured framework for exploiting data mining application for performance measures. The context is set in an airline company is illustrated for the use of such framework. The framework takes in consideration of how a knowledge worker interacts with performance information at the enterprise level to support them to make informed decision in managing the effectiveness of operations. A case study of applying data mining technology for performance data in an airline company is illustrated. The use of performance measures is specifically applied to assist in the aircraft delay management process. The increasingly dispersed and complex operations of airline operation put much strain on the part of knowledge worker in using search, acquiring and analyzing information to manage performance. One major problem faced with knowledge workers is the identification of root causes of performance deficiency. The large amount of factors involved in the analyze the root causes can be time consuming and the objective of applying data mining technology is to reduce the time and resources needed for such process. The increasing market competition for better performance management in various industries gives rises to need of the intelligent use of data. Because of this, the framework proposed here is very much generalizable to industries such as manufacturing. It could assist knowledge workers who are constantly looking for ways to improve operation effectiveness through new initiatives and the effort is required to be quickly done to gain competitive advantage in the marketplace.

  13. The utility of observational studies in clinical decision making: lessons learned from statin trials.

    Science.gov (United States)

    Foody, JoAnne M; Mendys, Phillip M; Liu, Larry Z; Simpson, Ross J

    2010-05-01

    Contemporary clinical decision making is well supported by a wide variety of information sources, including clinical practice guidelines, position papers, and insights from randomized controlled trials (RCTs). Much of our fundamental understanding of cardiovascular risk factors is based on multiple observations from major epidemiologic studies, such as The Seven Country Studies and the US-based Framingham Heart Study. These studies provided the framework for the development of clinical practice guidelines, including the National Cholesterol Education Program Adult Treatment Panel series. The objective of this article is to highlight the value of observational studies as a complement to clinical trial data for clinical decision making in real-world practice. Although RCTs are still the benchmark for assessing clinical efficacy and safety of a specific therapeutic approach, they may be of limited utility to practitioners who must then adapt the lessons learned from the trial into the patient care environment. The use of well-structured observational studies can improve our understanding of the translation of clinical trials into clinical practice, as demonstrated here with the example of statins. Although such studies have their own limitations, improved techniques for design and analysis have reduced the impact of bias and confounders. The introduction of the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines has provided more uniformity for such studies. When used together with RCTs, observational studies can enhance our understanding of effectiveness and utility in real-world clinical practice. In the examples of statin observational studies, the results suggest that relative effectiveness of different statins and potential impact of switching statins should be carefully considered in treating individual patients by practicing physicians.

  14. Study of Statin- and Loratadine-Induced Muscle Pain Mechanisms Using Human Skeletal Muscle Cells

    OpenAIRE

    Yat Hei Leung; Jacques Turgeon; Veronique Michaud

    2017-01-01

    Many drugs can cause unexpected muscle disorders, often necessitating the cessation of an effective medication. Inhibition of monocarboxylate transporters (MCTs) may potentially lead to perturbation of l-lactic acid homeostasis and muscular toxicity. Previous studies have shown that statins and loratadine have the potential to inhibit l-lactic acid efflux by MCTs (MCT1 and 4). The main objective of this study was to confirm the inhibitory potentials of atorvastatin, simvastatin (acid and lact...

  15. Personalized prediction of lifetime benefits with statin therapy for asymptomatic individuals: a modeling study.

    Directory of Open Access Journals (Sweden)

    Bart S Ferket

    Full Text Available BACKGROUND: Physicians need to inform asymptomatic individuals about personalized outcomes of statin therapy for primary prevention of cardiovascular disease (CVD. However, current prediction models focus on short-term outcomes and ignore the competing risk of death due to other causes. We aimed to predict the potential lifetime benefits with statin therapy, taking into account competing risks. METHODS AND FINDINGS: A microsimulation model based on 5-y follow-up data from the Rotterdam Study, a population-based cohort of individuals aged 55 y and older living in the Ommoord district of Rotterdam, the Netherlands, was used to estimate lifetime outcomes with and without statin therapy. The model was validated in-sample using 10-y follow-up data. We used baseline variables and model output to construct (1 a web-based calculator for gains in total and CVD-free life expectancy and (2 color charts for comparing these gains to the Systematic Coronary Risk Evaluation (SCORE charts. In 2,428 participants (mean age 67.7 y, 35.5% men, statin therapy increased total life expectancy by 0.3 y (SD 0.2 and CVD-free life expectancy by 0.7 y (SD 0.4. Age, sex, smoking, blood pressure, hypertension, lipids, diabetes, glucose, body mass index, waist-to-hip ratio, and creatinine were included in the calculator. Gains in total and CVD-free life expectancy increased with blood pressure, unfavorable lipid levels, and body mass index after multivariable adjustment. Gains decreased considerably with advancing age, while SCORE 10-y CVD mortality risk increased with age. Twenty-five percent of participants with a low SCORE risk achieved equal or larger gains in CVD-free life expectancy than the median gain in participants with a high SCORE risk. CONCLUSIONS: We developed tools to predict personalized increases in total and CVD-free life expectancy with statin therapy. The predicted gains we found are small. If the underlying model is validated in an independent cohort, the

  16. Safety of statins.

    Science.gov (United States)

    Brown, William Virgil

    2008-12-01

    To examine the evidence for the adverse effects that have been reported during the use of statins. We now have over twenty years of prescription use and many large well controlled trials with statin therapy for hypercholesterolemia. There is only one significant and well documented adverse effect with this group of drugs, rhabdomyolysis. Significant muscle damage is very rare when statin therapy is used in patients carefully screened for concomitant use of other drugs which may interfere with statin catabolism and excretion. Patients with severely impaired liver function are also at risk due to the importance of hepatic excretion of all statins. Chronic myalgias or other pain syndromes have not been confirmed by blinded placebo controlled trials. A significant and reproducible rise in liver enzymes (alanine and aspartate aminotransferases) is observed in 1 to 3% of patients but actual liver damage may not occur at all. Benign and transient proteinuria occurs without evidence of altered renal function. Creatinine clearance is usually increased by statins. Peripheral neuropathy may be a rare adverse effect and this needs further study. Statins are very effective at reducing the incidence of myocardial infarction, stroke and other manifestations of vascular disease. The adverse event rates are very uncommon and the benefit risk ratio is extremely high.

  17. Flaws in animal studies exploring statins and impact on meta-analysis.

    Science.gov (United States)

    Moja, Lorenzo; Pecoraro, Valentina; Ciccolallo, Laura; Dall'Olmo, Luigi; Virgili, Gianni; Garattini, Silvio

    2014-06-01

    Animal experiments should be appropriately designed, correctly analysed and transparently reported to increase their scientific validity and maximise the knowledge gained from each experiment. This systematic review of animal experiments investigating statins evaluates their quality of reporting and methodological aspects as well as their implications for the conduction of meta-analyses. We searched medline and embase for studies reporting research on statins in mice, rats and rabbits. We collected detailed information about the characteristics of studies, animals and experimental methods. We retrieved 161 studies. A little over half did not report randomisation (55%) and most did not describe blinding (88%). All studies reported details on the experimental procedure, although many omitted information about animal gender, age or weight. Four percent did not report the number of animals used. None reported the sample size. Fixed- and random-effects models gave different results (ratio of effect size increased by five folds). Heterogeneity was consistently substantial within animal models, for which accounting for covariates had minimal impact. Publication bias is highly suspected across studies. Although statins showed efficacy in animal models, preclinical studies highlighted fundamental problems in the way in which such research is conducted and reported. Results were often difficult to interpret and reproduce. Different meta-analytic approaches were highly inconsistent: a reliable approach to estimate the true parameter was imperceptible. Policies that address these issues are required from investigators, editors and institutions that care about the quality standards and ethics of animal research. © 2014 Stichting European Society for Clinical Investigation Journal Foundation.

  18. Applying data mining methods to the assessment of soil contamination and carbon sequestration under Mediterranean Climate. The case study of Guadiamar basin (SW Spain).

    Science.gov (United States)

    Muñoz Vallés, Sara; Pino-Mejías, Rafael; Blanco-Velázquez, Francisco J.; Anaya-Romero, María

    2017-04-01

    In the present background of increasing access to vast datasets of soil and environmental records, the application of the newest analytical techniques and approaches for modelling offer excellent opportunities to define recommendations and simulate processes for land degradation and management. In this regard, data mining techniques have been successfully applied in different fields of environmental sciences, performing an innovative tool to explore relevant questions and providing valuable results and useful applications through an efficient management and analysis of large and heterogeneous datasets. Soil Organic matter, pH and trace elements in soil perform close relationships, with ability to alter each other and lead to emerging, synergic properties for soils. In addition, effects associated to climate and land use change promotes mechanisms of feedback that could amplify the negative effects of soil contamination on human health, biodiversity conservation and soil ecosystem services maintenance. The aim of this study was to build and compare several data mining models for the prediction of potential and interrelated functions of soil contamination and carbon sequestration by soils. In this context, under the framework of the EU RECARE project (Preventing and Remediating degradation of Soils in Europe through Land Care), the Guadiamar valley (SW Spain) is used as case study. The area was affected by around four hm3 of acid waters and two hm3 of mud rich in heavy metals, resulting from a mine spill, in 1998, where more than 4,600 ha of agricultural and pasture land were affected. The area was subjected to a large-scale phyto-management project, and consequently protected as "Green Corridor". In this study, twenty environmental variables were taken into account and several base models for supervised classification problems were selected, including linear and quadratic discriminant analysis, logistic regression, neural networks and support vector machines. A

  19. Advanced Data Mining of Leukemia Cells Micro-Arrays

    Directory of Open Access Journals (Sweden)

    Richard S. Segall

    2009-12-01

    Full Text Available This paper provides continuation and extensions of previous research by Segall and Pierce (2009a that discussed data mining for micro-array databases of Leukemia cells for primarily self-organized maps (SOM. As Segall and Pierce (2009a and Segall and Pierce (2009b the results of applying data mining are shown and discussed for the data categories of microarray databases of HL60, Jurkat, NB4 and U937 Leukemia cells that are also described in this article. First, a background section is provided on the work of others pertaining to the applications of data mining to micro-array databases of Leukemia cells and micro-array databases in general. As noted in predecessor article by Segall and Pierce (2009a, micro-array databases are one of the most popular functional genomics tools in use today. This research in this paper is intended to use advanced data mining technologies for better interpretations and knowledge discovery as generated by the patterns of gene expressions of HL60, Jurkat, NB4 and U937 Leukemia cells. The advanced data mining performed entailed using other data mining tools such as cubic clustering criterion, variable importance rankings, decision trees, and more detailed examinations of data mining statistics and study of other self-organized maps (SOM clustering regions of workspace as generated by SAS Enterprise Miner version 4. Conclusions and future directions of the research are also presented.

  20. data mining in distributed database

    International Nuclear Information System (INIS)

    Ghunaim, A.A.A.

    2007-01-01

    as we march into the age of digital information, the collection and the storage of large quantities of data is increased, and the problem of data overload looms ominously ahead. it is estimated today that the volume of data stored by a company doubles every year but the amount of meaningful information is decreases rapidly. the ability to analyze and understand massive datasets lags far behind the ability to gather and store the data. the unbridled growth of data will inevitably lead to a situation in which it is increasingly difficult to access the desired information; it will always be like looking for a needle in a haystack, and where only the amount of hay will be growing all the time . so, a new generation of computational techniques and tools is required to analyze and understand the rapidly growing volumes of data . and, because the information technology (it) has become a strategic weapon in the modern life, it is needed to use a new decision support tools to be an international powerful competitor.data mining is one of these tools and its methods make it possible to extract decisive knowledge needed by an enterprise and it means that it concerned with inferring models from data , including statistical pattern recognition, applied statistics, machine learning , and neural networks. data mining is a tool for increasing productivity of people trying to build predictive models. data mining techniques have been applied successfully to several real world problem domains; but the application in the nuclear reactors field has only little attention . one of the main reasons, is the difficulty in obtaining the data sets

  1. Data Mining and Privacy of Social Network Sites' Users: Implications of the Data Mining Problem.

    Science.gov (United States)

    Al-Saggaf, Yeslam; Islam, Md Zahidul

    2015-08-01

    This paper explores the potential of data mining as a technique that could be used by malicious data miners to threaten the privacy of social network sites (SNS) users. It applies a data mining algorithm to a real dataset to provide empirically-based evidence of the ease with which characteristics about the SNS users can be discovered and used in a way that could invade their privacy. One major contribution of this article is the use of the decision forest data mining algorithm (SysFor) to the context of SNS, which does not only build a decision tree but rather a forest allowing the exploration of more logic rules from a dataset. One logic rule that SysFor built in this study, for example, revealed that anyone having a profile picture showing just the face or a picture showing a family is less likely to be lonely. Another contribution of this article is the discussion of the implications of the data mining problem for governments, businesses, developers and the SNS users themselves.

  2. Data mining utilizando redes neuronales

    OpenAIRE

    Ale, Juan María; Bot, Romina Laura

    2004-01-01

    Las Redes Neuronales son ampliamente utilizadas para tareas relacionadas con reconocimiento de patrones y clasificación. Aunque son clasificadores muy precisos, no son comúnmente utilizadas para Data Mining porque producen modelos de aprendizaje inexplicables. El algoritmo TREPAN extrae hipótesis explicables de una Red Neuronal entrenada. Las hipótesis producidas por el algoritmo se representan con un árbol de decisión que aproxima a la red. Los árboles de decisión extraídos por TREPAN no pue...

  3. DATA MINING IN SPORTS BETTING

    Directory of Open Access Journals (Sweden)

    Cristian Georgescu

    2013-12-01

    Full Text Available n this paper, we have made a brief analysis on how to make decisions in betting on European football with the help of data mining techniques. Whether you refer to betting a few days in advance of the sporting event or live betting, both options have been taken into consideration. By using a clustering algorithm for analyzing both the database containing events from football matches and the odds given by bookmakers, we have obtained graphs indicating the probabilities associated with analyzed events. Given the purely informative aspect of the current paper, we have only analyzed the number of corners from a match.

  4. Towards a unified European electricity market: The contribution of data-mining to support realistic simulation studies

    DEFF Research Database (Denmark)

    Pinto, Tiago; Santos, Gabriel; Pereira, Ivo F.

    2014-01-01

    Worldwide electricity markets have been evolving into regional and even continental scales. The aim at an efficient use of renewable based generation in places where it exceeds the local needs is one of the main reasons. A reference case of this evolution is the European Electricity Market, where...... countries are connected, and several regional markets were created, each one grouping several countries, and supporting transactions of huge amounts of electrical energy. The continuous transformations electricity markets have been experiencing over the years create the need to use simulation platforms...... to support operators, regulators, and involved players for understanding and dealing with this complex environment. This paper focuses on demonstrating the advantage that real electricity markets data has for the creation of realistic simulation scenarios, which allow the study of the impacts...

  5. Landslide detection using LiDAR data and data mining technology: Ali Mountain Highway case study (Taiwan)

    Science.gov (United States)

    Cheng, Youg-Sin; Yu, Teng-To; Tarolli, Paolo

    2017-04-01

    Taiwan mountains are severely affected each year by landslides, rock falls, and debris flows where the roads system suffer the most critical consequences. Among all mountain highways, Ali Highway, located into the main entrance of Alishan Mountain region, is one of the most landslide-prone areas in southern Taiwan. During the typhoon season, between May and August, the probability of occurrence of mass movements is at higher level than usual seeing great erosion rates. In fact, during Typhoon Morakot, in 2009, the intense rainfall caused abrupt interruption of the circulation for three months triggering several landslides (Liu et al. 2012). The topographic features such as slope, roughness and curvature among others have been extracted from 1 m DTM derived by a LiDAR dataset (collected in 2015) to investigate the slope failures along the Ali Mountain Highway. The high-resolution DTM highlighted that the hydrogeomorphological (e.g. density of stream, the distance from the ridge and terrain) features are one of the most influencing factors affecting the change and the instability of the slopes. To detect the landslide area, the decision tree classifier and the random forest algorithm (RF) have been adopted. The results provided a suitable analysis of the area involved in the failure. This will be a useful step in the understanding (and management) landslide processes of study area. References Liu CN, Dong JJ, Chen CJ, Lee WF (2012) Typical landslides and related mechanisms in Ali Mountain highway induced by typhoon Morakot: Perspectives from engineering geology. Landslides 9:239-254.

  6. Study of cyanotoxins presence from experimental cyanobacteria concentrations using a new data mining methodology based on multivariate adaptive regression splines in Trasona reservoir (Northern Spain).

    Science.gov (United States)

    Garcia Nieto, P J; Sánchez Lasheras, F; de Cos Juez, F J; Alonso Fernández, J R

    2011-11-15

    There is an increasing need to describe cyanobacteria blooms since some cyanobacteria produce toxins, termed cyanotoxins. These latter can be toxic and dangerous to humans as well as other animals and life in general. It must be remarked that the cyanobacteria are reproduced explosively under certain conditions. This results in algae blooms, which can become harmful to other species if the cyanobacteria involved produce cyanotoxins. In this research work, the evolution of cyanotoxins in Trasona reservoir (Principality of Asturias, Northern Spain) was studied with success using the data mining methodology based on multivariate adaptive regression splines (MARS) technique. The results of the present study are two-fold. On one hand, the importance of the different kind of cyanobacteria over the presence of cyanotoxins in the reservoir is presented through the MARS model and on the other hand a predictive model able to forecast the possible presence of cyanotoxins in a short term was obtained. The agreement of the MARS model with experimental data confirmed the good performance of the same one. Finally, conclusions of this innovative research are exposed. Copyright © 2011 Elsevier B.V. All rights reserved.

  7. Statistically significant relational data mining :

    Energy Technology Data Exchange (ETDEWEB)

    Berry, Jonathan W.; Leung, Vitus Joseph; Phillips, Cynthia Ann; Pinar, Ali; Robinson, David Gerald; Berger-Wolf, Tanya; Bhowmick, Sanjukta; Casleton, Emily; Kaiser, Mark; Nordman, Daniel J.; Wilson, Alyson G.

    2014-02-01

    This report summarizes the work performed under the project (3z(BStatitically significant relational data mining.(3y (BThe goal of the project was to add more statistical rigor to the fairly ad hoc area of data mining on graphs. Our goal was to develop better algorithms and better ways to evaluate algorithm quality. We concetrated on algorithms for community detection, approximate pattern matching, and graph similarity measures. Approximate pattern matching involves finding an instance of a relatively small pattern, expressed with tolerance, in a large graph of data observed with uncertainty. This report gathers the abstracts and references for the eight refereed publications that have appeared as part of this work. We then archive three pieces of research that have not yet been published. The first is theoretical and experimental evidence that a popular statistical measure for comparison of community assignments favors over-resolved communities over approximations to a ground truth. The second are statistically motivated methods for measuring the quality of an approximate match of a small pattern in a large graph. The third is a new probabilistic random graph model. Statisticians favor these models for graph analysis. The new local structure graph model overcomes some of the issues with popular models such as exponential random graph models and latent variable models.

  8. The Japan Statin Treatment Against Recurrent Stroke (J-STARS) Echo Study: Rationale and Trial Protocol.

    Science.gov (United States)

    Toyoda, Kazunori; Minematsu, Kazuo; Yasaka, Masahiro; Nagai, Yoji; Hosomi, Naohisa; Origasa, Hideki; Kitagawa, Kazuo; Uchiyama, Shinichiro; Koga, Masatoshi; Matsumoto, Masayasu

    2017-03-01

    The preventive effect of 3-hydroxy-3-methylglutaryl-coenzyme A reductase inhibitors (statins) on progression of carotid intima-media complex thickness (IMT) has been shown exclusively in nonstroke Western patients. The Japan Statin Treatment Against Recurrent Stroke (J-STARS) Echo Study aims to determine the effect of pravastatin on carotid IMT in Japanese patients with hyperlipidemia who developed noncardioembolic ischemic stroke. This is a substudy of the J-STARS, a multicenter, randomized, open-label, blinded-end point, parallel-group trial to examine whether pravastatin reduces stroke recurrence in patients with noncardioembolic stroke. The patients are randomized to receive pravastatin (10 mg daily) or not to receive any statins. Carotid ultrasonography is performed by well-trained certified examiners in each participating institute, and the recorded data are measured centrally. The primary outcome is change in the IMT of the distal wall in a consecutive 2-cm section on the central side of the common carotid artery bifurcation over 5 years of observation. The trial may help determine if the usual dose of pravastatin for daily clinical practice in Japan can affect carotid IMT in Japanese patients with noncardioembolic stroke. Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.

  9. Effect of statin on hepatocellular carcinoma in patients with type 2 diabetes: A nationwide nested case-control study.

    Science.gov (United States)

    Kim, Gyuri; Jang, Suk-Yong; Han, Eugene; Lee, Yong-Ho; Park, Se-Young; Nam, Chung Mo; Kang, Eun Seok

    2017-02-15

    Relationship on new statin use and the risk of hepatocellular carcinoma (HCC) in patients with incident type 2 diabetes mellitus (T2DM), who might be at the risk of developing HCC, is uncertained. A nationwide population-based nested case-control study was conducted within the National Health Insurance Service National Sample Cohort 2002-2013 in Korea. Newly prescribed statin after newly diagnosed T2DM was defined as statin use. Controls were matched to case patients on age, sex, follow-up time, and the date of diabetes diagnosis at a five-to-one ratio. Odds ratios (ORs) for associations of statin use with HCC were calculated using conditional logistic regression. After at least a 5-year HCC-free period, there were 229 incident HCC cases and 1,145 matched controls from 47,738 patients with incident diabetes. Of these 229 incident HCC cases, 27 (11.8%) were statin users, whereas 378 (33.0%) were statin users among 1,145 controls. Statin use was associated with a reduced risk of HCC development (adjusted OR [AOR]= 0.36, 95% confidence interval [CI] 0.22-0.60) after adjustment for chronic viral hepatitis, liver cirrhosis, alcoholic liver disease, previous cancer, aspirin use, insulin use, sulfonylurea use, metformin use, thiazolidinedione use, history of chronic obstructive pulmonary disease, Charlson comorbidity score, household income level, and residential area. Risk reduction was accentuated with an increase of cumulative defined daily doses (cDDD) compared with non-users (AORs 0.53, 0.36, 0.32, and 0.26 in ≤60, 60-180, 181-365, and >365cDDD, respectively; P for trend statin use before HCC diagnosis may have a beneficial inhibitory effect on HCC development in a dose-dependent manner, especially in individuals with liver disease. © 2016 UICC.

  10. A Data Mining Approach to Study the Impact of the Methodology Followed in Chemistry Lab Classes on the Weight Attributed by the Students to the Lab Work on Learning and Motivation

    Science.gov (United States)

    Figueiredo, M.; Esteves, L.; Neves, J.; Vicente, H.

    2016-01-01

    This study reports the use of data mining tools in order to examine the influence of the methodology used in chemistry lab classes, on the weight attributed by the students to the lab work on learning and own motivation. The answer frequency analysis was unable to discriminate the opinions expressed by the respondents according to the type of the…

  11. Primary Prevention With Statins

    DEFF Research Database (Denmark)

    Mortensen, Martin B; Afzal, Shoaib; Nordestgaard, Børge G

    2015-01-01

    BACKGROUND: Guidelines recommend initiating primary prevention for atherosclerotic cardiovascular disease (ASCVD) with statins based on absolute ASCVD risk assessment. Recently, alternative trial-based and hybrid approaches were suggested for statin treatment eligibility. OBJECTIVES: This study...... the population studied, 42% were eligible for statin therapy according to the 2013 American College of Cardiology/American Heart Association (ACC/AHA) risk assessment and cholesterol treatment guidelines approach, versus 56% with the trial-based approach and 21% with the hybrid approach. Among these statin......-eligible subjects, the ASCVD event rate per 1,000 person-years was 9.8, 6.8, and 11.2, respectively. The ACC/AHA-recommended absolute risk score was well calibrated around the 7.5% 10-year ASCVD risk treatment threshold and discriminated better than the trial-based or hybrid approaches. Compared with the ACC...

  12. Coenzyme Q10 supplementation decreases statin-related mild-to-moderate muscle symptoms: a randomized clinical study.

    Science.gov (United States)

    Skarlovnik, Ajda; Janić, Miodrag; Lunder, Mojca; Turk, Martina; Šabovič, Mišo

    2014-11-06

    Statin use is frequently associated with muscle-related symptoms. Coenzyme Q10 supplementation has yielded conflicting results in decreasing statin myopathy. Herein, we tested whether coenzyme Q10 supplementation could decrease statin-associated muscular pain in a specific group of patients with mild-to-moderate muscle symptoms. Fifty patients treated with statins and reporting muscle pain were recruited. The Q10 group (n=25) received coenzyme Q10 supplementation over a period of 30 days (50 mg twice daily), and the control group (n=25) received placebo. The Brief Pain Inventory (BPI) questionnaire was used and blood testing was performed at inclusion in the study and after 30 days of supplementation. The intensity of muscle pain, measured as the Pain Severity Score (PSS), in the Q10 group was reduced from 3.9±0.4 to 2.9±0.4 (PPain Interference Score (PIS) after Q10 supplementation was reduced from 4.0±0.4 to 2.6±0.4 (Pstatin-related muscle symptoms in 75% of patients. The relative values of PSS and PIS significantly decreased (-33.1% and -40.3%, respectively) in the Q10 group compared to placebo group (both Pmuscle enzymes or cholesterol values were found. The present results show that coenzyme Q10 supplementation (50 mg twice daily) effectively reduced statin-related mild-to-moderate muscular symptoms, causing lower interference of statin-related muscular symptoms with daily activities.

  13. Statins and morbidity and mortality in COPD in the COMIC study: a prospective COPD cohort study

    NARCIS (Netherlands)

    Citgez, Emanuel; van der Palen, Job; Koehorst-Ter Huurne, Kirsten; Movig, Kris; van der Valk, Paul; Brusse-Keizer, Marjolein

    2016-01-01

    BACKGROUND: Both chronic inflammation and cardiovascular comorbidity play an important role in the morbidity and mortality of patients with chronic obstructive pulmonary disease (COPD). Statins could be a potential adjunct therapy. The additional effects of statins in COPD are, however, still under

  14. Stratified sampling design based on data mining.

    Science.gov (United States)

    Kim, Yeonkook J; Oh, Yoonhwan; Park, Sunghoon; Cho, Sungzoon; Park, Hayoung

    2013-09-01

    To explore classification rules based on data mining methodologies which are to be used in defining strata in stratified sampling of healthcare providers with improved sampling efficiency. We performed k-means clustering to group providers with similar characteristics, then, constructed decision trees on cluster labels to generate stratification rules. We assessed the variance explained by the stratification proposed in this study and by conventional stratification to evaluate the performance of the sampling design. We constructed a study database from health insurance claims data and providers' profile data made available to this study by the Health Insurance Review and Assessment Service of South Korea, and population data from Statistics Korea. From our database, we used the data for single specialty clinics or hospitals in two specialties, general surgery and ophthalmology, for the year 2011 in this study. Data mining resulted in five strata in general surgery with two stratification variables, the number of inpatients per specialist and population density of provider location, and five strata in ophthalmology with two stratification variables, the number of inpatients per specialist and number of beds. The percentages of variance in annual changes in the productivity of specialists explained by the stratification in general surgery and ophthalmology were 22% and 8%, respectively, whereas conventional stratification by the type of provider location and number of beds explained 2% and 0.2% of variance, respectively. This study demonstrated that data mining methods can be used in designing efficient stratified sampling with variables readily available to the insurer and government; it offers an alternative to the existing stratification method that is widely used in healthcare provider surveys in South Korea.

  15. Statin Therapy and Outcome After Ischemic Stroke: Systematic Review and Meta-Analysis of Observational Studies and Randomized Trials.

    LENUS (Irish Health Repository)

    2013-01-03

    Background-Although experimental data suggest that statin therapy may improve neurological outcome after acute cerebral ischemia, the results from clinical studies are conflicting. We performed a systematic review and meta-analysis investigating the relationship between statin therapy and outcome after ischemic stroke. METHODS: The primary analysis investigated statin therapy at stroke onset (prestroke statin use) and good functional outcome (modified Rankin score 0 to 2) and death. Secondary analyses included the following: (1) acute poststroke statin therapy (≤72 hours after stroke), and (2) thrombolysis-treated patients. RESULTS: The primary analysis included 113 148 subjects (27 studies). Among observational studies, statin treatment at stroke onset was associated with good functional outcome at 90 days (pooled odds ratio [OR], 1.41; 95% confidence interval [CI], 1.29-1.56; P<0.001), but not 1 year (OR, 1.12; 95% CI, 0.9-1.4; P=0.31), and with reduced fatality at 90 days (pooled OR, 0.71; 95% CI, 0.62-0.82; P<0.001) and 1 year (OR, 0.80; 95% CI, 0.67-0.95; P=0.01). In the single randomized controlled trial reporting 90-day functional outcome, statin treatment was associated with good outcome (OR, 1.5; 95% CI, 1.0-2.24; P=0.05). No reduction in fatality was observed on meta-analysis of data from 3 randomized controlled trials (P=0.9). In studies of thrombolysis-treated patients, an association between statins and increased fatality at 90 days was observed (pooled OR, 1.25; 95% CI, 1.02-1.52; P=0.03, 3 studies, 4339 patients). However, this association was no longer present after adjusting for age and stroke severity in the largest study (adjusted OR, 1.14; 95% CI, 0.90-1.44; 4012 patients). CONCLUSIONS: In the largest meta-analysis to date, statin therapy at stroke onset was associated with improved outcome, a finding not observed in studies restricted to thrombolysis-treated patients. Randomized trials of statin therapy in acute ischemic stroke are needed.

  16. An Application of Multithreaded Data Mining in Educational Leadership Research

    OpenAIRE

    Fikis, David; Wang, Yinying; Bowers, Alex

    2015-01-01

    This study aims to apply high-performance computing to educational leadership research. Specifically, we applied an array of data acquisition and analytical techniques to the field of educational leadership research, including text data mining, probabiblistic topic modeling, and the use of software (CasperJS, GNU utilities, R, etc.) as well as hardware (the VELA batch computer and the multi-threaded data mining environment).  

  17. The Value of Data Mining in Music Education Research and Some Findings from Its Application to a Study of Instrumental Learning during Childhood

    Science.gov (United States)

    Faulkner, Robert; Davidson, Jane W.; McPherson, Gary E.

    2010-01-01

    The use of data mining for the analysis of data collected in natural settings is increasingly recognized as a legitimate mode of enquiry. This rule-inductive paradigm is an effective means of discovering relationships within large datasets--especially in research that has limited experimental design--and for the subsequent formulation of…

  18. Pocket data mining big data on small devices

    CERN Document Server

    Gaber, Mohamed Medhat; Gomes, Joao Bartolo

    2014-01-01

    Owing to continuous advances in the computational power of handheld devices like smartphones and tablet computers, it has become possible to perform Big Data operations including modern data mining processes onboard these small devices. A decade of research has proved the feasibility of what has been termed as Mobile Data Mining, with a focus on one mobile device running data mining processes. However, it is not before 2010 until the authors of this book initiated the Pocket Data Mining (PDM) project exploiting the seamless communication among handheld devices performing data analysis tasks that were infeasible until recently. PDM is the process of collaboratively extracting knowledge from distributed data streams in a mobile computing environment. This book provides the reader with an in-depth treatment on this emerging area of research. Details of techniques used and thorough experimental studies are given. More importantly and exclusive to this book, the authors provide detailed practical guide on the depl...

  19. Statins and physical activity in older men: the osteoporotic fractures in men study.

    Science.gov (United States)

    Lee, David S H; Markwardt, Sheila; Goeres, Leah; Lee, Christine G; Eckstrom, Elizabeth; Williams, Craig; Fu, Rongwei; Orwoll, Eric; Cawthon, Peggy M; Stefanick, Marcia L; Mackey, Dawn; Bauer, Douglas C; Nielson, Carrie M

    2014-08-01

    Muscle pain, fatigue, and weakness are common adverse effects of statin medications and may decrease physical activity in older men. To determine whether statin use is associated with physical activity, longitudinally and cross-sectionally. Men participating in the Osteoporotic Fractures in Men Study (N = 5994), a multicenter prospective cohort study of community-living men 65 years and older, enrolled between March 2000 and April 2002. Follow-up was conducted through 2009. Statin use as determined by an inventory of medications (taken within the last 30 days). In cross-sectional analyses (n = 4137), statin use categories were users and nonusers. In longitudinal analyses (n = 3039), categories were prevalent users (baseline use and throughout the study), new users (initiated use during the study), and nonusers (never used). Self-reported physical activity at baseline and 2 follow-up visits using the Physical Activity Scale for the Elderly (PASE). At the third visit, an accelerometer measured metabolic equivalents (METs [kilocalories per kilogram per hour]) and minutes of moderate activity (METs ≥3.0), vigorous activity (METs ≥6.0), and sedentary behavior (METs ≤1.5). At baseline, 989 men (24%) were users and 3148 (76%) were nonusers. The adjusted difference in baseline PASE between users and nonusers was -5.8 points (95% CI, -10.9 to -0.7 points). A total of 3039 men met the inclusion criteria for longitudinal analysis: 727 (24%) prevalent users, 845 (28%) new users, and 1467 (48%) nonusers. PASE score declined by a mean (95% CI) of 2.5 (2.0 to 3.0) points per year for nonusers and 2.8 (2.1 to 3.5) points per year for prevalent users, a nonstatistical difference (0.3 [-0.5 to 1.0] points). For new users, annual PASE score declined at a faster rate than nonusers (difference of 0.9 [95% CI, 0.1 to 1.7] points). A total of 3071 men had adequate accelerometry data, 1542 (50%) were statin users. Statin users expended less METs (0.03 [95% CI, 0.02-0.04] METs less

  20. Data mining in time series databases

    CERN Document Server

    Kandel, Abraham; Bunke, Horst

    2004-01-01

    Adding the time dimension to real-world databases produces Time SeriesDatabases (TSDB) and introduces new aspects and difficulties to datamining and knowledge discovery. This book covers the state-of-the-artmethodology for mining time series databases. The novel data miningmethods presented in the book include techniques for efficientsegmentation, indexing, and classification of noisy and dynamic timeseries. A graph-based method for anomaly detection in time series isdescribed and the book also studies the implications of a novel andpotentially useful representation of time series as strings. Theproblem of detecting changes in data mining models that are inducedfrom temporal databases is additionally discussed.

  1. Data Mining Solutions for the Business Environment

    Directory of Open Access Journals (Sweden)

    Ruxandra-Stefania PETRE

    2014-02-01

    Full Text Available Over the past years, data mining became a matter of considerable importance due to the large amounts of data available in the applications belonging to various domains. Data mining, a dynamic and fast-expanding field, that applies advanced data analysis techniques, from statistics, machine learning, database systems or artificial intelligence, in order to discover relevant patterns, trends and relations contained within the data, information impossible to observe using other techniques. The paper focuses on presenting the applications of data mining in the business environment. It contains a general overview of data mining, providing a definition of the concept, enumerating six primary data mining techniques and mentioning the main fields for which data mining can be applied. The paper also presents the main business areas which can benefit from the use of data mining tools, along with their use cases: retail, banking and insurance. Also the main commercially available data mining tools and their key features are presented within the paper. Besides the analysis of data mining and the business areas that can successfully apply it, the paper presents the main features of a data mining solution that can be applied for the business environment and the architecture, with its main components, for the solution, that would help improve customer experiences and decision-making

  2. Academic Performance: An Approach From Data Mining

    Directory of Open Access Journals (Sweden)

    David L. La Red Martinez

    2012-02-01

    Full Text Available The relatively low% of students promoted and regularized in Operating Systems Course of the LSI (Bachelor’s Degree in Information Systems of FaCENA (Faculty of Sciences and Natural Surveying - Facultad de Ciencias Exactas, Naturales y Agrimensura of UNNE (academic success, prompted this work, whose objective is to determine the variables that affect the academic performance, whereas the final status of the student according to the Res. 185/03 CD (scheme for evaluation and promotion: promoted, regular or free1. The variables considered are: status of the student, educational level of parents, secondary education, socio-economic level, and others. Data warehouse (Data Warehouses: DW and data mining (Data Mining: DM techniques were used to search pro.les of students and determine success or failure academic potential situations. Classifications through techniques of clustering according to different criteria have become. Some criteria were the following: mining of classification according to academic program, according to final status of the student, according to importance given to the study, mining of demographic clustering and Kohonen clustering according to final status of the student. Were conducted statistics of partition, detail of partitions, details of clusters, detail of fields and frequency of fields, overall quality of each process and quality detailed (precision, classification, reliability, arrays of confusion, diagrams of gain / elevation, trees, distribution of nodes, of importance of fields, correspondence tables of fields and statistics of cluster. Once certain profiles of students with low academic performance, it may address actions aimed at avoiding potential academic failures. This work aims to provide a brief description of aspects related to the data warehouse built and some processes of data mining developed on the same.

  3. Public data mining plus domestic experimental study defined involvement of the old-yet-uncharacterized gene matrix-remodeling associated 7 (MXRA7) in physiopathology of the eye.

    Science.gov (United States)

    Jia, Changkai; Zhang, Feng; Zhu, Ying; Qi, Xia; Wang, Yiqiang

    2017-10-20

    Matrix-remodeling associated 7 (MXRA7) gene was first reported in 2002 and named so for its co-expression with several genes known to relate with matrix-remodeling. However, not any studies had been intentionally performed to characterize this gene. We started defining the functions of MXRA7 by integrating bioinformatics analysis and experimental study. Data mining of MXRA7 expression in BioGPS, Gene Expression Omnibus and EurExpress platforms highlighted high level expression of Mxra7 in murine ocular tissues. Real-time PCR was employed to measure Mxra7 mRNA in tissues of adult C57BL/6 mice and demonstrated that Mxra7 was preferentially expressed at higher level in retina, corneas and lens than in other tissues. Then the inflammatory corneal neovascularization (CorNV) model and fungal corneal infections were induced in Balb/c mice, and mRNA levels of Mxra7 as well as several matrix-remodeling related genes (Mmp3, Mmp13, Ecm1, Timp1) were monitored with RT-PCR. The results demonstrated a time-dependent Mxra7 under-expression pattern (U-shape curve along timeline), while all other matrix-remodeling related genes manifested an opposite changes pattern (dome-shape curve). When limited data from BioGPS concerning human MXRA7 gene expression in human tissues were looked at, it was found that ocular tissue was also the one expressing highest level of MXRA7. To conclude, integrative assay of MXRA7 gene expression in public databank as well as domestic animal models revealed a selective high expression MXRA7 in murine and human ocular tissues, and its change patterns in two corneal disease models implied that MXRA7 might play a role in pathological processes or diseases involving injury, neovascularization and would healing. Copyright © 2017 Elsevier B.V. All rights reserved.

  4. Statin treatment in multiple sclerosis

    DEFF Research Database (Denmark)

    Pihl-Jensen, Gorm; Tsakiri, Anna; Frederiksen, Jette Lautrup

    2015-01-01

    BACKGROUND: Multiple sclerosis (MS) is a chronic inflammatory disease that leads to progressive disability. Statins [hydroxymethylglutaryl-CoA (HMG-CoA) reductase inhibitors] are widely prescribed drugs in hypercholesterolemia. They exert immunomodulatory and neurotrophic effects and are attractive...... candidates for MS treatment due to reliable safety profiles and favorable costs. Studies of statins in a murine MS model and in open-label trials in MS have shown decreased disease severity. OBJECTIVE: Our objective was to assess current evidence to support statin treatment in MS and clinically isolated......)-β treatment in RRMS, one of statin monotherapy in CIS, one of statin monotherapy in optic neuritis (ON)/CIS, and one of statin monotherapy in secondary progressive MS (SPMS)]. Three trials with eligible characteristics had not been published in peer-reviewed journals and were therefore not included. Due...

  5. Advances in Machine Learning and Data Mining for Astronomy

    Science.gov (United States)

    Way, Michael J.; Scargle, Jeffrey D.; Ali, Kamal M.; Srivastava, Ashok N.

    2012-03-01

    Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book's introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.

  6. Two non-synonymous markers in PTPN21, identified by genome-wide association study data-mining and replication, are associated with schizophrenia.

    LENUS (Irish Health Repository)

    Chen, Jingchun

    2011-09-01

    We conducted data-mining analyses of genome wide association (GWA) studies of the CATIE and MGS-GAIN datasets, and found 13 markers in the two physically linked genes, PTPN21 and EML5, showing nominally significant association with schizophrenia. Linkage disequilibrium (LD) analysis indicated that all 7 markers from PTPN21 shared high LD (r(2)>0.8), including rs2274736 and rs2401751, the two non-synonymous markers with the most significant association signals (rs2401751, P=1.10 × 10(-3) and rs2274736, P=1.21 × 10(-3)). In a meta-analysis of all 13 replication datasets with a total of 13,940 subjects, we found that the two non-synonymous markers are significantly associated with schizophrenia (rs2274736, OR=0.92, 95% CI: 0.86-0.97, P=5.45 × 10(-3) and rs2401751, OR=0.92, 95% CI: 0.86-0.97, P=5.29 × 10(-3)). One SNP (rs7147796) in EML5 is also significantly associated with the disease (OR=1.08, 95% CI: 1.02-1.14, P=6.43 × 10(-3)). These 3 markers remain significant after Bonferroni correction. Furthermore, haplotype conditioned analyses indicated that the association signals observed between rs2274736\\/rs2401751 and rs7147796 are statistically independent. Given the results that 2 non-synonymous markers in PTPN21 are associated with schizophrenia, further investigation of this locus is warranted.

  7. Study of the distribution patterns of the constituent herbs in classical Chinese medicine prescriptions treating respiratory disease by data mining methods.

    Science.gov (United States)

    Fu, Xian-Jun; Song, Xu-Xia; Wei, Lin-Bo; Wang, Zhen-Guo

    2013-08-01

    To provide the distribution pattern and compatibility laws of the constituent herbs in prescriptions, for doctor's convenience to make decision in choosing correct herbs and prescriptions for treating respiratory disease. Classical prescriptions treating respiratory disease were selected from authoritative prescription books. Data mining methods (frequent itemsets and association rules) were used to analyze the regular patterns and compatibility laws of the constituent herbs in the selected prescriptions. A total of 562 prescriptions were selected to be studied. The result exhibited that, Radix glycyrrhizae was the most frequently used in 47.2% prescriptions, other frequently used were Semen armeniacae amarum, Fructus schisandrae Chinese, Herba ephedrae, and Radix ginseng. Herbal ephedrae was always coupled with Semen armeniacae amarum with the confidence of 73.3%, and many herbs were always accompanied by Radix glycyrrhizae with high confidence. More over, Fructus schisandrae Chinese, Herba ephedrae and Rhizoma pinelliae was most commonly used to treat cough, dyspnoea and associated sputum respectively besides Radix glycyrrhizae and Semen armeniacae amarum. The prescriptions treating dyspnoea often used double herb group of Herba ephedrae & Radix glycyrrhizae, while prescriptions treating sputum often used double herb group of Rhizoma pinelliae & Radix glycyrrhizae and Rhizoma pinelliae & Semen armeniacae amarum, triple herb groups of Rhizoma pinelliae & Semen armeniacae amarum & Radix glycyrrhizae and Pericarpium citri reticulatae & Rhizoma pinelliae & Radix glycyrrhizae. The prescriptions treating respiratory disease showed common compatibility laws in using herbs and special compatibility laws for treating different respiratory symptoms. These principle patterns and special compatibility laws reported here could be useful for doctors to choose correct herbs and prescriptions in treating respiratory disease.

  8. Applications of Data Mining in Higher Education

    OpenAIRE

    Monika Goyal; Rajan Vohra

    2012-01-01

    Data analysis plays an important role for decision support irrespective of type of industry like any manufacturing unit and educations system. There are many domains in which data mining techniques plays an important role. This paper proposes the use of data mining techniques to improve the efficiency of higher education institution. If data mining techniques such as clustering, decision tree and association are applied to higher education processes, it would help to improve students performa...

  9. Data-Mining Research in Education

    OpenAIRE

    Cheng, Jiechao

    2017-01-01

    As an interdisciplinary discipline, data mining (DM) is popular in education area especially when examining students' learning performances. It focuses on analyzing educational related data to develop models for improving learners' learning experiences and enhancing institutional effectiveness. Therefore, DM does help education institutions provide high-quality education for its learners. Applying data mining in education also known as educational data mining (EDM), which enables to better un...

  10. Long-term effect of statins on the risk of new-onset osteoporosis: A nationwide population-based cohort study.

    Directory of Open Access Journals (Sweden)

    Tsung-Kun Lin

    Full Text Available Several observational cohort and meta-analytical studies in humans have shown that statin users have a lower risk of fractures or greater bone mineral densities (BMD than nonusers. However, some studies including randomized clinical trials have the opposite results, particularly in Asian populations.This study investigates the impacts of statins on new-onset osteoporosis in Taiwan.In a nationwide retrospective population-based cohort study, 45,342 subjects aged between 50-90 years having received statin therapy (statin-users since January 1 2001, and observed through December 31 2013 were selected from the National Health Insurance Research Database of Taiwan. Likewise, 115,594 patients had no statin therapy (statin-non-users were included as controls in this study. Multivariable Cox proportional hazards analysis for drug exposures was employed to evaluate the association between statin treatment and new-onset of osteoporosis risk. We also used the long-rank test to evaluate the difference of probability of osteoporosis-free survival.During the 13-year follow-up period, 16,146 of all enrolled subjects (10.03% developed osteoporosis, including 3097 statin-users (6.83% and 13,049 statin-non-users (11.29%. Overall, statin therapy reduced the risk of new-onset osteoporosis by 48% (adjusted hazard ratio [HR] 0.52; 95% CI 0.50 to 0.54. A dose-response relationship between statin treatment and the risk of new-onset osteoporosis was observed. The adjusted hazard ratios for new-onset osteoporosis were 0.84 (95% CI, 0.78 to 0.90, 0.56 (95% CI, 0.52 to 0.60 and 0.23 (95% CI, 0.21 to 0.25 when cumulative defined daily doses (cDDDs ranged from 28 to 90, 91 to 365, and more than 365, respectively, relative to nonusers. Otherwise, high-potency statins (rosuvastatin and atorvastatin and moderate-potency statin (simvastatin seemed to have a potential protective effect for osteoporosis.In this population-based cohort study, we found that statin use is associated

  11. Use of statins and risk of glioma

    DEFF Research Database (Denmark)

    Gaist, David; Andersen, L; Hallas, Jesper

    2013-01-01

    Laboratory studies and a single case-control study have suggested a protective effect of statins on the risk of glioma. We wished to investigate the influence of statin use on the risk of glioma in a population-based setting.......Laboratory studies and a single case-control study have suggested a protective effect of statins on the risk of glioma. We wished to investigate the influence of statin use on the risk of glioma in a population-based setting....

  12. Data Mining for Anomaly Detection

    Science.gov (United States)

    Biswas, Gautam; Mack, Daniel; Mylaraswamy, Dinkar; Bharadwaj, Raj

    2013-01-01

    The Vehicle Integrated Prognostics Reasoner (VIPR) program describes methods for enhanced diagnostics as well as a prognostic extension to current state of art Aircraft Diagnostic and Maintenance System (ADMS). VIPR introduced a new anomaly detection function for discovering previously undetected and undocumented situations, where there are clear deviations from nominal behavior. Once a baseline (nominal model of operations) is established, the detection and analysis is split between on-aircraft outlier generation and off-aircraft expert analysis to characterize and classify events that may not have been anticipated by individual system providers. Offline expert analysis is supported by data curation and data mining algorithms that can be applied in the contexts of supervised learning methods and unsupervised learning. In this report, we discuss efficient methods to implement the Kolmogorov complexity measure using compression algorithms, and run a systematic empirical analysis to determine the best compression measure. Our experiments established that the combination of the DZIP compression algorithm and CiDM distance measure provides the best results for capturing relevant properties of time series data encountered in aircraft operations. This combination was used as the basis for developing an unsupervised learning algorithm to define "nominal" flight segments using historical flight segments.

  13. Switching statins in Norway after new reimbursement policy: a nationwide prescription study.

    Science.gov (United States)

    Sakshaug, Solveig; Furu, Kari; Karlstad, Øystein; Rønning, Marit; Skurtveit, Svetlana

    2007-10-01

    To assess the changes in prescribing of statins in Norway after implementation of the new reimbursement regulations for statins in June 2005. Data were retrieved from the Norwegian Prescription Database covering the total population in Norway (4.6 million). Outcome measures were the proportion of atorvastatin users switching to simvastatin and changes in the proportion of new statin users receiving simvastatin. Based on retail costs for all statin prescriptions dispensed in Norway, expenditure was measured in Norwegian currency. One-year prevalences of statin use increased from 6.3 to 6.8% for women and from 7.5 to 8.1% for men from the year before to the year after the new statin regulations. Of atorvastatin users (N = 131,222), 39% switched to simvastatin during the 13-month period after the implementation. The proportion of switching was higher in women (41%) than in men (36%). In May 2005, 48% of the new statin users received simvastatin. The proportion of new users receiving simvastatin increased rapidly after implementation of the new regulations to 68% in June 2005 and reached 92% in June 2006. Expenditure was reduced from 120 million to 95 million Euro when comparing the year before with the year after the new statin regulations. The new reimbursement policy for statins has had a great impact on physicians' prescribing of statins in Norway. Physicians in Norway acknowledge the importance of contributing to cost containment.

  14. The Hazards of Data Mining in Healthcare.

    Science.gov (United States)

    Househ, Mowafa; Aldosari, Bakheet

    2017-01-01

    From the mid-1990s, data mining methods have been used to explore and find patterns and relationships in healthcare data. During the 1990s and early 2000's, data mining was a topic of great interest to healthcare researchers, as data mining showed some promise in the use of its predictive techniques to help model the healthcare system and improve the delivery of healthcare services. However, it was soon discovered that mining healthcare data had many challenges relating to the veracity of healthcare data and limitations around predictive modelling leading to failures of data mining projects. As the Big Data movement has gained momentum over the past few years, there has been a reemergence of interest in the use of data mining techniques and methods to analyze healthcare generated Big Data. Much has been written on the positive impacts of data mining on healthcare practice relating to issues of best practice, fraud detection, chronic disease management, and general healthcare decision making. Little has been written about the limitations and challenges of data mining use in healthcare. In this review paper, we explore some of the limitations and challenges in the use of data mining techniques in healthcare. Our results show that the limitations of data mining in healthcare include reliability of medical data, data sharing between healthcare organizations, inappropriate modelling leading to inaccurate predictions. We conclude that there are many pitfalls in the use of data mining in healthcare and more work is needed to show evidence of its utility in facilitating healthcare decision-making for healthcare providers, managers, and policy makers and more evidence is needed on data mining's overall impact on healthcare services and patient care.

  15. Collaborative Data Mining Tool for Education

    Science.gov (United States)

    Garcia, Enrique; Romero, Cristobal; Ventura, Sebastian; Gea, Miguel; de Castro, Carlos

    2009-01-01

    This paper describes a collaborative educational data mining tool based on association rule mining for the continuous improvement of e-learning courses allowing teachers with similar course's profile sharing and scoring the discovered information. This mining tool is oriented to be used by instructors non experts in data mining such that, its…

  16. A survey of temporal data mining

    Indian Academy of Sciences (India)

    Data mining is concerned with analysing large volumes of (often unstructured) data to automatically discover interesting regularities or relationships which in turn lead to better understanding of the underlying processes. The field of temporal data mining is concerned with such analysis in the case of ordered data streams ...

  17. Statins and the risk of acute pancreatitis: A population-based case-control study

    DEFF Research Database (Denmark)

    Thisted, Henriette; Jacobsen, Jacob; Munk, Estrid Muff

    2006-01-01

    BACKGROUND: Case reports have suggested that statins may cause acute pancreatitis. AIM: To examine if statins are associated with risk of acute pancreatitis. METHODS: We identified 2576 first-time admitted cases of acute pancreatitis from hospital discharge registers in three Danish counties, and......: Our findings speak against a strong causative effect of statins on the risk of acute pancreatitis, and may even indicate a mild protective effect....

  18. Statin use is not associated with improved progression free survival in cetuximab treated KRAS mutant metastatic colorectal cancer patients: results from the CAIRO2 study.

    Directory of Open Access Journals (Sweden)

    Lisanne L Krens

    Full Text Available Statins may inhibit the expression of the mutant KRAS phenotype by preventing the prenylation and thus the activation of the KRAS protein. This study was aimed at retrospectively evaluating the effect of statin use on outcome in KRAS mutant metastatic colorectal cancer patients (mCRC treated with cetuximab. Treatment data were obtained from patients who were treated with capecitabine, oxaliplatin bevacizumab ± cetuximab in the phase III CAIRO2 study. A total of 529 patients were included in this study, of whom 78 patients were on statin therapy. In patients with a KRAS wild type tumor (n = 321 the median PFS was 10.3 vs. 11.4 months for non-users compared to statin users and in patients with a KRAS mutant tumor (n = 208 this was 7.6 vs. 6.2 months, respectively. The hazard ratio (HR for PFS for statin users was 1.12 (95% confidence interval 0.78-1.61 and was not influenced by treatment arm, KRAS mutation status or the KRAS*statin interaction. Statin use adjusted for covariates was not associated with increased PFS (HR = 1.01, 95% confidence interval 0.71-1.54. In patients with a KRAS wild type tumor the median OS for non-users compared to statin users was 22.4 vs. 19.8 months and in the KRAS mutant tumor group the OS was 18.1 vs. 14.5 months. OS was significantly shorter in statin users versus non-users (HR = 1.54; 95% confidence interval 1.06-2.22. However, statin use, adjusted for covariates was not associated with increased OS (HR = 1.41, 95% confidence interval 0.95-2.10. In conclusion, the use of statins at time of diagnosis was not associated with an improved PFS in KRAS mutant mCRC patients treated with chemotherapy and bevacizumab plus cetuximab.

  19. Associations between patients' risk attitude and their adherence to statin treatment - a population based questionnaire and register study

    DEFF Research Database (Denmark)

    Barfoed, Benedicte Marie Lind; Paulsen, Maja Skov; Christensen, Palle Mark

    2016-01-01

    the risk-averse patients, OR 0.80 (95 %-CI 0.68-0.95) and OR 0.83 (95 %-CI 0.71-0.98), respectively. No significant association was found between adherence and financial risk attitude. Further, patients in the youngest age group and patients with no CVD were less adherent to statin treatment. CONCLUSION......: We find some indication that risk attitude is associated with adherence to statin treatment, and that risk-neutral and risk-seeking patients may have poorer adherence than risk-averse patients. This is important for clinicians to consider when discussing optimal treatment decisions...... on the association between risk attitude and adherence. The aim of the present study was to estimate associations between patients' adherence to statin treatment and different dimensions of risk attitude, and to identify subgroups of patients with poor adherence. METHODS: Population-based questionnaire and register...

  20. Persistent lipid abnormalities in statin-treated patients with diabetes mellitus in Europe and Canada: results of the Dyslipidaemia International Study

    NARCIS (Netherlands)

    Leiter, L. A.; Lundman, P.; da Silva, P. M.; Drexel, H.; Jünger, C.; Gitt, A. K.; Absenger, Guun; Albrich, Ernst; Allinger, Berndt; Allinger, Stephan; Anacher, Gerald; Angermayr, Gertraud; Angermeier, Hermann; Anzengruber, Aneas; Archimanitis, Gabriele; Arnsteiner, Patricia; Auberger, Wolfgang; Azhary, Mawaheb; Barfuss, Michael; Bauer, Christian; Bauer, Birgit Elisabeth; Beclin, Thomas; Binder, Thomas; Binder, Gabriele; Böhler, Dietmar; Brändle, Johann; Breslmair, Jörg; Brettlecker, Marlis; Bürger, Michael; Calvi, Inge; Dorfinger, Werner; Doringer-Schnepf, Elisabeth; Eer, Anton; Eckmayr, Christine; Eder, Franz; Egermann, Margit; Erath, Michael; Etzinger, Michael; Etzinger, Claudia; Fiedler, Lothar; Filip, Wolfgang; Filip, Michaela; Föchterle, Johann; Fodor, Anita; Frieden, Thomas; Gareiss, Mertens; Gföllner, Peter; Ghamarian, Thomas; Goritschan, Michael; Haar, Klaus; Habeler, Gerhard; Hadjiivanov, Valery; Haiböck, Christian; Hammer, Regina; Hartmann, Siegfried; Haschkovitz, Herbert; Hauer, Walter; Hauer, Josef; Haunschmidt, Christian; Heimayr, Christine; Hengl, Wolfgang; Hengl, Gunter; Hermann, Rudolf; Herrmann, Rainer; Hillebrand, Roswitha; Hintersteininger, Otto; Hirsch, Michael; Hitzinger, Martin; Hochegger, Tanja; Hockl, Wolfgang; Hoi, Michael; Hörmann, Jan; Hudler, Brigitte; Imb, Gerhard; Joichl, Anea; Jungbauer, Karl; Kapl, Gerlinde; Kerschbaum, Margit; Kienesberger, Franz; Killinger, Gerhard; Kitzler, Gerhard; Klein, Franz; Kleinbichler, Dietmar; Kohr, Anton; Kopetzky, Michael; Korthals, Christian; Kortschak, Werner; Koschutnik, Martin; Kraus, Werner A.; Kurzemann, Susanne; Lavicka, Claus; Lehner, Guido; Lenz, Jürgen; Lepuschütz, Sabine; Lichtenwallner, Michael; Lober, Reinhard; Loidl, Christine; Lopatka, Eduard; Ludwig, Rudolf; Maca, Thomas; Mair, Anneliese; Mandak, Michael; Margreiter, Maria; Margreiter, Anea; Markovics, Michael; Matejicek, Frieich; Mohilla, Maximillian; Moll, Christian; Mörz, Beate; Mörz, Reinhard; Nagl, Heinz; Neumayr, Günther; Oberroitmair, Helmut; Oberzinner, Michael; Pallamar, Walter; Pangratz, Sibylle; Parandian, Laurenz; Paulus, Alexana; Pfaffenwimmer, Christoph; Plaichinger, Peter; Pokorn, Thomas; Polanec, Helmuth; Pöll-Weiss, Barbara; Pralea, Doralina; Puttinger, Johann; Quinton, Thomas; Ranegger, Matthias; Rass, Sepp; Rauch, Heribert; Riehs, Manfred; Robetin, Erich; Rohringer, Jörg; Rupprechter, Josef; Sadjed, Eduard; Schimbach, Johann Alois; Schmid, Jutta; Schneiderbauer, Rotraud; Schopper, Wolfgang; Schulze-Bauer, Alfred; Schuster, Gottfried; Schwarz, Johann; Schwarz, Maria; Schweighofer, Christoph; Schwelle, Franz; Simma, Hanspeter; Sock, Renate; Sock, Reinhard; Sprenger, Fritz; Stiglmayr, Thomas; Stocker, Ilse; Stütz, Pia; Tama, Mustafa; Teleky, Ursula; Tschauko, Werner; Veits, Martin; Vikydal, Gerhard; Vlaschitz, Karl; Wais, Elisabeth; Wais, Adam; Wegmann, Robert; Wehle, Franz; Weindl, Manfred; Weinhandl, Manuela; Wendt, Ursula; Wendt, Klaus; Werner-Tutschku, Volker; Werner-Tutschku, Christine; Wilscher, Josef; Wind, Norbert; Winter, Aneas; Wolfschütz, Gerald; Wolfsgruber, Markus; Wolfsgruber, Brigitte; Wurm, Renate; Ziebart-Schroth, Arno; Zimmermann, Maximillian; Zinnagl, Aneas; Zirm, Anea; Zirm Canada, Bernhard; Bokenfohr, Grace Mary; Liu, Edmond K. H.; Melling, Gordon W.; Papp, Edward William; Sachdeva, Ashok K.; Snyman, Ernst Retief; Varma, Sonya; Ward, Richard A.; Tiong Wong, Anew Pak; Basson, Paul J.; Brodie, Brian D.; Chahal, Sukhjiwan Jeevyn; Chan, William Y.; Chow, John C.; Cormack, Maura; Eddy, Donald H. J.; Ezekiel, Daniel; Farquhar, Anew; Gu, Shian; Hii, Ting H.; Ho-Asjoe, Marianne P. K.; Hosie, Anew; Jaffer, Shahin; Jakubowski, Anew T.; Karim, Mandy; Kiai, Cristina; Kooy, Jacobus; Lytle, Craig R.; Mcleod, Kevin Lain; Morgan, David C.; Myckatyn, Michael M.; Ng, John P. Y.; Schriemer, Ronnald; Schumacher, Gerhard; Grey Stopforth, James; Hoo Tsui, Winston Wai; Wilson, Robin T.; Wong, Danny; Wong, Wilfred T.; Yeung, Margaret M. W.; Cram, David Harvey; Kumari Dissanayake, Dilani Tamara; Gerber, Johan Daniel W.; Haligowski, David; Hrabarchuk, Blair; Kroczak, Tadeusz J.; Lipson, Alan H.; Mahay, Raj K.; Wessels Mare, Abraham Carel; Mohamdee, Feisal John; Olynyk, Frederick Theodore; Pieterse, Wickus; Ramgoolam, Rajenanath; Rothova, Anna; Saunders, Kevin Kenneth; Szajkowski, Stanley; van Gend, Richard F.; van Rensburg, Nicolaas Marthinus Jansen; Anand, Sanjiv; Baer, Carolyn E. H.; Basque, Eric J. Y.; Benaya, Sebastian; Bessoudo, Ricardo; Bhalla, Jaswinder; Chettiar, Nataraj V.; Craig, Brian N.; Desrosiers, France; Ranjani Imbulgoda, Manel; Morgan, Gareth M.; Nowak, Zbigniew J.; Scott, Daniel G.; Searles, Gregory R.; Slorach, J. Ninian; Stevenson, Robert N.; Browne, Noel John; Bruff, Karl Joseph; Collingwood, John Maurice; Collins, Wayne; Over, Aidan; Gabriel, Anthony M.; Govender, Moonsamy; Hart, David G.; Hatcher, Lydia B.; Janes, John; Kielty, John F.; Krisdaphongs, Michoke; Lush, Richard Boyd; Moulton, William Bertram; Riche, Cyril R.; Rideout, Gary M.; Roberts, Bernard C.; Walsh, Paul E.; Wight, Harold G.; Woodland, K. Heather; Woodland, Robert C.; Atkinson, Bradley Charles; Chow, Carlyle S. H. A.; Collins, James A.; Graham, Robert D.; Hosein, Jalal; Machel, Teresa M.; Mahaney, Gordon Ralston; Mclean, James Robert Bruce; Murray, Michael R.; Myatt, Gregory Alexander; Ozere, Christopher P.; Saha, Amal Krishna; Sanders, David Herbert; Seaman, Donald Maxwell; Seaman, James Gordon; Swinamer, Deanna; Voon Yee, Kenny Yew; Ali, Mohamed Mustapha; Bankay, Clarence D. C.; Beduhn, Eitel Erich Reinhold; Callaghan, Denis J.; Chan, Yun Kai; Chaudhri, Arif R.; Chen, Richard Y. Y.; Conway, James Robin; Cunningham, William L.; Cusimano, Steven Lawrence; Souza, Eleanor De; de Souza, Selwyn X.; Deyoung, John Paul; Epstein, Ralph; Faiers, Alan Arthur; Figurado, Victor John; Forbes, F. Basil Trayer; Gabor, Zsuzsanna; Gallardo, Rodolfo Canonizado; Gaur, Shiva K.; George, Elizabeth; Hartford, Brian J.; Shiu-Chung Ho, Michael; Ho, Chung; Ismail, Shiraz H.; Bhushan Kalra, Bharat; Koprowicz, Kinga; Kumar, Naresh; Lam, Clement; Lau, Ming-Jarm; Law, Hugo Kwok Cheung; Fung, Max Leung Sui; Liutkus, Joanne Frances; Lotfallah, Talaat K.; Luton, Robert G.; Meneses, Gloria S.; Miller, Mark Lee; Nagji, Noorbegum; Ng, Ken H. M.; Ng Thow Hing, Roland E.; Pandey, Amritanshu Shekhar; Petrov, Ivan; Rosenthall, Wendy; Rudner, Howard; Russell, Alan Douglas; Sanchez, Zenia A.; Shaban, Joseph A.; Shariff, Shiraz B. K.; Shih, Chung Ming; Sinclair, Duncan W.; Spink, Donald Richard; Tung, Tommy Hak Tsun; Vizel, Saul; Yanover, David Frederick; Zavodni, Louis S.; Cusack, Paul; Dewar, Charles M.; Hooley, Peter; Kassner, Rachel Anne; Mackinnon, Randy James; Molyneaux, Harold W.; Shetty, Karunakara Naduhithlu; Barrière, Ginette; Berjat, Maria B.; Bernucci, Bruno; Bérubé, Claude; Boueau, Ghyslain; Chehayeb, Raja; Ciricillo, Domenico; Constance, Christian M.; Côté, Gilles; Desroches, Jacques; Gagnon, Robert; Gaueau, Gilles; Godbout, Jean Louis; Harvey, Pierre; Hassan, Youssef; Hoang, Ngoc Vinh; Houde, Danielle; Lalonde, Alain-Paul; Lavoie, Régis; Leclair, Normand; Meagher, Luc; Ouimet, Alain; Plourde, Simon; Rioux, Denis W.; Roberge, Claude; Roy, Bruno; Sasseville, Richard; Serfaty, Samuel; Theriault, Lyne; Timothée, Jean R.; Tjia, Sabine; Tremblay, Bruno; Turcotte, Jean; Bose, Sabyasachi; Aletta Bouwer, Hester; Chernesky, Patricia A.; Johnson, Mervin Louis; Kemp, David R.; Lai, Raymond Pong-Che; Lee, Frank R.; Lipsett, William G. C.; Lombard, Schalk J.; Majid, Falah S.; Malan, Johannes J.; Maree, Narinda; Nayar, Arun; Nel, Mandi; Oduntan, Oluwole O.; Rajakumar, Alphonsus R. J.; Baraka Ramadan, Fauzi; Shamsuzzaman, Mohammed; Vermeulen, Abraham P. M.; Fred, C.; Anthonsen, Birgitte; Ardest, Steen Pennerup; Arnold-Larsen, Susanne Kajsa; Axelsen, Allan; Barfoed, Klaus; Birkler, Niels Erik; Blokkebak, Jens; Boserup, Jørgen; Kettrup Brassøe, Jens Ole; Chovanec, Martin; Lykke Christensen, Bendt; Christensen, Micael; Skjøth Christensen, Randi; Eidner, Per Olav; Eisbo, Jørn; Elsvor, Jan; Engmann, Ida Veng-Christensen; Eriksen, Rene Milling; Frederiksen, Thorkil; Frølund, Hanne Charlotte; Garne, Susanne; Giørtz, Agnete; Gregersen, Bettina; Halkier, Merete Lundbye; Hansen, Jens Georg; Harder, Jan; Jørgen, Hans; Henriksen, O.; Kirkeby Hoffmann, Michael; Holk, Erik; Hollensen, Jan; Jacobsen, Rune; Jakobsen, Lotte; Jensen, Christian; Jensen, Morten; Jensen, Vibeke; Jepsen, Peter; Johannsen, Jens Arne; Verner Johansen, Lars; Johansen, Ole Steen; Juul, Kristian; Jørgensen, Arvid Frank; Jørgensen, Peter; Jørgensen, Ulrik Miilmann; Kensmark, Lars; Kjellerup, Carsten; Kjaer, Ejner; Kjaersgaard, Morten; Klubien, Peter; Kolby, Peter; Korsgaard Thomsen, Kristian; Krebs, Peter; Kristiansen, Tom; Lyng, Flemming; Madsen, Natalia V.; Meyer-Christensen, Jesper; Mogensen, Ole; Mortensen, Finn; Nielsen, Lotta Marie; Nielsen, Per Schiwe; Nielsen, Søren Kjærem; Ommen, Henrik; Juhl Otte, Jens; Østergaard Paridon, Volle; Parm, Michael; Peampour, Kian; Petersen, Kirsten; Pilgaard, Peder Jensen; Poulsen, Svend Erik; Preisler, Thomas; Hast Prins, Søren Ulrik; Randløv, Annette; Rasmussen, Birgit Reindahl; Elmegaard Rasmussen, Peter; Rasmussen, Regnar; Roed, Søren Flemming; Sander, Kirsten Foltmar; Schmidt, Ejnar Ørum; Jørgen Schultz, Paul; Smidemann, Margit; Solgaard, Jørgen; Stripp, Tommy; Søderlund, Michael Rene M.; Søgaard, Henning; Søndergaard, Dorte E.; Sørensen, Birgitte H.; Sørensen, Gerhard Seth; Thøgersen, Niels; Toftdahl, Hans; Uggerhøj, Hanne; Uhrenholt, Bjarne; Veronika Ullisch, Eva; Valentiner-Branth, Christian; Vinberg, Jørgen; Vinter, Svend Aage; Vittrup, Preben; Winther-Pedersen, Niels; Wøldike, Anne Grete; Zederkof, Jørgen M.; Thue Østergaard, Merete; Abiven, Patrick; Abraham, Dominique; de Beaumais, Philippe Adam; Ado, Jean Pierre; Affres, Helene; Agache, Regis; Airault Leman, Anne Marie; Moussarih, Abdallah Al; Albaric, Christian; Allaouchiche, Thierry; Allignol, Christian; Ammor, Mohammed; Ammoun Bourdelas, Corinne; Amsallem, Luc; Anquez, Denis; Antonini, Jean Michel; Assuied, Virginia; Attia, Gerard; Audebert, Olivier; Audibert, Henri; Ayach, Claude; Bagdadlian, Serge; Bagni, Marina; Baillet, Jean; Ballivian Cardozo, Fernando; Baranes, Robert; Barbier, Patricia; Barousse, Francoise; Bas, Sylvie; Battaglia, Jean Marc; Baudonnat, Bruno; Bauple, Jean Louis; Domengetroy, Frederic Baylac; Beard, Thierry; Beaumier, Eric; Beaumont, Jean Francois; Baylac Domengetroy, Frederic; Beck, Christian; Behar, Michel; Behr, Bernard; Benady, Richard; Benghanem, Mohamed Mounir; Benichou, Herve; Bensoussan, Jean Marc; Bensussan, Pierre; Bercegeay, Pascal; Berneau, Jean Baptiste; Bertolotti, Alexane; Bertrand, Sylviane; Besson, Alain; Bezanson, Christophe; Bezier, Christophe; Bezzina, Remy; Bichon, Herve; Bickar, Pierre; Billot, Pierre; Billot Belmere, Marie Claude; Bisson, Francois; Blanc, Dominique; Bloch, Jean Luc; Bloch, Bernard; Blondin, Hyacinthe; Blot, Jacques; Bloud, Raymond; Blouin, Pascal; Boesch, Christophe; Boiteux, Jean Luc; Bonnafous, Pierre; Bonneau, Yanick; Bonnefoy, Laurent; Borg, Bernard; Borys, Jean Michel; Brunehaut Petaut, Myriam; Boschmans, Sabine; Said, Rami Bou; Bouallouche, Abderrahmane; Bouchet, Jacques; Bouchlaghem, Khaled; Boulen, Yvon; Bouline, Benoit; Bounekhla, Mohamed Salah; Bouquin, Vincent; Bourgeois, Marie Brigitte; Bourgois, Didier; Brandily, Christian; Brandt, Pierre; Branquart, Frederic; Breilh, Patrick; Brilleman, Fabrice; Brisson, Thierry; Brocard, Francis; Bruel, Pierre; Brun, Jean Pierre; Buisson, Jean Gabriel; Buisson Virmoux, Isabelle; Bur, Christian; Cabal Malville, Elodie; Cabantous, Serge; Cabrol, Pierre; Cagnoli Gromovoi, Sylviane; Caillaux, Bruno Xavier; Caillot, Didier; Canchon Ottaviani, Isabelle; Canu, Philippe; Caramella, Alexana; Caramella, Alexane; Cardaillac, Christian; Carrivale, Alain; Cartal, Jean Pierre; Cassany, Bernard; Cauon, Bernard; Causeret, Jean Marie; Caye, Philippe; Cayet, Jean Paul; Cazor, Gilles; Cesarini, Joel; Chakra, Georges; Chambeau, Bernadette; Chambon, Valerie; Chanas, Jack; Chapuzot, Patrick; Charon, Ane; Charpin, Eric; Charton, Frederic; Cheikel, Jean; Chemin, Philippe; Chennouf, Kamel; Chequel, Henri; Chevrier, Denis; Ciroux, Patrick; Cissou, Yves; Claeys, Jean Luc; Clariond, Yves; Classen, Olivier; Cloerec, Ane; Clouet, Sophie; Cloup Lefeuvre, Anne Marie; Cochet, Chantal; Cocuau, Didier; Cohen, Henri; Cohen Presberg, Pascale Cohen; Colin, Stephane; Colin, Remy; Colucci, Robert; Come, Philippe; Condouret, Pierre; Conturie, Agnes; Corbin, Ane; Corticelli, Paola; Coste, Daniel; Cotrel, Olivier; Coueau, Sylvie; Coulon, Paul; Courdy, Christian; Courtin, Marc; Courtot, Pierre; Coutrey, Laurent; Couval, Rene; Cravello, Patrick; Cressey, Olivier; Cuisinier, Yves; Cunin, Bernard; Cunnington, Bernard; Cusseau, Herve; Cuvelier, Christian; Arailh, Bruno D.; Dabboura, Adib; Dages, Laurence; Dahmani, Noureddine; Dandignac, Jean Christophe; Daney, Dominique; Dannel, Bernard; Darbois, Dominique; Dareths, Philippe; Daubin, Daniel; David, Jean Claude; de Foiard, Patrick; de Mallmann Guyot, Veronique De; de Wit, Marie Astrid; Debast, Francoise; Deboute, Eric; Debuc, Jean Pierre; Dechoux, Edouard; Decloux, Olivier; Decruyenaere, Yannick; Dejans, Jacques Maurice; Delarue, Michel; Delattre, Xavier; Delmaire, Patrick; Denis, Lucien; Deschamps Ben Ayed, Myriam; Devins, Pascal; Dezou, Sylvie; Dieuzaide, Pierre; Dirheimer, Bertrand; Dominguez, Paul; Donadille, Florence; Dondain, Benoit; Doridan, Pierre; Ouhet, Pascal; Dubois, Arnaud; Dubois, Ane; Ducharme, Pascal; Duchez, Paul; Dulard, Catherine; Dumoulin, Marc; Duprey, Georges; Durand, Jacques; Mohamed, Ibrahim; Chehab, El; Emery, Bernard; Emmanuel, Georges; Ashari, Ghazaleh Esna; Evrard, Eric; Fargeot Lamy, Aleth; Farges, Jean Louis; Faucher, Patrick; Faucie, Alain; Faure, Yves; Favre, Jean Jacques; Felipe, Jean Louis; Feret, Daniel; Ferragu, Alain; Ferrandin, Gerard; Ferriot, Francois; Finelle, Laurent; Flond, Jacques; Foieri, Jean; Fol, Stephane; Fontaine, Brigitte; Forichon, Dominique; Foucry, Michel; Fournier, Jean Francois; Fregeac, Bernard; Fuchs, Martin; Gabriel, Franck; Gaimard, Didier; Gallois, Stephane; Garapon, Georges; Garas, Mamdouh; Garcia, Pierre; Garcia, Jean Michel; Garcia, Marie Pierre; Garman, Waddah; Garzuel, Dominique; Gaspard, Jean Marc; Gauci, Laurent; Gautheron, Patrick; Gauthier, Jacques; Gauthier Lafaye, Pierre Yves; Gay, Michel Charles; Gay Duc, Bernadette; Gayout, Olivier; Gegu, Yann; Gentile, Francois; Germain, Emmanuel; Gharbi, Gerard; Gigandet Tamarelle, Catherine; Gilardie, Alain; Gilles Verliat, Martine; Gillet, Thierry; Gnana, Philippe; Goguey, Alain; Gombert, Alain; Gonin, Bernard; Gonzales, Philippe; Goulesque, Xavier; Graba, Jean Marc; Granier, Alain; Greiner, Olivier; Groboz, Martial; Gromoff, Serge; Grossemy, Xavier; Grossi, Christian; Guenin, Frederic; Gueranger, Pierre; Guerin, Patrick; Guerineau, Jean Pierre; Guessous Zghal, Fathia; Guicheux, Dominique; Guillere, Jacqueline; Guyonnet, Gilles; Haddad, Samir; Hadj, Nordine; Hamani, Djamel; Hamm, Jacky; Hammoudi, Djamal; Harle, Xavier; Harnie Coussau, Pierre; Hazen, Richard; Hembert, Francois; Hemon, Pierre; Hergue, Michel; Hestin, Christian; Heyraud, Luc; Hindennach, Dieter; Hirot, Etienne; Ho Wang Yin, Chan Shing; Hocquelet Denis, Catherine; Hoppe, Patrice; Horovitz, Daniel; Hours, Jean Michel; Houta, Benjamin; Hua, Gerard; Hui Bon Hoa, Nicole; Humez, Philippe; Hurier, Michel; Husson, Gerald; Hyvernat, Guy; Ichard, Jean Francois; Impens, Claude; Iovescu, Decebal; Jacob, Philippe; Jacob, Gildas; Jacquemart, Jean Pierre; Jacquier, Philippe; Jahanshahi Honorat, Shideh; Jalladeau, Jean Francois; Jan, Luc; Jannel, Yves; Jarrige, Vincent; Jeremiasz, Richard; Annick Jestin Depond, Marie; Joseph, Michel; Joseph Henri Fargue, Helene; Joubrel, Alain; Jouet, Alain; Julien, Bruno; Jullien, Francois; Jullien, Jean Louis; Kadoche, David; Kahl, Etienne; Kanawati, Aiman; Khalife, Sami; Khettou, Christophe; Kiers, Jean Paul; Kissel, Christian; Klein, Jean Claude; Klopfenstein, Samuel; Koch, Alexis; Koenig, Georges; Kohler, Philippe; Koriche, Abdelmalek; Labernardiere, Nicole; Labet, Philippe; Lablanche, Fabien; Laborde Laulhe, Vincent; Lagorce, Xavier; Laine, Eric; Lalague, Pascal; Laleu, Jean Noel; Lambert, Michel; Lambert Ledain, Mireille Lambert; Lambertyn, Xavier; Lame, Jean Francois; Langlois, Frederic; Lanoix, Eric; Laprade, Michel; Lasseri, Charaf; Laterrade, Bernard; Laurent, Jean Claude; Laurier, Bernard; Laval, Laurent; Le Borgne, Patrick; Le Franc, Pierre; Le Henaff, Patrick; Le Noir de Carlan, Herve; Le Roy, Jean Pierre; Le Roy Hennion, Florence; Lebon, Louis; Lecler, Olivier; Leclerc, Philippe; Ledieu, Christian; Lefebvre, Bernard; Lefevre, Philippe; Lehujeur, Catherine; Leiber, Christian; Leick, Gerard; Lemberthe, Thierry; Lenevez, Norbert; Lenoble, Patrick; Leriche, Philippe; Leroux, Eric; Leroy, Jean Michel; Leroy, Christian; Lescaillez, Dominique; Leurele, Christian; Lhermann, Sophie; Libermann, Pierre; Licari, Gilbert; Lo Re, Antoine; Long, Philippe; Long, Jean Louis; Lormeau, Boris; Louchart, Jean Christophe; Lucas, Jean Pierre; Luquet, Thierry; Lussato, Philippe; Maarouf, Moustapha; Mabilais, Francois; Magnier Sinclair, Christine; Mahot Moreau, Pascale; Malafosse, Denis; Mandirac, Jean Paul; Manolis, Jerome; Mante, Jean Pierre; Maquaire, Claude; Marchal, Thierry; Marchand, Guillaume; Marillesse, Olivier; Marmier, Gabriel; Herve Maron, Yves; Marrachelli, Nadine; Marsaux, Michel; Martin, Bruno; Martin, Michel; Deiss, Pascale Martin; Masson, Arnaud; Mativa, Bruno; Matton, Jean Francois; Mauffrey, Jean; Mauriere, Serge; Maurois, Georges; Maury, Joceline; Mayer, Frederic; Menu, Pierre; Mercier, Bernard; Messmer, Daniel; Mestiri, Sami; Meyer, Gilles; Michaelides, Michael; Michaud, Gilles; Michenaud, Bernard; Mielot, Stephane; Millory Marco, Jerry Anne; Mingam, Stephane; Mira, Reginald; Mius, Stephane; Monnier Meteau, Marie Paule; Mora, Francis; Morbois Trabut, Louise; Morosi, Laurent; Mougeolle, Jean Luc; Mouget, Jean Louis; Mouroux, Daniel; Mouthon, Jean Marie; Muller, Jacques; Nakache, Ane; Narbonne, Herve; Navarranne Roumec, Anne; Navarro, Pierre; Neubrand, Jean Yves; Nguyen, Quang Thieu; Nguyen Quang, Guy; Nguyen Xuan, Thong; Niot, Patrice; Oudart, Jean Maurice; Outteryck, Alain; Pages, Jean Marie; Paillet, Charles; Pain, Jean Marie; Pangaud de Gouville, Patricia; Paquin, Olivier; Parent, Vincent; Parer Richard, Claire; Parrot, Francine; Parthenay, Pascal; Pascariello, Jean Claude; Passebon, Jean Claude; Pere, Alain; Perelstein, Laurent; Perot, Michel; Petit, Richard; Petit, Philippe; Petit, Francois; Petruzzi, Philippe; Phelipeau, Denis; Philippon, Jean Claude; Philippon, Gilles; Picard, Bruno; Picard, Jean Claude; Picot, Bernard; Piera, Jean Francois; Pieri, Alain; Piffoux, Eric; Pilard, Patrick; Pillet, Alain; Pinot, Philippe; Pinzani, Alain; Pleskof, Alain; Plessier, Jean Claude; Plisson, Alain; Pochon, Claude; Poggi, Valerie; Poirat, Alain; Poiree, Maurice; Polleux, Janick; Noel Pontecaille, Jean; Posocco, Regis; Pospiech, Jean Claude; Pradies, Felix; Prevot, Remi; Pueyo, Jean Bernard; Quaelli, Jacques; Rabbia, Michel; Rabemananjara, Aimery; Rami, Saad; Rapin, Jean Jacques; Rasquin, Corinne; Ratinaud, Didier; Reboud, Bruno; Reboul, Philippe; Reichman, Jean Jacques; Reinhardt, Patrick; Renard Houta, Catherine Renard; Reverdy, Olivier; Revol, Michel; Rey, Pierre Alain; Richardeau, Yves; Rives, Bernard; Robida, Christine; Rochez Fraiberg, Muriel; Rodet, Jean Pierre; Rolland, Jean Francois; Romand, Bruno; Romano, Jean Paul; Rosati Gretere, Chantal; Rosey, Alain; Rosset, Martial; Rossi, Jean Pierre; Rouquette, Georges; Rousseau, Michel; Rousselon, Xavier; Roy, Christophe; Royer, Denis; Ruetsch, Marcel; Saade, Maurice; Saby Kuchler, Nicolas; Samar, Guy; Sanchez, Pierre Yves; Sane, Alain; Sanz, Jean Paul; Sardon, Michel; Sarrazin, Marc Eric; Sasportes, Gilbert; Saudou, Francis; Sauze, Elisabeth; Savary, Pascal; Schenowitz, Alain; Schmartz, Pierre; Schoepfer, Marc Olivier; Seewagen, Jacques; Serramoune, Denis; Serre, Christian; Sicard Guroo, Helene; Sichãc, Jean Philippe; Sifaoui, Sylvain; Simoncello, Marc; Simonin, Marie Jeanne; Simonnet, Jean Francois; Spindler, Didier; Steier, Alain; Sultan, Charles Raphael; Taghipour, Kouroch; Talayrach, Bruno; Talbot, Francois; Talhouarn, Vanessa; Tallec, Yves; Tarasco Schenrey, Elisabeth; Tarrene, Michel; Tater, Dominique; Tessier, Bernard; Teste, Marie; Thierry, Dominique; Thiollier, Patrice; Thoreau, Frederic; Thual, Jean; Traen, Vincent; Trigano, Jacques Alexane; Troussier, Jean Bernard; Truong Ky Minh, Bernard; van Melckebeke, Gerard; Vaque, Philippe; Vaucelle, Celine; Vedel, Eric; Venu, Didier; Verdavoine, Patrick; Vergeron, Jean; Viallon, Philippe; Viault, Dominique; Vieules, Jean Max; Vigier, Jean Paul; Vilain, Jean Marie; Villard, Bruno; Vitoux, Jean Francois; Viviand, Paul; Vivien, Olivier; Walter, Patrice; Waquier, Patrick; Waszkiewicz, Jean Marc; Weidich, Stephane; Westerfeld, Raymond; Weynachter, Gerald; Wilhelm, Pierre; Wolff, Claude; Wursthorn, Marc; Zammattio, Didier; Zylinski, Bernard; Lauer, Peter; Kühn, Uwe; Weltzel, Wolfgang; Mohr, Hella; Weyland, Klaus; Spittel, Bärbel; Böhm, Günter; Ferdowsy, Said; Hanusch, Peter; Spiekermann, Josef; Albert, Edwin; Stuff, Karl; Jungmair, Wolfgang; Koller, Sabine; Schubert, Wilhelm; Schlehahn, Fred; Bormann, Gundula; Graf, Kristof; Stiehler, Gisela; Bock, Manfred; Müller, Angelika; Haufe, Michael; Nielsen, Lorenz; Raum, Doris; Rogler, Karin; Bürstner, Joachim; Völk, Hans-Jörg; Sachse, Michael; Escher, Torsten; Doumit, Adel; O'dey, Hildegard; Holzmann, Ulrike; Sauer, Hermann; Schellenberg, Gottfried; Carius, Jürgen; Dänschel, Wilfried; Kopf, Aneas; Zerr, Elena; Tatalovic, Ratko; Rupp, Heiun; Anders, Elfriede; Mende, Marion; Volk, Ulrich; Hagenow, Aneas; Lang, Thomas; Schmitz, Karl-Heinz; Gössling, Jan-Henik; Mutsch, Günther; Steidel, Joachim; Osten, Klaus; Giokoglu, Kiriakos; Bellisch, Sabine; Füll, Katja; Walther, Wolfgang; Flick, Sabine; Dünnebier, Rosemarie; Dharmawan, Ichsan; Schönmehl, Wolfgang; Hoss, Valentin; Kipping, Stephan; Wolf, Hans-Joachim; Wolf, Hans-Frieich; Willmann, Volker; Bugarski, Bruno; Hoffschröer, Josef; Von Wallfeld, Siegrun; Ruhland, Guun; Bulling, Daniel; Häusler, Maren; Haustein, Gabriele; Kallenbach, Cornelia; Schwemmler, Claudia; Frank, Antje; Lodder-Bender, Ulrike; Rawe, Klaus; Reinert, Hans-Ferdinand; Schönhof, Petra; Fahrenschon, Klaus; Schorcht, Elisabeth; Etzold, Erika; Brehm, Michael; Paust, Wolf-Dieter; Schulte-Kemna, Achim; Pötter, Klaus-Werner; Ott-Voigtländer, Ulrike; Schwenke, Reto; Thinesse-Mallwitz, Manuela; Siml, Steffi; Stern, Hirene; Roelen, Harald; Scherhag, Klaus-Peter; Matulla, Petra; Herrmann, Hans Joachim; Neumann, Gerhard; Barbuia, Marius; Vormann, Reinhold; Hitzler, Karl; Linum, Aneas; Hanke, Klaus; Hohberg, Hans-Joachim; Klingel, Roger; Hohnstädter, Rainer; Klasen, Hartmut; Aschermann, Peter; Grau, Wilfried; Killinger, Paul; Gross, Kathrin; Naus, Rainer; Todoroff, Karin; Zühlke, Wolfgang; Kellner, Hanns-Ulrich; Hager, Eva; Thieme, Jochen; Kornitzky, Michael; Rösch, Volker; Heinze, Elke; Hiederer, Wolfgang; Konz, Karl-Heinz; Köhler, Michael; Diekmann, Martin; Junghans, Edith; Dietermann, Friedgard; Kerp, Ekkehard; Schäfer-Lehnhausen, Silvia; Kruck, Irmtraut; Ettelt, Rolf; Hölscher, Aneas; Kittler, Sybil; Jung, Heiun; Mailänder, Albert; Nowara, Peter; Ritschl, Harald; Mödl, Bernhard; Gallwitz, Torsten; Meyer, Stephan; Peter, Anton; Peters, Otto; Pflaum, Petra; Fröhlich, Karl-Heinz; Mertens, Hans-Jürgen; Merlin-Sprünken, Verena; Erpenbach, Klaus; Fervers, Frank; Kuhl, Ulrike; Halsig, Friedemann; Rein, Wilfried; Hauser, Ernst-Richard; Laubenthal, Florin; Richard, Frank; Langer, Claus; Lange, Rainer; Eska, Jan; Mohanty, George; Lange, Isengard; Eltges, Nicole; Kuntz, Christoph; Mechery, Thomas; Vöckl, Josef; Viergutz, Christoph; Stähle-Klose, Claudia; Sohr, Katja; Böhler, Steffen; Brecke, Georg; Burls, Malcolm; Werner, Karl-Michael; Vorpahl, Ralf; Stahl-Weigert, Beate; Bunge, Gerd; Thomsen, Jutta; Blessing, Erwin; Bengel, Bengel; Buhlmann, Ulla; Tröger, Tröger; Sippel, Sippel; Vossschulte, Vossschulte; Wilms, Wilms; Appelt, Appelt; Dauterstedt, Dauterstedt; Witte, Witte; Böttger, Uta; Wyborski, Waltraud; Strache, Sabine; Böttger, Werner; Zeiner, Luise; Wuttke, Wanda; Stoidner- Amann, Annette; Stoermer, Brigitte; Bock, Stephan; Groos-März, Cornelia; Thamm, Maria-Elisabeth; Meier, Josef; Schneider, Martin; Niessen, Ulrich; Storm, Gernot-Rainer; Streitbürger, Elmar; Münkel, Thomas; Palfi, Mihai; Naumann, Ulrich; Tannhof, Gabriele; Streibhardt, Frank; Gebhardt, Wolfgang; Nieswandt, Gerhard; Gerke, Ulrich; Nöhring, Axel; Bott, Jochen; Goertz, Jutta; Winkler, Dietmar; Lotter, Edith; Kraaz, Katja; Bärwinkel, Petra; Hildebrandt, Diana; Weyers, Georg; Kubin-Siring, Birgit; Baier, Eduard; Weber, Thomas; Holz, Dirk-Egbert; Wolfers, Johannes; Kihm, Wolfgang; Kamali-Ernst, Schirin; Amann, Wolfgang; Kaase, Hans-Jürgen; Banning, Ottmar; Voigt, Thomas; Grünert, Frank; Gürtler, Michael; Pferdmenges, Karin; van Treek, Heiko; Möller, Bernd; Weigel, Sybille; Jun Hassler, Normann; Mauer, Helmuth; Beckers, Erwin; Weber, Clemens-August; Hawash, Hana; Ladke, Dietrich; Labitzky, Gerlinde; Kunkel, Petra; Hartung, Wolfgang; Pomykaj, Thomas; Prokop, Heiun; Schleif, Thomas; Cascino, Luisa; Exner, Petra; Daelman, Eric; Dietrich, Aneas; Prasse, Thomas; Brundisch, Stefanie; Schipper, Ralf; Duderstaedt, Bernd; de Haan, Fokko; Schmidt-Reinwald, Astrid; Seidel, Peter; Schmitz, Joachim; Bülent, Ergec; Ja Pique, Pyoong; Ding, Roland; Eggeling, Thomas; Duderstaedt, Elvira; Ferchland, Hans-Peter; Kruth, Renate; Gralla, Dieter; Köhler, Angelika; Laborge, Joachim Rene; Hammer, Harald; Richter, Ilona; Sauldie, Happy; Valk-Denkema, Inge Van Der; van der Valk, Leo; Feely, John; Dunne, Liam; Cox, John; Doyle, Michael; O'Gorman, Mary; Kennedy, John; Maher, Brian; Forde, Derek; Harrington, Peter; Cronin, Brian; Coady, Anew; Craig, John; O'Dowd, Caroline; O'Doherty, Brian; O'Connor, Patrick; Ling, Roland; Perry, Majella; Crowley, James; Keaveney, Lynda; Townley, Eadaoin; O'Shea, Eamonn; Regan, Michael; Cunningham, Seamus; Bluett, Desmond; Whyte, Oliver; Casey, Michael; Ruane, Fergal; Fitzgerald, Eleanor; O'Beirn, Eugene; Faller, Eamonn; Moffatt, Sean; Coleman, Michael; Day, Brendan; Mcadam, Brendan; O'Neill, Daragh; Mac Mahon, Conor; Wheeler, Mark; Byrne, Sheila; Fulcher, Kieran; CAREY, Owen; O'Connell, Kieran; Keane, Jack; Almarsomi, Laith; Vaughan, Carl; O'Callaghan, Tom; Grufferty, Tadgh; Shanahan, Eamon; Crowley, Brendan; Moran, Joe; Cotter, Jeremy; Healy, Colin; Curtin, Tom; Dillon, Joe; Dennehy, Thomas; Murphy, Elaine; Kennedy, Michael; Coffey, Donal; Carroll, Paul O.; Oliver, Barry; Mccarthy, Shane; Joyce, Peter; O'Shea, Gerard; Apperloo, A. J.; Basart, D. C. G.; Bax, M.; Beysens, P. A. J.; Breed, J. G. S.; Derks, A.; Eijgenraam, J. W.; Hermann, J. P. R.; Janus, C. L.; Kaasjager, H. A. H.; Klomps, H. C.; Koole, M. A. C.; Koster, T.; Kroon, C.; Lieverse, A. G.; Massaar-Hagen, B. E. M.; Moghaddam, F.; Oldenburg-Ligtenberg, P. C.; Potter van Loon, B. J.; Stroes, E. S. G.; Twickler, Th B.; van Asperdt, F. G. M. H.; van Asseldonk, J. P. M.; van der Loos, T. L. J. M.; van der Velde, R. Y.; van der Vring, J. A. F.; van Dorp, W. T.; van Essen, G. G.; van Kalmthout, P. M.; van Liebergen, R. A. M.; van Wissem, S.; Waanders, H.; Withagen, A. J. A. M.; Andersen, Per Vidar Klemet; Andersen, Randi F.; Andersson, Egil; Arnstad, Asle; Belguendouz, Larbi; Birkeland, Inge Arve; Bjørkum, Kari; Bredvold, Thor; Brevig, Leif Harald; Buchman, Erik; Burkeland-Matre, Rune; Burski, Krzysztoft; Byre, Roald; Bø, Per Erik; Dahl, Erik; Duch, Anna; Duong, Khoa; Dvergsdal, Peter; Edvardsen, Magne; Ernø, Asbjørn; Fredwall, Svein Otto; Glasø, Morten; Glasø, Jan; Grini, Asbjørn; Hallaråker, Arne; Normann Hansen, Age Normann; Haugland, Helge Haugland; Henrichsen, Svein Høegh; Hestnes, Atle; Idehen, Norman I. E.; Jacobsen, Kristin Løland; Johansen, Ture; Johnsen, Roald; Jonasmo, Kåre; Kirknes, Svetalana; Kjetså, Arild; Kjaer, Peter; Knoph, Erik; Knutssøn, Carl; Koss, Arne; Kravtchenko, Oleg; Krogsæter, Dagfinn; Langaker, Kåre; Lind, Knut W.; Lund, Kjell Rømyhr; Madsbu, Sverre; Mehlum, Yvonne E. Mazurek; Moon, Philipp; Movafagh, Aram; Myhrer, Kurt; Nørager, Dan Michael; Ore, Stephan; Rafat, Hooshang B.; Rød, Reinert; Schmidt-Melbye, Torgeir; Singh, Navneet; Singsås, Tore; Skjelvan, Gunnar; Smet, Arthur; Staalesen, Staale; Storeheier, Espen; Storhaug, Sidsel; Storm-Larsen, Ane; Sundby, Jon Eivind; Syverstad, Dag Eivind; Sørensen, Anne Sissel; Torjusen, Trygve B.; Torkelsen, Arne; Tunby, Jan Reidar; Vanberg, Pål Johan; Vevatne, Audun; Vikse, Arild; Wahlstrøm, Viktor; Walaas, Kirsten; Walløe, Arne Eyolf; Wear-Hansen, Hans-Gunnar; Ole Ystgaard, Ole Aneas; Zimmermann, Birgit; Øvsthus, Knut; Aião, Julio; Albuquerque, Mario; Alves, Fernando; Esteves, Antonio; Amaral, Maria Fatima; Amaral, Fátima; Amorim, Helena; Anade, Benilde; Anade, Maria Benilde; Antonio, Godinho; Araujo, Francisco; Arriaga, Antonio; Baeta, Sonia; Afonso, Francisca Banha; Beato, Vitor; Beirão, Paula; Martins, Ausenda Belo; Bernardes, Jose; Botas, Luis; Baeta, Antonio; Ramos, Manuel Braga; Brandão, Peo; Brandão, Antonio G.; Brandão, Antonio; Raposo, Antonio Caetano; Carrilho, Francisco; Carvalho, Isabel; Carvalho, Patricia; Castel-Branco, Ana; Castellano, Maria Desamparados; Corredoura, Ana; Corredoura, Ana Sofia; Costa, Vitor; Coutinho, João; Crujo, Francisco; Cunha, Damião; Dias, Manuela; Fernandes, Maria Emilia; Ferreira, Gustavo; Ferreira, Dirce; Ferreira, Jorge; Ferreira, Antonio M.; Fonseca, Antonio; Freitas, Paula; Gago, Amandio; Galego, Rosa; Garrett, Antonio Viriato; Gavina, Cristina; Simões, José Geraldes; Gomes, Maria Fatima; Gomes, Norberto; Gomez, Brigitte; Graça, Peo; Gravato, Antonio; Guedes, Nuno Filipe; Guerra, Fernanda; Issa, Custódio; João, Isabel Fernandes; João, Isabel; Jorge, Vasco; Leite, Maria Salome; Lousada, Nuno; Macedo, Filipe M.; Madeira Lopes, João; Magalhães, Jorge; Marinho, Jose Carlos; Marques, Carlos; Marques, Jose Augusto; Marques Ferreira, Antonio; Martins, Jose Carlos; Martins, J. Belo; Matos, Alice; Melo, Miguel; Miguel, Antonia; Monteiro, Filomena; Monteiro, Francisco; Monteiro, Filomena B.; Sarmento, João Morais; Morato Sá, Maria José; Mota, Joana; Moura, Luis; Moura, Brenda; Neves, Lena; Neves, Celestino; Oliveira, Maria; Oliveira Ramos, Manuel; Osorio, Ramos; Pacheco, Joao; Palma, Isabel; Peixoto, Maria Cristina; Pereira, Helder; Pestana, João; Pignatelli, Duarte; Pinho, Hernani; Puig, Jorge; Raindo, Maria; Ramos, Helena; Rebelo, Marta; Roigues, Antonio; Roigues, Alvaro; Roigues, Elisabete; Rola, José; Rovytchcva, Milena; Sa, João; Santos, Fernando; Santos, João Cesar; Sequeira Duarte, Joao; Serra E Silva, Polybio; Silva, Bernardino; Silva, Paula; Silva, Maria; Silva, Francisco; Silva, Dora; Silva, José; Silvestre, Isabel; Simões, Heleno; Soares, Manuela; Sousa, Nelson; Sousa, Antonio; Souto, Delfina; Teixeira, Esmeralda; Torres, Isabel; Valle, Tahydi; Ventura, Carlos; Vicente, Ana; Vieira, Muriel; Alfaro, Rafael; Alonso, Roigo; Alvarez, Juan Carlos; Allut, Germán; Amado, Jose A.; Ampuero, Javier; Angel, Luis Fernando; Antolín, Eduardo; Anton, Javier; Aranda, Jose Luis; Argimon, Jordi; Arques, Francesc; Arribas, Jose Peo; Arroya, Concepción; Arroyo, Jose Antonio; Auladell, Maria Antonia; Bajo, Julian; BALVIN, Alberto; Ballester, Jose Vicente; Barreda Glez, Maria Jesus; Becerra, Antonio; Bermejo, Juan Carlos; Bernacer, Luis; Besada, Ricardo; Blasco, Jesús; Bravo, Manuel; Bueno, Francisco Manuel; Campo, Ignacio; Carrasco, Jose Luis; Catalán, José Ignacio; Cobo, Jose; Coello, Ignacio; Combarro, Jesús; Contreras, Juan A.; Correa, Julian; Cortilla, Alberto; Cuatrecasas, Guillem; Chicharro, Sana; de Dios, Juan; de Los Arcos, Enrique; de Portugal, Jose; del Cañizo, Francisco; del Molino, Fatima; Díaz, Jose Luis; Domingo, Javier; Escobar, Carlos; Escoda, Jaume; Espinosa, Eugenio; Ester, Francisco; Fernandez, Antonio; Ferreiro, Manuel; Fondas, Jose Maria; Fraile, Angel Luis; Franco, Miguel; Fuentes, Francisco; Garcia, Jose Antonio; Garcia, Domingo; Garcia, Manuel Enrique; García, Luis; Garcia, Jesus; Gilabert, Rosa; Goiria, Begoña; Gomez, Purificación; Gomez-Calcerrada, David; Gonzalez, Manuel; Gonzalez, Jose Manuel; Guijarro, Carlos; Guirao Gujarro, Victor; Herrera, Carlos; Herrera, Maria Carmen; Herrero, Miguel; Ibarguren, Amaya; Irigoyen, Luis; Jimenez, Blas; Lamelas, Jose Antonio; Laplaza, Ismael; Laporta, Felix; Lazo, Victor; Leal, Mariano; Ledesma, Vicente; Lopez, Peo; Lopez, Pablo; Lopez, Alberto; López, Maria Jose; Lopez-Cepero, Eduardo; Lorenzo, Francisco; Lucena, Javier; Luquín, Rafael; Lloveras, Ariadna; Maceda, Teresa; Macia, Ramon; Marti, Cristina; Martin, Jose Maria; Martin, Isodoro; Martín Lesende, Iñaki; Martinez, Mercedes; Martinez, Juan Alberto; Martinez, Peo; Martinez, Angel; Mato, Fernando; Medel, Federico; Mederos, Ana Maria; Mediavilla, Javier; Mediavilla, Gregorio; Mestron, Antonio; Michans, Antonio; Millán, Jesús; Molina, Carlos; Monroy, Carmelo; Monte, Inés; Montes, Jose Maria; Morales, Clotilde; Morales, Francisco J.; Morata, Carmen; Mori, Carlos; Muñoz, Jaime; Muñoz, Maria Jose; Núnez, Julio; Nuñez, Alfonso; Ocaña, Fermin; Olaz, Fernando; Ollero Artigas, Anes; Ortega, Juan; Oteo, Olga; Pascual, Jose Maria; Paya, Jose Antonio; Pechuan, Joaquín; Penedo Suarez, Ramón; Perez, Eugenia; Pesquera, Carlos; Pia, Gonzalo; Piea, Maria; Pinilla, Martin; Pita, Alejano; Pose, Antonio; Prieto Díaz, Miguel Angel; Quesada, Carmen; Ramirez, Francisco; Ramirez, Carmen; Ramirez, Luisa; Reinares, Leonardo; Rey, Salvador; Ribas, Montse; Ridaura, Amparo; Ridocci, Francisco; Rigueiro, Peo; Rivera, Salomón; Robles, Antonio; Rodero, Estrella; Roiguez, Jose Angel; Romero, Fernando; Romero Hernandez, Franklin; Romeu, Regina; Rubio Buisán, Lorenzo; Salas, Fernando; Sánchez, Carlos; Sánchez, Jesus; Saponi, Jose Maria; Serres, Miguel; Suarez, Saturnino; Suarez, Carmen; Tato, Maria; Tebar, Francisco Javier; Toda, Maria Roca; Tofe, Santiago; Urdiain, Raquel; Vaamonde, Leopoldo; Valderrama, Javier; Vazquez, Jose Antonio; Velazquez, Osvaldo; Venell, Federico; Vilariño, Ruben; Villa, Maria Jesus; Villar, Maria Dolores; Zarauza, Jesus; Zuñiga, Manuel; Abab, Jose Luis; Abad, Eduardo; Abad, Rafael; Afonso, Carmen; Aguilar, Gerardo; Alberiche, Maria Del Pino; Alcolea, Rosa; Alegria, Eduardo; Almagro, Fátima; Almenara, Africa; Almenos, Maria Cruz; Alonso, Javier; Alvarez, Manuel; Ampudia, Javier; Andia, Victor Manuel; Anglada, Jordi; Aranda, Miguel Ángel; Arbelo, Lorenzo; Armengol, Francesc; Arnau, Asunción; Arrarte, Vicente; Arribas, Bienvenido; Artiñano, Yolanda; Avilés, Benjamín; Ayensa, Javier; Ballestar, Enric; Ballester, Javier; Barcelo, Bartolome; Barcena, Felix; Barranco, Mercedes; Barrena, Isabel; Barriales, Vicente; Barrot, Joan; Bartolome, Jose A.; Belmonte, Joan; Bellés, Amadeo; Benito, Josefina; Bernad, Antonio; Biendicho, Armando; Blanco, Rubén; Boix, Evangelina; Bonora, Carlos; Boxó, Jose Ramon; Brea, Angel; Caballero, Peo; Cabrera, Peo; Cabrero, Juan Jose; Calduch, Lourdes; Calero, Francisco; Calvo Garcia, Jose Javier; Camacho, Jose; Canales, Juan Jose; Caparros, Jorge; Carbonell, Francisco; Caro, Manuel; Castilla, Miguel Angel; Castillo, Luis; Cepero, Daniel; Cerdan, Miguel; Cimbora, Antonio; Civera, Miguel; Colchero, Justo; Comas Fuentes, Angel; Corpas, Clara; Corrales, Juan Antonio; Cotobal, Eusebio; Cruz, Carmen; Cruz, Inmaculada; de La Flor, Manuel D.; de Luis, Alberto; del Alamo, Alberto; del Rosario, Victor; Diego, Carlos; D'Lacoste, Marta; Doganis Peppas, Constantino; Dominguez, Jose Ramon; Durá, Francisco Javier; Durand, Jose L.; Ena, Javier; Encinas, Ana Rosa; Erdozain, Juan Peo; Escribano, Jose; Escriva, Blanca; Esteve, Eduardo; Facila, Lorenzo; Fenoll, Federico; Fernandez, Eugenio; Fernandez, Celia; Fernandez, Maria Jesus; Fernandez, Antonia; Fernandez, Jacinto; Fernandez, Severo; Fernandez, Jose Manuel; Fernandez, Jose Manuel Fernandez; Ferrer, Juan Carlos; Ferrer, Peo; Ferrer Bascuñana, Peo; Fierro, Maria Jose; Flores, Julio; Fuentes, Fernando; Fuertes, Jorge; Galgo, Alberto; Galvez, Angel; Gallego, Anea; Garcia, Maria Angeles; Garcia, Jose; Garcia, Maria Luisa; Garcia, Peo; Garcia, Javier; García, Francisco; Garrido, Nícolas Garrido; Gil, Manuel Gil; Ginés Gascón, Ramón; Godoy, Diego; Gomez, Carlos Manuel; Gonzalez, Miguel; Gonzalez, Rosa; Gonzalez, Rocío; Gonzalez, Enrique; Gonzalez, Juan Jose; Gonzalez, Joaquin; Gonzalez Huambos, Adan; Guerrero, Jordi; Guillen, Rosario; Guirao, Lorenzo; Gutierrez, Fernando; Gutierrez, Diego; Hernandez, Alberto; Hernandez, Antonio; Hernandis, Vicenta; Herrero, Jose Vicente; Herreros, Benjamin; Hevia Roiguez, Eduardo; Horgue, Antonio; Illan, Fatima; Inigo, Pilar; Ibrahim Jaber, Ali; Jimenez, Manuel; Jornet, Agusti; Juanola, Ester; Laguna, Alfonso; Latorre, Juan; Lebron, Jose Antonio; Lecube, Albert; Ledesma, Claudio; Ligorria, Cristina; Lima, Joan; López, Jose Enrique; Lopez, Manuel; López, José Antonio; López, Jaime; López, Isio; Lozano, Jose Vicente; Mangas, Miguel Angel; Mangas, Alipio; Manzano, Antonio; Maraver, Juan; Marco, Maria Dolores; Marchán, Enrique; Marchante, Francisco; Marin, Fernando; Marreo, Josefa Esther; Martin, Manuel; Martin, Alberto; Martin, Francisco Javier; Martinez, Antonio; Martinez, Guillermo; Martínez, Luis; Martinez Barselo, Antonio Pablo; Mas, Emili; Mascareño, Isabel; Mascarós, Enrique; Massa, Rita; Mazón, Pilar; Mediavilla, Juan Diego; Mena, Candido; Mendez, Jose; Mendez, Jose Maria; Mezquita Raya, Peo; Millan, Jose Maria; Millaruelo, Jose; Minguela, Ester; Miret, Pere; Molina, Mariano; Molina, Carmen; Montagud, Blanca; Montalban, Coral; Montiel, Angel; Montoro, Javier; Monze, Bernardo; Moreno, Francisco Luis; Morillas, Antonio; Moro, Jose Antonio; Moya, Ana; Muñiz, Ovidio; Muñoz, Manuel; Navarro, Vicente Luis; Nerin, Jesus; Nicolas, Ricardo; Nogueiras, Concepción; Ojeda, Benito; Olmerilla, Javier; Oller, Guillermo; Ortega, Antonio; Ortega, Manuel; Ortega, Miguel; Ortiz, Maria Jose; Otegui Alarduya, Luis; Palet, Jordi; Palomo, Jesus; Paytubí, Carlos; Peiro, Rafael; Pelaez, Carmen; Peña, Peo; Peñafiel, Javier; Perez, Antonia; Perez, Elvira; Perez, Tomas; Peso, Miguel; Pilar, Juan Manuel; Piñeiro, Carlos; Plaza, Jose Antonio; Polo, Noelia; Portal, Maria; Prieto, Jesus; Prieto, Luis; Prieto Novo, Manuel; Puñal, Peo; Quesada, Miguel; Quindimil, Jose Antonio; Rabade, Jose Manuel; Ramila Beraza, Luis Antonio; Ramirez, José Manuel; Ramos, Jose Antonio; Ramos, Francisco; Rayo, Manuel; Reixa Vizoso, Sol; Reyes, Antonio; Rico, Miguel Angel; Ripoll, Tomas; Rivera, Antonio; Robres, Mariano; Rodilla, Enrique; Roiguez, Miguel Angel; Roiguez, Zoilo Jesus; Roiguez, Carlos; Roiguez, Pilar; Roiguez, Melchor; Roiguez, Alfonso; Rojas, Domingo; Rosell, Luis; Rossignoli, Carlos; Rueda, Antonio; Rueda, Eloy; Ruix, Anes; Ruiz, Jose Antonio; Ruiz, Luis; Saban, Jose; Saez, Francisco Jose; Salleras, Narcis; Sánchez, Gerardo; Sanchez, Gloria; Sanchez, Angel; Sanfeliu, Josep Maria; Sangros Gonzalez, Javier; Santos, Francisco; Santus, Eufrosina; Sebastian, Alfredo; Seguro, Maria Eugenia; Selles, David; Serrano, Daniel; Serrano, Soledad; Serrano, Adalberto; Sestorain, Francisco; Solbes, Ruben; Soriano, Cristina; Suárez, Héctor; Surroca, Maria Luisa; Tarabini, Ada; Tarraga, Peo; Teixido, Eulalia; Terron, Raquel; Torres, Antonio; Tortosa, Jose Maria; Tortosa, Frederic; Valdés, Carmen; Valdés, Peo; Valiente, Jose Ignacio; Varo, Antonio; Vazquez, Enrique; Vázquez, Luis; Vela Ruiz de Morales, Jose Manuel; Vericat, Antonio; Vicioso, Peo; Vilaplana, Carlos; Villazón, Francisco; Lidia Viñas, Lidia Viñas; Zuagoitia, Jose Felix; Nörgaard, Faris; Dziamski, Ryszard; Haglund, Lars; Holm, Daniela; Sars, Mikael; Jagunic, Ivica; Östgård, Per; Kumlin, Lars; Jacobsson, Michael; Hamad, Yousef; Jäger, Wanje; Särhammar, Lars; Olsson, Anders; Boldt-Christmas, Antonina; Nyborg, Karin; Kjellström, Thomas; Ghazal, Faris; Wikström, Lene; Holby, Torulf; Bhiladvala, Pallonji; Kynde, Sara Maria; Eizyk, Enrique Julio; Tengblad, Anders; Christoffersson, Ole; Sjöström, Astrid; Kynde, Christian; Katzman, Per; Tenhunen, Anita; Lennermo, Klas; Lindholm, Carl-Johan; Löndahl, Magnus; Elfstrand, Aino; Grönlund-Brown, Inger; Ziedén, Bo; Minnhagen, Karin; Lindvall, Peter; Fant, Kristina; Kaczynski, Jacek; Wallmark, Anders; Wallén, Carl-Erik; Wallberg, Håkan; Grönquist, Lennart; Hansen, John Albert; Björkander, Inge; Timberg, Ingar; Rosenqvist, Ulf; Fries, Robert; Carlsson, Jan-Erik; Rautio, Aslak Tauno; Sjöberg, Lennart; Wirdby, Alexander; Höök, Peter; Larsson, Åsa; Bergström, Catharina Lysell; Jwayed, Addnan; Smolowicz, Adam; Lindman, Anne-Christine; Nilsson, Per; Tarrach, Gerrit; Carlsson, Ingolf; Wieloch, Mattias; Rindevall, Peter; Strömblad, Gunnar; Holmberg, Göran; Shahnazarian, Henrik; Melchior, Jan; Younan, Kamal; Hansson, Anders; Bjurklint, Dag; Borgencrantz, Bertil; Sjöström, Malin; Mullaart, Mikael; Munoz, Marjatta; Jakkola, Vallentina; Romot, Jaan; Dash, Rabinarayan; Magnusson, Jan-Olof; Ahmed, Saman; Jonsson, Christina; Pipkorn, Owe; Bray, Edward; Wolff, Aneas; Black, Iain; Head, Christopher; Allan, Anthony

    2011-01-01

    To assess the prevalence of persistent lipid abnormalities in statin-treated patients with diabetes with and without the metabolic syndrome. This was a cross-sectional study of 22,063 statin-treated outpatients consecutively recruited by clinicians in Canada and 11 European countries. Patient

  1. Statin use is not associated with improved progression free survival in cetuximab treated KRAS mutant metastatic colorectal cancer patients: results from the CAIRO2 study

    NARCIS (Netherlands)

    Krens, Lisanne L.; Simkens, Lieke H. J.; Baas, Jara M.; Koomen, Els R.; Gelderblom, Hans; Punt, Cornelis J. A.; Guchelaar, Henk-Jan

    2014-01-01

    Statins may inhibit the expression of the mutant KRAS phenotype by preventing the prenylation and thus the activation of the KRAS protein. This study was aimed at retrospectively evaluating the effect of statin use on outcome in KRAS mutant metastatic colorectal cancer patients (mCRC) treated with

  2. EXTRACTING KNOWLEDGE FROM DATA - DATA MINING

    Directory of Open Access Journals (Sweden)

    DIANA ELENA CODREANU

    2011-04-01

    Full Text Available Managers of economic organizations have at their disposal a large volume of information and practically facing an avalanche of information, but they can not operate studying reports containing detailed data volumes without a correlation because of the good an organization may be decided in fractions of time. Thus, to take the best and effective decisions in real time, managers need to have the correct information is presented quickly, in a synthetic way, but relevant to allow for predictions and analysis.This paper wants to highlight the solutions to extract knowledge from data, namely data mining. With this technology not only has to verify some hypotheses, but aims at discovering new knowledge, so that economic organization to cope with fierce competition in the market.

  3. Prediction of thermodynamic properties of refrigerants using data mining

    International Nuclear Information System (INIS)

    Kuecueksille, Ecir Ugur; Selbas, Resat; Sencan, Arzu

    2011-01-01

    The analysis of vapor compression refrigeration systems requires the availability of simple and efficient mathematical formulations for the determination of thermodynamic properties of refrigerants. The aim of this study is to determine thermodynamic properties as enthalpy, entropy and specific volume of alternative refrigerants using data mining method. Alternative refrigerants used in the study are R134a, R404a, R407c and R410a. The results obtained from data mining have been compared to actual data from the literature. The study shows that the data mining methodology is successfully applicable to determine enthalpy, entropy and specific volume values for any temperature and pressure of refrigerants. Therefore, computation time reduces and simulation of vapor compression refrigeration systems is fairly facilitated.

  4. The multiple zeta value data mine

    International Nuclear Information System (INIS)

    Buemlein, J.; Broadhurst, D.J.

    2009-07-01

    We provide a data mine of proven results for multiple zeta values (MZVs) of the form ζ(s 1 ,s 2 ,..,s k ) = sum ∞ n 1 >n 2 >...>n k >0 {1/(n 1 s 1 ..n k s k )} with weight w = sum K i=1 s i and depth k and for Euler sums of the form sum ∞ n 1 >n 2 >...>n k >0 {(ε 1 n 1 ..ε 1 n k )/(n 1 s 1 ..n k s k )} with signs ε i = ± 1. Notably, we achieve explicit proven reductions of all MZVs with weights w≤22, and all Euler sums with weights w≤12, to bases whose dimensions, bigraded by weight and depth, have sizes in precise agreement with the Broadhurst. Kreimer and Broadhurst conjectures. Moreover, we lend further support to these conjectures by studying even greater weights (w≤30), using modular arithmetic. To obtain these results we derive a new type of relation for Euler sums, the Generalized Doubling Relations. We elucidate the ''pushdown'' mechanism, whereby the ornate enumeration of primitive MZVs, by weight and depth, is reconciled with the far simpler enumeration of primitive Euler sums. There is some evidence that this pushdown mechanism finds its origin in doubling relations. We hope that our data mine, obtained by exploiting the unique power of the computer algebra language FORM, will enable the study of many more such consequences of the double-shuffle algebra of MZVs, and their Euler cousins, which are already the subject of keen interest, to practitioners of quantum field theory, and to mathematicians alike. (orig.)

  5. Genetic determinants of statin intolerance

    Directory of Open Access Journals (Sweden)

    Pollex Rebecca L

    2007-03-01

    Full Text Available Abstract Background Statin-related skeletal muscle disorders range from benign myalgias – such as non-specific muscle aches or joint pains without elevated serum creatinine kinase (CK concentration – to true myositis with >10-fold elevation of serum CK, to rhabdomyolysis and myoglobinuria. The genetic basis of statin-related muscle disorders is largely unknown. Because mutations in the COQ2 gene are associated with severe inherited myopathy, we hypothesized that common, mild genetic variation in COQ2 would be associated with inter-individual variation in statin intolerance. We studied 133 subjects who developed myopathy on statin monotherapy and 158 matched controls who tolerated statins without incident or complaint. Results COQ2 genotypes, based on two single nucleotide polymorphisms (SNP1 and SNP2 and a 2-SNP haplotype, all showed significant associations with statin intolerance. Specifically, the odds ratios (with 95% confidence intervals for increased risk of statin intolerance among homozygotes for the rare alleles were 2.42 (0.99 to 5.89, 2.33 (1.13 to 4.81 and 2.58 (1.26 to 5.28 for SNP1 and SNP2 genotypes, and the 2-SNP haplotype, respectively. Conclusion These preliminary pharmacogenetic results, if confirmed, are consistent with the idea that statin intolerance which is manifested primarily through muscle symptoms is associated with genomic variation in COQ2 and thus perhaps with the CoQ10 pathway.

  6. Educational data mining and learning analytics

    OpenAIRE

    Vera Hernández, Joan Carles

    2017-01-01

    Treball basat en Educational Data Mining & Learning Analitics d'anàlisi de la matriculació dels alumnes i el seu impacte sobre la decisió de tornar-se a matricular. Trabajo basado en Educational Data Mining & Learning Analytics análisis de la matriculación de los alumnos y su impacto sobre la decisión de volverse a matricular. Work based on Educational Data Mining & Learning Analytics analysis of student enrollment and its impact on the decision to re-enroll.

  7. Data Mining Tools for Malware Detection

    CERN Document Server

    Masud, Mehedy; Thuraisingham, Bhavani; Andreasson, Kim J

    2011-01-01

    Although the use of data mining for security and malware detection is quickly on the rise, most books on the subject provide high-level theoretical discussions to the near exclusion of the practical aspects. Breaking the mold, Data Mining Tools for Malware Detection provides a step-by-step breakdown of how to develop data mining tools for malware detection. Integrating theory with practical techniques and experimental results, it focuses on malware detection applications for email worms, malicious code, remote exploits, and botnets. The authors describe the systems they have designed and devel

  8. Data Mining and Statistics for Decision Making

    CERN Document Server

    Tufféry, Stéphane

    2011-01-01

    Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge. Data mining is usually associated with a business or an organization's need to identify trends and profiles, allowing, for example, retailers to discover patterns on which to base marketing objectives. This book looks at both classical and recent techniques of data mining, such as clustering, discriminant analysis, logistic regression, generalized lin

  9. Data mining-aided materials discovery and optimization

    Directory of Open Access Journals (Sweden)

    Wencong Lu

    2017-09-01

    Full Text Available Recent developments in data mining-aided materials discovery and optimization are reviewed in this paper, and an introduction to the materials data mining (MDM process is provided using case studies. Both qualitative and quantitative methods in machine learning can be adopted in the MDM process to accomplish different tasks in materials discovery, design, and optimization. State-of-the-art techniques in data mining-aided materials discovery and optimization are demonstrated by reviewing the controllable synthesis of dendritic Co3O4 superstructures, materials design of layered double hydroxide, battery materials discovery, and thermoelectric materials design. The results of the case studies indicate that MDM is a powerful approach for use in materials discovery and innovation, and will play an important role in the development of the Materials Genome Initiative and Materials Informatics.

  10. Meta-analysis of genome-wide association studies of HDL cholesterol response to statins

    NARCIS (Netherlands)

    Postmus, Iris; Warren, Helen R.; Trompet, Stella; Arsenault, Benoit J.; Avery, Christy L.; Bis, Joshua C.; Chasman, Daniel I.; de Keyser, Catherine E.; Deshmukh, Harshal A.; Evans, Daniel S.; Feng, QiPing; Li, Xiaohui; Smit, Roelof A. J.; Smith, Albert V.; Sun, Fangui; Taylor, Kent D.; Arnold, Alice M.; Barnes, Michael R.; Barratt, Bryan J.; Betteridge, John; Boekholdt, S. Matthijs; Boerwinkle, Eric; Buckley, Brendan M.; Chen, Y.-D. Ida; de Craen, Anton J. M.; Cummings, Steven R.; Denny, Joshua C.; Dubé, Marie Pierre; Durrington, Paul N.; Eiriksdottir, Gudny; Ford, Ian; Guo, Xiuqing; Harris, Tamara B.; Heckbert, Susan R.; Hofman, Albert; Hovingh, G. Kees; Kastelein, John J. P.; Launer, Leonore J.; Liu, Ching-Ti; Liu, Yongmei; Lumley, Thomas; McKeigue, Paul M.; Munroe, Patricia B.; Neil, Andrew; Nickerson, Deborah A.; Nyberg, Fredrik; O'Brien, Eoin; O'Donnell, Christopher J.; Post, Wendy; Poulter, Neil; Vasan, Ramachandran S.; Rice, Kenneth; Rich, Stephen S.; Rivadeneira, Fernando; Sattar, Naveed; Sever, Peter; Shaw-Hawkins, Sue; Shields, Denis C.; Slagboom, P. Eline; Smith, Nicholas L.; Smith, Joshua D.; Sotoodehnia, Nona; Stanton, Alice; Stott, David J.; Stricker, Bruno H.; Stürmer, Til; Uitterlinden, André G.; Wei, Wei-Qi; Westendorp, Rudi G. J.; Whitsel, Eric A.; Wiggins, Kerri L.; Wilke, Russell A.; Ballantyne, Christie M.; Colhoun, Helen M.; Cupples, L. Adrienne; Franco, Oscar H.; Gudnason, Vilmundur; Hitman, Graham; Palmer, Colin N. A.; Psaty, Bruce M.; Ridker, Paul M.; Stafford, Jeanette M.; Stein, Charles M.; Tardif, Jean-Claude; Caulfield, Mark J.; Jukema, J. Wouter; Rotter, Jerome I.; Krauss, Ronald M.

    2016-01-01

    In addition to lowering low density lipoprotein cholesterol (LDL-C), statin therapy also raises high density lipoprotein cholesterol (HDL-C) levels. Inter-individual variation in HDL-C response to statins may be partially explained by genetic variation. We performed a meta-analysis of genome-wide

  11. Meta-analysis of genome-wide association studies of HDL cholesterol response to statins

    NARCIS (Netherlands)

    D. Postmus (Douwe); H. Warren (Helen); S. Trompet (Stella); B.J. Arsenault (Benoit J.); C.L. Avery; J.C. Bis (Joshua); D.I. Chasman (Daniel); C.E. de Keyser (Catherina Elisabeth); H. Deshmukh (Harshal); D.S. Evans (Daniel); Feng, Q. (QiPing); X. Li (Xiaohui); Smit, R.A.J. (Roelof A.J.); A.V. Smith (Albert Vernon); F. Sun (Fangui); K.D. Taylor (Kent); A.M. Arnold (Alice M.); M.J. Barnes (Michael); B.J. Barratt (Bryan J.); J. Betteridge (John); S.M. Boekholdt (Matthijs); E.A. Boerwinkle (Eric); B.M. Buckley (Brendan M.); Y.D. Chen (Y.); A.J. de Craen (Anton); S. Cummings; Denny, J.C. (Joshua C.); G.P. Dubé (Gregory); P.N. Durrington (Paul); G. Eiriksdottir (Gudny); I. Ford (Ian); X. Guo (Xiuqing); T.B. Harris (Tamara); S.R. Heckbert (Susan); A. Hofman (Albert); G. Kees Hovingh; J.J.P. Kastelein (John); Launer, L.J. (Leonore J.); Liu, C.-T. (Ching-Ti); Y. Liu (YongMei); T. Lumley (Thomas); P.M. Mckeigue (Paul); P. Munroe (Patricia); A. Neil (Andrew); D.A. Nickerson (Deborah); F. Nyberg (Fredrik); E. O'Brien (Eoin); C.J. O'Donnell (Christopher); W.S. Post (Wendy S.); N.R. Poulter (Neil); R.S. Vasan (Ramachandran Srini); K.M. Rice (Kenneth); S.S. Rich (Stephen); F. Rivadeneira Ramirez (Fernando); N. Sattar (Naveed); P. Sever (Peter); S. Shaw-Hawkins (Sue); D.C. Shields (Denis C.); P.E. Slagboom (Eline); N.L. Smith (Nicholas); J.D. Smith (Joshua D.); N. Sotoodehnia (Nona); A. Stanton (Alice); D.J. Stott (David. J.); B.H.Ch. Stricker (Bruno); T. Stürmer; A.G. Uitterlinden (André); W.-Q. Wei (Wei-Qi); R.G.J. Westendorp (Rudi); E.A. Whitsel (Eric A.); K.L. Wiggins (Kerri); R.A. Wilke (Russell A.); C. Ballantyne (Christie); H.M. Colhoun (H.); L.A. Cupples (Adrienne); O.H. Franco (Oscar); V. Gudnason (Vilmundur); G.A. Hitman (Graham); C.N.A. Palmer (Colin); B.M. Psaty (Bruce); P.M. Ridker (Paul); J.M. Stafford (Jeanette M.); Stein, C.M. (Charles M.); J.-C. Tardif (Jean-Claude); M. Caulfield (Mark); J.W. Jukema (Jan Wouter); Rotter, J.I. (Jerome I.); R.M. Krauss (Ronald)

    2016-01-01

    textabstractBackground In addition to lowering low density lipoprotein cholesterol (LDL-C), statin therapy also raises high density lipoprotein cholesterol (HDL-C) levels. Interindividual variation in HDL-C response to statins may be partially explained by genetic variation. Methods and results We

  12. Clinical Profile of Statin Intolerance in the Phase 3 GAUSS-2 Study

    NARCIS (Netherlands)

    Cho, Leslie; Rocco, Michael; Colquhoun, David; Sullivan, David; Rosenson, Robert S.; Dent, Ricardo; Xue, Allen; Scott, Rob; Wasserman, Scott M.; Stroes, Erik

    2016-01-01

    Recent evidence suggests that statin intolerance may be more common than reported in randomized trials. However, the statin-intolerant population is not well characterized. The goal of this report is to characterize the population enrolled in the phase 3 Goal Achievement after Utilizing an

  13. Data mining approach to model the diagnostic service management.

    Science.gov (United States)

    Lee, Sun-Mi; Lee, Ae-Kyung; Park, Il-Su

    2006-01-01

    Korea has National Health Insurance Program operated by the government-owned National Health Insurance Corporation, and diagnostic services are provided every two year for the insured and their family members. Developing a customer relationship management (CRM) system using data mining technology would be useful to improve the performance of diagnostic service programs. Under these circumstances, this study developed a model for diagnostic service management taking into account the characteristics of subjects using a data mining approach. This study could be further used to develop an automated CRM system contributing to the increase in the rate of receiving diagnostic services.

  14. Towards Cooperative Predictive Data Mining in Competitive Environments

    Science.gov (United States)

    Lisý, Viliam; Jakob, Michal; Benda, Petr; Urban, Štěpán; Pěchouček, Michal

    We study the problem of predictive data mining in a competitive multi-agent setting, in which each agent is assumed to have some partial knowledge required for correctly classifying a set of unlabelled examples. The agents are self-interested and therefore need to reason about the trade-offs between increasing their classification accuracy by collaborating with other agents and disclosing their private classification knowledge to other agents through such collaboration. We analyze the problem and propose a set of components which can enable cooperation in this otherwise competitive task. These components include measures for quantifying private knowledge disclosure, data-mining models suitable for multi-agent predictive data mining, and a set of strategies by which agents can improve their classification accuracy through collaboration. The overall framework and its individual components are validated on a synthetic experimental domain.

  15. Teaching Financial Data Mining using Stocks and Futures Contracts

    Directory of Open Access Journals (Sweden)

    Gary Boetticher

    2005-06-01

    Full Text Available Financial data mining models is considered to be "the hardest way to make easy money." Data miners are certainly motivated by the prospect of discovering a financial "Holy Grail." However, designing and implementing a successful model poses many intellectual challenges. These include securing and cleaning data; acquiring a sufficient amount of financial domain knowledge; bounding the complexity of the problem; and properly validating results. Teaching financial data mining is especially difficult due to the student's limited financial domain knowledge and the relatively short period (one semester for building financial models. This paper describes an application of a financial data mining term project based on Stock and E-Mini futures contracts and discusses "lessons learned" from assigning similar term projects over six different semesters. Results of each case study results are presented and discussed.

  16. IT Data Mining Tool Uses in Aerospace

    Science.gov (United States)

    Monroe, Gilena A.; Freeman, Kenneth; Jones, Kevin L.

    2012-01-01

    Data mining has a broad spectrum of uses throughout the realms of aerospace and information technology. Each of these areas has useful methods for processing, distributing, and storing its corresponding data. This paper focuses on ways to leverage the data mining tools and resources used in NASA's information technology area to meet the similar data mining needs of aviation and aerospace domains. This paper details the searching, alerting, reporting, and application functionalities of the Splunk system, used by NASA's Security Operations Center (SOC), and their potential shared solutions to address aircraft and spacecraft flight and ground systems data mining requirements. This paper also touches on capacity and security requirements when addressing sizeable amounts of data across a large data infrastructure.

  17. A survey of temporal data mining

    Indian Academy of Sciences (India)

    other subtle relationships in the data using a combination of techniques from ... stamped list of items bought by customers lends itself to data mining analysis that ...... Frequent episode mining can be used here as part of an alarm management.

  18. DATA MINING THE GALAXY ZOO MERGERS

    Data.gov (United States)

    National Aeronautics and Space Administration — DATA MINING THE GALAXY ZOO MERGERS STEVEN BAEHR, ARUN VEDACHALAM, KIRK BORNE, AND DANIEL SPONSELLER Abstract. Collisions between pairs of galaxies usually end in the...

  19. Data Mining and Homeland Security: An Overview

    National Research Council Canada - National Science Library

    Seifert, Jeffrey W

    2008-01-01

    .... Often used as a means for detecting fraud, assessing risk, and product retailing, data mining involves the use of data analysis tools to discover previously unknown, valid patterns and relationships in large data sets...

  20. Data Mining and Homeland Security: An Overview

    National Research Council Canada - National Science Library

    Seifert, Jeffrey W

    2007-01-01

    .... Often used as a means for detecting fraud, assessing risk, and product retailing, data mining involves the use of data analysis tools to discover previously unknown, valid patterns and relationships in large data sets...

  1. Data Mining and Homeland Security: An Overview

    National Research Council Canada - National Science Library

    Seifert, Jeffrey W

    2006-01-01

    .... Often used as a means for detecting fraud, assessing risk, and product retailing, data mining involves the use of data analysis tools to discover previously unknown, valid patterns and relationships in large data sets...

  2. Open-source tools for data mining.

    Science.gov (United States)

    Zupan, Blaz; Demsar, Janez

    2008-03-01

    With a growing volume of biomedical databases and repositories, the need to develop a set of tools to address their analysis and support knowledge discovery is becoming acute. The data mining community has developed a substantial set of techniques for computational treatment of these data. In this article, we discuss the evolution of open-source toolboxes that data mining researchers and enthusiasts have developed over the span of a few decades and review several currently available open-source data mining suites. The approaches we review are diverse in data mining methods and user interfaces and also demonstrate that the field and its tools are ready to be fully exploited in biomedical research.

  3. Data mining and business analytics with R

    CERN Document Server

    Ledolter, Johannes

    2013-01-01

    Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification. Highlighting both underlying concepts and practical computational skills, Data Mining

  4. Challenges in computational statistics and data mining

    CERN Document Server

    Mielniczuk, Jan

    2016-01-01

    This volume contains nineteen research papers belonging to the areas of computational statistics, data mining, and their applications. Those papers, all written specifically for this volume, are their authors’ contributions to honour and celebrate Professor Jacek Koronacki on the occcasion of his 70th birthday. The book’s related and often interconnected topics, represent Jacek Koronacki’s research interests and their evolution. They also clearly indicate how close the areas of computational statistics and data mining are.

  5. Data mining theories, algorithms, and examples

    CERN Document Server

    Ye, Nong

    2013-01-01

    AN OVERVIEW OF DATA MINING METHODOLOGIESIntroduction to data mining methodologiesMETHODOLOGIES FOR MINING CLASSIFICATION AND PREDICTION PATTERNSRegression modelsBayes classifiersDecision treesMulti-layer feedforward artificial neural networksSupport vector machinesSupervised clusteringMETHODOLOGIES FOR MINING CLUSTERING AND ASSOCIATION PATTERNSHierarchical clusteringPartitional clusteringSelf-organized mapProbability distribution estimationAssociation rulesBayesian networksMETHODOLOGIES FOR MINING DATA REDUCTION PATTERNSPrincipal components analysisMulti-dimensional scalingLatent variable anal

  6. Data Mining Solutions for the Business Environment

    OpenAIRE

    Ruxandra-Stefania PETRE

    2013-01-01

    Over the past years, data mining became a matter of considerable importance due to the large amounts of data available in the applications belonging to various domains. Data mining, a dynamic and fast-expanding field, that applies advanced data analysis techniques, from statistics, machine learning, database systems or artificial intelligence, in order to discover relevant patterns, trends and relations contained within the data, information impossible to observe using other techniques. The p...

  7. Possibility of Integrated Data Mining of Clinical Data

    Directory of Open Access Journals (Sweden)

    Akinori Abe

    2007-03-01

    Full Text Available In this paper, we introduce integrated data mining. Because of recent rapid progress in medical science as well as clinical diagnosis and treatment, integrated and cooperative research among medical researchers, biology, engineering, cultural science, and sociology is required. Therefore, we propose a framework called Cyber Integrated Medical Infrastructure (CIMI. Within this framework, we can deal with various types of data and consequently need to integrate those data prior to analysis. In this study, for medical science, we analyze the features and relationships among various types of data and show the possibility of integrated data mining.

  8. Data Mining Process Optimization in Computational Multi-agent Systems

    OpenAIRE

    Kazík, O.; Neruda, R. (Roman)

    2015-01-01

    In this paper, we present an agent-based solution of metalearning problem which focuses on optimization of data mining processes. We exploit the framework of computational multi-agent systems in which various meta-learning problems have been already studied, e.g. parameter-space search or simple method recommendation. In this paper, we examine the effect of data preprocessing for machine learning problems. We perform the set of experiments in the search-space of data mining processes which is...

  9. Does Googling lead to statin intolerance?

    Science.gov (United States)

    Khan, Sarah; Holbrook, Anne; Shah, Baiju R

    2018-07-01

    The nocebo effect, where patients with expectations of adverse effects are more likely to experience them, may contribute to the high rate of statin intolerance found in observational studies. Information that patients read on the internet may be a precipitant of this effect. The objective of the study was to establish whether the number of websites about statin side effects found using Google is associated with the prevalence of statin intolerance. The prevalence of statin intolerance in 13 countries across 5 continents was established in a recent study via a web-based survey of primary care physicians and specialists. Using the Google search engine for each country, the number of websites about statin side effects was determined, and standardized to the number of websites about statins overall. Searches were restricted to pages in the native language, and were conducted after connecting to each country using a virtual private network (VPN). English-speaking countries (Australia, Canada, UK, USA) had the highest prevalence of statin intolerance and also had the largest standardized number of websites about statin side effects. The sample Pearson correlation coefficient between these two variables was 0.868. Countries where patients using Google are more likely to find websites about statin side effects have greater levels of statin intolerance. The nocebo effect driven by online information may be contributing to statin intolerance. Copyright © 2018 Elsevier B.V. All rights reserved.

  10. Data mining and Pattern Recognizing Models for Identifying Inherited Diseases: Challenges and Implications

    OpenAIRE

    Lahiru Iddamalgoda; Partha Sarathi Das; Partha Sarathi Das; Achala Aponso; Vijayaraghava Seshadri Sundararajan; Prashanth Suravajhala; Prashanth Suravajhala; Prashanth Suravajhala; Jayaraman K Valadi

    2016-01-01

    Data mining and pattern recognition methods reveal interesting findings in genetic studies, especially on how genetic makeup is associated with inherited diseases. Although researchers have proposed various data mining models for biomedical approaches, there remains a challenge in accurately determining the responsible genetic factors for prioritizing the single nucleotide polymorphisms (SNP) associated with the disease. In this commentary, we review the state-of-art data mining and pattern r...

  11. Data Mining and Pattern Recognition Models for Identifying Inherited Diseases: Challenges and Implications

    OpenAIRE

    Iddamalgoda, Lahiru; Das, Partha S.; Aponso, Achala; Sundararajan, Vijayaraghava S.; Suravajhala, Prashanth; Valadi, Jayaraman K.

    2016-01-01

    Data mining and pattern recognition methods reveal interesting findings in genetic studies, especially on how the genetic makeup is associated with inherited diseases. Although researchers have proposed various data mining models for biomedical approaches, there remains a challenge in accurately prioritizing the single nucleotide polymorphisms (SNP) associated with the disease. In this commentary, we review the state-of-art data mining and pattern recognition models for identifying inherited ...

  12. Factors influencing dyslipidemia in statin-treated patients in Lebanon and Jordan: results of the Dyslipidemia International Study.

    Science.gov (United States)

    Azar, Sami T; Hantash, Hadi Abu; Jambart, Selim; El-Zaheri, Mohamed M; Rachoin, Rachoin; Chalfoun, Amal; Lahoud, Layla; Okkeh, Osama; Bramlage, Peter; Brudi, Philippe; Ambegaonkar, Baishali M

    2014-01-01

    Cardiovascular disease is the leading cause of death and disability worldwide. Therefore, as part of the Dyslipidemia International Study (DYSIS), we have analyzed the prevalence of lipid abnormalities and risk factors for dyslipidemia in statin-treated patients in Lebanon and Jordan. This cross-sectional, multicenter study enrolled 617 patients at 13 hospitals in Lebanon and Jordan. Patients were at least 45 years old and had been treated with statins for at least 3 months. Multivariate logistic regression analysis was used to determine patient characteristics contributing to dyslipidemia during statin therapy. Our findings indicated that 55.9% of statin-treated patients (mean age 60.3 years, 47% female) in Lebanon and Jordan did not achieve goal levels for low-density lipoprotein cholesterol which were dependent on Systematic Coronary Risk Evaluation (SCORE) risk, and 70% of patients (76% men and 63.3% of women) were at very high cardiovascular risk. Low-density lipoprotein cholesterol goals were not achieved in 67.2% of those with very high cardiovascular risk. The most commonly prescribed statin was atorvastatin (44.6%), followed by simvastatin (27.7%), rosuvastatin (21.2%), fluvastatin (3.3%), pravastatin (3%), and lovastatin (0.2%). Approximately half of the population was treated with a statin dose potency of 4, equaling 40 mg of simvastatin. In Lebanon and Jordan, the strongest independent associations with low-density lipoprotein cholesterol not at goal were current smoking (odds ratio [OR] 1.96; 95% confidence [CI] 1.25-3.08), diabetes mellitus (OR 2.53; 95% CI 1.70-3.77), and ischemic heart disease (OR 2.26; 95% CI 1.45-3.53), while alcohol consumption was associated with reduced risk (OR 0.12; 95% CI 0.03-0.57). We observed that many patients in Lebanon and Jordan experienced persistent dyslipidemia during statin treatment, supporting the notion that novel lipid-lowering strategies need to be developed. Also, social programs aimed at combating the

  13. Data Mining: A Hybrid Methodology for Complex and Dynamic Research

    Science.gov (United States)

    Lang, Susan; Baehr, Craig

    2012-01-01

    This article provides an overview of the ways in which data and text mining have potential as research methodologies in composition studies. It introduces data mining in the context of the field of composition studies and discusses ways in which this methodology can complement and extend our existing research practices by blending the best of what…

  14. Prevalence of dyslipidaemia in statin-treated patients in Ireland: Irish results of the DYSlipidaemia International Study (DYSIS).

    LENUS (Irish Health Repository)

    Horgan, S

    2012-02-01

    BACKGROUND: Statins are proven to reduce cardiovascular risk; however, substantial risk remains in patients on statin therapy. Persisting dyslipidaemia is likely to play a contributory role. AIM: To assess the prevalence of persisting lipid abnormalities in patients treated with statins. METHODS: DYSIS was a cross-sectional study of 22,063 patients in Europe and Canada. 900 Irish patients participated. All patients were >\\/= 45 years and treated with statins for >\\/= 3 months. Data were collected from the patients\\' records. ESC guidelines were used to classify risk and to define lipid levels. RESULTS: Mean age was 66.1 years with women representing 40.7%. 78.6% were high-risk patients; that is 53.9% with cardiovascular disease (CVD), 20.1% with diabetes and 15.9% with a SCORE risk >\\/= 5%. Total cholesterol was not at goal in 34.4% of all patients. LDL-C was elevated in 30.8% of all patients and in 30% at high risk. Low HDL-C was found in 34.7% of high-risk patients compared to 16.9% of patients with an ESC score <5%. In diabetics without CVD, low HDL-C and elevated TGs were found in 46 and 44.3%, respectively. CONCLUSIONS: Despite statin therapy, a significant number of patients have persistent dyslipidaemia. While LDL-C targets are suboptimal in three out of ten patients, the prevalence of low HDL-C and high TGs in high-risk patients is greater than one in three. A more integrated approach to the treatment of patients with dyslipidaemia is warranted. Clinical trials are needed to assess the impact of therapies that raise HDL-C and lower elevated TGs.

  15. The association of statin use with reduced incidence of venous thromboembolism: a population-based cohort study.

    Science.gov (United States)

    Lassila, Riitta; Jula, Antti; Pitkäniemi, Janne; Haukka, Jari

    2014-11-05

    Venous thromboembolism (VTE) continues to be a frequent medical emergency requiring rapid recognition so as to reach diagnosis and initiate anticoagulation therapy. The use of statins in addition to reducing the incidence of arterial thrombosis for decreasing the incidence and reoccurrence of VTE is reported. The aim of our study was to explore the association between statin usage and the incidence of new VTE at the population level during a 10-year follow-up. Population-based historic cohort. The Health 2000 Survey was based on a nationally representative sample. 8028 individuals aged 30 years or over in Finland. The primary end point event was the first ever hospitalisation due to one of the following causes: pulmonary embolism (International Classification of Diseases-10 I26), cerebral venous non-pyogenic thrombosis (I63.6), or venous thrombosis (I80.9-189). The preselected explanatory variables applied to the Poisson regression model were statin usage (no/yes) during follow-up (2000-2011) and several baseline data (age, sex; usage of blood glucose lowering drugs, vitamin K antagonists and antiplatelet agents). We observed 136 VTE events, the incidence of 1.72 (95% CI 1.44 to 2.04) per 1000 person-years. Current statin usage did not associate with the incidence of VTE according to the univariate model (rate ratio (RR) 0.93, 0.56 to 1.52), but when adjusted with baseline variables (age, sex, medications) the RR declined to 0.60 (0.36 to 1.00, p=0.04). Statin use offers protection against first ever VTE events and appears as a primary prevention tool in patients without anticoagulation or antiplatelet medication. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  16. An Application of Data Mining Algorithms for Shipbuilding Cost Estimation

    NARCIS (Netherlands)

    Kaluzny, B.L.; Barbici, S.; Berg, G.; Chiomento, R.; Derpanis,D.; Jonsson, U.; Shaw, R.H.A.D.; Smit, M.C.; Ramaroson, F.

    2011-01-01

    This article presents a novel application of known data mining algorithms to the problem of estimating the cost of ship development and construction. The work is a product of North Atlantic Treaty Organization Research and Technology Organization Systems Analysis and Studies 076 Task Group “NATO

  17. Dengue fatality prediction using data mining | Rahim | Journal of ...

    African Journals Online (AJOL)

    The aim of this research is to study the current implementation of dengue outbreak control in Malaysia and predict dengue fever cases using data mining techniques. Real data on dengue fever and weather are collected from the Ministry of Health in its Perak Tengah district office and Perak Meteorological office respectively ...

  18. Guideline concordance of new statin prescriptions: who got a statin?

    Science.gov (United States)

    Cascino, Thomas; Vali, Marzieh; Redberg, Rita; Bravata, Dawn M; Boscardin, John; Eilkhani, Elnaz; Keyhani, Salomeh

    2017-09-01

    Statins are recommended to reduce serum cholesterol in patients at risk for atherosclerotic cardiovascular disease. Despite the prevalence of statin use, little is known about the indications for new prescriptions. We assessed the concordance of new statin prescriptions in the Veterans Health Administration (VHA) compared with the Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III, or ATP III) guidelines (the guidelines in force in 2012) and the American College of Cardiology (ACC)-American Heart Association (AHA) 2013 guidelines. Cross-sectional study. We identified every patient who received a new prescription (no statin use in the prior year) in the VHA in 2012. Patients were excluded if they had incomplete data, triglycerides greater than 400 mg/dL, or fewer than 2 primary care visits to ensure adequate baseline data to calculate Framingham and ACC-AHA 2013 risk scores. We identified 250,243 new statin prescriptions in 2012 in the VHA, with 121,081 meeting inclusion criteria. Among new prescriptions, 68% were prescribed for primary prevention and 32% were prescribed for secondary prevention. Among patients receiving new statins for primary prevention, 48% did not have an indication supported by the ATP III guideline and 20% did not have an indication supported by the ACC/AHA guideline. Overall, approximately 19% of patients may have received a statin for an indication not supported by either guideline. Veterans are commonly prescribed statins for indications not supported by professional society guidelines. The finding of common use of statins outside established guidelines represents an opportunity to improve the quality and value of the healthcare delivery.

  19. Consumer behavior in the setting of over-the-counter statin availability: lessons from the consumer use study of OTC Mevacor.

    Science.gov (United States)

    Brass, Eric P

    2004-11-04

    Despite the proven benefits of statins, large numbers of patients meeting guideline criteria for therapy are not receiving these drugs. It has been suggested that over-the-counter (OTC) availability of statins would allow more consumers to use statins and achieve cardiovascular risk reduction. However, concerns have been raised as to the consumers' ability to self-manage hyperlipidemia and use statins safely. The Consumer Use Study of OTC Mevacor (CUSTOM) was designed to define consumer behaviors in the setting of OTC statin availability. The study was conducted in a simulated OTC setting and allowed consumers to purchase once-daily lovastatin 20 mg. The CUSTOM dataset includes >3,300 consumers who evaluated OTC lovastatin for potential purchase at study sites and follow-up information on purchasers for up to 6 months of self-managed therapy. These data have been analyzed to address consumers' knowledge of their cholesterol concentrations as well as their ability to make OTC use decisions based on their cardiovascular risk, avoid drug-drug interactions, self-manage their cholesterol treatment after deciding to use the OTC product, and maintain interactions with physicians while using lovastatin OTC. The results showed that most study participants appropriately self-selected OTC statin therapy and managed their treatment. Use of OTC statins by consumers needing more intensive statin therapy or facing the risk of potential drug-drug interactions remains an area of concern but occurred infrequently in CUSTOM. These data are important for making an informed risk-benefit decision concerning OTC statin availability.

  20. The multiple zeta value data mine

    Energy Technology Data Exchange (ETDEWEB)

    Buemlein, J. [Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany); Broadhurst, D.J. [Open Univ., Milton Keynes (United Kingdom). Physics and Astronomy Dept.; Vermaseren, J.A.M. [Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany); NIKHEF, Amsterdam (Netherlands)

    2009-07-15

    We provide a data mine of proven results for multiple zeta values (MZVs) of the form {zeta}(s{sub 1},s{sub 2},..,s{sub k}) = sum {sup {infinity}}{sub n{sub 1}}{sub >n{sub 2}}{sub >...>n{sub k}}{sub >0} {l_brace}1/(n{sub 1}{sup s{sub 1}}..n{sub k}{sup s{sub k}}){r_brace} with weight w = sum {sup K}{sub i=1}s{sub i} and depth k and for Euler sums of the form sum {sup {infinity}}{sub n{sub 1}}{sub >n{sub 2}}{sub >...>n{sub k}}{sub >0} {l_brace}({epsilon}{sub 1}{sup n{sub 1}}..{epsilon}{sub 1}{sup n{sub k}})/(n{sub 1}{sup s{sub 1}}..n{sub k}{sup s{sub k}}){r_brace} with signs {epsilon}{sub i} = {+-} 1. Notably, we achieve explicit proven reductions of all MZVs with weights w{<=}22, and all Euler sums with weights w{<=}12, to bases whose dimensions, bigraded by weight and depth, have sizes in precise agreement with the Broadhurst. Kreimer and Broadhurst conjectures. Moreover, we lend further support to these conjectures by studying even greater weights (w{<=}30), using modular arithmetic. To obtain these results we derive a new type of relation for Euler sums, the Generalized Doubling Relations. We elucidate the ''pushdown'' mechanism, whereby the ornate enumeration of primitive MZVs, by weight and depth, is reconciled with the far simpler enumeration of primitive Euler sums. There is some evidence that this pushdown mechanism finds its origin in doubling relations. We hope that our data mine, obtained by exploiting the unique power of the computer algebra language FORM, will enable the study of many more such consequences of the double-shuffle algebra of MZVs, and their Euler cousins, which are already the subject of keen interest, to practitioners of quantum field theory, and to mathematicians alike. (orig.)

  1. Data Mining and Machine Learning in Astronomy

    Science.gov (United States)

    Ball, Nicholas M.; Brunner, Robert J.

    We review the current state of data mining and machine learning in astronomy. Data Mining can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results. We cover common machine learning algorithms, such as artificial neural networks and support vector machines, applications from a broad range of astronomy, emphasizing those in which data mining techniques directly contributed to improving science, and important current and future directions, including probability density functions, parallel algorithms, Peta-Scale computing, and the time domain. We conclude that, so long as one carefully selects an appropriate algorithm and is guided by the astronomical problem at hand, data mining can be very much the powerful tool, and not the questionable black box.

  2. [Statins and muscle pain].

    Science.gov (United States)

    Yosef, Yoni; Schurr, Daniel; Constantini, Naama

    2014-07-01

    Statins are used for the prevention and treatment of cardiovascular disease. The treatment is quite safe but not free of side effects, particularly muscle pain. Fear of pain may prevent patients from carrying out exercise or diminish their motivation to return and engage in it, even though both the statins and the exercise have a proven benefit in both treatment and prevention, and a synergistic effect enhances this benefit. Prevalence of muscular pain ranges from 1-30%. Pain usually appears at the beginning of treatment, but can occur even after months and under any of the existing agents. The creatine phosphokinase (CPK) enzyme level may rise, but not necessarily. Increases to exceptional values (10 times the upper normal level) are relatively rare and rhabdomyolysis is extremely rare. The risk increases with age, co-morbidities and especially when taken concurrently with drugs that are metabolized in a similar pathway. Pain usually passes within a month after discontinuing treatment, but may persist for six months or more. Studies have examined the effect of statin therapy on the ability to perform physical activity, but results are inconsistent. The increased rise of CPK was observed under statin therapy, a tendency that increased with age. However, it was not accompanied by an increased incidence of muscle pain or rhabdomyolysis. Considering the above we recommend encouraging patients to exercise. However, patients should be instructed to report new or worsening muscular pains. Discontinuation, lowering dose or replacement should be considered when pain is suspected to be related with treatment.

  3. Hypercholesterolemia, Stroke And Statins

    Directory of Open Access Journals (Sweden)

    Prabhakar S

    2005-01-01

    Full Text Available The link between serum cholesterol levels and the incidence of stroke still remain to be established. There are conflicting reports from a series of observational cohort studies. However, clinical trials with HMG CoA reductase inhibitors (also called statins have shown that cholesterol lowering therapy used in the primary and secondary prevention of myocardial infarction significantly reduced cardiovascular events including strokes. Meta analysis of trials with statins have shown a relative risk reduction in stroke of 12 to 48% in patients with coronary heart disease after MI. It has been postulated that the clinical action of statins is the result of pleiotropic / antiatherogenic effects rather than simply a reduction in cholesterol. The putative beneficial effect of statins in stroke involve blocking of macrophage and platelet activation, improvement of endothelial cell vasomotor function, enhancement of endothelial fibrinolytic function, immunosuppressive and anti-inflammatory action, inhibition of smooth muscle cell proliferation and particularly enhancement of endothelial nitric oxide synthase (eNOS.

  4. Patients' perspectives on statin therapy for treatment of hypercholesterolaemia: a qualitative study.

    Science.gov (United States)

    Tolmie, Elizabeth P; Lindsay, Grace M; Kerr, Susan M; Brown, Malcom R; Ford, Ian; Gaw, Allan

    2003-07-01

    Health Care Practitioners' attempts to implement secondary prevention targets for coronary heart disease (CHD) may be restricted by low rates of persistence with statin therapy. There is a need to understand why some patients, despite having established CHD and elevated cholesterol, do not comply with their prescribed statin regimen. To explore patients' perspectives on compliance with statin therapy. Primary care, West of Scotland. The research approach was qualitative. Thirty-three patients prescribed statin therapy and identified as having different patterns of compliance (poor moderate and good) were interviewed. The in-depth interviews were conducted on a one to one basis. Patients prescribed statin therapy for less than three months were excluded. Data were analysed thematically with the assistance of QSR Nudist. From analysis of the narrative data, two broad categories, i.e. 'Patient-health care provider communication' and 'Health beliefs' were identified. These categories encompassed six main themes: 'Initiation of therapy'; 'Subsequent feedback'; 'Sources of misconceptions'; 'Unconditional acceptance'; 'Conditional acceptance'; 'Deferment and Rejection'. Acceptance of and compliance with statin therapy appeared to be associated with the provision, interpretation and feedback of information during patient-practitioner consultations, and patients' beliefs about personal health status, cholesterol, and recommended cholesterol-lowering strategies. Patients' beliefs and understanding about cholesterol, and the role of cholesterol modifying strategies should be determined prior to the initiation of therapy and at appropriate intervals thereafter.

  5. [In vitro study over statins effects on cellular growth curves and its reversibility with mevalonate].

    Science.gov (United States)

    Millan Núñez-Cortés, Jesús; Alvarez Rodriguez, Ysmael; Alvarez Novés, Granada; Recarte Garcia-Andrade, Carlos; Alvarez-Sala Walther, Luis

    2014-01-01

    HMG-CoA-Reductase inhibitors, also known as statins, are currently the most powerful cholesterol-lowering drugs available on the market. Clinical trials and experimental evidence suggest that statins have heavy anti-atherosclerotic effects. These are in part consequence of lipid lowering but also result from pleiotropic actions of the drugs. These so-called pleiotropic properties affect various aspects of cell function, inflammation, coagulation, and vasomotor activity. These effects are mediated either indirectly through LDL-c reduction or via a direct effect on cellular functions. Although many of the pleiotropic properties of statins may be a class effect, some may be unique to certain agents and account for differences in their pharmacological activity. So, although statins typically have similar effects on LDL-c levels, differences in chemical structure and pharmacokinetic profile can lead to variations in pleiotropic effects. In this paper we analize the in vitro effects of different statins over different cell lines from cells implicated in atherosclerotic process: endothelial cells, fibroblasts, and vascular muscular cells. In relation with our results we can proof that the effects of different dosis of different statins provides singular effects over growth curves of different cellular lines, a despite of a class-dependent effects. So, pleiotropic effects and its reversibility with mevalonate are different according with the molecule and the dosis. Copyright © 2013 Elsevier España, S.L. y SEA. All rights reserved.

  6. APLIKASI DATA MINING UNTUK MENAMPILKAN INFORMASI TINGKAT KELULUSAN MAHASISWA

    Directory of Open Access Journals (Sweden)

    Yuli Asriningtias

    2014-01-01

    Full Text Available Perguruan tinggi dituntut memiliki keunggulan bersaing dengan memanfaatkan sumber dayanya, termasuk sumber daya manusia dalam hal ini adalah mahasiswa.Tidak semua mahasiswa dapat menyelesaikan study tepat waktu, disamping  IPK yang beragam. Lama waktu mahasiswa dalam menempuh studi dan IPK menjadi salah satu faktor tingkat keunggulan sebuah Perguruan Tinggi.  Nilai potensi tersebut dapat digali menggunakan teknik data mining.Data mining adalah kegiatan menemukan pola yang menarik dari data dalam jumlah besar, data dapat disimpan dalam database, data warehouse, atau penyimpanan informasi lainnya. Data warehouse merupakan penyimpanan data yang berorientasi objek, terintegrasi, mempunyai variant waktu, dan menyimpan data dalam bentuk nonvolatile sebagai pendukung manejemen dalam proses pengambilan keputusan. Penelitian ini dikembangkan dengan cara menscan data pada database secara langsung sehingga menghasilkan informasi yag dibutuhkan. Aplikasi data mining ini dibangun menggunakan bahasa pemrograman Borland Delphi 7 dan menggunakan database SQL Server 2000 sebagai media penyimpan data. Hasil dari penelitian bahwa dapat diketahui tingkat ketepatan waktu dan nilai kelulusan mahasiswa yang berelasi dengan atribut data masuk mahasiswa. Kata Kunci : Data mining, data warehouse, kelulusan mahasiswa.

  7. Applying data mining techniques to improve diagnosis in neonatal jaundice

    Directory of Open Access Journals (Sweden)

    Ferreira Duarte

    2012-12-01

    Full Text Available Abstract Background Hyperbilirubinemia is emerging as an increasingly common problem in newborns due to a decreasing hospital length of stay after birth. Jaundice is the most common disease of the newborn and although being benign in most cases it can lead to severe neurological consequences if poorly evaluated. In different areas of medicine, data mining has contributed to improve the results obtained with other methodologies. Hence, the aim of this study was to improve the diagnosis of neonatal jaundice with the application of data mining techniques. Methods This study followed the different phases of the Cross Industry Standard Process for Data Mining model as its methodology. This observational study was performed at the Obstetrics Department of a central hospital (Centro Hospitalar Tâmega e Sousa – EPE, from February to March of 2011. A total of 227 healthy newborn infants with 35 or more weeks of gestation were enrolled in the study. Over 70 variables were collected and analyzed. Also, transcutaneous bilirubin levels were measured from birth to hospital discharge with maximum time intervals of 8 hours between measurements, using a noninvasive bilirubinometer. Different attribute subsets were used to train and test classification models using algorithms included in Weka data mining software, such as decision trees (J48 and neural networks (multilayer perceptron. The accuracy results were compared with the traditional methods for prediction of hyperbilirubinemia. Results The application of different classification algorithms to the collected data allowed predicting subsequent hyperbilirubinemia with high accuracy. In particular, at 24 hours of life of newborns, the accuracy for the prediction of hyperbilirubinemia was 89%. The best results were obtained using the following algorithms: naive Bayes, multilayer perceptron and simple logistic. Conclusions The findings of our study sustain that, new approaches, such as data mining, may support

  8. Data Mining Techniques for Customer Relationship Management

    Science.gov (United States)

    Guo, Feng; Qin, Huilin

    2017-10-01

    Data mining have made customer relationship management (CRM) a new area where firms can gain a competitive advantage, and play a key role in the firms’ management decision. In this paper, we first analyze the value and application fields of data mining techniques for CRM, and further explore how data mining applied to Customer churn analysis. A new business culture is developing today. The conventional production centered and sales purposed market strategy is gradually shifting to customer centered and service purposed. Customers’ value orientation is increasingly affecting the firms’. And customer resource has become one of the most important strategic resources. Therefore, understanding customers’ needs and discriminating the most contributed customers has become the driving force of most modern business.

  9. The Top Ten Algorithms in Data Mining

    CERN Document Server

    Wu, Xindong

    2009-01-01

    From classification and clustering to statistical learning, association analysis, and link mining, this book covers the most important topics in data mining research. It presents the ten most influential algorithms used in the data mining community today. Each chapter provides a detailed description of the algorithm, a discussion of available software implementation, advanced topics, and exercises. With a simple data set, examples illustrate how each algorithm works and highlight the overall performance of each algorithm in a real-world application. Featuring contributions from leading researc

  10. Supporting Solar Physics Research via Data Mining

    Science.gov (United States)

    Angryk, Rafal; Banda, J.; Schuh, M.; Ganesan Pillai, K.; Tosun, H.; Martens, P.

    2012-05-01

    In this talk we will briefly introduce three pillars of data mining (i.e. frequent patterns discovery, classification, and clustering), and discuss some possible applications of known data mining techniques which can directly benefit solar physics research. In particular, we plan to demonstrate applicability of frequent patterns discovery methods for the verification of hypotheses about co-occurrence (in space and time) of filaments and sigmoids. We will also show how classification/machine learning algorithms can be utilized to verify human-created software modules to discover individual types of solar phenomena. Finally, we will discuss applicability of clustering techniques to image data processing.

  11. Quantification of Operational Risk Using A Data Mining

    Science.gov (United States)

    Perera, J. Sebastian

    1999-01-01

    What is Data Mining? - Data Mining is the process of finding actionable information hidden in raw data. - Data Mining helps find hidden patterns, trends, and important relationships often buried in a sea of data - Typically, automated software tools based on advanced statistical analysis and data modeling technology can be utilized to automate the data mining process

  12. Cholesterol suppresses antimicrobial effect of statins

    Directory of Open Access Journals (Sweden)

    Mohammad Reza Haeri

    2015-12-01

    Full Text Available Objective(s:Isoprenoid biosynthesis is a key metabolic pathway to produce a wide variety of biomolecules such as cholesterol and carotenoids, which target cell membranes. On the other hand, it has been reported that statins known as inhibitors of isoprenoid biosynthesis and cholesterol lowering agents, may have a direct antimicrobial effect on the some bacteria. The exact action of statins in microbial metabolism is not clearly understood. It is possible that statins inhibit synthesis or utilization of some sterol precursor necessary for bacterial membrane integrity. Accordingly, this study was designed in order to examine if statins inhibit the production of a compound, which can be used in the membrane, and whether cholesterol would replace it and rescue bacteria from toxic effects of statins. Materials and Methods: To examine the possibility we assessed antibacterial effect of statins with different classes; lovastatin, simvastatin, and atorvastatin, alone and in combination with cholesterol on two Gram-positive (Staphylococcus aureus and Enterococcus faecalis and two Gram-negative (Pseudomonas aeruginosa and Escherichia coli bacteria using gel diffusion assay. Results: Our results showed that all of the statins except for lovastatin had significant antibacterial property in S. aureus, E. coli, and Enter. faecalis. Surprisingly, cholesterol nullified the antimicrobial action of effective statins in statin-sensitive bacteria. Conclusion: It is concluded that statins may deprive bacteria from a metabolite responsible for membrane stability, which is effectively substituted by cholesterol.

  13. Statin intolerance - a question of definition.

    Science.gov (United States)

    Algharably, Engi Abdel-Hady; Filler, Iris; Rosenfeld, Stephanie; Grabowski, Katja; Kreutz, Reinhold

    2017-01-01

    Statin therapy is the backbone of pharmacologic therapy for low-density lipoproteins cholesterol lowering and plays a pivotal role in cardiovascular disease prevention. Statin intolerance is understood as the inability to continue using a statin to reduce individual cardiovascular risk sufficiently, due to the development of symptoms or laboratory abnormalities attributable to the initiation or dose escalation of a statin. Muscle symptoms are the most common side effects observed. Areas covered: The main aim of this article is to present a review on published definitions of statin intolerance. In addition, a brief review on clinical aspects and risk factors of statin intolerance is provided and features for a common definition for statin intolerance are suggested. Expert opinion: A definition of statin intolerance by major drug regulatory agencies is not available. In clinical studies, different definitions are chosen and results are not comparable; different medical associations do not agree on one common definition. There is an unmet need to establish a common definition of statin intolerance to ensure an appropriate clinical use of this important drug class. Further work is required to develop a consensus definition on statin intolerance that could have significant positive impact on both research and clinical management.

  14. Tools for Educational Data Mining: A Review

    Science.gov (United States)

    Slater, Stefan; Joksimovic, Srecko; Kovanovic, Vitomir; Baker, Ryan S.; Gasevic, Dragan

    2017-01-01

    In recent years, a wide array of tools have emerged for the purposes of conducting educational data mining (EDM) and/or learning analytics (LA) research. In this article, we hope to highlight some of the most widely used, most accessible, and most powerful tools available for the researcher interested in conducting EDM/LA research. We will…

  15. Data Mining Gets Traction in Education

    Science.gov (United States)

    Sparks, Sarah D.

    2011-01-01

    The new and rapidly growing field of educational data mining is using the chaff from data collected through normal school activities to explore learning in more detail than ever, and researchers say the day when educators can make use of Amazon.com-like feedback on student learning behaviors may be closer than most people think. Educational data…

  16. Highly Robust Methods in Data Mining

    Czech Academy of Sciences Publication Activity Database

    Kalina, Jan

    2013-01-01

    Roč. 8, č. 1 (2013), s. 9-24 ISSN 1452-4864 Institutional support: RVO:67985807 Keywords : data mining * robust statistics * high-dimensional data * cluster analysis * logistic regression * neural networks Subject RIV: BB - Applied Statistics, Operational Research

  17. Engaging Business Students with Data Mining

    Science.gov (United States)

    Brandon, Dan

    2016-01-01

    The Economist calls it "a golden vein", and many business experts now say it is the new science of winning. Business and technologists have many names for this new science, "business intelligence" (BI), " data analytics," and "data mining" are among the most common. The job market for people skilled in this…

  18. Traffic Flow Management: Data Mining Update

    Science.gov (United States)

    Grabbe, Shon R.

    2012-01-01

    This presentation provides an update on recent data mining efforts that have been designed to (1) identify like/similar days in the national airspace system, (2) cluster/aggregate national-level rerouting data and (3) apply machine learning techniques to predict when Ground Delay Programs are required at a weather-impacted airport

  19. Comparative genomics using data mining tools

    Indian Academy of Sciences (India)

    We have analysed the genomes of representatives of three kingdoms of life, namely, archaea, eubacteria and eukaryota using data mining tools based on compositional analyses of the protein sequences. The representatives chosen in this analysis were Methanococcus jannaschii, Haemophilus influenzae and ...

  20. Mining Views : database views for data mining

    NARCIS (Netherlands)

    Blockeel, H.; Calders, T.; Fromont, É.; Goethals, B.; Prado, A.

    2008-01-01

    We present a system towards the integration of data mining into relational databases. To this end, a relational database model is proposed, based on the so called virtual mining views. We show that several types of patterns and models over the data, such as itemsets, association rules and decision

  1. Mining Views : database views for data mining

    NARCIS (Netherlands)

    Blockeel, H.; Calders, T.; Fromont, É.; Goethals, B.; Prado, A.; Nijssen, S.; De Raedt, L.

    2007-01-01

    We propose a relational database model towards the integration of data mining into relational database systems, based on the so called virtual mining views. We show that several types of patterns and models over the data, such as itemsets, association rules, decision trees and clusterings, can be

  2. Study of Statin- and Loratadine-Induced Muscle Pain Mechanisms Using Human Skeletal Muscle Cells

    Directory of Open Access Journals (Sweden)

    Yat Hei Leung

    2017-10-01

    Full Text Available Many drugs can cause unexpected muscle disorders, often necessitating the cessation of an effective medication. Inhibition of monocarboxylate transporters (MCTs may potentially lead to perturbation of l-lactic acid homeostasis and muscular toxicity. Previous studies have shown that statins and loratadine have the potential to inhibit l-lactic acid efflux by MCTs (MCT1 and 4. The main objective of this study was to confirm the inhibitory potentials of atorvastatin, simvastatin (acid and lactone forms, rosuvastatin, and loratadine on l-lactic acid transport using primary human skeletal muscle cells (SkMC. Loratadine (IC50 31 and 15 µM and atorvastatin (IC50 ~130 and 210 µM demonstrated the greatest potency for inhibition of l-lactic acid efflux at pH 7.0 and 7.4, respectively (~2.5-fold l-lactic acid intracellular accumulation. Simvastatin acid exhibited weak inhibitory potency on l-lactic acid efflux with an intracellular lactic acid increase of 25–35%. No l-lactic acid efflux inhibition was observed for simvastatin lactone or rosuvastatin. Pretreatment studies showed no change in inhibitory potential and did not affect lactic acid transport for all tested drugs. In conclusion, we have demonstrated that loratadine and atorvastatin can inhibit the efflux transport of l-lactic acid in SkMC. Inhibition of l-lactic acid efflux may cause an accumulation of intracellular l-lactic acid leading to the reported drug-induced myotoxicity.

  3. A Framework for Investigating Influence of Organizational Decision Makers on Data Mining Process Achievement

    Directory of Open Access Journals (Sweden)

    Hanieh Hajisafari

    2012-02-01

    Full Text Available Currently, few studies deal with evaluation of data mining plans in context of solvng organizational problems. A successful data miner is searching to solve a fully defined business problem. To make the data mining (DM results actionable, the data miner must explain them to the business insider. The interaction process between the business insiders and data miners is actually a knowledge-sharing process. In this study through representing a framwork, influence of organizational decision makers on data mining process and results investigated. By investigating research literature, the critical success factors of data mining plans was identified and the role of organizational decision makers in each step of data mining was investigated.‌ Then, the conceptual framework of influence of organizational decision makers on data mining process achievement was designed. By getting expert opinions, the proposed framework was analyzed and evantually designed the final framework of influence of organizational decision makers on data mining process achievement. Analysis of experts opinions showed that by knowledge sharing of data ming results with decision makers, "learning", "action or internalization" and "enforcing/unlearning" will become as critical success factors. Also, results of examining importance of decision makers' feedback on data mining steps showed that getting feedback from decision makers could have most influence on "knowledge extraction and representing model" step and least on "data cleaning and preprocessing" step.

  4. Factors influencing dyslipidemia in statin-treated patients in Lebanon and Jordan: results of the Dyslipidemia International Study

    Directory of Open Access Journals (Sweden)

    Azar ST

    2014-05-01

    Full Text Available Sami T Azar,1 Hadi Abu Hantash,2 Selim Jambart,3 Mohammad M El-Zaheri,4 Rachoin Rachoin,5 Amal Chalfoun,6 Layla Lahoud,6 Osama Okkeh,2 Peter Bramlage,7 Philippe Brudi,8 Baishali M Ambegaonkar81American University of Beirut Medical Center, Beirut, Lebanon; 2Istishari Hospital, Amman, Jordan; 3St Joseph University Faculty of Medicine, Beirut, Lebanon; 4Jordan Hospital, Amman, Jordan; 5Notre Dame des Secours Hospital, Jbeil, Lebanon; 6MSD Levant, Beirut, Lebanon; 7Institut für Pharmakologie und präventive Medizin, Mahlow, Germany; 8Merck and Co, Inc., Whitehouse Station, NJ, USABackground: Cardiovascular disease is the leading cause of death and disability worldwide. Therefore, as part of the Dyslipidemia International Study (DYSIS, we have analyzed the prevalence of lipid abnormalities and risk factors for dyslipidemia in statin-treated patients in Lebanon and Jordan.Methods: This cross-sectional, multicenter study enrolled 617 patients at 13 hospitals in Lebanon and Jordan. Patients were at least 45 years old and had been treated with statins for at least 3 months. Multivariate logistic regression analysis was used to determine patient characteristics contributing to dyslipidemia during statin therapy.Results: Our findings indicated that 55.9% of statin-treated patients (mean age 60.3 years, 47% female in Lebanon and Jordan did not achieve goal levels for low-density lipoprotein cholesterol which were dependent on Systematic Coronary Risk Evaluation (SCORE risk, and 70% of patients (76% men and 63.3% of women were at very high cardiovascular risk. Low-density lipoprotein cholesterol goals were not achieved in 67.2% of those with very high cardiovascular risk. The most commonly prescribed statin was atorvastatin (44.6%, followed by simvastatin (27.7%, rosuvastatin (21.2%, fluvastatin (3.3%, pravastatin (3%, and lovastatin (0.2%. Approximately half of the population was treated with a statin dose potency of 4, equaling 40 mg of simvastatin. In

  5. Recent advances in environmental data mining

    Science.gov (United States)

    Leuenberger, Michael; Kanevski, Mikhail

    2016-04-01

    Due to the large amount and complexity of data available nowadays in geo- and environmental sciences, we face the need to develop and incorporate more robust and efficient methods for their analysis, modelling and visualization. An important part of these developments deals with an elaboration and application of a contemporary and coherent methodology following the process from data collection to the justification and communication of the results. Recent fundamental progress in machine learning (ML) can considerably contribute to the development of the emerging field - environmental data science. The present research highlights and investigates the different issues that can occur when dealing with environmental data mining using cutting-edge machine learning algorithms. In particular, the main attention is paid to the description of the self-consistent methodology and two efficient algorithms - Random Forest (RF, Breiman, 2001) and Extreme Learning Machines (ELM, Huang et al., 2006), which recently gained a great popularity. Despite the fact that they are based on two different concepts, i.e. decision trees vs artificial neural networks, they both propose promising results for complex, high dimensional and non-linear data modelling. In addition, the study discusses several important issues of data driven modelling, including feature selection and uncertainties. The approach considered is accompanied by simulated and real data case studies from renewable resources assessment and natural hazards tasks. In conclusion, the current challenges and future developments in statistical environmental data learning are discussed. References - Breiman, L., 2001. Random Forests. Machine Learning 45 (1), 5-32. - Huang, G.-B., Zhu, Q.-Y., Siew, C.-K., 2006. Extreme learning machine: theory and applications. Neurocomputing 70 (1-3), 489-501. - Kanevski, M., Pozdnoukhov, A., Timonin, V., 2009. Machine Learning for Spatial Environmental Data. EPFL Press; Lausanne, Switzerland, p.392

  6. Data Mining Application in Customer Relationship Management for Hospital Inpatients

    OpenAIRE

    Lee, Eun Whan

    2012-01-01

    Objectives This study aims to discover patients loyal to a hospital and model their medical service usage patterns. Consequently, this study proposes a data mining application in customer relationship management (CRM) for hospital inpatients. Methods A recency, frequency, monetary (RFM) model has been applied toward 14,072 patients discharged from a university hospital. Cluster analysis was conducted to segment customers, and it modeled the patterns of the loyal customers' medical services us...

  7. Coenzyme Q10 Supplementation Decreases Statin-Related Mild-to-Moderate Muscle Symptoms: A Randomized Clinical Study

    OpenAIRE

    Skarlovnik, Ajda; Janić, Miodrag; Lunder, Mojca; Turk, Martina; Šabovič, Mišo

    2014-01-01

    Background Statin use is frequently associated with muscle-related symptoms. Coenzyme Q10 supplementation has yielded conflicting results in decreasing statin myopathy. Herein, we tested whether coenzyme Q10 supplementation could decrease statin-associated muscular pain in a specific group of patients with mild-to-moderate muscle symptoms. Material/Methods Fifty patients treated with statins and reporting muscle pain were recruited. The Q10 group (n=25) received coenzyme Q10 supplementation o...

  8. Personalized Prediction of Lifetime Benefits with Statin Therapy for Asymptomatic Individuals: A Modeling Study

    NARCIS (Netherlands)

    B.S. Ferket (Bart); B.J.H. van Kempen (Bob); J. Heeringa (Jan); S. Spronk (Sandra); K.E. Fleischmann (Kirsten); R.L. Nijhuis (Rogier); A. Hofman (Albert); E.W. Steyerberg (Ewout); M.G.M. Hunink (Myriam)

    2012-01-01

    textabstractBackground: Physicians need to inform asymptomatic individuals about personalized outcomes of statin therapy for primary prevention of cardiovascular disease (CVD). However, current prediction models focus on short-term outcomes and ignore the competing risk of death due to other causes.

  9. Data Mining Supercomputing with SAS JMP® Genomics

    Directory of Open Access Journals (Sweden)

    Richard S. Segall

    2011-02-01

    Full Text Available JMP® Genomics is statistical discovery software that can uncover meaningful patterns in high-throughput genomics and proteomics data. JMP® Genomics is designed for biologists, biostatisticians, statistical geneticists, and those engaged in analyzing the vast stores of data that are common in genomic research (SAS, 2009. Data mining was performed using JMP® Genomics on the two collections of microarray databases available from National Center for Biotechnology Information (NCBI for lung cancer and breast cancer. The Gene Expression Omnibus (GEO of NCBI serves as a public repository for a wide range of highthroughput experimental data, including the two collections of lung cancer and breast cancer that were used for this research. The results for applying data mining using software JMP® Genomics are shown in this paper with numerous screen shots.

  10. Associations Between Statin Use and Physical Function in Older Adults from The Netherlands and Australia: Longitudinal Aging Study Amsterdam and Australian Longitudinal Study on Women's Health.

    Science.gov (United States)

    van Boheemen, Laurette; Tett, Susan E; Sohl, Evelien; Hugtenburg, Jacqueline G; van Schoor, Natasja M; Peeters, G M E E

    2016-06-01

    Statin therapy may cause myopathy, but long-term effects on physical function are unclear. We investigated whether statin use is associated with poorer physical function in two population-based cohorts of older adults. Data were from 691 men and women (aged 69-102 years in 2005/2006) in the LASA (Longitudinal Aging Study Amsterdam) and 5912 women (aged 79-84 years in 2005) in the ALSWH (Australian Longitudinal Study on Women's Health). Statin use and dose were sourced from containers (LASA) and administrative databases (ALSWH). Physical function was assessed using performance tests, questionnaires on functional limitations and the SF-12 (LASA) and SF-36 (ALSWH) questionnaires. Cross-sectional (both studies) and 3-year prospective associations (ALSWH) were analysed for different statin dosage using linear and logistic regression. In total, 25 % of participants in LASA and 61 % in ALSWH used statins. In the cross-sectional models in LASA, statin users were less likely to have functional limitations (percentage of subjects with at least 1 limitation 63.9 vs. 64.2; odds ratio [OR] 0.6; 95 % confidence interval [CI] 0.3-0.9) and had better SF-12 physical component scores (mean [adjusted] 47.3 vs. 44.5; beta [B] = 2.8; 95 % CI 1.1-4.5); in ALSWH, statin users had better SF-36 physical component scores (mean [adjusted] 37.4 vs. 36.5; B = 0.9; 95 % CI 0.3-1.5) and physical functioning subscale scores (mean [adjusted] 55.1 vs. 52.6; B = 2.4; 95 % CI 1.1-3.8) than non-users. Similar associations were found for low- and high-dose users and in the prospective models. In contrast, no significant associations were found with performance tests. Two databases from longitudinal population studies in older adults gave comparable results, even though different outcome measures were used. In these two large cohorts, statin use was associated with better self-perceived physical function.

  11. Using Data Mining to Predict Possible Future Depression Cases

    OpenAIRE

    Daimi, Kevin; Banitaan, Shadi

    2014-01-01

    Depression is a disorder characterized by misery and gloominess felt over a period of time. Some symptoms of depression overlap with somatic illnesses implying considerable difficulty in diagnosing it. This paper contributes to its diagnosis through the application of data mining, namely classification, to predict patients who will most likely develop depression or are currently suffering from depression. Synthetic data is used for this study. To acquire the results, the popular suite of mach...

  12. Data Mining Thesis Topics in Finland

    OpenAIRE

    Bajo Rouvinen, Ari

    2017-01-01

    The Theseus open repository contains metadata about more than 100,000 thesis publications from the different universities of applied sciences in Finland. Different data mining techniques were applied to the Theseus dataset to build a web application to explore thesis topics and degree programmes using different libraries in Python and JavaScript. Thesis topics were extracted from manually annotated keywords by the authors and curated subjects by the librarians. During the project, the quality...

  13. Data mining teaching throughout cards game competition

    OpenAIRE

    Antoñanzas-Torres, Javier; Urraca, Ruben; Sodupe-Ortega, Enrique; Martínez-de-Pison, Francisco; Pernía-Espinoza, Alpha

    2015-01-01

    [EN] Data-mining techniques and statistical metrics learning can be complicated because of the complexity and overwhelming nature of this field. In this paper a class competition to improve learning of designing Decision Support Systems (DSS) by playing a classic cards game named "Copo" is proposed. The fact that this game is based on a probabilistic problem and that different solutions can be obtained represents a very typical kind of problem in the field of engineering and compu...

  14. A Data Mining Approach to Intelligence Operations

    DEFF Research Database (Denmark)

    Memon, Nasrullah; Hicks, David; Harkiolakis, Nicholas

    2008-01-01

    agencies.   An emphasis in the paper is placed on Social Network Analysis and Investigative Data Mining, and the use of these technologies in the counterterrorism domain.  Tools and techniques from both areas are described, along with the important tasks for which they can be used to assist...... with the investigation and analysis of terrorist organizations.  The process of collecting data about these organizations is also considered along with the inherent difficulties that are involved....

  15. Solar Data Mining at Georgia State University

    Science.gov (United States)

    Angryk, R.; Martens, P. C.; Schuh, M.; Aydin, B.; Kempton, D.; Banda, J.; Ma, R.; Naduvil-Vadukootu, S.; Akkineni, V.; Küçük, A.; Filali Boubrahimi, S.; Hamdi, S. M.

    2016-12-01

    In this talk we give an overview of research projects related to solar data analysis that are conducted at Georgia State University. We will provide update on multiple advances made by our research team on the analysis of image parameters, spatio-temporal patterns mining, temporal data analysis and our experiences with big, heterogeneous solar data visualization, analysis, processing and storage. We will talk about up-to-date data mining methodologies, and their importance for big data-driven solar physics research.

  16. Differential impact of statin on new-onset diabetes in different age groups: a population-based case-control study in women from an asian country.

    Directory of Open Access Journals (Sweden)

    Chih-Wei Chen

    Full Text Available BACKGROUND: Statins reduce cardiovascular risks but increase the risk of new-onset diabetes (NOD. The aim of this study is to determine what effect, if any, statins have on the risk of NOD events in a population-based case-control study. An evaluation of the relationship between age and statin-exposure on NOD risks was further examined in a female Asian population. METHOD: In a nationwide case-controlled study, the authors assessed 1065 female NOD patients and 10650 controls with matching ages, genders and physician visit dates. The impact of statin-exposure on NOD was examined through multiple logistic regression models. Subgroup analysis for exploring the risk of NOD and statin-exposure in different age groups was performed. RESULTS: Statin-exposure was statistically significantly associated with increased new-onset diabetes risks using multivariate analysis. Interaction effect between age and statin-exposure on NOD risk was noted. For atorvastatin, the risk of cDDDs>60 was highest among the 55-64 year-olds (adjusted odds ratio [OR], 8.0; 95% confidence interval [CI], 2.57-24.90. For rosuvastatin, the risk of cDDDs>60 was highest among the 40-54 year-olds (adjusted OR, 14.8; 95% CI, 2.27-96.15. For simvastatin, the risk of cDDDs>60 was highest among the 55-64 year-olds (adjusted OR, 15.8; 95% CI, 5.77-43.26. For pravastatin, the risk of cDDDs>60 was highest among the 55-64 year-olds (adjusted OR, 14.0; 95% CI, 1.56-125.18. CONCLUSIONS: This population-based study found that statin use is associated with an increased risk of NOD in women. The risk of statin-related NOD was more evident for women aged 40-64 years compared to women aged 65 or more, and was cumulative-dose dependent. The use of statins should always be determined by weighing the clinical benefits and potential risks for NOD, and the patients should be continuously monitored for adverse effects.

  17. Statin use and vitreoretinal surgery: Findings from a Finnish population-based cohort study.

    Science.gov (United States)

    Loukovaara, Sirpa; Sahanne, Sari; Takala, Annika; Haukka, Jari

    2018-01-16

    Vitreoretinal (VR) surgery is the third most common intraocular surgery after refractive and cataract surgery. The impact of statin therapy on VR surgery outcomes remains unclear, despite a potentially beneficial effect. We explored the association of preoperative statin therapy and the need for revitrectomy after primary vitrectomy. Our historical, population-based, register-based, VR surgery cohort consisted of 5709 patients operated in a tertiary, academic referral hospital in Finland, during 2008-2014, covering 6.5 years. Subgroup analysis was performed as follows: eyes operated due to (i) rhegmatogenous retinal detachment (RRD), (ii) VR interface diseases (macular pucker/hole), (iii) diabetic maculopathy or proliferative retinopathy, (iv) vitreous haemorrhage, (v) lens subluxation, (vi) vitreous opacities or (vii) other VR indication. The primary end-point event was revitrectomy during a postoperative follow-up period of 1 year due to retinal redetachment, vitreous rehaemorrhage, postoperative endophthalmitis, recurrent pucker or unclosed macular hole. Rhegmatogenous retinal detachment (RRD) was the second most frequent indication of VR surgery, including 1916 patients, with 305 re-operations with rate 0.20 (95% CI 0.18-0.23) per person-year. Statin treatment in time of operation was associated with lower risk of re-operation according to relative scale (incidence rate ratio 0.72, 95% CI 0.53-0.97), but not in absolute scale (incidence rate difference -0.58, 95% CI -4.30 to 3.15 for 100 person-years). No association with statin therapy and vitrectomy outcome was observed in the other VR subgroups. Use of statin treatment was associated with a 28% lower risk of revitrectomy in patients operated due to RRD. Further randomized clinical trials are highly warranted. © 2018 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

  18. Educational data mining applications and trends

    CERN Document Server

    2014-01-01

    This book is devoted to the Educational Data Mining arena. It highlights works that show relevant proposals, developments, and achievements that shape trends and inspire future research.  After a rigorous revision process sixteen manuscripts were accepted and organized into four parts as follows: ·     Profile: The first part embraces three chapters oriented to: 1) describe the nature of educational data mining (EDM); 2) describe how to pre-process raw data to facilitate data mining (DM); 3) explain how EDM supports government policies to enhance education. ·     Student modeling: The second part contains five chapters concerned with: 4) explore the factors having an impact on the students academic success; 5) detect student's personality and behaviors in an educational game; 6) predict students performance to adjust content and strategies; 7) identify students who will most benefit from tutor support; 8) hypothesize the student answer correctness based on eye metrics and mouse click. ·     As...

  19. A Data Mining Approach for Cardiovascular Diagnosis

    Directory of Open Access Journals (Sweden)

    Pereira Joana

    2017-12-01

    Full Text Available The large amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analysed by traditional methods. Data mining can improve decision-making by discovering patterns and trends in large amounts of complex data. In the healthcare industry specifically, data mining can be used to decrease costs by increasing efficiency, improve patient quality of life, and perhaps most importantly, save the lives of more patients. The main goal of this project is to apply data mining techniques in order to make possible the prediction of the degree of disability that patients will present when they leave hospitalization. The clinical data that will compose the data set was obtained from one single hospital and contains information about patients who were hospitalized in Cardio Vascular Disease’s (CVD unit in 2016 for having suffered a cardiovascular accident. To develop this project, it will be used the Waikato Environment for Knowledge Analysis (WEKA machine learning Workbench since this one allows users to quickly try out and compare different machine learning methods on new data sets

  20. Time Dependent Data Mining in RAVEN

    Energy Technology Data Exchange (ETDEWEB)

    Cogliati, Joshua Joseph [Idaho National Lab. (INL), Idaho Falls, ID (United States); Chen, Jun [Idaho National Lab. (INL), Idaho Falls, ID (United States); Patel, Japan Ketan [Idaho National Lab. (INL), Idaho Falls, ID (United States); Mandelli, Diego [Idaho National Lab. (INL), Idaho Falls, ID (United States); Maljovec, Daniel Patrick [Idaho National Lab. (INL), Idaho Falls, ID (United States); Alfonsi, Andrea [Idaho National Lab. (INL), Idaho Falls, ID (United States); Talbot, Paul William [Idaho National Lab. (INL), Idaho Falls, ID (United States); Rabiti, Cristian [Idaho National Lab. (INL), Idaho Falls, ID (United States)

    2016-09-01

    RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The goal of this type of analyses is to understand the response of such systems in particular with respect their probabilistic behavior, to understand their predictability and drivers or lack of thereof. Data mining capabilities are the cornerstones to perform such deep learning of system responses. For this reason static data mining capabilities were added last fiscal year (FY 15). In real applications, when dealing with complex multi-scale, multi-physics systems it seems natural that, during transients, the relevance of the different scales, and physics, would evolve over time. For these reasons the data mining capabilities have been extended allowing their application over time. In this writing it is reported a description of the new RAVEN capabilities implemented with several simple analytical tests to explain their application and highlight the proper implementation. The report concludes with the application of those newly implemented capabilities to the analysis of a simulation performed with the Bison code.

  1. Statins Decrease Oxidative Stress and ICD Therapies

    Directory of Open Access Journals (Sweden)

    Heather L. Bloom

    2010-01-01

    Full Text Available Recent studies demonstrate that statins decrease ventricular arrhythmias in internal cardioverter defibrillator (ICD patients. The mechanism is unknown, but evidence links increased inflammatory and oxidative states with increased arrhythmias. We hypothesized that statin use decreases oxidation. Methods. 304 subjects with ICDs were surveyed for ventricular arrhythmia. Blood was analyzed for derivatives of reactive oxygen species (DROMs and interleukin-6 (IL-6. Results. Subjects included 252 (83% men, 58% on statins, 20% had ventricular arrhythmias. Average age was 63 years and ejection fraction (EF 20%. ICD implant duration was 29 ± 27 months. Use of statins correlated with lower ICD events (r=0.12, P=.02. Subjects on statins had lower hsCRP (5.2 versus 6.3; P=.05 and DROM levels (373 versus 397; P=.03. Other factors, including IL-6 and EF did not differ between statin and nonstatin use, nor did beta-blocker or antiarrhythmic use. Multivariate cross-correlation analysis demonstrated that DROMs, statins, IL-6 and EF were strongly associated with ICD events. Multivariate regression shows DROMs to be the dominant predictor. Conclusion. ICD event rate correlates with DROMs, a measure of lipid peroxides. Use of statins is associated with reduced DROMs and fewer ICD events, suggesting that statins exert their effect through reducing oxidation.

  2. Data Mining and Optimization Tools for Developing Engine Parameters Tools

    Science.gov (United States)

    Dhawan, Atam P.

    1998-01-01

    This project was awarded for understanding the problem and developing a plan for Data Mining tools for use in designing and implementing an Engine Condition Monitoring System. Tricia Erhardt and I studied the problem domain for developing an Engine Condition Monitoring system using the sparse and non-standardized datasets to be available through a consortium at NASA Lewis Research Center. We visited NASA three times to discuss additional issues related to dataset which was not made available to us. We discussed and developed a general framework of data mining and optimization tools to extract useful information from sparse and non-standard datasets. These discussions lead to the training of Tricia Erhardt to develop Genetic Algorithm based search programs which were written in C++ and used to demonstrate the capability of GA algorithm in searching an optimal solution in noisy, datasets. From the study and discussion with NASA LeRC personnel, we then prepared a proposal, which is being submitted to NASA for future work for the development of data mining algorithms for engine conditional monitoring. The proposed set of algorithm uses wavelet processing for creating multi-resolution pyramid of tile data for GA based multi-resolution optimal search.

  3. Data mining application in customer relationship management for hospital inpatients.

    Science.gov (United States)

    Lee, Eun Whan

    2012-09-01

    This study aims to discover patients loyal to a hospital and model their medical service usage patterns. Consequently, this study proposes a data mining application in customer relationship management (CRM) for hospital inpatients. A recency, frequency, monetary (RFM) model has been applied toward 14,072 patients discharged from a university hospital. Cluster analysis was conducted to segment customers, and it modeled the patterns of the loyal customers' medical services usage via a decision tree. Patients were divided into two groups according to the variables of the RFM model and the group which had significantly high frequency of medical use and expenses was defined as loyal customers, a target market. As a result of the decision tree, the predictable factors of the loyal clients were; length of stay, certainty of selectable treatment, surgery, number of accompanying treatments, kind of patient room, and department from which they were discharged. Particularly, this research showed that when a patient within the internal medicine department who did not have surgery stayed for more than 13.5 days, their probability of being a classified as a loyal customer was 70.0%. To discover a hospital's loyal patients and model their medical usage patterns, the application of data-mining has been suggested. This paper suggests practical use of combining segmentation, targeting, positioning (STP) strategy and the RFM model with data-mining in CRM.

  4. Data Mining Application in Customer Relationship Management for Hospital Inpatients

    Science.gov (United States)

    2012-01-01

    Objectives This study aims to discover patients loyal to a hospital and model their medical service usage patterns. Consequently, this study proposes a data mining application in customer relationship management (CRM) for hospital inpatients. Methods A recency, frequency, monetary (RFM) model has been applied toward 14,072 patients discharged from a university hospital. Cluster analysis was conducted to segment customers, and it modeled the patterns of the loyal customers' medical services usage via a decision tree. Results Patients were divided into two groups according to the variables of the RFM model and the group which had significantly high frequency of medical use and expenses was defined as loyal customers, a target market. As a result of the decision tree, the predictable factors of the loyal clients were; length of stay, certainty of selectable treatment, surgery, number of accompanying treatments, kind of patient room, and department from which they were discharged. Particularly, this research showed that when a patient within the internal medicine department who did not have surgery stayed for more than 13.5 days, their probability of being a classified as a loyal customer was 70.0%. Conclusions To discover a hospital's loyal patients and model their medical usage patterns, the application of data-mining has been suggested. This paper suggests practical use of combining segmentation, targeting, positioning (STP) strategy and the RFM model with data-mining in CRM. PMID:23115740

  5. Data mining of routine laboratory tests can predict liver disease progression in Egyptian diabetic patients with hepatitis C virus (G4) infection: a cohort study of 71 806 patients.

    Science.gov (United States)

    Saad, Yasmin; Awad, Abobakr; Alakel, Wafaa; Doss, Wahid; Awad, Tahany; Mabrouk, Mahasen

    2018-02-01

    Hepatitis C virus (HCV) and diabetes mellitus (DM) are prevalent diseases worldwide, associated with significant morbidity, mortality, and mutual association. The aims of this study were as follows: (i) find the prevalence of DM among 71 806 Egyptian patients with chronic HCV infection and its effect on liver disease progression and (ii) using data mining of routine tests to predict hepatic fibrosis in diabetic patients with HCV infection. A retrospective multicentered study included laboratory and histopathological data of 71 806 patients with HCV infection collected by Egyptian National Committee for control of viral hepatitis. Using data mining analysis, we constructed decision tree algorithm to assess predictors of fibrosis progression in diabetic patients with HCV. Overall, 12 018 (16.8%) patients were diagnosed as having diabetes [6428: fasting blood glucose ≥126 mg/dl (9%) and 5590: fasting blood glucose ≥110-126 mg/dl (7.8%)]. DM was significantly associated with advanced age, high BMI and α-fetoprotein (AFP), and low platelets and serum albumin (P≤0.001). Advanced liver fibrosis (F3-F4) was significantly correlated with DM (P≤0.001) irrespective of age. Of 16 attributes, decision tree model for fibrosis showed AFP was most decisive with cutoff of 5.25 ng/ml as starting point of fibrosis. AFP level greater than cutoff in patients was the first important splitting attribute; age and platelet count were second important splitting attributes. (i) Chronic HCV is significantly associated with DM (16.8%). (ii) Advanced age, high BMI and AFP, low platelets count and albumin show significant association with DM in HCV. (iii) AFP cutoff of 5.25 is a starting point of fibrosis development and integrated into mathematical model to predict development of liver fibrosis in diabetics with HCV (G4) infection.

  6. Review Article Therapeutic Potential of Statins in Age-related ...

    African Journals Online (AJOL)

    2011-08-09

    Aug 9, 2011 ... Keywords: Age-related macular, Non-invasive treatment, Pleiotropic effects, Prevention, Statins. Received 14 June ... two types: non-exudative or “dry', characterised by .... Dam Eye Study in Wisconsin, statin use at the 10-.

  7. An Overview on Data Mining of Nighttime Light Remote Sensing

    Directory of Open Access Journals (Sweden)

    LI Deren

    2015-06-01

    Full Text Available When observing the Earth from above at night, it is clear that the human settlement and major economic regions emit glorious light. At cloud-free nights, some remote sensing satellites can record visible radiance source, including city light, fishing boat light and fire, and these nighttime cloud-free images are remotely sensed nighttime light images. Different from daytime remote sensing, nighttime light remote sensing provides a unique perspective on human social activities, thus it has been widely used for spatial data mining of socioeconomic domains. Historically, researches on nighttime light remote sensing mostly focus on urban land cover and urban expansion mapping using DMSP/OLS imagery, but the nighttime light images are not the unique remote sensing source to do these works. Through decades of development of nighttime light product, the nighttime light remote sensing application has been extended to numerous interesting and scientific study domains such as econometrics, poverty estimation, light pollution, fishery and armed conflict. Among the application cases, it is surprising to see the Gross Domestic Production (GDP data can be corrected using the nighttime light data, and it is interesting to see mechanism of several diseases can be revealed by nighttime light images, while nighttime light are the unique remote sensing source to do the above works. As the nighttime light remote sensing has numerous applications, it is important to summarize the application of nighttime light remote sensing and its data mining fields. This paper introduced major satellite platform and sensors for observing nighttime light at first. Consequently, the paper summarized the progress of nighttime light remote sensing data mining in socioeconomic parameter estimation, urbanization monitoring, important event evaluation, environmental and healthy effects, fishery dynamic mapping, epidemiological research and natural gas flaring monitoring. Finally, future

  8. Data mining application in industrial energy audit for lighting

    Energy Technology Data Exchange (ETDEWEB)

    Maricar, N.M.; Kim, G.C.; Jamal, N. [Kolej Univ., Melaka (Malaysia). Faculty of Electrical Engineering

    2005-07-01

    A data mining application for lighting energy audits at industrial sites was presented. Data collection was based on the parameters needed for the analysis part of the audit. Data collection included the activity for which the room was used; its dimension; light level readings in lux; the number of luminaries; the number of lamps per luminaries; lamp fixtures; and lamp wattage. The lumen method was used to calculate the recommended numbers of luminaries in the room. The number was then compared with the existing system's luminaries. The installed load efficacy ratio (ILER) was then used to determine proper retrofit action to maximize energy usage. The difference between the calculated lux and the standard lux was used to create data subsets. A data mining algorithm was used to determine that the ILER plays an important role in calculating the efficiency of lighting systems. It was also concluded that the method can be used to minimize the time needed to analyze large amounts of lighting data. The results of case studies were also used to show that the combined data mining algorithm provided accurate assessments using existing calculated data. 7 refs., 8 tabs., 5 figs.

  9. Genetically Guided Statin Therapy

    Science.gov (United States)

    2017-03-01

    number of new statin prescriptions, and (4) patient reported quality of life, physical activity, perceptions regarding statin therapy , and pain as...outcomes known to be prevented by statin therapy , we examined hospitalizations for three diagnoses: acute myocardial infarction (MI), stroke, and...cholesterol. However, the ultimate goal of statin therapy is to decrease incidence of CAD, acute myocardial infarction and perhaps stroke. However, there is a

  10. Impact of statins on risk of new onset diabetes mellitus: a population-based cohort study using the Korean National Health Insurance claims database

    Directory of Open Access Journals (Sweden)

    Lee J

    2016-10-01

    Full Text Available Jimin Lee,1 Yoojin Noh,1 Sooyoung Shin,1 Hong-Seok Lim,2 Rae Woong Park,3 Soo Kyung Bae,4 Euichaul Oh,4 Grace Juyun Kim,5 Ju Han Kim,5 Sukhyang Lee1 1Division of Clinical Pharmacy, College of Pharmacy, Ajou University, Suwon, South Korea; 2Department of Cardiology, School of Medicine, Ajou University, Suwon, South Korea; 3Department of Biomedical Informatics, School of Medicine, Ajou University, Suwon, South Korea; 4Division of Pharmaceutical Sciences, College of Pharmacy, The Catholic University of Korea, Bucheon, South Korea; 5Division of Biomedical Informatics, College of Medicine, Seoul National University, Seoul, South Korea Abstract: Statin therapy is beneficial in reducing cardiovascular events and mortalities in patients with atherosclerotic cardiovascular diseases. Yet, there have been concerns of increased risk of diabetes with statin use. This study was aimed to evaluate the association between statins and new onset diabetes mellitus (NODM in patients with ischemic heart disease (IHD utilizing the Korean Health Insurance Review and Assessment Service claims database. Among adult patients with preexisting IHD, new statin users and matched nonstatin users were identified on a 1:1 ratio using proportionate stratified random sampling by sex and age. They were subsequently propensity score matched further with age and comorbidities to reduce the selection bias. Overall incidence rates, cumulative rates and hazard ratios (HRs between statin use and occurrence of NODM were estimated. The subgroup analyses were performed according to sex, age groups, and the individual agents and intensities of statins. A total of 156,360 patients (94,370 in the statin users and 61,990 in the nonstatin users were included in the analysis. The incidence rates of NODM were 7.8% and 4.8% in the statin users and nonstatin users, respectively. The risk of NODM was higher among statin users (crude HR 2.01, 95% confidence interval [CI] 1.93–2.10; adjusted HR 1

  11. Knowledge Discovery and Data Mining in Iran's Climatic Researches

    Science.gov (United States)

    Karimi, Mostafa

    2013-04-01

    Advances in measurement technology and data collection is the database gets larger. Large databases require powerful tools for analysis data. Iterative process of acquiring knowledge from information obtained from data processing is done in various forms in all scientific fields. However, when the data volume large, and many of the problems the Traditional methods cannot respond. in the recent years, use of databases in various scientific fields, especially atmospheric databases in climatology expanded. in addition, increases in the amount of data generated by the climate models is a challenge for analysis of it for extraction of hidden pattern and knowledge. The approach to this problem has been made in recent years uses the process of knowledge discovery and data mining techniques with the use of the concepts of machine learning, artificial intelligence and expert (professional) systems is overall performance. Data manning is analytically process for manning in massive volume data. The ultimate goal of data mining is access to information and finally knowledge. climatology is a part of science that uses variety and massive volume data. Goal of the climate data manning is Achieve to information from variety and massive atmospheric and non-atmospheric data. in fact, Knowledge Discovery performs these activities in a logical and predetermined and almost automatic process. The goal of this research is study of uses knowledge Discovery and data mining technique in Iranian climate research. For Achieve This goal, study content (descriptive) analysis and classify base method and issue. The result shown that in climatic research of Iran most clustering, k-means and wards applied and in terms of issues precipitation and atmospheric circulation patterns most introduced. Although several studies in geography and climate issues with statistical techniques such as clustering and pattern extraction is done, Due to the nature of statistics and data mining, but cannot say for

  12. Uncertainty modeling for data mining a label semantics approach

    CERN Document Server

    Qin, Zengchang

    2014-01-01

    Outlining a new research direction in fuzzy set theory applied to data mining, this volume proposes a number of new data mining algorithms and includes dozens of figures and illustrations that help the reader grasp the complexities of the concepts.

  13. Data Mining Based on Cloud-Computing Technology

    Directory of Open Access Journals (Sweden)

    Ren Ying

    2016-01-01

    Full Text Available There are performance bottlenecks and scalability problems when traditional data-mining system is used in cloud computing. In this paper, we present a data-mining platform based on cloud computing. Compared with a traditional data mining system, this platform is highly scalable, has massive data processing capacities, is service-oriented, and has low hardware cost. This platform can support the design and applications of a wide range of distributed data-mining systems.

  14. Comparsion analysis of data mining models applied to clinical research in traditional Chinese medicine.

    Science.gov (United States)

    Zhao, Yufeng; Xie, Qi; He, Liyun; Liu, Baoyan; Li, Kun; Zhang, Xiang; Bai, Wenjing; Luo, Lin; Jing, Xianghong; Huo, Ruili

    2014-10-01

    To help researchers selecting appropriate data mining models to provide better evidence for the clinical practice of Traditional Chinese Medicine (TCM) diagnosis and therapy. Clinical issues based on data mining models were comprehensively summarized from four significant elements of the clinical studies: symptoms, symptom patterns, herbs, and efficacy. Existing problems were further generalized to determine the relevant factors of the performance of data mining models, e.g. data type, samples, parameters, variable labels. Combining these relevant factors, the TCM clinical data features were compared with regards to statistical characters and informatics properties. Data models were compared simultaneously from the view of applied conditions and suitable scopes. The main application problems were the inconsistent data type and the small samples for the used data mining models, which caused the inappropriate results, even the mistake results. These features, i.e. advantages, disadvantages, satisfied data types, tasks of data mining, and the TCM issues, were summarized and compared. By aiming at the special features of different data mining models, the clinical doctors could select the suitable data mining models to resolve the TCM problem.

  15. A Survey of Educational Data-Mining Research

    Science.gov (United States)

    Huebner, Richard A.

    2013-01-01

    Educational data mining (EDM) is an emerging discipline that focuses on applying data mining tools and techniques to educationally related data. The discipline focuses on analyzing educational data to develop models for improving learning experiences and improving institutional effectiveness. A literature review on educational data mining topics…

  16. Using Data Mining to Teach Applied Statistics and Correlation

    Science.gov (United States)

    Hartnett, Jessica L.

    2016-01-01

    This article describes two class activities that introduce the concept of data mining and very basic data mining analyses. Assessment data suggest that students learned some of the conceptual basics of data mining, understood some of the ethical concerns related to the practice, and were able to perform correlations via the Statistical Package for…

  17. 76 FR 14637 - State Medicaid Fraud Control Units; Data Mining

    Science.gov (United States)

    2011-03-17

    ...] State Medicaid Fraud Control Units; Data Mining AGENCY: Office of Inspector General (OIG), HHS. ACTION... and analyzing State Medicaid claims data, known as data mining. To support and modernize MFCU efforts... (FFP) in the costs of defined data mining activities under specified conditions. In addition, we...

  18. Data Mining and Knowledge Management in Higher Education -Potential Applications.

    Science.gov (United States)

    Luan, Jing

    This paper introduces a new decision support tool, data mining, in the context of knowledge management. The most striking features of data mining techniques are clustering and prediction. The clustering aspect of data mining offers comprehensive characteristics analysis of students, while the predicting function estimates the likelihood for a…

  19. Study protocol for statin web-based investigation of side effects (StatinWISE) : a series of randomised controlled N-of-1 trials comparing atorvastatin and placebo in UK primary care

    NARCIS (Netherlands)

    Herrett, Emily; Williamson, Elizabeth; Beaumont, Danielle; Prowse, Danielle; Youssouf, Nabila; Brack, Kieran; Armitage, Jane; Goldacre, Ben; MacDonald, Thomas; Staa, Tjeerd P van; Roberts, Ian; Shakur-Still, Haleema; Smeeth, Liam

    2017-01-01

    INTRODUCTION: Statins are effective at preventing cardiovascular disease, widely prescribed and their use is growing. Uncertainty persists about whether they cause symptomatic muscle adverse effects, such as pain and weakness, in the absence of statin myopathy. Discrepancies between data from

  20. [Statins in the secondary prevention of stroke: New evidence from the SPARCL Study].

    Science.gov (United States)

    Castilla-Guerra, Luis; Fernández-Moreno, María Del Carmen; López-Chozas, José Manuel

    2016-01-01

    Until recently there was little evidence that statin therapy reduced the risk of stroke recurrence. The SPARCL trial, published in 2006, was the first trial to show the benefits of statin therapy in preventing recurrent stroke. The SPARCL trial showed that treatment with atorvastatin 80mg/day reduced recurrent stroke in patients with a recent stroke or transient ischemic attack (TIA). Several post hoc analyses of different subgroups followed the SPARCL trial. They have not revealed any significant differences when patients were grouped by age, sex or type of stroke. The SPARCL trial has also helped to identify patients who may have a greater benefit from statins: Patients with carotid stenosis, with more intense lipid lowering, and those who achieve optimal levels of LDL-C, HDL-C, triglycerides, and blood pressure. The trial has also helped to identify individuals at high risk of new vascular events. Clearly there is a before and after in stroke prevention since the SPARCL trial was published. Copyright © 2015 Sociedad Española de Arteriosclerosis. Publicado por Elsevier España, S.L.U. All rights reserved.

  1. Data mining, mining data : energy consumption modelling

    Energy Technology Data Exchange (ETDEWEB)

    Dessureault, S. [Arizona Univ., Tucson, AZ (United States)

    2007-09-15

    Most modern mining operations are accumulating large amounts of data on production and business processes. Data, however, provides value only if it can be translated into information that appropriate users can utilize. This paper emphasized that a new technological focus should emerge, notably how to concentrate data into information; analyze information sufficiently to become knowledge; and, act on that knowledge. Researchers at the Mining Information Systems and Operations Management (MISOM) laboratory at the University of Arizona have created a method to transform data into action. The data-to-action approach was exercised in the development of an energy consumption model (ECM), in partnership with a major US-based copper mining company, 2 software companies, and the MISOM laboratory. The approach begins by integrating several key data sources using data warehousing techniques, and increasing the existing level of integration and data cleaning. An online analytical processing (OLAP) cube was also created to investigate the data and identify a subset of several million records. Data mining algorithms were applied using the information that was isolated by the OLAP cube. The data mining results showed that traditional cost drivers of energy consumption are poor predictors. A comparison was made between traditional methods of predicting energy consumption and the prediction formed using data mining. Traditionally, in the mines for which data were available, monthly averages of tons and distance are used to predict diesel fuel consumption. However, this article showed that new information technology can be used to incorporate many more variables into the budgeting process, resulting in more accurate predictions. The ECM helped mine planners improve the prediction of energy use through more data integration, measure development, and workflow analysis. 5 refs., 11 figs.

  2. Proactive data mining with decision trees

    CERN Document Server

    Dahan, Haim; Rokach, Lior; Maimon, Oded

    2014-01-01

    This book explores a proactive and domain-driven method to classification tasks. This novel proactive approach to data mining not only induces a model for predicting or explaining a phenomenon, but also utilizes specific problem/domain knowledge to suggest specific actions to achieve optimal changes in the value of the target attribute. In particular, the authors suggest a specific implementation of the domain-driven proactive approach for classification trees. The book centers on the core idea of moving observations from one branch of the tree to another. It introduces a novel splitting crite

  3. Patent data mining method and apparatus

    Science.gov (United States)

    Boyack, Kevin W.; Grafe, V. Gerald; Johnson, David K.; Wylie, Brian N.

    2002-01-01

    A method of data mining represents related patents in a multidimensional space. Distance between patents in the multidimensional space corresponds to the extent of relationship between the patents. The relationship between pairings of patents can be expressed based on weighted combinations of several predicates. The user can select portions of the space to perceive. The user also can interact with and control the communication of the space, focusing attention on aspects of the space of most interest. The multidimensional spatial representation allows more ready comprehension of the structure of the relationships among the patents.

  4. Temporal data mining for hospital management

    Science.gov (United States)

    Tsumoto, Shusaku; Hirano, Shoji

    2009-04-01

    It has passed about twenty years since clinical information are stored electronically as a hospital information system since 1980's. Stored data include from accounting information to laboratory data and even patient records are now started to be accumulated: in other words, a hospital cannot function without the information system, where almost all the pieces of medical information are stored as multimedia databases. In this paper, we applied temporal data mining and exploratory data analysis techniques to hospital management data. The results show several interesting results, which suggests that the reuse of stored data will give a powerful tool for hospial management.

  5. Analyzing Log Files using Data-Mining

    Directory of Open Access Journals (Sweden)

    Marius Mihut

    2008-01-01

    Full Text Available Information systems (i.e. servers, applications and communication devices create a large amount of monitoring data that are saved as log files. For analyzing them, a data-mining approach is helpful. This article presents the steps which are necessary for creating an ‘analyzing instrument’, based on an open source software called Waikato Environment for Knowledge Analysis (Weka [1]. For exemplification, a system log file created by a Windows-based operating system, is used as input file.

  6. Data Mining Methods for Recommender Systems

    Science.gov (United States)

    Amatriain, Xavier; Jaimes*, Alejandro; Oliver, Nuria; Pujol, Josep M.

    In this chapter, we give an overview of the main Data Mining techniques used in the context of Recommender Systems. We first describe common preprocessing methods such as sampling or dimensionality reduction. Next, we review the most important classification techniques, including Bayesian Networks and Support Vector Machines. We describe the k-means clustering algorithm and discuss several alternatives. We also present association rules and related algorithms for an efficient training process. In addition to introducing these techniques, we survey their uses in Recommender Systems and present cases where they have been successfully applied.

  7. Multimedia data mining and analytics disruptive innovation

    CERN Document Server

    Baughman, Aaron; Pan, Jia-Yu; Petrushin, Valery A

    2015-01-01

    This authoritative text/reference provides fresh insights into the cutting edge of multimedia data mining, reflecting how the research focus has shifted towards networked social communities, mobile devices and sensors. Presenting a detailed exploration into the progression of the field, the book describes how the history of multimedia data processing can be viewed as a sequence of disruptive innovations. Across the chapters, the discussion covers the practical frameworks, libraries, and open source software that enable the development of ground-breaking research into practical applications.

  8. Geographical variation in morphometry, craniometry, and diet of amammalian species (Stone marten, Martes foina) using data mining

    OpenAIRE

    PAPAKOSTA, MALAMATI; KITIKIDOU, KYRIAKI; BAKALOUDIS, DIMITRIOS; VLACHOS, CHRISTOS; CHATZINIKOS, EVANGELOS; ALEXANDROU, OLGA; SAKOULIS, ANASTASIOS

    2018-01-01

    Ecologists use various data mining techniques to make predictions and estimations, to identify patterns in datasets and relationships between qualitative and quantitative variables, or to classify variables. The aim of this study was to investigate if the application of data mining could be used to study geographical variation in the morphometry, craniometry, and diet of a mammalian species (Martes foina), and to determine whether data mining can complement genetic analysis to recognize subsp...

  9. Statins Reduces the Risk of Dementia in Patients with Late-Onset Depression: A Retrospective Cohort Study.

    Science.gov (United States)

    Yang, Ya-Hsu; Teng, Hao-Wei; Lai, Yen-Ting; Li, Szu-Yuan; Lin, Chih-Ching; Yang, Albert C; Chan, Hsiang-Lin; Hsieh, Yi-Hsuan; Lin, Chiao-Fan; Hsu, Fu-Ying; Liu, Chih-Kuang; Liu, Wen-Sheng

    2015-01-01

    Patients with late-onset depression (LOD) have been reported to run a higher risk of subsequent dementia. The present study was conducted to assess whether statins can reduce the risk of dementia in these patients. We used the data from National Health Insurance of Taiwan during 1996-2009. Standardized Incidence Ratios (SIRs) were calculated for LOD and subsequent dementia. The criteria for LOD diagnoses included age ≥65 years, diagnosis of depression after 65 years of age, at least three service claims, and treatment with antidepressants. The time-dependent Cox proportional hazards model was applied for multivariate analyses. Propensity scores with the one-to-one nearest-neighbor matching model were used to select matching patients for validation studies. Kaplan-Meier curve estimate was used to measure the group of patients with dementia living after diagnosis of LOD. Totally 45,973 patients aged ≥65 years were enrolled. The prevalence of LOD was 12.9% (5,952/45,973). Patients with LOD showed to have a higher incidence of subsequent dementia compared with those without LOD (Odds Ratio: 2.785; 95% CI 2.619-2.958). Among patients with LOD, lipid lowering agent (LLA) users (for at least 3 months) had lower incidence of subsequent dementia than non-users (Hazard Ratio = 0.781, 95% CI 0.685-0.891). Nevertheless, only statins users showed to have reduced risk of dementia (Hazard Ratio = 0.674, 95% CI 0.547-0.832) while other LLAs did not, which was further validated by Kaplan-Meier estimates after we used the propensity scores with the one-to-one nearest-neighbor matching model to control the confounding factors. Statins may reduce the risk of subsequent dementia in patients with LOD.

  10. Statin treatment and risk of recurrent venous thromboembolism

    DEFF Research Database (Denmark)

    Nguyen, Cu Dinh; Andersson, Charlotte; Jensen, Thomas Bo

    2013-01-01

    Objectives Statins may decrease the risk of primary venous thromboembolism (VTE), that is, deep vein thrombosis (DVT) and pulmonary embolism (PE) but the effect of statins in preventing recurrent VTE is less clear. The aim of this study was therefore to investigate the association between statin ...

  11. Quantifying the contribution of statins to the decline in population mean cholesterol by socioeconomic group in England 1991 - 2012: a modelling study.

    Directory of Open Access Journals (Sweden)

    Chris Kypridemos

    Full Text Available Serum total cholesterol is one of the major targets for cardiovascular disease prevention. Statins are effective for cholesterol control in individual patients. At the population level, however, their contribution to total cholesterol decline remains unclear. The aim of this study was to quantify the contribution of statins to the observed fall in population mean cholesterol levels in England over the past two decades, and explore any differences between socioeconomic groups.This is a modelling study based on data from the Health Survey for England. We analysed changes in observed mean total cholesterol levels in the adult England population between 1991-92 (baseline and 2011-12. We then compared the observed changes with a counterfactual 'no statins' scenario, where the impact of statins on population total cholesterol was estimated and removed. We estimated uncertainty intervals (UI using Monte Carlo simulation, where confidence intervals (CI were impractical. In 2011-12, 13.2% (95% CI: 12.5-14.0% of the English adult population used statins at least once per week, compared with 1991-92 when the proportion was just 0.5% (95% CI: 0.3-1.0%. Between 1991-92 and 2011-12, mean total cholesterol declined from 5.86 mmol/L (95% CI: 5.82-5.90 to 5.17 mmol/L (95% CI: 5.14-5.20. For 2011-12, mean total cholesterol was lower in more deprived groups. In our 'no statins' scenario we predicted a mean total cholesterol of 5.36 mmol/L (95% CI: 5.33-5.40 for 2011-12. Statins were responsible for approximately 33.7% (95% UI: 28.9-38.8% of the total cholesterol reduction since 1991-92. The statin contribution to cholesterol reduction was greater among the more deprived groups of women, while showing little socio-economic gradient among men.Our model suggests that statins explained around a third of the substantial falls in total cholesterol observed in England since 1991. Approximately two thirds of the cholesterol decrease can reasonably be attributed non

  12. Big data mining: In-database Oracle data mining over hadoop

    Science.gov (United States)

    Kovacheva, Zlatinka; Naydenova, Ina; Kaloyanova, Kalinka; Markov, Krasimir

    2017-07-01

    Big data challenges different aspects of storing, processing and managing data, as well as analyzing and using data for business purposes. Applying Data Mining methods over Big Data is another challenge because of huge data volumes, variety of information, and the dynamic of the sources. Different applications are made in this area, but their successful usage depends on understanding many specific parameters. In this paper we present several opportunities for using Data Mining techniques provided by the analytical engine of RDBMS Oracle over data stored in Hadoop Distributed File System (HDFS). Some experimental results are given and they are discussed.

  13. LIFESTAT – Living with statins

    DEFF Research Database (Denmark)

    Christensen, Christa Lykke; Helge, Jørn Wulff; Krasnik, Allan

    2016-01-01

    AIM: LIFESTAT is an interdisciplinary project that leverages approaches and knowledge from medicine, the humanities and the social sciences to analyze the impact of statin use on health, lifestyle and well-being in cohorts of Danish citizens. The impetus for the study is the fact that 10....... The study investigates the biological consequences of statin treatment; determines the mechanism(s) by which statin use causes muscle and mitochondrial dysfunction; and analyzes achievement of treatment goals, people's perception of disease risk, media influence on people's risk and health perception...... and unintended side effects (e.g. myalgia, and glucose and exercise intolerance). METHODS: The LIFESTAT project combines invasive human experiments, biomedical analyses, nationwide surveys, epidemiological studies, qualitative interviews, media content analyses, and ethnographic participant observations...

  14. Management Strategies for Statin-Associated Muscle Symptoms: How Useful Is Same-Statin Rechallenge?

    Science.gov (United States)

    Brennan, Emily T; Joy, Tisha R

    2017-05-01

    Statin-associated muscle symptoms (SAMS) are common. Rechallenge with the same statin (same-statin rechallenge) has recently been included as part of a proposed scoring index for diagnosing SAMS, but data regarding tolerability and efficacy of same-statin rechallenge, compared with other strategies, is minimal. In this study we evaluated the tolerability, percent change in low-density lipoprotein cholesterol (LDL-C), and proportion of patients achieving their LDL-C targets among 3 common management strategies-same-statin rechallenge, switching to a different statin (statin switch), and use of nonstatin medications only. We performed a retrospective analysis of 118 patients referred to our tertiary care centre for management of SAMS, defined as development of muscle-related symptoms with 2 or more statins. Baseline and last follow-up lipid parameters were documented. Patients were classified as tolerant of a strategy if, at their last follow-up, they remained on that strategy. After a median follow-up of 17 months, most (n = 79; 67%) patients were able to tolerate a statin. Tolerability was similar among the 3 treatment strategies (71% same-statin rechallenge vs 53% statin switch vs 57% for nonstatin therapy only; P = 0.11). Those in the same-statin rechallenge and statin switch groups achieved greater LDL-C reductions compared with those who only tolerated nonstatins (-38.8 ± 3.4% vs -36.4 ± 2.9% vs -17.3 ± 4.5%; P = 0.0007). A greater proportion of patients in the same-statin rechallenge group achieved their target LDL-C compared with those in the nonstatin therapy only group (50% vs 15%; odds ratio, 6.8; 95% confidence interval, 1.5-40.7; P = 0.04). Among individuals with a history of SAMS, most will tolerate statin therapy. Same-statin rechallenge was highly tolerable and efficacious. Thus, same-statin rechallenge might warrant increased utilization. Copyright © 2017 Canadian Cardiovascular Society. Published by Elsevier Inc. All rights reserved.

  15. Optimal sampling strategy for data mining

    International Nuclear Information System (INIS)

    Ghaffar, A.; Shahbaz, M.; Mahmood, W.

    2013-01-01

    Latest technology like Internet, corporate intranets, data warehouses, ERP's, satellites, digital sensors, embedded systems, mobiles networks all are generating such a massive amount of data that it is getting very difficult to analyze and understand all these data, even using data mining tools. Huge datasets are becoming a difficult challenge for classification algorithms. With increasing amounts of data, data mining algorithms are getting slower and analysis is getting less interactive. Sampling can be a solution. Using a fraction of computing resources, Sampling can often provide same level of accuracy. The process of sampling requires much care because there are many factors involved in the determination of correct sample size. The approach proposed in this paper tries to find a solution to this problem. Based on a statistical formula, after setting some parameters, it returns a sample size called s ufficient sample size , which is then selected through probability sampling. Results indicate the usefulness of this technique in coping with the problem of huge datasets. (author)

  16. Data mining: childhood injury control and beyond.

    Science.gov (United States)

    Tepas, Joseph J

    2009-08-01

    Data mining is defined as the automatic extraction of useful, often previously unknown information from large databases or data sets. It has become a major part of modern life and is extensively used in industry, banking, government, and health care delivery. The process requires a data collection system that integrates input from multiple sources containing critical elements that define outcomes of interest. Appropriately designed data mining processes identify and adjust for confounding variables. The statistical modeling used to manipulate accumulated data may involve any number of techniques. As predicted results are periodically analyzed against those observed, the model is consistently refined to optimize precision and accuracy. Whether applying integrated sources of clinical data to inferential probabilistic prediction of risk of ventilator-associated pneumonia or population surveillance for signs of bioterrorism, it is essential that modern health care providers have at least a rudimentary understanding of what the concept means, how it basically works, and what it means to current and future health care.

  17. Asymmetric threat data mining and knowledge discovery

    Science.gov (United States)

    Gilmore, John F.; Pagels, Michael A.; Palk, Justin

    2001-03-01

    Asymmetric threats differ from the conventional force-on- force military encounters that the Defense Department has historically been trained to engage. Terrorism by its nature is now an operational activity that is neither easily detected or countered as its very existence depends on small covert attacks exploiting the element of surprise. But terrorism does have defined forms, motivations, tactics and organizational structure. Exploiting a terrorism taxonomy provides the opportunity to discover and assess knowledge of terrorist operations. This paper describes the Asymmetric Threat Terrorist Assessment, Countering, and Knowledge (ATTACK) system. ATTACK has been developed to (a) data mine open source intelligence (OSINT) information from web-based newspaper sources, video news web casts, and actual terrorist web sites, (b) evaluate this information against a terrorism taxonomy, (c) exploit country/region specific social, economic, political, and religious knowledge, and (d) discover and predict potential terrorist activities and association links. Details of the asymmetric threat structure and the ATTACK system architecture are presented with results of an actual terrorist data mining and knowledge discovery test case shown.

  18. Pleiotropic effects of statins in stroke prevention

    Directory of Open Access Journals (Sweden)

    Yenny Yenny

    2016-02-01

    Full Text Available Cardiovascular disease is the leading cause of death and disability, and  contributes substantially to healthcare budgets. The lipid-lowering drugs, 3-hydroxy-3-methylgulutaryl-coenzyme A (HMG-CoA reductase inhibitor or statins, reducing mortality and cardiovascular morbidity in patients with established cardiovascular disease. Statins therefore have a place in the secondary prevention of cardiovascular disease. Recent experimental and clinical studies suggest that statins may exert vascular protective effect beyond cholesterol reduction. The cholesterol-independet or “pleiotropic” effects of statin include the upregulation and activation of endothelial nitric acid synthase (eNOS that can increase nitric oxide (NO production. Augmentation of NO production increases cerebral blood flow, which can lead to neuroprotection during brain ischaemia. By inhibiting mevalonate synthesis, statins prevent the formation of several isoprenoids (including farnesylpyrophosphate and geranylgeranylpyrophosphate. Inhibiting geranylgeranylation of RhoA small G proteins increases the stability of eNOS mRNA through the remodeling of endothelial actin microfilamens. Moreover, statins directly increase eNOS activity within minutes by activating the pathway involving phosphoinositide 3-kinase and protein kinase B. In the secondary prevention of stroke, the use of statins reduces the incidence of either recurrent stroke or other major vascular events and treatment should be initiated soon after the event. The use of statins does not increase hemorrhagic stroke or cancer and may also favor atherosclerotic plaque regression.

  19. Pleiotropic effects of statins in stroke prevention

    Directory of Open Access Journals (Sweden)

    Yenny

    2009-08-01

    Full Text Available Cardiovascular disease is the leading cause of death and disability, and contributes substantially to healthcare budgets. The lipid-lowering drugs, 3-hydroxy-3-methylgulutaryl-coenzyme A (HMG-CoA reductase inhibitor or statins, reducing mortality and cardiovascular morbidity in patients with established cardiovascular disease. Statins therefore have a place in the secondary prevention of cardiovascular disease. Recent experimental and clinical studies suggest that statins may exert vascular protective effect beyond cholesterol reduction. The cholesterol-independet or “pleiotropic” effects of statin include the upregulation and activation of endothelial nitric acid synthase (eNOS that can increase nitric oxide (NO production. Augmentation of NO production increases cerebral blood flow, which can lead to neuroprotection during brain ischaemia. By inhibiting mevalonate synthesis, statins prevent the formation of several isoprenoids (including farnesylpyrophosphate and geranylgeranylpyrophosphate. Inhibiting geranylgeranylation of RhoA small G proteins increases the stability of eNOS mRNA through the remodeling of endothelial actin microfilamens. Moreover, statins directly increase eNOS activity within minutes by activating the pathway involving phosphoinositide 3-kinase and protein kinase B. In the secondary prevention of stroke, the use of statins reduces the incidence of either recurrent stroke or other major vascular events and treatment should be initiated soon after the event. The use of statins does not increase hemorrhagic stroke or cancer and may also favor atherosclerotic plaque regression.

  20. Maternal vaccination and preterm birth: using data mining as a screening tool

    DEFF Research Database (Denmark)

    Orozova-Bekkevold, Ivanka; Jensen, Henrik; Stensballe, Lone

    2007-01-01

    Objective The main purpose of this study was to identify possible associations between medicines used in pregnancy and preterm deliveries using data mining as a screening tool. Settings Prospective cohort study. Methods We used data mining to identify possible correlates between preterm delivery...... measure Preterm birth, a delivery occurring before the 259th day of gestation (i.e., less than 37 full weeks). Results Data mining had indicated that maternal vaccination (among other factors) might be related to preterm birth. The following regression analysis showed that, the women who reported being...... further studies. Data mining, especially with additional refinements, may be a valuable and very efficient tool to screen large databases for relevant information which can be used in clinical and public health research....

  1. Anti-HMGCR antibodies as a biomarker for immune-mediated necrotizing myopathies: A history of statins and experience from a large international multi-center study.

    Science.gov (United States)

    Musset, Lucile; Allenbach, Yves; Benveniste, Olivier; Boyer, Olivier; Bossuyt, Xavier; Bentow, Chelsea; Phillips, Joe; Mammen, Andrew; Van Damme, Philip; Westhovens, René; Ghirardello, Anna; Doria, Andrea; Choi, May Y; Fritzler, Marvin J; Schmeling, Heinrike; Muro, Yoshinao; García-De La Torre, Ignacio; Ortiz-Villalvazo, Miguel A; Bizzaro, Nicola; Infantino, Maria; Imbastaro, Tiziana; Peng, Qinglin; Wang, Guochun; Vencovský, Jiří; Klein, Martin; Krystufkova, Olga; Franceschini, Franco; Fredi, Micaela; Hue, Sophie; Belmondo, Thibaut; Danko, Katalin; Mahler, Michael

    2016-10-01

    In an effort to find naturally occurring substances that reduce cholesterol by inhibiting 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR), statins were first discovered by Endo in 1972. With the widespread prescription and use of statins to decrease morbidity from myocardial infarction and stroke, it was noted that approximately 5% of all statin users experienced muscle pain and weakness during treatment. In a smaller proportion of patients, the myopathy progressed to severe morbidity marked by proximal weakness and severe muscle wasting. Remarkably, Mammen and colleagues were the first to discover that the molecular target of statins, 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR), is an autoantibody target in patients that develop an immune-mediated necrotizing myopathy (IMNM). These observations have been confirmed in a number of studies but, until today, a multi-center, international study of IMNM, related idiopathic inflammatory myopathies (IIM), other auto-inflammatory conditions and controls has not been published. Accordingly, an international, multi-center study investigated the utility of anti-HMGCR antibodies in the diagnosis of statin-associated IMNM in comparison to different forms of IIM and controls. This study included samples from patients with different forms of IIM (n=1250) and patients with other diseases (n=656) that were collected from twelve sites and tested for anti-HMGCR antibodies by ELISA. This study confirmed that anti-HMGCR autoantibodies, when found in conjunction with statin use, characterize a subset of IIM who are older and have necrosis on muscle biopsy. Taken together, the data to date indicates that testing for anti-HMGCR antibodies is important in the differential diagnosis of IIM and might be considered for future classification criteria. Copyright © 2016. Published by Elsevier B.V.

  2. A Framework for Investigating Influence of Organizational Decision Makers on Data Mining Process Achievement

    OpenAIRE

    Hanieh Hajisafari; Shaaban Elahi

    2012-01-01

    Currently, few studies deal with evaluation of data mining plans in context of solvng organizational problems. A successful data miner is searching to solve a fully defined business problem. To make the data mining (DM) results actionable, the data miner must explain them to the business insider. The interaction process between the business insiders and data miners is actually a knowledge-sharing process. In this study through representing a framwork, influence of organizational decision mak...

  3. Increasing incidence of statin treatmentamong the elderly and those withoutprevious cardiovascular conditions. A nationwide register study

    DEFF Research Database (Denmark)

    Kildemoes, Helle Wallach; Andersen, Morten

      Background: Supported by the growing evidence of the beneficial effects of statins in a range of conditions, statin utilization has increased considerably in most Western countries over the last decade. Objectives: To estimate to what extent a widening of indication scope for statins accounts...... with discharge diagnoses and surgical procedures performed during 1977-2005. The disease status for all cohort members was assigned by means of disease markers for seven cardiovascular conditions, corresponding to a hierarchy of broad indications for statin therapy. Using the indication hierarchy, we computed...

  4. Adherence to drug label recommendations for avoiding drug interactions causing statin-induced myopathy--a nationwide register study.

    Directory of Open Access Journals (Sweden)

    Jennifer Settergren

    Full Text Available To investigate the extent to which clinicians avoid well-established drug-drug interactions that cause statin-induced myopathy. We hypothesised that clinicians would avoid combining erythromycin or verapamil/diltiazem respectively with atorvastatin or simvastatin. In patients with statin-fibrate combination therapy, we hypothesised that gemfibrozil was avoided to the preference of bezafibrate or fenofibrate. When combined with verapamil/diltiazem or fibrates, we hypothesized that the dispensed doses of atorvastatin/simvastatin would be decreased.Cross-sectional analysis of nationwide dispensing data. Odds ratios of interacting erythromycin, verapamil/diltiazem versus respective prevalence of comparator drugs doxycycline, amlodipine/felodipine in patients co-dispensed interacting statins simvastatin/atorvastatin versus patients unexposed (pravastatin/fluvastatin/rosuvastatin was calculated. For fibrates, OR of gemfibrozil versus fenofibrate/bezafibrate in patients co-dispensed any statin was assessed.OR of interacting erythromycin versus comparator doxycycline did not differ between patients on interacting and comparator statins either in patients dispensed high or low statin doses (adjusted OR 0.87; 95% CI 0.60-1.25 and 0.92; 95% CI 0.69-1.23. Interacting statins were less common among patients dispensed verapamil/diltiazem as compared to patients on amlodipine/felodipine (OR high dose 0.62; CI 0.56-0.68 and low dose 0.63; CI 0.58-0.68. Patients on any statin were to a lesser extent dispensed gemfibrozil compared to patients not dispensed a statin (OR high dose 0.65; CI 0.55-0.76 and low dose 0.70; CI 0.63-0.78. Mean DDD (SD for any statin was substantially higher in patients co-dispensed gemfibrozil 178 (149 compared to patients on statin monotherapy 127 (93, (p<0.001.Prescribers may to some extent avoid co-prescription of statins with calcium blockers and fibrates with an increased risk of myopathy. We found no evidence for avoiding co

  5. Data Mining Activities for Bone Discipline - Current Status

    Science.gov (United States)

    Sibonga, J. D.; Pietrzyk, R. A.; Johnston, S. L.; Arnaud, S. B.

    2008-01-01

    The disciplinary goals of the Human Research Program are broadly discussed. There is a critical need to identify gaps in the evidence that would substantiate a skeletal health risk during and after spaceflight missions. As a result, data mining activities will be engaged to gather reviews of medical data and flight analog data and to propose additional measures and specific analyses. Several studies are briefly reviewed which have topics that partially address these gaps in knowledge, including bone strength recovery with recovery of bone mass density, current renal stone formation knowledge, herniated discs, and a review of bed rest studies conducted at Ames Human Research Facility.

  6. Data Mining Methods to Generate Severe Wind Gust Models

    Directory of Open Access Journals (Sweden)

    Subana Shanmuganathan

    2014-01-01

    Full Text Available Gaining knowledge on weather patterns, trends and the influence of their extremes on various crop production yields and quality continues to be a quest by scientists, agriculturists, and managers. Precise and timely information aids decision-making, which is widely accepted as intrinsically necessary for increased production and improved quality. Studies in this research domain, especially those related to data mining and interpretation are being carried out by the authors and their colleagues. Some of this work that relates to data definition, description, analysis, and modelling is described in this paper. This includes studies that have evaluated extreme dry/wet weather events against reported yield at different scales in general. They indicate the effects of weather extremes such as prolonged high temperatures, heavy rainfall, and severe wind gusts. Occurrences of these events are among the main weather extremes that impact on many crops worldwide. Wind gusts are difficult to anticipate due to their rapid manifestation and yet can have catastrophic effects on crops and buildings. This paper examines the use of data mining methods to reveal patterns in the weather conditions, such as time of the day, month of the year, wind direction, speed, and severity using a data set from a single location. Case study data is used to provide examples of how the methods used can elicit meaningful information and depict it in a fashion usable for management decision making. Historical weather data acquired between 2008 and 2012 has been used for this study from telemetry devices installed in a vineyard in the north of New Zealand. The results show that using data mining techniques and the local weather conditions, such as relative pressure, temperature, wind direction and speed recorded at irregular intervals, can produce new knowledge relating to wind gust patterns for vineyard management decision making.

  7. Importance-Performance Analysis of Service Attributes based on Customers Segmentation with a Data Mining Approach: a Study in the Mobile Telecommunication Market in Yazd Province

    Directory of Open Access Journals (Sweden)

    Seyed Yaghoub Hosseini

    2012-12-01

    Full Text Available In customer relationship management (CRM systems, importance and performance of the attributes that define a service is very important. Importance-Performance analysis is an effective tool for prioritizing service attributes based on customer needs and expectations and also for identifying strengths and weaknesses of organization in the market. In this study with the purpose of increasing reliability and accuracy of results, customers are segmented based on their demographic characteristics and perception of service attributes performance and then individual IPA matrixes are developed for each segment. Self-Organizing Maps (SOM has been used for segmentation and a feed forward neural network has been used to estimate the importance of attributes. Research findings show that mobile subscribers in Yazd province can be categorized in three segments. Individual IPA matrixes have been provided for each of these segments. Based on these results, recommendations are offered to companies providing mobile phone services.

  8. A Knowledge Model Sharing Based Approach to Privacy-Preserving Data Mining

    OpenAIRE

    Hongwei Tian; Weining Zhang; Shouhuai Xu; Patrick Sharkey

    2012-01-01

    Privacy-preserving data mining (PPDM) is an important problem and is currently studied in three approaches: the cryptographic approach, the data publishing, and the model publishing. However, each of these approaches has some problems. The cryptographic approach does not protect privacy of learned knowledge models and may have performance and scalability issues. The data publishing, although is popular, may suffer from too much utility loss for certain types of data mining applications. The m...

  9. Parallel object-oriented data mining system

    Science.gov (United States)

    Kamath, Chandrika; Cantu-Paz, Erick

    2004-01-06

    A data mining system uncovers patterns, associations, anomalies and other statistically significant structures in data. Data files are read and displayed. Objects in the data files are identified. Relevant features for the objects are extracted. Patterns among the objects are recognized based upon the features. Data from the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) sky survey was used to search for bent doubles. This test was conducted on data from the Very Large Array in New Mexico which seeks to locate a special type of quasar (radio-emitting stellar object) called bent doubles. The FIRST survey has generated more than 32,000 images of the sky to date. Each image is 7.1 megabytes, yielding more than 100 gigabytes of image data in the entire data set.

  10. Detecting Internet Worms Using Data Mining Techniques

    Directory of Open Access Journals (Sweden)

    Muazzam Siddiqui

    2008-12-01

    Full Text Available Internet worms pose a serious threat to computer security. Traditional approaches using signatures to detect worms pose little danger to the zero day attacks. The focus of malware research is shifting from using signature patterns to identifying the malicious behavior displayed by the malwares. This paper presents a novel idea of extracting variable length instruction sequences that can identify worms from clean programs using data mining techniques. The analysis is facilitated by the program control flow information contained in the instruction sequences. Based upon general statistics gathered from these instruction sequences we formulated the problem as a binary classification problem and built tree based classifiers including decision tree, bagging and random forest. Our approach showed 95.6% detection rate on novel worms whose data was not used in the model building process.

  11. Data mining in bioinformatics using Weka.

    Science.gov (United States)

    Frank, Eibe; Hall, Mark; Trigg, Len; Holmes, Geoffrey; Witten, Ian H

    2004-10-12

    The Weka machine learning workbench provides a general-purpose environment for automatic classification, regression, clustering and feature selection-common data mining problems in bioinformatics research. It contains an extensive collection of machine learning algorithms and data pre-processing methods complemented by graphical user interfaces for data exploration and the experimental comparison of different machine learning techniques on the same problem. Weka can process data given in the form of a single relational table. Its main objectives are to (a) assist users in extracting useful information from data and (b) enable them to easily identify a suitable algorithm for generating an accurate predictive model from it. http://www.cs.waikato.ac.nz/ml/weka.

  12. The Association of Statin Use with Age-Related Macular Degeneration Progression: The Age-Related Eye Disease Study 2 Report Number 9.

    Science.gov (United States)

    Al-Holou, Shaza N; Tucker, William R; Agrón, Elvira; Clemons, Traci E; Cukras, Catherine; Ferris, Frederick L; Chew, Emily Y

    2015-12-01

    To evaluate the association of statin use with progression of age-related macular degeneration (AMD). Preplanned, prospective cohort study within a controlled clinical trial of oral supplementation for age-related eye diseases. Age-Related Eye Disease Study 2 (AREDS2) participants, aged 50 to 85 years. Factors, including age, gender, smoking status, aspirin use, and history of diabetes, hypertension, heart disease, angina, and stroke-all known to be associated with statin use-were included in a logistic regression model to estimate propensity scores for each participant. Age-adjusted proportional hazards regression models, with and without propensity score matching, were performed to evaluate the association of statin use with progression to late AMD. Analyses adjusting for the competing risk of death were also performed. Baseline and annual stereoscopic fundus photographs were assessed centrally by masked graders for the development of late AMD, either neovascular AMD or geographic atrophy (GA). Of the 3791 participants (2462 with bilateral large drusen and 1329 with unilateral late AMD at baseline), 1659 (43.8%) were statin users. The overall analysis, with no matching of propensity scores and no adjustment for death as a competing risk, showed that statin use was not associated with progression to late AMD (hazard ratio [HR], 1.08; 95% confidence interval [CI], 0.83-1.41; P = 0.56). When matched for propensity scores and adjusted for death as a competing risk, the result was not statistically significant (HR, 0.81; 95% CI, 0.55-1.20; P = 0.29). Furthermore, subgroup analyses of persons with or without late AMD at baseline and the various components of late AMD (neovascular AMD, central GA, or any GA) also showed no statistically significant association of statin use with progression to AMD. Statin use was not statistically significantly associated with progression to late AMD in the AREDS2 participants, and these findings are consistent with findings in the

  13. The Association of Statin Use with Age-Related Macular Degeneration Progression The Age-Related Eye Disease Study 2 Report Number 9

    Science.gov (United States)

    Al-Holou, Shaza N.; Tucker, William R.; Agrón, Elvira; Clemons, Traci E.; Cukras, Catherine; Ferris, Frederick L.; Chew, Emily Y.

    2015-01-01

    Objective/purpose To evaluate the association of statin use with progression of age-related macular degeneration (AMD). Design Preplanned, prospective cohort study within a controlled clinical trial of oral supplementation for age-related eye diseases. Subjects Age-Related Eye Disease Study 2 participants, aged 50 to 85 years. Methods Factors, including age, gender, smoking status, aspirin use, and history of diabetes, hypertension, heart disease, angina, and stroke, all known to be associated with statin use, were included in a logistic regression model to estimate propensity scores for each participant. Age-adjusted proportional hazards regression models, with and without propensity score matching, were performed to evaluate the association of statin use with progression to late AMD. Analyses were also performed adjusting for the competing risk of death. Main Outcome Measures Baseline and annual stereoscopic fundus photographs were assessed centrally by masked graders for the development of late AMD, either neovascular AMD or geographic atrophy (GA). Results Of the 3791 participants (2462 with bilateral large drusen and 1329 with unilateral late AMD at baseline), 1659 (43.8%) were statin users. The overall analysis, with no matching of propensity scores and no adjustment for death as a competing risk, showed that statin use was not associated with progression to late AMD (hazard ratios [HR] of 1.08, 95% confidence intervals [CI] of 0.83–1.41, P=0.56). When matched for propensity scores and adjusted for death as a competing risk, the result was not statistically significant with HR: 0.81, 95% CI: 0.55–1.20, P=0.29. Further subgroup analyses of persons with or without late AMD at baseline to the various components of late AMD (neovascular, central geographic atrophy, or any geographic atrophy) also showed no statistically significant association of statin use with progression to AMD. Conclusions Statin use was not statistically significantly associated with the

  14. Virtual Observatories, Data Mining, and Astroinformatics

    Science.gov (United States)

    Borne, Kirk

    The historical, current, and future trends in knowledge discovery from data in astronomy are presented here. The story begins with a brief history of data gathering and data organization. A description of the development ofnew information science technologies for astronomical discovery is then presented. Among these are e-Science and the virtual observatory, with its data discovery, access, display, and integration protocols; astroinformatics and data mining for exploratory data analysis, information extraction, and knowledge discovery from distributed data collections; new sky surveys' databases, including rich multivariate observational parameter sets for large numbers of objects; and the emerging discipline of data-oriented astronomical research, called astroinformatics. Astroinformatics is described as the fourth paradigm of astronomical research, following the three traditional research methodologies: observation, theory, and computation/modeling. Astroinformatics research areas include machine learning, data mining, visualization, statistics, semantic science, and scientific data management.Each of these areas is now an active research discipline, with significantscience-enabling applications in astronomy. Research challenges and sample research scenarios are presented in these areas, in addition to sample algorithms for data-oriented research. These information science technologies enable scientific knowledge discovery from the increasingly large and complex data collections in astronomy. The education and training of the modern astronomy student must consequently include skill development in these areas, whose practitioners have traditionally been limited to applied mathematicians, computer scientists, and statisticians. Modern astronomical researchers must cross these traditional discipline boundaries, thereby borrowing the best of breed methodologies from multiple disciplines. In the era of large sky surveys and numerous large telescopes, the potential

  15. Applying Fuzzy Data Mining to Telecom Churn Management

    Science.gov (United States)

    Liao, Kuo-Hsiung; Chueh, Hao-En

    Customers tend to change telecommunications service providers in pursuit of more favorable telecommunication rates. Therefore, how to avoid customer churn is an extremely critical topic for the intensely competitive telecommunications industry. To assist telecommunications service providers in effectively reducing the rate of customer churn, this study used fuzzy data mining to determine effective marketing strategies by analyzing the responses of customers to various marketing activities. These techniques can help telecommunications service providers determine the most appropriate marketing opportunities and methods for different customer groups, to reduce effectively the rate of customer turnover.

  16. Visualizing data mining results with the Brede tools

    DEFF Research Database (Denmark)

    Nielsen, Finn Årup

    2009-01-01

    has expanded and now includes its own database with coordinates along with ontologies for brain regions and functions: The Brede Database. With Brede Toolbox and Database combined we setup automated workflows for extraction of data, mass meta-analytic data mining and visualizations. Most of the Web......A few neuroinformatics databases now exist that record results from neuroimaging studies in the form of brain coordinates in stereotaxic space. The Brede Toolbox was originally developed to extract, analyze and visualize data from one of them --- the BrainMap database. Since then the Brede Toolbox...

  17. Statin and NSAID Use and Prostate Cancer Risk

    Science.gov (United States)

    Coogan, Patricia F.; Kelly, Judith Parsells; Strom, Brian L.; Rosenberg, Lynn

    2010-01-01

    Purpose Some studies have reported reduced risks of advanced, but not early, prostate cancer among statin users, and one study found a reduced risk only among statin users who had also used nonsteroidal anti-inflammatory drugs (NSAIDs). We have previously reported no association between statin use and prostate cancer in our hospital-based Case Control Surveillance Study. The purpose of the present analyses was to update the findings by cancer stage and to evaluate the joint use of statins and NSAIDs. Methods Cases were 1367 men with prostate cancer and controls were 2007 men with diagnoses unrelated to statin or NSAID use. We used multivariable logistic regression analyses to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for statin use compared with no use, and joint use of statin and NSAIDs compared with use of neither. Results The odds ratio among regular statin users was 1.1 (95% CI 0.9–1.5), and odds ratios were similar among early and late stage cancers. The odds ratio among joint statin and NSAID users was 1.1 (95% CI 0.7–1.6). Conclusion The present results do not support a protective effect of statin use, or statin and NSAID use, on the risk of advanced prostate cancer. PMID:20582910

  18. Statin utilization according to indication and age: A Danish cohort study on changing prescribing and purchasing behaviour

    DEFF Research Database (Denmark)

    Kildemoes, Helle Wallach; Vass, Mikkel; Hendriksen, Carsten

    2012-01-01

    indications and ages. Conclusion: While patent expiry and lower prices most likely boosted the general increase in statin utilization, the gradually altered indication and age pattern seems to be driven by guidelines, influencing both reimbursement rules and general healthcare policies. A media debate...... on statin side effects may have modified the general attitudes. (C) 2012 Elsevier Ireland Ltd. All rights reserved....

  19. Using data mining to segment healthcare markets from patients' preference perspectives.

    Science.gov (United States)

    Liu, Sandra S; Chen, Jie

    2009-01-01

    This paper aims to provide an example of how to use data mining techniques to identify patient segments regarding preferences for healthcare attributes and their demographic characteristics. Data were derived from a number of individuals who received in-patient care at a health network in 2006. Data mining and conventional hierarchical clustering with average linkage and Pearson correlation procedures are employed and compared to show how each procedure best determines segmentation variables. Data mining tools identified three differentiable segments by means of cluster analysis. These three clusters have significantly different demographic profiles. The study reveals, when compared with traditional statistical methods, that data mining provides an efficient and effective tool for market segmentation. When there are numerous cluster variables involved, researchers and practitioners need to incorporate factor analysis for reducing variables to clearly and meaningfully understand clusters. Interests and applications in data mining are increasing in many businesses. However, this technology is seldom applied to healthcare customer experience management. The paper shows that efficient and effective application of data mining methods can aid the understanding of patient healthcare preferences.

  20. Efficacy and safety of alirocumab in patients with hypercholesterolemia not adequately controlled with non-statin lipid-lowering therapy or the lowest strength of statin: ODYSSEY NIPPON study design and rationale.

    Science.gov (United States)

    Teramoto, Tamio; Kondo, Akira; Kiyosue, Arihiro; Harada-Shiba, Mariko; Ishigaki, Yasushi; Tobita, Kimimasa; Kawabata, Yumiko; Ozaki, Asuka; Baccara-Dinet, Marie T; Sata, Masataka

    2017-06-17

    Statins are generally well-tolerated and serious side effects are infrequent, but some patients experience adverse events and reduce their statin dose or discontinue treatment altogether. Alirocumab is a highly specific, fully human monoclonal antibody to proprotein convertase subtilisin/kexin type 9 (PCSK9), which can produce substantial and sustained reductions of low-density lipoprotein cholesterol (LDL-C). The randomized, double-blind, placebo-controlled, parallel-group, phase 3 ODYSSEY NIPPON study will explore alirocumab 150 mg every 4 weeks (Q4W) in 163 Japanese patients with hypercholesterolemia who are on the lowest-strength dose of atorvastatin (5 mg/day) or are receiving a non-statin lipid-lowering therapy (LLT) (fenofibrate, bezafibrate, ezetimibe, or diet therapy alone). Hypercholesterolemia is defined as LDL-C ≥ 100 mg/dL (2.6 mmol/L) in patients with heterozygous familial hypercholesterolemia or non-familial hypercholesterolemia with a history of documented coronary heart disease, or ≥120 mg/dL (3.1 mmol/L) in patients with non-familial hypercholesterolemia classified as primary prevention category III (i.e. high-risk patients). During the 12-week double-blind treatment period, patients will be randomized (1:1:1) to receive alirocumab subcutaneously (SC) 150 mg Q4W alternating with placebo for alirocumab Q4W, or alirocumab 150 mg SC every 2 weeks (Q2W), or SC placebo Q2W. The primary efficacy endpoint is the percentage change in calculated LDL-C from baseline to week 12. The long-term safety and tolerability of alirocumab will also be investigated. The ODYSSEY NIPPON study will provide insights into the efficacy and safety of alirocumab 150 mg Q4W or 150 mg Q2W among Japanese patients with hypercholesterolemia who are on the lowest-strength dose of atorvastatin, or are receiving a non-statin LLT (including diet therapy alone). ClinicalTrials.gov number: NCT02584504.

  1. Increasing incidence of statin prescribing for the elderly without previous cardiovascular conditions:  A nation wide register study

    DEFF Research Database (Denmark)

    Kildemoes, Helle Wallach; Andersen, Morten

    Supported by the growing evidence of the beneficial effects of statins in a range of conditions, statin utilization has increased considerably in most Western countries over the last decade. Objectives To estimate to what extent a widening of indication scope for statins accounts for the increasing...... Danish statin utilization during 1996-2005, applying treatment incidence as a measure of changing prescribing behaviour Methods From three nationwide registers, we retrieved individual records on demographics, dispensed prescription drugs and hospital discharges. Danish inhabitants were followed...... for seven cardiovascular conditions, corresponding to a hierarchy of statin indications. Poisson regression analyses were applied to quantify the incidence growth, according to age and indication.  Results Treatment incidence increased from 4/1000 person years in 2000 to 17/1000 in 2005, the increase being...

  2. Effect of statins on skeletal muscle function.

    Science.gov (United States)

    Parker, Beth A; Capizzi, Jeffrey A; Grimaldi, Adam S; Clarkson, Priscilla M; Cole, Stephanie M; Keadle, Justin; Chipkin, Stuart; Pescatello, Linda S; Simpson, Kathleen; White, C Michael; Thompson, Paul D

    2013-01-01

    Many clinicians believe that statins cause muscle pain, but this has not been observed in clinical trials, and the effect of statins on muscle performance has not been carefully studied. The Effect of Statins on Skeletal Muscle Function and Performance (STOMP) study assessed symptoms and measured creatine kinase, exercise capacity, and muscle strength before and after atorvastatin 80 mg or placebo was administered for 6 months to 420 healthy, statin-naive subjects. No individual creatine kinase value exceeded 10 times normal, but average creatine kinase increased 20.8±141.1 U/L (Pmuscle strength or exercise capacity with atorvastatin, but more atorvastatin than placebo subjects developed myalgia (19 versus 10; P=0.05). Myalgic subjects on atorvastatin or placebo had decreased muscle strength in 5 of 14 and 4 of 14 variables, respectively (P=0.69). These results indicate that high-dose atorvastatin for 6 months does not decrease average muscle strength or exercise performance in healthy, previously untreated subjects. Nevertheless, this blinded, controlled trial confirms the undocumented impression that statins increase muscle complaints. Atorvastatin also increased average creatine kinase, suggesting that statins produce mild muscle injury even among asymptomatic subjects. This increase in creatine kinase should prompt studies examining the effects of more prolonged, high-dose statin treatment on muscular performance. URL: http://www.clinicaltrials.gov. Unique identifier: NCT00609063.

  3. Seminal quality prediction using data mining methods.

    Science.gov (United States)

    Sahoo, Anoop J; Kumar, Yugal

    2014-01-01

    Now-a-days, some new classes of diseases have come into existences which are known as lifestyle diseases. The main reasons behind these diseases are changes in the lifestyle of people such as alcohol drinking, smoking, food habits etc. After going through the various lifestyle diseases, it has been found that the fertility rates (sperm quantity) in men has considerably been decreasing in last two decades. Lifestyle factors as well as environmental factors are mainly responsible for the change in the semen quality. The objective of this paper is to identify the lifestyle and environmental features that affects the seminal quality and also fertility rate in man using data mining methods. The five artificial intelligence techniques such as Multilayer perceptron (MLP), Decision Tree (DT), Navie Bayes (Kernel), Support vector machine+Particle swarm optimization (SVM+PSO) and Support vector machine (SVM) have been applied on fertility dataset to evaluate the seminal quality and also to predict the person is either normal or having altered fertility rate. While the eight feature selection techniques such as support vector machine (SVM), neural network (NN), evolutionary logistic regression (LR), support vector machine plus particle swarm optimization (SVM+PSO), principle component analysis (PCA), chi-square test, correlation and T-test methods have been used to identify more relevant features which affect the seminal quality. These techniques are applied on fertility dataset which contains 100 instances with nine attribute with two classes. The experimental result shows that SVM+PSO provides higher accuracy and area under curve (AUC) rate (94% & 0.932) among multi-layer perceptron (MLP) (92% & 0.728), Support Vector Machines (91% & 0.758), Navie Bayes (Kernel) (89% & 0.850) and Decision Tree (89% & 0.735) for some of the seminal parameters. This paper also focuses on the feature selection process i.e. how to select the features which are more important for prediction of

  4. Statin use in adults at high risk of cardiovascular disease mortality: cross-sectional analysis of baseline data from The Irish Longitudinal Study on Ageing (TILDA).

    LENUS (Irish Health Repository)

    Murphy, Catriona

    2015-07-01

    This study aims to examine the extent to which statins are used by adults at high risk of cardiovascular disease (CVD) compared to European clinical guidelines. The high-risk groups examined are those with (1) known CVD, (2) known diabetes and (3) a high or very high risk (≥5%) of CVD mortality based on Systematic COronary Risk Evaluation (SCORE).

  5. The DYSlipidemia International Study (DYSIS-Egypt: A report on the prevalence of lipid abnormalities in Egyptian patients on chronic statin treatment

    Directory of Open Access Journals (Sweden)

    Adel El Etriby

    2013-09-01

    Conclusions: Despite chronic statin treatment, two-thirds of patients in the DYSIS-Egypt study had elevated LDL–C levels. A dual strategy, comprising modification of lifestyle factors together with novel treatment options, appears to be necessary to combat the rise in cardiovascular-related morbidity and mortality.

  6. Data Mining Practical Machine Learning Tools and Techniques

    CERN Document Server

    Witten, Ian H; Hall, Mark A

    2011-01-01

    Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place

  7. Statin use and survival in colorectal cancer: Results from a population-based cohort study and an updated systematic review and meta-analysis.

    Science.gov (United States)

    Gray, Ronan T; Coleman, Helen G; Hughes, Carmel; Murray, Liam J; Cardwell, Chris R

    2016-12-01

    The aim of this study was to investigate the association between statin use and survival in a population-based colorectal cancer (CRC) cohort and perform an updated meta-analysis to quantify the magnitude of any association. A cohort of 8391 patients with newly diagnosed Dukes' A-C CRC (2009-2012) was identified from the Scottish Cancer Registry. This cohort was linked to the Prescribing Information System and the National Records of Scotland Death Records (until January 2015) to identify 1064 colorectal cancer-specific deaths. Adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for cancer-specific mortality by statin use were calculated using time dependent Cox regression models. The systematic review included relevant studies published before January 2016. Meta-analysis techniques were used to derive combined HRs for associations between statin use and cancer-specific and overall mortality. In the Scottish cohort, statin use before diagnosis (HR=0.84, 95% CI 0.75-0.94), but not after (HR=0.90, 95% CI 0.77-1.05), was associated with significantly improved cancer-specific mortality. The systematic review identified 15 relevant studies. In the meta-analysis, there was consistent (I 2 =0%,heterogeneity P=0.57) evidence of a reduction in cancer-specific mortality with statin use before diagnosis in 6 studies (n=86,622, pooled HR=0.82, 95% CI 0.79-0.86) but this association was less apparent and more heterogeneous (I 2 =67%,heterogeneity P=0.03) with statin use after diagnosis in 4 studies (n=19,152, pooled HR=0.84, 95% CI 0.68-1.04). In a Scottish CRC cohort and updated meta-analysis there was some evidence that statin use was associated with improved survival. However, these associations were weak in magnitude and, particularly for post-diagnosis use, varied markedly between studies. Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. Equity in statin use in New Zealand

    Directory of Open Access Journals (Sweden)

    Norris P

    2014-03-01

    Full Text Available INTRODUCTION: Preventive medications such as statins are used to reduce cardiovascular risk. There is some evidence to suggest that people of lower socioeconomic position are less likely to be prescribed statins. In New Zealand, Maori have higher rates of cardiovascular disease. AIM: This study aimed to investigate statin utilisation by socioeconomic position and ethnicity in a region of New Zealand. METHODS: This was a cross-sectional study in which data were collected on all prescriptions dispensed from all pharmacies in one city during 2005/6. Linkage with national datasets provided information on patients' age, gender and ethnicity. Socioeconomic position was identified using the New Zealand Index of Socioeconomic Deprivation 2006. RESULTS: Statin use increased with age until around 75 years. Below age 65 years, those in the most deprived socioeconomic areas were most likely to receive statins. In the 55-64 age group, 22.3% of the most deprived population received a statin prescription (compared with 17.5% of the mid and 18.6% of the least deprived group. At ages up to 75 years, use was higher amongst Maori than non-Maori, particularly in middle age, where Maori have a higher risk of cardiovascular disease. In the 45-54 age group, 11.6% of Maori received a statin prescription, compared with 8.7% of non-Maori. DISCUSSION: Statin use approximately matched the pattern of need, in contrast to other studies which found under-treatment of people of low socioeconomic position. A PHARMAC campaign to increase statin use may have increased use in high-risk groups in New Zealand.

  9. Statins and angiogenesis: Is it about connections?

    International Nuclear Information System (INIS)

    Khaidakov, Magomed; Wang, Wenze; Khan, Junaid A.; Kang, Bum-Yong; Hermonat, Paul L.; Mehta, Jawahar L.

    2009-01-01

    Statins, inhibitors of 3-hydroxy-3-methyl-glutaryl-coenzyme A reductase, have been shown to induce both angiogenic and angiostatic responses. We attempted to resolve this controversy by studying the effects of two different statins, rosuvastatin and simvastatin, in two different assay systems. In the matrigel angiogenesis assay, both statins enhanced tube formation by human umbilical vein endothelial cells (HUVECs, p < 0.01 vs. control). In the ex vivo mouse aortic ring sprouting assay, both statins virtually abolished new vessel formation (p < 0.01). As a basic difference between the two models of angiogenesis is dispersed state of endothelial cells vs. compact monolayer, we analyzed influence of statins on endothelial junction proteins. RT-PCR analysis and cytoimmunostaining of HUVECs treated with simvastatin revealed increased expression of VE-cadherin (p < 0.05). The blockade of VE-cadherin with a specific antibody reversed simvastatin-induced tube formation (p < 0.002). These data suggest that statins through VE-cadherin stimulation modulate cell-cell adhesion and diminish the ability of cells to proliferate and migrate. The observations of reduced angiogenesis in the intact vessel may relate to anti-atherosclerotic and anti-cancer effects of statins, and provide a feasible explanation for conflicting data under different experimental conditions.

  10. Statins and risk of breast cancer recurrence

    Directory of Open Access Journals (Sweden)

    Sakellakis M

    2016-11-01

    Full Text Available Minas Sakellakis,1 Karolina Akinosoglou,1 Anastasia Kostaki,2 Despina Spyropoulou,1 Angelos Koutras,1 1Department of Medicine, Division of Oncology, University Hospital, Patras Medical School, Patras, 2Department of Statistics, Athens University of Economics and Business, Athens, Greece Background: The primary end point of our study was to test whether the concurrent use of a statin is related to a lower risk of recurrence and increased relapse-free survival in patients with early breast cancer. Materials and methods: We reviewed 610 female patients with stage I, II, or III breast cancer who had been surgically treated and who had subsequently received at least adjuvant chemotherapy in order to prevent recurrence. Results: Among the 610 patients with breast cancer, 83 (13.6% were receiving a statin on a chronic basis for other medical purposes. Overall, statin users displayed longer mean relapse-free survival (16.6 vs 10.2 years, P=0.028. After data had been adjusted for patient and disease characteristics, statin users maintained a lower risk of recurrence. This favorable outcome in statin users was particularly evident when we included only younger patients in the analysis (20 vs 10 years, P=0.006. Conclusion: Statins may be linked to a favorable outcome in early breast cancer patients, especially in younger age-groups. Keywords: statins, breast, cancer, adjuvant, recurrence

  11. DATA MINING APPLICATION IN CREDIT CARD FRAUD DETECTION SYSTEM

    Directory of Open Access Journals (Sweden)

    FRANCISCA NONYELUM OGWUELEKA

    2011-06-01

    Full Text Available Data mining is popularly used to combat frauds because of its effectiveness. It is a well-defined procedure that takes data as input and produces models or patterns as output. Neural network, a data mining technique was used in this study. The design of the neural network (NN architecture for the credit card detection system was based on unsupervised method, which was applied to the transactions data to generate four clusters of low, high, risky and high-risk clusters. The self-organizing map neural network (SOMNN technique was used for solving the problem of carrying out optimal classification of each transaction into its associated group, since a prior output is unknown. The receiver-operating curve (ROC for credit card fraud (CCF detection watch detected over 95% of fraud cases without causing false alarms unlike other statistical models and the two-stage clusters. This shows that the performance of CCF detection watch is in agreement with other detection software, but performs better.

  12. Machine Learning and Data Mining Methods in Diabetes Research.

    Science.gov (United States)

    Kavakiotis, Ioannis; Tsave, Olga; Salifoglou, Athanasios; Maglaveras, Nicos; Vlahavas, Ioannis; Chouvarda, Ioanna

    2017-01-01

    The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.

  13. Application of Data Mining in direct marketing

    Directory of Open Access Journals (Sweden)

    Dejana Pavlović

    2014-04-01

    Full Text Available The key to successful business operations lies in good communication with clients. There are a growing number of brokers in the financial market who collect excess funds from the clients and perform transfers to those who need the funds. However, many external and internal factors influence the decision on disposal of available funds. This paper identifies and researches into clients’ satisfaction in the banking system. By application of the disclosure of data legality we will try to point to the factors that influence the clients' decision to invest their long-term deposits in the parent bank. Upon classification and clustering, we will interpret and indentify the strengths and weaknesses of the target results. This analysis provides the guidelines through the use of the decision-making tree, application of data mining and the possibility to use a large set of data increases the value and accuracy of this technique. The problem with this technique is accuracy of the data submitted by the client.

  14. Using Data Mining for Wine Quality Assessment

    Science.gov (United States)

    Cortez, Paulo; Teixeira, Juliana; Cerdeira, António; Almeida, Fernando; Matos, Telmo; Reis, José

    Certification and quality assessment are crucial issues within the wine industry. Currently, wine quality is mostly assessed by physicochemical (e.g alcohol levels) and sensory (e.g. human expert evaluation) tests. In this paper, we propose a data mining approach to predict wine preferences that is based on easily available analytical tests at the certification step. A large dataset is considered with white vinho verde samples from the Minho region of Portugal. Wine quality is modeled under a regression approach, which preserves the order of the grades. Explanatory knowledge is given in terms of a sensitivity analysis, which measures the response changes when a given input variable is varied through its domain. Three regression techniques were applied, under a computationally efficient procedure that performs simultaneous variable and model selection and that is guided by the sensitivity analysis. The support vector machine achieved promising results, outperforming the multiple regression and neural network methods. Such model is useful for understanding how physicochemical tests affect the sensory preferences. Moreover, it can support the wine expert evaluations and ultimately improve the production.

  15. Statins and Cancer Prevention

    Science.gov (United States)

    ... opposed to the use of another type of lipid-lowering drug, fibrates). [Statins and the risk of colorectal cancer. Poynter, JN., et al. New England Journal of Medicine , May 26, 2005, (352:2184–92]. Is NCI supporting research with statins to prevent other types of cancer? ...

  16. Statin resistance and export

    DEFF Research Database (Denmark)

    2015-01-01

    The present invention relates e.g. to methods of producing statins in transgenic, non-filamentous microorganisms such as Saccharomyces cerevisiae. In addition, the present invention relates to the transgenic, non-filamentous microorganisms as such as well as various uses of transmembrane statin e...

  17. Prediction of Allogeneic Hematopoietic Stem-Cell Transplantation Mortality 100 Days After Transplantation Using a Machine Learning Algorithm: A European Group for Blood and Marrow Transplantation Acute Leukemia Working Party Retrospective Data Mining Study.

    Science.gov (United States)

    Shouval, Roni; Labopin, Myriam; Bondi, Ori; Mishan-Shamay, Hila; Shimoni, Avichai; Ciceri, Fabio; Esteve, Jordi; Giebel, Sebastian; Gorin, Norbert C; Schmid, Christoph; Polge, Emmanuelle; Aljurf, Mahmoud; Kroger, Nicolaus; Craddock, Charles; Bacigalupo, Andrea; Cornelissen, Jan J; Baron, Frederic; Unger, Ron; Nagler, Arnon; Mohty, Mohamad

    2015-10-01

    Allogeneic hematopoietic stem-cell transplantation (HSCT) is potentially curative for acute leukemia (AL), but carries considerable risk. Machine learning algorithms, which are part of the data mining (DM) approach, may serve for transplantation-related mortality risk prediction. This work is a retrospective DM study on a cohort of 28,236 adult HSCT recipients from the AL registry of the European Group for Blood and Marrow Transplantation. The primary objective was prediction of overall mortality (OM) at 100 days after HSCT. Secondary objectives were estimation of nonrelapse mortality, leukemia-free survival, and overall survival at 2 years. Donor, recipient, and procedural characteristics were analyzed. The alternating decision tree machine learning algorithm was applied for model development on 70% of the data set and validated on the remaining data. OM prevalence at day 100 was 13.9% (n=3,936). Of the 20 variables considered, 10 were selected by the model for OM prediction, and several interactions were discovered. By using a logistic transformation function, the crude score was transformed into individual probabilities for 100-day OM (range, 3% to 68%). The model's discrimination for the primary objective performed better than the European Group for Blood and Marrow Transplantation score (area under the receiver operating characteristics curve, 0.701 v 0.646; Prisk evaluation of patients with AL before HSCT, and is available online (http://bioinfo.lnx.biu.ac.il/∼bondi/web1.html). It is presented as a continuous probabilistic score for the prediction of day 100 OM, extending prediction to 2 years. The DM method has proved useful for clinical prediction in HSCT. © 2015 by American Society of Clinical Oncology.

  18. The role of metformin and statins in the incidence of epithelial ovarian cancer in type 2 diabetes: a cohort and nested case-control study.

    Science.gov (United States)

    Urpilainen, E; Marttila, M; Hautakoski, A; Arffman, M; Sund, R; Ilanne-Parikka, P; Arima, R; Kangaskokko, J; Puistola, U; Läärä, E; Hinkula, M

    2018-02-07

    To obtain evidence of the effects of metformin and statins on the incidence of ovarian cancer in women with type 2 diabetes (T2D). A retrospective cohort study and nested case-control study. The data were obtained from a diabetes database (FinDM) combining information from several nationwide registers. A cohort of 137 643 women over 40 years old and diagnosed with T2D during 1996-2011 in Finland. In full cohort analysis Poisson regression was used to estimate the hazard ratios (HR) in relation to ever use of metformin, insulin other oral anti-diabetic medication or statins. In the nested case-control analysis 20 controls were matched to each case of ovarian cancer. Conditional logistic regression was used to estimate HRs in relation to medication use and cumulative use of different medications. The estimates were adjusted for age and duration of T2D. Incidence of ovarian cancer. In all, 303 women were diagnosed with ovarian cancer during the follow up. Compared with other forms of oral anti-diabetic medication, metformin (HR 1.02, 95% CI: 0.72-1.45) was not found to be associated with the incidence of ovarian cancer. Neither was there evidence for statins to affect the incidence (HR 0.99, 95% CI: 0.78-1.25). In nested case-control analysis the results were essentially similar. No evidence of an association between the use of metformin or statins and the incidence of ovarian cancer in women with T2D was found. No evidence found for metformin or statins reducing the incidence of ovarian cancer. © 2018 Royal College of Obstetricians and Gynaecologists.

  19. Introduction to the special section on educational data mining

    NARCIS (Netherlands)

    Calders, T.G.K.; Pechenizkiy, M.

    2012-01-01

    Educational Data Mining (EDM) is an emerging multidisciplinary research area, in which methods and techniques for exploring data originating from various educational information systems have been developed. EDM is both a learning science, as well as a rich application area for data mining, due to

  20. Exploring the Integration of Data Mining and Data Visualization

    Science.gov (United States)

    Zhang, Yi

    2011-01-01

    Due to the rapid advances in computing and sensing technologies, enormous amounts of data are being generated everyday in various applications. The integration of data mining and data visualization has been widely used to analyze these massive and complex data sets to discover hidden patterns. For both data mining and visualization to be…

  1. Application and Exploration of Big Data Mining in Clinical Medicine.

    Science.gov (United States)

    Zhang, Yue; Guo, Shu-Li; Han, Li-Na; Li, Tie-Ling

    2016-03-20

    To review theories and technologies of big data mining and their application in clinical medicine. Literatures published in English or Chinese regarding theories and technologies of big data mining and the concrete applications of data mining technology in clinical medicine were obtained from PubMed and Chinese Hospital Knowledge Database from 1975 to 2015. Original articles regarding big data mining theory/technology and big data mining's application in the medical field were selected. This review characterized the basic theories and technologies of big data mining including fuzzy theory, rough set theory, cloud theory, Dempster-Shafer theory, artificial neural network, genetic algorithm, inductive learning theory, Bayesian network, decision tree, pattern recognition, high-performance computing, and statistical analysis. The application of big data mining in clinical medicine was analyzed in the fields of disease risk assessment, clinical decision support, prediction of disease development, guidance of rational use of drugs, medical management, and evidence-based medicine. Big data mining has the potential to play an important role in clinical medicine.

  2. Data Mine and Forget It?: A Cautionary Tale

    Science.gov (United States)

    Tada, Yuri; Kraft, Norbert Otto; Orasanu, Judith M.

    2011-01-01

    With the development of new technologies, data mining has become increasingly popular. However, caution should be exercised in choosing the variables to include in data mining. A series of regression trees was created to demonstrate the change in the selection by the program of significant predictors based on the nature of variables.

  3. Model architecture of intelligent data mining oriented urban transportation information

    Science.gov (United States)

    Yang, Bogang; Tao, Yingchun; Sui, Jianbo; Zhang, Feizhou

    2007-06-01

    Aiming at solving practical problems in urban traffic, the paper presents model architecture of intelligent data mining from hierarchical view. With artificial intelligent technologies used in the framework, the intelligent data mining technology improves, which is more suitable for the change of real-time road condition. It also provides efficient technology support for the urban transport information distribution, transmission and display.

  4. Data mining in e-commerce: A survey

    Indian Academy of Sciences (India)

    R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22

    it is only apposite to seek the services of data mining to make (business) sense out of these data sets. Data mining ..... for the simple reason that for practical purposes, it is sufficient to include snapshots of data taken at say, weekly ..... of the mining environment and the expenses the user is willing to incur). The authors have.

  5. Informatics, Data Mining, Econometrics and Financial Economics: A Connection

    NARCIS (Netherlands)

    C-L. Chang (Chia-Lin); M.J. McAleer (Michael); W.-K. Wong (Wing-Keung)

    2015-01-01

    textabstractThis short communication reviews some of the literature in econometrics and financial economics that is related to informatics and data mining. We then discuss some of the research on econometrics and financial economics that could be extended to informatics and data mining beyond the

  6. Set-oriented data mining in relational databases

    NARCIS (Netherlands)

    Houtsma, M.A.W.; Swami, Arun

    1995-01-01

    Data mining is an important real-life application for businesses. It is critical to find efficient ways of mining large data sets. In order to benefit from the experience with relational databases, a set-oriented approach to mining data is needed. In such an approach, the data mining operations are

  7. Expressive power of an algebra for data mining

    NARCIS (Netherlands)

    Calders, T.; Lakshmanan, L.V.S.; Ng, R.T.; Paredaens, J.

    2006-01-01

    The relational data model has simple and clear foundations on which significant theoretical and systems research has flourished. By contrast, most research on data mining has focused on algorithmic issues. A major open question is: what's an appropriate foundation for data mining, which can

  8. The viability of business data mining in the sports environment ...

    African Journals Online (AJOL)

    Data mining can be viewed as the process of extracting previously unknown information from large databases and utilising this information to make crucial business decisions (Simoudis, 1996: 26). This paper considers the viability of using data mining tools and techniques in sports, particularly with regard to mining the ...

  9. Experienced ethical issues of personalized data-mined media services

    DEFF Research Database (Denmark)

    Sørensen, Jannick Kirk

    2008-01-01

    This tentative PhD project description concerns the ethnographic examination of users’ experience of privacy issues and usability related to personalized data mined (web-) services for media content.......This tentative PhD project description concerns the ethnographic examination of users’ experience of privacy issues and usability related to personalized data mined (web-) services for media content....

  10. Statin Intolerance: the Clinician's Perspective.

    Science.gov (United States)

    Stulc, Tomáš; Ceška, Richard; Gotto, Antonio M

    2015-12-01

    Muscle problems and other adverse symptoms associated with statin use are frequent reasons for non-adherence and discontinuation of statin therapy, which results in inadequate control of hyperlipidemia and increased cardiovascular risk. However, most patients who experience adverse symptoms during statin use are able to tolerate at least some degree of statin therapy. Given the profound cardiovascular benefits derived from statins, an adequate practical approach to statin intolerance is, therefore, of great clinical importance. Statin intolerance can be defined as the occurrence of myalgia or other adverse symptoms that are attributed to statin therapy and that lead to its discontinuation. In reality, these symptoms are actually unrelated to statin use in many patients, especially in those with atypical presentations following long periods of treatment. Thus, the first step in approaching patients with adverse symptoms during the course of statin therapy is identification of those patients for whom true statin intolerance is unlikely, since most of these patients would probably be capable of tolerating adequate statin therapy. In patients with statin intolerance, an altered dosing regimen of very low doses of statins should be attempted and, if tolerated, should gradually be increased to achieve the highest tolerable doses. In addition, other lipid-lowering drugs may be needed, either in combination with statins, or alone, if statins are not tolerated at all. Stringent control of other risk factors can aid in reducing cardiovascular risk if attaining lipid treatment goals proves difficult.

  11. Big data mining analysis method based on cloud computing

    Science.gov (United States)

    Cai, Qing Qiu; Cui, Hong Gang; Tang, Hao

    2017-08-01

    Information explosion era, large data super-large, discrete and non-(semi) structured features have gone far beyond the traditional data management can carry the scope of the way. With the arrival of the cloud computing era, cloud computing provides a new technical way to analyze the massive data mining, which can effectively solve the problem that the traditional data mining method cannot adapt to massive data mining. This paper introduces the meaning and characteristics of cloud computing, analyzes the advantages of using cloud computing technology to realize data mining, designs the mining algorithm of association rules based on MapReduce parallel processing architecture, and carries out the experimental verification. The algorithm of parallel association rule mining based on cloud computing platform can greatly improve the execution speed of data mining.

  12. Kajian Data Mining Customer Relationship Management pada Lembaga Keuangan Mikro

    Directory of Open Access Journals (Sweden)

    Tikaridha Hardiani

    2016-01-01

    Full Text Available Companies are required to be ready to face the competition will be intense with other companies, including micro-finance institutions. Faced more intense competition, has led to many businesses in microfinance institutions find profitable strategy to distinguish from the others. Strategy that can be applied is implementing Customer Relationship Management (CRM and data mining. Data mining can be used to microfinance institutions that have a large enough data. Determine the potential customers with customer segmentation can help the decision-making marketing strategy that will be implemented . This paper discusses several data mining techniques that can be used for customer segmentation. Proposed method of data mining technique is fuzzy clustering with fuzzy C-Means algorithm and fuzzy RFM. Keywords : Customer relationship management; Data mining; Fuzzy clustering; Micro-finance institutions; Fuzzy C-Means; Fuzzy RFM

  13. Software tool for data mining and its applications

    Science.gov (United States)

    Yang, Jie; Ye, Chenzhou; Chen, Nianyi

    2002-03-01

    A software tool for data mining is introduced, which integrates pattern recognition (PCA, Fisher, clustering, hyperenvelop, regression), artificial intelligence (knowledge representation, decision trees), statistical learning (rough set, support vector machine), computational intelligence (neural network, genetic algorithm, fuzzy systems). It consists of nine function models: pattern recognition, decision trees, association rule, fuzzy rule, neural network, genetic algorithm, Hyper Envelop, support vector machine, visualization. The principle and knowledge representation of some function models of data mining are described. The software tool of data mining is realized by Visual C++ under Windows 2000. Nonmonotony in data mining is dealt with by concept hierarchy and layered mining. The software tool of data mining has satisfactorily applied in the prediction of regularities of the formation of ternary intermetallic compounds in alloy systems, and diagnosis of brain glioma.

  14. Underutilization of high-intensity statin therapy after hospitalization for coronary heart disease.

    Science.gov (United States)

    Rosenson, Robert S; Kent, Shia T; Brown, Todd M; Farkouh, Michael E; Levitan, Emily B; Yun, Huifeng; Sharma, Pradeep; Safford, Monika M; Kilgore, Meredith; Muntner, Paul; Bittner, Vera

    2015-01-27

    National guidelines recommend use of high-intensity statins after hospitalization for coronary heart disease (CHD) events. This study sought to estimate the proportion of Medicare beneficiaries filling prescriptions for high-intensity statins after hospital discharge for a CHD event and to analyze whether statin intensity before hospitalization is associated with statin intensity after discharge. We conducted a retrospective cohort study using a 5% random sample of Medicare beneficiaries between 65 and 74 years old. Beneficiaries were included in the analysis if they filled a statin prescription after a CHD event (myocardial infarction or coronary revascularization) in 2007, 2008, or 2009. High-intensity statins included atorvastatin 40 to 80 mg, rosuvastatin 20 to 40 mg, and simvastatin 80 mg. Among 8,762 Medicare beneficiaries filling a statin prescription after a CHD event, 27% of first post-discharge fills were for a high-intensity statin. The percent filling a high-intensity statin post-discharge was 23.1%, 9.4%, and 80.7%, for beneficiaries not taking statins pre-hospitalization, taking low/moderate-intensity statins, and taking high-intensity statins before their CHD event, respectively. Compared with beneficiaries not on statin therapy pre-hospitalization, multivariable adjusted risk ratios for filling a high-intensity statin were 4.01 (3.58-4.49) and 0.45 (0.40-0.52) for participants taking high-intensity and low/moderate-intensity statins before their CHD event, respectively. Only 11.5% of beneficiaries whose first post-discharge statin fill was for a low/moderate-intensity statin filled a high-intensity statin within 365 days of discharge. The majority of Medicare beneficiaries do not fill high-intensity statins after hospitalization for CHD. Copyright © 2015 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

  15. Rising statin use and effect on ischemic stroke outcome

    Directory of Open Access Journals (Sweden)

    Haymore Joseph

    2004-03-01

    Full Text Available Abstract Background Statins (3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors have neuroprotective effects in experimental stroke models and are commonly prescribed in clinical practice. The aim of this study was to determine if patients taking statins before hospital admission for stroke had an improved clinical outcome. Methods This was an observational study of 436 patients admitted to the National Institutes of Health Suburban Hospital Stroke Program between July 2000 and December 2002. Self-reported risk factors for stroke were obtained on admission. Stroke severity was determined by the admission National Institutes of Health Stroke Scale score. Good outcome was defined as a Rankin score Results There were 436 patients with a final diagnosis of ischemic stroke; statin data were available for 433 of them. A total of 95/433 (22% of patients were taking a statin when they were admitted, rising from 16% in 2000 to 26% in 2002. Fifty-one percent of patients taking statins had a good outcome compared to 38% of patients not taking statins (p = 0.03. After adjustment for confounding factors, statin pretreatment was associated with a 2.9 odds (95% CI: 1.2–6.7 of a good outcome at the time of hospital discharge. Conclusions The proportion of patients taking statins when they are admitted with stroke is rising rapidly. Statin pretreatment was significantly associated with an improved functional outcome at discharge. This finding could support the early initiation of statin therapy after stroke.

  16. Statin-related myotoxicity.

    Science.gov (United States)

    Fernandes, Vera; Santos, Maria Joana; Pérez, Antonio

    2016-05-01

    Statin therapy has a very important role in decreasing cardiovascular risk, and treatment non-compliance may therefore be a concern in high cardiovascular risk patients. Myotoxicity is a frequent side effect of statin therapy and one of the main causes of statin discontinuation, which limits effective treatment of patients at risk of or with cardiovascular disease. Because of the high proportion of patients on statin treatment and the frequency of statin-related myotoxicity, this is a subject of concern in clinical practice. However, statin-related myotoxicity is probably underestimated because there is not a gold standard definition, and its diagnosis is challenging. Moreover, information about pathophysiology and optimal therapeutic options is scarce. Therefore, this paper reviews the knowledge about the definition, pathophysiology and predisposing conditions, diagnosis and management of statin-related myotoxicity, and provides a practical scheme for its management in clinical practice. Copyright © 2016 SEEN. Published by Elsevier España, S.L.U. All rights reserved.

  17. Clinical Effectiveness of Statin Therapy After Ischemic Stroke: Primary Results From the Statin Therapeutic Area of the Patient-Centered Research Into Outcomes Stroke Patients Prefer and Effectiveness Research (PROSPER) Study.

    Science.gov (United States)

    O'Brien, Emily C; Greiner, Melissa A; Xian, Ying; Fonarow, Gregg C; Olson, DaiWai M; Schwamm, Lee H; Bhatt, Deepak L; Smith, Eric E; Maisch, Lesley; Hannah, Deidre; Lindholm, Brianna; Peterson, Eric D; Pencina, Michael J; Hernandez, Adrian F

    2015-10-13

    In patients with ischemic stroke, data on the real-world effectiveness of statin therapy for clinical and patient-centered outcomes are needed to better inform shared decision making. Patient-Centered Research Into Outcomes Stroke Patients Prefer and Effectiveness Research (PROSPER) is a Patient-Centered Outcomes Research Institute-funded research program designed with stroke survivors to evaluate the effectiveness of poststroke therapies. We linked data on patients ≥65 years of age enrolled in the Get With The Guidelines-Stroke Registry to Medicare claims. Two-year to postdischarge outcomes of those discharged on a statin versus not on a statin were adjusted through inverse probability weighting. Our coprimary outcomes were major adverse cardiovascular events and home time (days alive and out of a hospital or skilled nursing facility). Secondary outcomes included all-cause mortality, all-cause readmission, cardiovascular readmission, and hemorrhagic stroke. From 2007 to 2011, 77 468 patients who were not taking statins at the time of admission were hospitalized with ischemic stroke; of these, 71% were discharged on statin therapy. After adjustment, statin therapy at discharge was associated with a lower hazard of major adverse cardiovascular events (hazard ratio, 0.91; 95% confidence interval, 0.87-0.94), 28 more home-time days after discharge (PStatin therapy at discharge was not associated with increased risk of hemorrhagic stroke (hazard ratio, 0.94; 95% confidence interval, 0.72-1.23). Among statin-treated patients, 31% received a high-intensity dose; after risk adjustment, these patients had outcomes similar to those of recipients of moderate-intensity statin. In older ischemic stroke patients who were not taking statins at the time of admission, discharge statin therapy was associated with lower risk of major adverse cardiovascular events and nearly 1 month more home time during the 2-year period after hospitalization. © 2015 American Heart Association

  18. Statin use and risk of endometrial cancer

    DEFF Research Database (Denmark)

    Sperling, Cecilie D.; Verdoodt, Freija; Friis, Soren

    2017-01-01

    INTRODUCTION: Laboratory and epidemiological evidence have suggested that statin use may protect against the development of certain cancers, including endometrial cancer. In a nationwide registry-based case-control study, we examined the association between statin use and risk of endometrial cancer....... MATERIAL AND METHODS: Cases were female residents of Denmark with a primary diagnosis of endometrial cancer during 2000-2009. For each case, we selected 15 female population controls matched on date of birth (±one month) using risk-set sampling. Ever use of statin was defined as two or more prescriptions...... on separate dates. Conditional logistic regressions were used to estimate age-matched (by design) and multivariable-adjusted odds ratios (ORs) and 95% confidence intervals (CI) for endometrial cancer associated with statin use. The multivariable-adjusted models included parity, hormone replacement therapy...

  19. Current treatment of dyslipidaemia: PCSK9 inhibitors and statin intolerance.

    Science.gov (United States)

    Koskinas, Konstantinos; Wilhelm, Matthias; Windecker, Stephan

    2016-01-01

    Statins are the cornerstone of the management of dyslipidaemias and prevention of cardiovascular disease. Although statins are, overall, safe and well tolerated, adverse events can occur and constitute an important barrier to maintaining long-term adherence to statin treatment. In patients who cannot tolerate statins, alternative treatments include switch to another statin, intermittent-dosage regimens and non-statin lipid-lowering medications. Nonetheless, a high proportion of statin-intolerant patients are unable to achieve recommended low-density lipoprotein (LDL) cholesterol goals, thereby resulting in substantial residual cardiovascular risk. Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a protease implicated in LDL receptor degradation and plays a central role in cholesterol metabolism. In recent studies, PCSK9 inhibition by means of monoclonal antibodies achieved LDL cholesterol reductions of 50% to 70% across various patient populations and background lipid-lowering therapies, while maintaining a favourable safety profile. The efficacy and safety of the monoclonal antibodies alirocumab and evolocumab were confirmed in statin-intolerant patients, indicating that PCSK9 inhibitors represent an attractive treatment option in this challenging clinical setting. PCSK9 inhibitors recently received regulatory approval for clinical use and may be considered in properly selected patients according to current consensus documents, including patients with statin intolerance. In this review we summarise current evidence regarding diagnostic evaluation of statin-related adverse events, particularly statin-associated muscle symptoms, and we discuss current recommendations on the management of statin-intolerant patients. In view of emerging evidence of the efficacy and safety of PCSK9 inhibitors, we further discuss the role of monoclonal PCSK9 antibodies in the management of statin-intolerant hypercholesterolaemic patients.

  20. Statins Suppress Ebola Virus Infectivity by Interfering with Glycoprotein Processing.

    Science.gov (United States)

    Shrivastava-Ranjan, Punya; Flint, Mike; Bergeron, Éric; McElroy, Anita K; Chatterjee, Payel; Albariño, César G; Nichol, Stuart T; Spiropoulou, Christina F

    2018-05-01

    Ebola virus (EBOV) infection is a major public health concern due to high fatality rates and limited effective treatments. Statins, widely used cholesterol-lowering drugs, have pleiotropic mechanisms of action and were suggested as potential adjunct therapy for Ebola virus disease (EVD) during the 2013-2016 outbreak in West Africa. Here, we evaluated the antiviral effects of statin (lovastatin) on EBOV infection in vitro Statin treatment decreased infectious EBOV production in primary human monocyte-derived macrophages and in the hepatic cell line Huh7. Statin treatment did not interfere with viral entry, but the viral particles released from treated cells showed reduced infectivity due to inhibition of viral glycoprotein processing, as evidenced by decreased ratios of the mature glycoprotein form to precursor form. Statin-induced inhibition of infectious virus production and glycoprotein processing was reversed by exogenous mevalonate, the rate-limiting product of the cholesterol biosynthesis pathway, but not by low-density lipoprotein. Finally, statin-treated cells produced EBOV particles devoid of the surface glycoproteins required for virus infectivity. Our findings demonstrate that statin treatment inhibits EBOV infection and suggest that the efficacy of statin treatment should be evaluated in appropriate animal models of EVD. IMPORTANCE Treatments targeting Ebola virus disease (EVD) are experimental, expensive, and scarce. Statins are inexpensive generic drugs that have been used for many years for the treatment of hypercholesterolemia and have a favorable safety profile. Here, we show the antiviral effects of statins on infectious Ebola virus (EBOV) production. Our study reveals a novel molecular mechanism in which statin regulates EBOV particle infectivity by preventing glycoprotein processing and incorporation into virus particles. Additionally, statins have anti-inflammatory and immunomodulatory effects. Since inflammation and dysregulation of the immune

  1. An Intelligent Archive Testbed Incorporating Data Mining

    Science.gov (United States)

    Ramapriyan, H.; Isaac, D.; Yang, W.; Bonnlander, B.; Danks, D.

    2009-01-01

    interoperability, and being able to convert data to information and usable knowledge in an efficient, convenient manner, aided significantly by automation (Ramapriyan et al. 2004; NASA 2005). We can look upon the distributed provider environment with capabilities to convert data to information and to knowledge as an Intelligent Archive in the Context of a Knowledge Building system (IA-KBS). Some of the key capabilities of an IA-KBS are: Virtual Product Generation, Significant Event Detection, Automated Data Quality Assessment, Large-Scale Data Mining, Dynamic Feedback Loop, and Data Discovery and Efficient Requesting (Ramapriyan et al. 2004).

  2. Statins: antimicrobial resistance breakers or makers?

    Directory of Open Access Journals (Sweden)

    Humphrey H.T. Ko

    2017-10-01

    Full Text Available Introduction The repurposing of non-antibiotic drugs as adjuvant antibiotics may help break antimicrobial resistance (AMR. Statins are commonly prescribed worldwide to lower cholesterol. They also possess qualities of AMR “breakers”, namely direct antibacterial activity, synergism with antibiotics, and ability to stimulate the host immune system. However, statins’ role as AMR breakers may be limited. Their current extensive use for cardiovascular protection might result in selective pressures for resistance, ironically causing statins to be AMR “makers” instead. This review examines statins’ potential as AMR breakers, probable AMR makers, and identifies knowledge gaps in a statin-bacteria-human-environment continuum. The most suitable statin for repurposing is identified, and a mechanism of antibacterial action is postulated based on structure-activity relationship analysis. Methods A literature search using keywords “statin” or “statins” combined with “minimum inhibitory concentration” (MIC was performed in six databases on 7th April 2017. After screening 793 abstracts, 16 relevant studies were identified. Unrelated studies on drug interactions; antifungal or antiviral properties of statins; and antibacterial properties of mevastatin, cerivastatin, antibiotics, or natural products were excluded. Studies involving only statins currently registered for human use were included. Results Against Gram-positive bacteria, simvastatin generally exerted the greatest antibacterial activity (lowest MIC compared to atorvastatin, rosuvastatin, and fluvastatin. Against Gram-negative bacteria, atorvastatin generally exhibited similar or slightly better activity compared to simvastatin, but both were more potent than rosuvastatin and fluvastatin. Discussion Statins may serve as AMR breakers by working synergistically with existing topical antibiotics, attenuating virulence factors, boosting human immunity, or aiding in wound healing. It

  3. Explaining and predicting workplace accidents using data-mining techniques

    International Nuclear Information System (INIS)

    Rivas, T.; Paz, M.; Martin, J.E.; Matias, J.M.; Garcia, J.F.; Taboada, J.

    2011-01-01

    Current research into workplace risk is mainly conducted using conventional descriptive statistics, which, however, fail to properly identify cause-effect relationships and are unable to construct models that could predict accidents. The authors of the present study modelled incidents and accidents in two companies in the mining and construction sectors in order to identify the most important causes of accidents and develop predictive models. Data-mining techniques (decision rules, Bayesian networks, support vector machines and classification trees) were used to model accident and incident data compiled from the mining and construction sectors and obtained in interviews conducted soon after an incident/accident occurred. The results were compared with those for a classical statistical techniques (logistic regression), revealing the superiority of decision rules, classification trees and Bayesian networks in predicting and identifying the factors underlying accidents/incidents.

  4. Healthcare Scheduling by Data Mining: Literature Review and Future Directions

    Directory of Open Access Journals (Sweden)

    Maria M. Rinder

    2012-01-01

    Full Text Available This article presents a systematic literature review of the application of industrial engineering methods in healthcare scheduling, with a focus on the role of patient behavior in scheduling. Nine articles that used mathematical programming, data mining, genetic algorithms, and local searches for optimum schedules were obtained from an extensive search of literature. These methods are new approaches to solve the problems in healthcare scheduling. Some are adapted from areas such as manufacturing and transportation. Key findings from these studies include reduced time for scheduling, capability of solving more complex problems, and incorporation of more variables and constraints simultaneously than traditional scheduling methods. However, none of these methods modeled no-show and walk-ins patient behavior. Future research should include more variables related to patient and/or environment.

  5. Data mining techniques for thermophysical properties of refrigerants

    International Nuclear Information System (INIS)

    Kuecueksille, Ecir Ugur; Selbas, Resat; Sencan, Arzu

    2009-01-01

    This study presents ten modeling techniques within data mining process for the prediction of thermophysical properties of refrigerants (R134a, R404a, R407c and R410a). These are linear regression (LR), multi layer perception (MLP), pace regression (PR), simple linear regression (SLR), sequential minimal optimization (SMO), KStar, additive regression (AR), M5 model tree, decision table (DT), M5'Rules models. Relations depending on temperature and pressure were carried out for the determination of thermophysical properties as the specific heat capacity, viscosity, heat conduction coefficient, density of the refrigerants. Obtained model results for every refrigerant were compared and the best model was investigated. Results indicate that use of derived formulations from these techniques will facilitate design and optimize of heat exchangers which is component of especially vapor compression refrigeration system

  6. Explaining and predicting workplace accidents using data-mining techniques

    Energy Technology Data Exchange (ETDEWEB)

    Rivas, T., E-mail: trivas@uvigo.e [Dpto. Ingenieria de los Recursos Naturales y Medio Ambiente, E.T.S.I. Minas, University of Vigo, Campus Lagoas, 36310 Vigo (Spain); Paz, M., E-mail: mpaz.minas@gmail.co [Dpto. Ingenieria de los Recursos Naturales y Medio Ambiente, E.T.S.I. Minas, University of Vigo, Campus Lagoas, 36310 Vigo (Spain); Martin, J.E., E-mail: jmartin@cippinternacional.co [CIPP International, S.L. Parque Tecnologico de Asturias, Parcela 43, Oficina 11, 33428 Llanera (Spain); Matias, J.M., E-mail: jmmatias@uvigo.e [Dpto. Estadistica e Investigacion Operativa, E.T.S.I. Minas, University of Vigo, Campus Lagoas, 36310 Vigo (Spain); Garcia, J.F., E-mail: jgarcia@cippinternacional.co [CIPP International, S.L. Parque Tecnologico de Asturias, Parcela 43, Oficina 11, 33428 Llanera (Spain); Taboada, J., E-mail: jtaboada@uvigo.e [Dpto. Ingenieria de los Recursos Naturales y Medio Ambiente, E.T.S.I. Minas, University of Vigo, Campus Lagoas, 36310 Vigo (Spain)

    2011-07-15

    Current research into workplace risk is mainly conducted using conventional descriptive statistics, which, however, fail to properly identify cause-effect relationships and are unable to construct models that could predict accidents. The authors of the present study modelled incidents and accidents in two companies in the mining and construction sectors in order to identify the most important causes of accidents and develop predictive models. Data-mining techniques (decision rules, Bayesian networks, support vector machines and classification trees) were used to model accident and incident data compiled from the mining and construction sectors and obtained in interviews conducted soon after an incident/accident occurred. The results were compared with those for a classical statistical techniques (logistic regression), revealing the superiority of decision rules, classification trees and Bayesian networks in predicting and identifying the factors underlying accidents/incidents.

  7. Application of data mining techniques for nuclear data and instrumentation

    International Nuclear Information System (INIS)

    Toshniwal, Durga

    2013-01-01

    Data mining is defined as the discovery of previously unknown, valid, novel, potentially useful, and understandable patterns in large databases. It encompasses many different techniques and algorithms which differ in the kinds of data that can be analyzed and the form of knowledge representation used to convey the discovered knowledge. Patterns in the data can be represented in many different forms, including classification rules, association rules, clusters, etc. Data mining thus deals with the discovery of hidden trends and patterns from large quantities of data. The field of data mining is emerging as a new, fundamental research area with important applications to science, engineering, medicine, business, and education. It is an interdisciplinary research area and draws upon several roots, including database systems, machine learning, information systems, statistics and expert systems. Data mining, when performed on time series data, is known as time series data mining (TSDM). A time series is a sequence of real numbers, each number representing a value at a point of time. During the past few years, there has been an explosion of research in the area of time series data mining. This includes attempts to model time series data, to design languages to query such data, and to develop access structures to efficiently process queries on such data. Time series data arises naturally in many real-world applications. Efficient discovery of knowledge through time series data mining can be helpful in several domains such as: Stock market analysis, Weather forecasting etc. An important application area of data mining techniques is in nuclear power plant and related data. Nuclear power plant data can be represented in form of time sequences. Often it may be of prime importance to analyze such data to find trends and anomalies. The general goals of data mining include feature extraction, similarity search, clustering and classification, association rule mining and anomaly

  8. BOOK REVIEW EDUCATIONAL DATA MINING: APPLICATIONS AND TRENDS

    Directory of Open Access Journals (Sweden)

    Aylin OZTURK

    2016-04-01

    Full Text Available Educational Data Mining (EDM is a developing field based on data mining techniques. EDM emerged as a combination of areas such as machine learning, statistics, computer science, education, cognitive science, and psychometry. EDM focuses on learner characteristics, behaviors, academic achievements, process of learning, educational functionalities, domain knowledge content, assessments, and applications. Educational data mining is defined by Baker (2010 as ‘‘an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand students, and the settings which they learn in’’. EDM is concerned with improving the learning process and environment.

  9. Data Mining at NASA: From Theory to Applications

    Science.gov (United States)

    Srivastava, Ashok N.

    2009-01-01

    This slide presentation demonstrates the data mining/machine learning capabilities of NASA Ames and Intelligent Data Understanding (IDU) group. This will encompass the work done recently in the group by various group members. The IDU group develops novel algorithms to detect, classify, and predict events in large data streams for scientific and engineering systems. This presentation for Knowledge Discovery and Data Mining 2009 is to demonstrate the data mining/machine learning capabilities of NASA Ames and IDU group. This will encompass the work done re cently in the group by various group members.

  10. Data mining for the social sciences an introduction

    CERN Document Server

    Attewell, Paul

    2015-01-01

    We live in a world of big data: the amount of information collected on human behavior each day is staggering, and exponentially greater than at any time in the past. Additionally, powerful algorithms are capable of churning through seas of data to uncover patterns. Providing a simple and accessible introduction to data mining, Paul Attewell and David B. Monaghan discuss how data mining substantially differs from conventional statistical modeling familiar to most social scientists. The authors also empower social scientists to tap into these new resources and incorporate data mining

  11. Development of an Enhanced Generic Data Mining Life Cycle (DMLC)

    OpenAIRE

    Hofmann, Markus; Tierney, Brendan

    2017-01-01

    Data mining projects are complex and have a high failure rate. In order to improve project management and success rates of such projects a life cycle is vital to the overall success of the project. This paper reports on a research project that was concerned with the life cycle development for large scale data mining projects. The paper provides a detailed view of the design and development of a generic data mining life cycle called DMLC. The life cycle aims to support all members of data mini...

  12. Controlling Cholesterol with Statins

    Science.gov (United States)

    ... For Consumers Home For Consumers Consumer Updates Controlling Cholesterol with Statins Share Tweet Linkedin Pin it More ... not, the following tips can help keep your cholesterol in check: Talk with your healthcare provider about ...

  13. Statins: pros and cons.

    Science.gov (United States)

    Pinal-Fernandez, Iago; Casal-Dominguez, Maria; Mammen, Andrew L

    2018-05-23

    Statins inhibit the critical step of cholesterol synthesis in which 3-hydroxy-3-methylglutaryl coenzyme A (HMGC) is transformed to mevalonate by the enzyme HMGC reductase. By doing so, they have a potent lipid-lowering effect that reduces cardiovascular risk and decreases mortality. Since the mevalonate pathway also influences endothelial function, the inflammatory response, and coagulation, the effects of statins reach well beyond their cholesterol lowering properties. As with all drugs, statins may have adverse effects; these include musculoskeletal symptoms, increased risk of diabetes, and higher rates of hemorrhagic stroke. However, the frequency of adverse effects is extremely low and, in selected patient populations, the benefits of statins considerably outweigh the potential risks. Published by Elsevier España, S.L.U.

  14. Data Mining Methods Applied to Flight Operations Quality Assurance Data: A Comparison to Standard Statistical Methods

    Science.gov (United States)

    Stolzer, Alan J.; Halford, Carl

    2007-01-01

    In a previous study, multiple regression techniques were applied to Flight Operations Quality Assurance-derived data to develop parsimonious model(s) for fuel consumption on the Boeing 757 airplane. The present study examined several data mining algorithms, including neural networks, on the fuel consumption problem and compared them to the multiple regression results obtained earlier. Using regression methods, parsimonious models were obtained that explained approximately 85% of the variation in fuel flow. In general data mining methods were more effective in predicting fuel consumption. Classification and Regression Tree methods reported correlation coefficients of .91 to .92, and General Linear Models and Multilayer Perceptron neural networks reported correlation coefficients of about .99. These data mining models show great promise for use in further examining large FOQA databases for operational and safety improvements.

  15. An application of data mining in district heating substations for improving energy performance

    Science.gov (United States)

    Xue, Puning; Zhou, Zhigang; Chen, Xin; Liu, Jing

    2017-11-01

    Automatic meter reading system is capable of collecting and storing a huge number of district heating (DH) data. However, the data obtained are rarely fully utilized. Data mining is a promising technology to discover potential interesting knowledge from vast data. This paper applies data mining methods to analyse the massive data for improving energy performance of DH substation. The technical approach contains three steps: data selection, cluster analysis and association rule mining (ARM). Two-heating-season data of a substation are used for case study. Cluster analysis identifies six distinct heating patterns based on the primary heat of the substation. ARM reveals that secondary pressure difference and secondary flow rate have a strong correlation. Using the discovered rules, a fault occurring in remote flow meter installed at secondary network is detected accurately. The application demonstrates that data mining techniques can effectively extrapolate potential useful knowledge to better understand substation operation strategies and improve substation energy performance.

  16. Data Mining and Pattern Recognition Models for Identifying Inherited Diseases: Challenges and Implications.

    Science.gov (United States)

    Iddamalgoda, Lahiru; Das, Partha S; Aponso, Achala; Sundararajan, Vijayaraghava S; Suravajhala, Prashanth; Valadi, Jayaraman K

    2016-01-01

    Data mining and pattern recognition methods reveal interesting findings in genetic studies, especially on how the genetic makeup is associated with inherited diseases. Although researchers have proposed various data mining models for biomedical approaches, there remains a challenge in accurately prioritizing the single nucleotide polymorphisms (SNP) associated with the disease. In this commentary, we review the state-of-art data mining and pattern recognition models for identifying inherited diseases and deliberate the need of binary classification- and scoring-based prioritization methods in determining causal variants. While we discuss the pros and cons associated with these methods known, we argue that the gene prioritization methods and the protein interaction (PPI) methods in conjunction with the K nearest neighbors' could be used in accurately categorizing the genetic factors in disease causation.

  17. Data mining and Pattern Recognizing Models for Identifying Inherited Diseases: Challenges and Implications

    Directory of Open Access Journals (Sweden)

    Lahiru Iddamalgoda

    2016-08-01

    Full Text Available Data mining and pattern recognition methods reveal interesting findings in genetic studies, especially on how genetic makeup is associated with inherited diseases. Although researchers have proposed various data mining models for biomedical approaches, there remains a challenge in accurately determining the responsible genetic factors for prioritizing the single nucleotide polymorphisms (SNP associated with the disease. In this commentary, we review the state-of-art data mining and pattern recognition models for identifying inherited diseases and deliberate the need of binary classification and scoring based prioritization methods for determining causal variants. While we discuss the pros and cons associated with these methods known, we argue that the gene prioritization methods and the protein interaction (PPI methods in conjunction with the K nearest neighbors’ could be used in accurately categorizing the genetic factors in disease causation

  18. Data mining algorithms for land cover change detection: a review

    Indian Academy of Sciences (India)

    Sangram Panigrahi

    2017-11-24

    Nov 24, 2017 ... values, poor quality measurement, high resolution and high dimensional data. The land cover .... These data sets also include quality assurance information, ...... 2012 A new data mining framework for forest fire mapping.

  19. Warehousing Structured and Unstructured Data for Data Mining.

    Science.gov (United States)

    Miller, L. L.; Honavar, Vasant; Barta, Tom

    1997-01-01

    Describes an extensible object-oriented view system that supports the integration of both structured and unstructured data sources in either the multidatabase or data warehouse environment. Discusses related work and data mining issues. (AEF)

  20. Usage reporting on recorded lectures using educational data mining

    NARCIS (Netherlands)

    Gorissen, Pierre; Van Bruggen, Jan; Jochems, Wim

    2012-01-01

    Gorissen, P., Van Bruggen, J., & Jochems, W. M. G. (2012). Usage reporting on recorded lectures using educational data mining. International Journal of Learning Technology, 7, 23-40. doi:10.1504/IJLT.2012.046864

  1. Usage of Data Mining at Financial Decision Making

    Directory of Open Access Journals (Sweden)

    Levent BORAN

    2014-06-01

    Full Text Available The knowledge age requires controlling every kind of information. Recognition of patterns in data may provide previously unknown and useful information that can provide competitive advantages. If related techniques are applied on financial statements, it is possible to acquire valuable information about companies’ financial situations. It is considered that data mining could be an alternative of common financial analysis techniques such as vertical analysis, horizontal analysis, trend analysis and ratio analysis. Against existing financial analysis methods, data mining provides some advantages, which are ability of manipulation of huge data and competence of obtaining previously unknown information. There exist two major constraints of data mining implementation that are lack of experts on both data mining and related domains and cost of computer software and hardware used.

  2. Visual Data Mining of Robot Performance Data, Phase II

    Data.gov (United States)

    National Aeronautics and Space Administration — We propose to design and develop VDM/RP, a visual data mining system that will enable analysts to acquire, store, query, analyze, and visualize recent and historical...

  3. 2nd International Conference on Computational Intelligence in Data Mining

    CERN Document Server

    Mohapatra, Durga

    2016-01-01

    The book is a collection of high-quality peer-reviewed research papers presented in the Second International Conference on Computational Intelligence in Data Mining (ICCIDM 2015) held at Bhubaneswar, Odisha, India during 5 – 6 December 2015. The two-volume Proceedings address the difficulties and challenges for the seamless integration of two core disciplines of computer science, i.e., computational intelligence and data mining. The book addresses different methods and techniques of integration for enhancing the overall goal of data mining. The book helps to disseminate the knowledge about some innovative, active research directions in the field of data mining, machine and computational intelligence, along with some current issues and applications of related topics.

  4. Application of Data Mining for Card Fraud Detection

    Directory of Open Access Journals (Sweden)

    I.V. Andrianov

    2012-03-01

    Full Text Available This paper focuses on implementing Data Mining methods for card fraud detection. The approach to classification and prediction tasks for detection of unauthorized transactions is considered.

  5. 1st International Conference on Computational Intelligence in Data Mining

    CERN Document Server

    Behera, Himansu; Mandal, Jyotsna; Mohapatra, Durga

    2015-01-01

    The contributed volume aims to explicate and address the difficulties and challenges for the seamless integration of two core disciplines of computer science, i.e., computational intelligence and data mining. Data Mining aims at the automatic discovery of underlying non-trivial knowledge from datasets by applying intelligent analysis techniques. The interest in this research area has experienced a considerable growth in the last years due to two key factors: (a) knowledge hidden in organizations’ databases can be exploited to improve strategic and managerial decision-making; (b) the large volume of data managed by organizations makes it impossible to carry out a manual analysis. The book addresses different methods and techniques of integration for enhancing the overall goal of data mining. The book helps to disseminate the knowledge about some innovative, active research directions in the field of data mining, machine and computational intelligence, along with some current issues and applications of relate...

  6. Data mining of air traffic control operational errors

    Science.gov (United States)

    2006-01-01

    In this paper we present the results of : applying data mining techniques to identify patterns and : anomalies in air traffic control operational errors (OEs). : Reducing the OE rate is of high importance and remains a : challenge in the aviation saf...

  7. Accounting and Financial Data Analysis Data Mining Tools

    Directory of Open Access Journals (Sweden)

    Diana Elena Codreanu

    2011-05-01

    Full Text Available Computerized accounting systems in recent years have seen an increase in complexity due to thecompetitive economic environment but with the help of data analysis solutions such as OLAP and DataMining can be a multidimensional data analysis, can detect the fraud and can discover knowledge hidden indata, ensuring such information is useful for decision making within the organization. In the literature thereare many definitions for data mining but all boils down to same idea: the process takes place to extract newinformation from large data collections, information without the aid of data mining tools would be verydifficult to obtain. Information obtained by data mining process has the advantage that only respond to thequestion of what happens but at the same time argue and show why certain things are happening. In this paperwe wish to present advanced techniques for analysis and exploitation of data stored in a multidimensionaldatabase.

  8. Transparent data mining for big and small data

    CERN Document Server

    Quercia, Daniele; Pasquale, Frank

    2017-01-01

    This book focuses on new and emerging data mining solutions that offer a greater level of transparency than existing solutions. Transparent data mining solutions with desirable properties (e.g. effective, fully automatic, scalable) are covered in the book. Experimental findings of transparent solutions are tailored to different domain experts, and experimental metrics for evaluating algorithmic transparency are presented. The book also discusses societal effects of black box vs. transparent approaches to data mining, as well as real-world use cases for these approaches. As algorithms increasingly support different aspects of modern life, a greater level of transparency is sorely needed, not least because discrimination and biases have to be avoided. With contributions from domain experts, this book provides an overview of an emerging area of data mining that has profound societal consequences, and provides the technical background to for readers to contribute to the field or to put existing approaches to prac...

  9. artery disease guidelines with extracted knowledge from data mining

    Directory of Open Access Journals (Sweden)

    Peyman Rezaei-Hachesu

    2017-06-01

    Conclusion: Guidelines confirm the achieved results from data mining (DM techniques and help to rank important risk factors based on national and local information. Evaluation of extracted rules determined new patterns for CAD patients.

  10. Perioperative Statin Therapy Is Not Associated With Reduced Risk of Anastomotic Leakage After Colorectal Resection

    DEFF Research Database (Denmark)

    Bisgård, Anne Sofie; Noack, Morten Westergaard; Klein, Mads

    2013-01-01

    Anastomotic leakage is a serious complication of colorectal surgery. Several studies have demonstrated the beneficial pleiotropic effects of statins, and preliminary studies have suggested that perioperative statin treatment may be associated with reduced risk of anastomotic leakage....

  11. Generic atorvastatin, the Belgian statin market and the cost-effectiveness of statin therapy.

    Science.gov (United States)

    Simoens, Steven; Sinnaeve, Peter R

    2013-02-01

    This study examines how the market entry of generic atorvastatin influences the Belgian statin market and the cost-effectiveness of statin therapy. Using IMS Health data, the Belgian 2000-2011 statin market was analyzed in terms of total expenditure, annual price of statin treatment, and patient numbers. A simulation analysis projected statin market shares from 2012 to 2015 following market entry of generic atorvastatin. This analysis was based on three scenarios regarding the number of patients taking specific statins. Savings associated with an atorvastatin price reduction of 50-70 % were calculated. A literature review of economic evaluations assessed the cost-effectiveness of generic atorvastatin. Statin expenditure increased from €113 million in 2000 to €285 million in 2011 due to higher expenditure on atorvastatin and rosuvastatin. Although the number of patients treated with simvastatin increased by nearly 800 %, the resulting increase in expenditure was partially offset by price reductions. Atorvastatin is projected to become the dominant product in the Belgian statin market (market share of 47-66 % by 2015). Annual savings would attain €108.6-€153.7 million for a 50 % reduction in the atorvastatin price and €152.0-€215.2 million for a 70 % price reduction. The literature suggests that generic atorvastatin is cost-effective as compared to simvastatin. The limited evidence about the cost-effectiveness of rosuvastatin as compared with generic atorvastatin is inconclusive. Generic atorvastatin is cost-effective as compared to simvastatin, is projected to become the dominant product in the Belgian statin market and is expected to generate substantial savings to health care payers.

  12. DATA MINING TECHNIQUES FOR EDUCATIONAL DATA: A REVIEW

    OpenAIRE

    Pragati Sharma; Dr. Sanjiv Sharma

    2018-01-01

    Recently, data mining is gaining more popularity among researcher. Data mining provides various techniques and methods for analysing data produced by various applications of different domain. Similarly, Educational mining is providing a way for analyzing educational data set. Educational mining concerns with developing methods for discovering knowledge from data that come from educational field and it helps to extract the hidden patterns and to discover new knowledge from large educational da...

  13. Report from Dagstuhl Seminar 12331 Mobility Data Mining and Privacy

    OpenAIRE

    Clifton, Christopher W.; Kuijpers, Bart; Morik, Katharina; Saygin, Yucel

    2012-01-01

    This report documents the program and the outcomes of Dagstuhl Seminar 12331 “Mobility Data Mining and Privacy”. Mobility data mining aims to extract knowledge from movement behaviour of people, but this data also poses novel privacy risks. This seminar gathered a multidisciplinary team for a conversation on how to balance the value in mining mobility data with privacy issues. The seminar focused on four key issues: Privacy in vehicular data, in cellular data, context- dependent privacy, and ...

  14. [Aspects for data mining implementation in gerontology and geriatrics].

    Science.gov (United States)

    Mikhal'skiĭ, A I

    2014-01-01

    Current challenges facing theory and practice in ageing sciences need new methods of experimental data investigation. This is a result as of experimental basis developments in biological research, so of information technology progress. These achievements make it possible to use well proven in different fields of science and engineering data mining methods for tasks in gerontology and geriatrics. Some examples of data mining methods implementation in gerontology are presented.

  15. An Intelligent Agent based Architecture for Visual Data Mining

    OpenAIRE

    Hamdi Ellouzi; Hela Ltifi; Mounir Ben Ayed

    2016-01-01

    the aim of this paper is to present an intelligent architecture of Decision Support System (DSS) based on visual data mining. This architecture applies the multi-agent technology to facilitate the design and development of DSS in complex and dynamic environment. Multi-Agent Systems add a high level of abstraction. To validate the proposed architecture, it is implemented to develop a distributed visual data mining based DSS to predict nosocomial infectionsoccurrence in intensive care units. Th...

  16. A REVIEW ON PREDICTIVE ANALYTICS IN DATA MINING

    OpenAIRE

    Arumugam.S

    2016-01-01

    The data mining its main process is to collect, extract and store the valuable information and now-a-days it’s done by many enterprises actively. In advanced analytics, Predictive analytics is the one of the branch which is mainly used to make predictions about future events which are unknown. Predictive analytics which uses various techniques from machine learning, statistics, data mining, modeling, and artificial intelligence for analyzing the current data and to make predictions about futu...

  17. Data mining in e-commerce: A survey

    Indian Academy of Sciences (India)

    Data mining has matured as a field of basic and applied research in computer science in general and e-commerce in particular. In this paper, we survey some of the recent approaches and architectures where data mining has been applied in the fields of e-commerce and e-business. Our intent is not to survey the plethora ...

  18. Predictive models in churn data mining: a review

    OpenAIRE

    García, David L.; Vellido Alcacena, Alfredo; Nebot Castells, M. Àngela

    2007-01-01

    The development of predictive models of customer abandonment plays a central role in any churn management strategy. These models can be developed using either qualitative approaches or can take a data-centred point of view. In the latter case, the use of Data Mining procedures and techniques can provide useful and actionable insights into the processes leading to abandonment. In this report, we provide a brief and structured review of some of the Data Mining approaches that have been put forw...

  19. DECISION SUPPORT SYSTEM TO SUPPORT DECISION PROCESSES WITH DATA MINING

    OpenAIRE

    Rupnik, Rok; Kukar, Matjaž

    2007-01-01

    Traditional techniques of data analysis do not enable the solution of all kind of problems and for that reason they have become insufficient. This caused a newinterdisciplinary field of data mining to arise, encompassing both classical statistical, and modern machine learning techniques to support the data analysis and knowledge discovery from data. Data mining methods are powerful in dealing with large quantities of data, but on the other hand they are difficult to master by business users t...

  20. Survey of Analysis of Crime Detection Techniques Using Data Mining and Machine Learning

    Science.gov (United States)

    Prabakaran, S.; Mitra, Shilpa

    2018-04-01

    Data mining is the field containing procedures for finding designs or patterns in a huge dataset, it includes strategies at the convergence of machine learning and database framework. It can be applied to various fields like future healthcare, market basket analysis, education, manufacturing engineering, crime investigation etc. Among these, crime investigation is an interesting application to process crime characteristics to help the society for a better living. This paper survey various data mining techniques used in this domain. This study may be helpful in designing new strategies for crime prediction and analysis.

  1. Consequences of succinylcholine administration to patients using statins.

    Science.gov (United States)

    Turan, Alparslan; Mendoza, Maria L; Gupta, Shipra; You, Jing; Gottlieb, Alexandru; Chu, Weihan; Saager, Leif; Sessler, Daniel I

    2011-07-01

    Statins cause structural changes in myocytes and provoke myotoxicity, myopathy, and myalgias. Thus, patients taking statins may be especially susceptible to succinylcholine-induced muscle injury. The authors tested the hypothesis that succinylcholine increases plasma concentrations of myoglobin, potassium, and creatine kinase more in patients who take statins than in those who do not and that succinylcholine-induced postoperative muscle pain is aggravated in statin users. Patients who took statins for at least 3 months and those who had never used statins were enrolled. General anesthesia was induced and included 1.5 mg/kg succinylcholine for intubation. The incidence and degree of fasciculation after succinylcholine administration were recorded. Blood samples were obtained before induction and 5 and 20 min and 24 h after succinylcholine administration. Patients were interviewed 2 and 24 h after surgery to determine the degree of myalgia. The authors enrolled 38 patients who used statins and 32 who did not. At 20 min, myoglobin was higher in statin users versus nonusers (ratio of medians 1.34 [95% CI: 1.1, 1.7], P = 0.018). Fasciculations in statin users were more intense than in nonusers (P = 0.047). However, plasma potassium and creatine kinase concentrations were similar in statin users and nonusers, as was muscle pain. The plasma myoglobin concentration at 20 min was significantly greater in statin users than nonusers, although the difference seems unlikely to be clinically important. The study results suggest that the effect of succinylcholine given to patients taking statins is likely to be small and probably of limited clinical consequence.

  2. Adaptation to statins restricts human tumour growth in Nude mice

    International Nuclear Information System (INIS)

    Follet, Julie; Rémy, Lionel; Hesry, Vincent; Simon, Brigitte; Gillet, Danièle; Auvray, Pierrick; Corcos, Laurent; Le Jossic-Corcos, Catherine

    2011-01-01

    Statins have long been used as anti-hypercholesterolemia drugs, but numerous lines of evidence suggest that they may also bear anti-tumour potential. We have recently demonstrated that it was possible to isolate cancer cells adapted to growth in the continuous presence of lovastatin. These cells grew more slowly than the statin-sensitive cells of origin. In the present study, we compared the ability of both statin-sensitive and statin-resistant cells to give rise to tumours in Nude mice. HGT-1 human gastric cancer cells and L50 statin-resistant derivatives were injected subcutaneously into Nude mice and tumour growth was recorded. At the end of the experiment, tumours were recovered and marker proteins were analyzed by western blotting, RT-PCR and immunohistochemistry. L50 tumours grew more slowly, showed a strong decrease in cyclin B1, over-expressed collagen IV, and had reduced laminin 332, VEGF and CD34 levels, which, collectively, may have restricted cell division, cell adhesion and neoangiogenesis. Taken together, these results showed that statin-resistant cells developed into smaller tumours than statin-sensitive cells. This may be reflective of the cancer restricting activity of statins in humans, as suggested from several retrospective studies with subjects undergoing statin therapy for several years

  3. Moderate-intensity statin therapy seems ineffective in primary cardiovascular prevention in patients with type 2 diabetes complicated by nephropathy. A multicenter prospective 8 years follow up study.

    Science.gov (United States)

    Sasso, Ferdinando Carlo; Lascar, Nadia; Ascione, Antonella; Carbonara, Ornella; De Nicola, Luca; Minutolo, Roberto; Salvatore, Teresa; Rizzo, Maria Rosaria; Cirillo, Plinio; Paolisso, Giuseppe; Marfella, Raffaele

    2016-10-13

    Although numerous studies and metanalysis have shown the beneficial effect of statin therapy in CVD secondary prevention, there is still controversy such the use of statins for primary CVD prevention in patients with DM. The purpose of this study was to evaluate the occurrence of total major adverse cardio-vascular events (MACE) in a cohort of patients with type 2 diabetes complicated by nephropathy treated with statins, in order to verify real life effect of statin on CVD primary prevention. We conducted an observational prospective multicenter study on 564 patients with type 2 diabetic nephropathy free of cardiovascular disease attending 21 national outpatient diabetes clinics and followed them up for 8 years. 169 of them were treated with statins (group A) while 395 were not on statins (group B). Notably, none of the patients was treated with a high-intensity statin therapy according to last ADA position statement. Total MACE occurred in 32 patients from group A and in 68 patients from group B. Fatal MACE occurred in 13 patients from group A and in 30 from group B; nonfatal MACE occurred in 19 patients from group A and in 38 patients from group B. The analysis of the Kaplan-Meier survival curves showed a not statistically significant difference in the incidence of total (p 0.758), fatal (p 0.474) and nonfatal (p 0.812) MACE between the two groups. HbA1c only showed a significant difference in the incidence of MACE between the two groups (HR 1.201, CI 1.041-1.387, p 0.012). These findings suggest that, in a real clinical setting, moderate-intensity statin treatment is ineffective in cardiovascular primary prevention for patients with diabetic nephropathy. Trial registration ClinicalTrials.gov Identifier NCT00535925. Date of registration: September 24, 2007, retrospectively registered.

  4. Data mining applications in the context of casemix.

    Science.gov (United States)

    Koh, H C; Leong, S K

    2001-07-01

    In October 1999, the Singapore Government introduced casemix-based funding to public hospitals. The casemix approach to health care funding is expected to yield significant benefits, including equity and rationality in financing health care, the use of comparative casemix data for quality improvement activities, and the provision of information that enables hospitals to understand their cost behaviour and reinforces the drive for more cost-efficient services. However, there is some concern about the "quicker and sicker" syndrome (that is, the rapid discharge of patients with little regard for the quality of outcome). As it is likely that consequences of premature discharges will be reflected in the readmission data, an analysis of possible systematic patterns in readmission data can provide useful insight into the "quicker and sicker" syndrome. This paper explores potential data mining applications in the context of casemix by using readmission data as an illustration. In particular, it illustrates how data mining can be used to better understand readmission data and to detect systematic patterns, if any. From a technical perspective, data mining (which is capable of analysing complex non-linear and interaction relationships) supplements and complements traditional statistical methods in data analysis. From an applications perspective, data mining provides the technology and methodology to analyse mass volume of data to detect hidden patterns in data. Using readmission data as an illustrative data mining application, this paper explores potential data mining applications in the general casemix context.

  5. The effects of 2 weeks of statin treatment on mitochondrial respiratory capacity in middle-aged males: the LIFESTAT study.

    Science.gov (United States)

    Asping, Magnus; Stride, Nis; Søgaard, Ditte; Dohlmann, Tine Lovsø; Helge, Jørn W; Dela, Flemming; Larsen, Steen

    2017-06-01

    Statins are used to lower cholesterol in plasma and are one of the most used drugs in the world. Many statin users experience muscle pain, but the mechanisms are unknown at the moment. Many studies have hypothesized that mitochondrial function could be involved in these side effects. The aim of the study was to investigate mitochondrial function after 2 weeks of treatment with simvastatin (S; n = 10) or pravastatin (P; n = 10) in healthy middle-aged participants. Mitochondrial respiratory capacity and substrate sensitivity were measured in permeabilized muscle fibers by high-resolution respirometry. Mitochondrial content (citrate synthase (CS) activity), antioxidant content, as well as coenzyme Q 10 concentration (Q 10 ) were determined. Fasting plasma glucose and insulin concentrations were measured, and whole body maximal oxygen uptake (VO 2max ) was determined. No differences were seen in mitochondrial respiratory capacity although a tendency was observed for a reduction when complex IV respiration was analyzed in both S (229 (169; 289 (95% confidence interval)) vs. 179 (146; 211) pmol/s/mg, respectively; P = 0.062) and P (214 (143; 285) vs. 162 (104; 220) pmol/s/mg, respectively; P = 0.053) after treatment. A tendency (1.64 (1.28; 2.00) vs. 1.28 (0.99; 1.58) mM, respectively; P = 0.092) for an increased mitochondrial substrate sensitivity (complex I-linked substrate; glutamate) was seen only in S after treatment. No differences were seen in Q 10 , CS activity, or antioxidant content after treatment. Fasting glucose and insulin as well as VO 2max were not changed after treatment. Two weeks of statin (S or P) treatment have no major effect on mitochondrial function. The tendency for an increased mitochondrial substrate sensitivity after simvastatin treatment could be an early indication of the negative effects linked to statin treatment.

  6. Analisis Data Lulusan dengan Data Mining untuk Mendukung Strategi Promosi Universitas Lancang Kuning

    Directory of Open Access Journals (Sweden)

    Elvira Asril

    2015-11-01

    Full Text Available Setiap perusahaan maupun organisasi yang ingin tetap bertahan perlu untuk menentukan strategi promosi yang tepat. Penentuan strategi promosi yang tepat akan dapat mengurangi biaya promosi dan mencapai sasaran promosi yang tepat. Salah satu cara yang dapat dilakukan untuk penentuan strategi promosi adalah dengan menggunakan teknik data mining. Teknik data mining yang digunakan dalam hal ini adalah dengan menggunakan algoritma Clustering K-Means. Clustering merupakan pengelompokkan record, observasi, atau kasus ke dalam kelas-kelas objek yang mirip. K-Means adalah metode klaster data non-hirarkis yang mencoba untuk membagi data ke dalam satu atau lebih klaster. Penelitian dilakukan dengan mengamati beberapa variabel penelitian yang sering dipertimbangkan oleh perguruan tinggi dalam menentukan sasaran promosinya yaitu asal sekolah, daerah, dan jurusan. Hasil penelitian ini adalah berupa pola menarik hasil data mining yang merupakan informasi penting untuk mendukung strategi promosi yang tepat dalam mendapatkan calon mahasiswa baru.Kata kunci: Data Mining, Clustering, K-Means Each company or organization that wants to survive needs to determine appropriate promotional strategies. Determination of appropriate promotional strategies will be able to reduce costs and achieve the goals the promotion of proper promotion. One way that can be done to determine campaign strategy is to use data mining techniques. Data mining techniques used in this case is to use a K-Means clustering algorithm. Clustering is the grouping of records, observation, or in the case of the object classes that are similar. K-Means is a method of non-hierarchical clustering of data that is trying to divide the data into one or more clusters. The study was conducted by observing some of the variables that are often considered by the college in determining the target of promotion that the school of origin, region, and department. Results of this study are interesting pattern of

  7. Statin Intolerance: A Literature Review and Management Strategies.

    Science.gov (United States)

    Saxon, David R; Eckel, Robert H

    Statin intolerance is a commonly encountered clinical problem for which useful management strategies exist. Although many patients report statin-related muscle symptoms, studies indicate that the majority of these patients can tolerate a statin upon re-challenge. Alternative statin dosing strategies are an effective way to modify and reintroduce statin therapy for patients reporting adverse symptoms. Correction of vitamin D deficiency and hypothyroidism may improve statin tolerability in some patients. CoQ10 supplementation has been found to be of no benefit for statin-related muscle symptoms in most recent clinical trials. PCSK9 inhibitors are a new therapeutic option that if confirmed as safe and effective by outcomes trials may be of substantial benefit to select patients at high ASCVD risk who are unable to achieve adequate low-density lipoprotein cholesterol (LDL-C) lowering on maximally tolerated statin therapy. Other available medications to lower LDL-C in statin intolerant patients include ezetimibe, bile acid sequestrants, niacin, and fibrates. Published by Elsevier Inc.

  8. Statin-induced myotoxicity is exacerbated by aging: A biophysical and molecular biology study in rats treated with atorvastatin

    International Nuclear Information System (INIS)

    Camerino, Giulia Maria; De Bellis, Michela; Conte, Elena; Liantonio, Antonella; Musaraj, Kejla; Cannone, Maria; Fonzino, Adriano; Giustino, Arcangela; De Luca, Annamaria; Romano, Rossella; Camerino, Claudia; Laghezza, Antonio; Loiodice, Fulvio; Desaphy, Jean-Francois; Conte Camerino, Diana; Pierno, Sabata

    2016-01-01

    Statin-induced skeletal muscle damage in rats is associated to the reduction of the resting sarcolemmal chloride conductance (gCl) and ClC-1 chloride channel expression. These drugs also affect the ClC-1 regulation by increasing protein kinase C (PKC) activity, which phosphorylate and close the channel. Also the intracellular resting calcium (restCa) level is increased. Similar alterations are observed in skeletal muscles of aged rats, suggesting a higher risk of statin myotoxicity. To verify this hypothesis, we performed a 4–5-weeks atorvastatin treatment of 24-months-old rats to evaluate the ClC-1 channel function by the two-intracellular microelectrodes technique as well as transcript and protein expression of different genes sensitive to statins by quantitative real-time-PCR and western blot analysis. The restCa was measured using FURA-2 imaging, and histological analysis of muscle sections was performed. The results show a marked reduction of resting gCl, in agreement with the reduced ClC-1 mRNA and protein expression in atorvastatin-treated aged rats, with respect to treated adult animals. The observed changes in myocyte-enhancer factor-2 (MEF2) expression may be involved in ClC-1 expression changes. The activity of PKC was also increased and further modulate the gCl in treated aged rats. In parallel, a marked reduction of the expression of glycolytic and mitochondrial enzymes demonstrates an impairment of muscle metabolism. No worsening of restCa or histological features was found in statin-treated aged animals. These findings suggest that a strong reduction of gCl and alteration of muscle metabolism coupled to muscle atrophy may contribute to the increased risk of statin-induced myopathy in the elderly. - Highlights: • This work characterizes the causes of atorvastatin related myotoxicity in aged rats. • Skeletal muscle chloride channel ClC-1 is a target of statin-induced side effects. • ClC-1 dysfunction is worsened by aging process. • Age

  9. Statin-induced myotoxicity is exacerbated by aging: A biophysical and molecular biology study in rats treated with atorvastatin

    Energy Technology Data Exchange (ETDEWEB)

    Camerino, Giulia Maria; De Bellis, Michela; Conte, Elena; Liantonio, Antonella; Musaraj, Kejla; Cannone, Maria; Fonzino, Adriano [Section of Pharmacology, Department of Pharmacy & Drug Sciences, University of Bari - Aldo Moro, Bari (Italy); Giustino, Arcangela [Department of Biomedical Sciences and Human Oncology, University of Bari - Aldo Moro, Medical School, Bari (Italy); De Luca, Annamaria; Romano, Rossella [Section of Pharmacology, Department of Pharmacy & Drug Sciences, University of Bari - Aldo Moro, Bari (Italy); Camerino, Claudia [Department of Medical Sciences, Neurosciences and Sense Organs, University of Bari - Aldo Moro, Bari (Italy); Laghezza, Antonio; Loiodice, Fulvio [Section of Medicinal Chemistry, Department of Pharmacy & Drug Sciences, University of Bari - Aldo Moro, Bari (Italy); Desaphy, Jean-Francois [Department of Biomedical Sciences and Human Oncology, University of Bari - Aldo Moro, Medical School, Bari (Italy); Conte Camerino, Diana [Section of Pharmacology, Department of Pharmacy & Drug Sciences, University of Bari - Aldo Moro, Bari (Italy); Pierno, Sabata, E-mail: sabata.pierno@uniba.it [Section of Pharmacology, Department of Pharmacy & Drug Sciences, University of Bari - Aldo Moro, Bari (Italy)

    2016-09-01

    Statin-induced skeletal muscle damage in rats is associated to the reduction of the resting sarcolemmal chloride conductance (gCl) and ClC-1 chloride channel expression. These drugs also affect the ClC-1 regulation by increasing protein kinase C (PKC) activity, which phosphorylate and close the channel. Also the intracellular resting calcium (restCa) level is increased. Similar alterations are observed in skeletal muscles of aged rats, suggesting a higher risk of statin myotoxicity. To verify this hypothesis, we performed a 4–5-weeks atorvastatin treatment of 24-months-old rats to evaluate the ClC-1 channel function by the two-intracellular microelectrodes technique as well as transcript and protein expression of different genes sensitive to statins by quantitative real-time-PCR and western blot analysis. The restCa was measured using FURA-2 imaging, and histological analysis of muscle sections was performed. The results show a marked reduction of resting gCl, in agreement with the reduced ClC-1 mRNA and protein expression in atorvastatin-treated aged rats, with respect to treated adult animals. The observed changes in myocyte-enhancer factor-2 (MEF2) expression may be involved in ClC-1 expression changes. The activity of PKC was also increased and further modulate the gCl in treated aged rats. In parallel, a marked reduction of the expression of glycolytic and mitochondrial enzymes demonstrates an impairment of muscle metabolism. No worsening of restCa or histological features was found in statin-treated aged animals. These findings suggest that a strong reduction of gCl and alteration of muscle metabolism coupled to muscle atrophy may contribute to the increased risk of statin-induced myopathy in the elderly. - Highlights: • This work characterizes the causes of atorvastatin related myotoxicity in aged rats. • Skeletal muscle chloride channel ClC-1 is a target of statin-induced side effects. • ClC-1 dysfunction is worsened by aging process. • Age

  10. The relationship between statins and breast cancer prognosis varies by statin type and exposure time: a meta-analysis.

    Science.gov (United States)

    Liu, Binliang; Yi, Zongbi; Guan, Xiuwen; Zeng, Yi-Xin; Ma, Fei

    2017-07-01

    Breast cancer is the most common cancer in females and the leading cause of death worldwide. The effects of statins on breast cancer prognosis have long been controversial; thus, it is important to investigate the relationship between statin type, exposure time, and breast cancer prognosis. This study sought to explore the effect of statins, as well as the different effects of statin solubility and variable follow-up times, on breast cancer prognosis. We searched the MEDLINE (via PubMed), EMBASE (via OvidSP), Cochrane Library, and ISI Web of Knowledge databases using combinations of the terms "breast neoplasms[MeSH]," "statins" or "lipid-lowering drug," "prognosis" or "survival," or "mortality" or "outcome" with no limit on the publication date. We searched the databases between inception and October 15, 2016. Reference lists of the included studies and relevant reviews were also manually screened. The initial search identified 71 publications, and 7 of these studies, which included a total of 197,048 women, met the selection criteria. Two authors independently screened each study for inclusion and extracted the data. The data were analyzed using Stata/SE 11.0. Overall statin use was associated with lower cancer-specific mortality and all-cause mortality, although the benefit appeared to be constrained by statin type and follow-up time. Lipophilic statins were associated with decreased breast cancer-specific and all-cause mortality; however, hydrophilic statins were weakly protective against only all-cause mortality and not breast cancer-specific mortality. Of note, one group with more than 4 years of follow-up did not show a significant correlation between statin use and cancer-specific mortality or all-cause mortality, whereas groups with less than 4 years of follow-up still showed the protective effect of statins against cancer-specific mortality and all-cause mortality. Although statins can reduce breast cancer patient mortality, the benefit appears to be

  11. Towards educational data mining: Using data mining methods for automated chat analysis to understand and support inquiry learning processes

    OpenAIRE

    Anjewierden , Anjo; Kolloffel , Bas; Hulshof , Casper

    2007-01-01

    In this paper we investigate the application of data mining methods to provide learners with real-time adaptive feedback on the nature and patterns of their on-line communication while learning collaboratively.We derived two models for classifying chat messages using data mining techniques and tested these on an actual data set [16]. The reliability of the classification of chat messages is established by comparing the models performance to that of humans. Results indicate that the classifica...

  12. A Meta-Analysis of Educational Data Mining on Improvements in Learning Outcomes

    Science.gov (United States)

    AlShammari, Iqbal A.; Aldhafiri, Mohammed D.; Al-Shammari, Zaid

    2013-01-01

    A meta-synthesis study was conducted of 60 research studies on educational data mining (EDM) and their impacts on and outcomes for improving learning outcomes. After an overview, an examination of these outcomes is provided (Romero, Ventura, Espejo, & Hervas, 2008; Romero, "et al.", 2011). Then, a review of other EDM-related research…

  13. Predicting Dropout Student: An Application of Data Mining Methods in an Online Education Program

    Science.gov (United States)

    Yukselturk, Erman; Ozekes, Serhat; Turel, Yalin Kilic

    2014-01-01

    This study examined the prediction of dropouts through data mining approaches in an online program. The subject of the study was selected from a total of 189 students who registered to the online Information Technologies Certificate Program in 2007-2009. The data was collected through online questionnaires (Demographic Survey, Online Technologies…

  14. Statins for aortic valve stenosis

    Directory of Open Access Journals (Sweden)

    Luciana Thiago

    Full Text Available ABSTRACT BACKGROUND: Aortic valve stenosis is the most common type of valvular heart disease in the USA and Europe. Aortic valve stenosis is considered similar to atherosclerotic disease. Some studies have evaluated statins for aortic valve stenosis. OBJECTIVES: To evaluate the effectiveness and safety of statins in aortic valve stenosis. METHODS: Search methods: We searched the Cochrane Central Register of Controlled Trials (CENTRAL, MEDLINE, Embase, LILACS - IBECS, Web of Science and CINAHL Plus. These databases were searched from their inception to 24 November 2015. We also searched trials in registers for ongoing trials. We used no language restrictions. Selection criteria: Randomized controlled clinical trials (RCTs comparing statins alone or in association with other systemic drugs to reduce cholesterol levels versus placebo or usual care. Data collection and analysis: Primary outcomes were severity of aortic valve stenosis (evaluated by echocardiographic criteria: mean pressure gradient, valve area and aortic jet velocity, freedom from valve replacement and death from cardiovascular cause. Secondary outcomes were hospitalization for any reason, overall mortality, adverse events and patient quality of life. Two review authors independently selected trials for inclusion, extracted data and assessed the risk of bias. The GRADE methodology was employed to assess the quality of result findings and the GRADE profiler (GRADEPRO was used to import data from Review Manager 5.3 to create a 'Summary of findings' table. MAIN RESULTS: We included four RCTs with 2360 participants comparing statins (1185 participants with placebo (1175 participants. We found low-quality evidence for our primary outcome of severity of aortic valve stenosis, evaluated by mean pressure gradient (mean difference (MD -0.54, 95% confidence interval (CI -1.88 to 0.80; participants = 1935; studies = 2, valve area (MD -0.07, 95% CI -0.28 to 0.14; participants = 127; studies = 2

  15. Statins for aortic valve stenosis.

    Science.gov (United States)

    Thiago, Luciana; Tsuji, Selma Rumiko; Nyong, Jonathan; Puga, Maria Eduarda Dos Santos; Góis, Aécio Flávio Teixeira de; Macedo, Cristiane Rufino; Valente, Orsine; Atallah, Álvaro Nagib

    2016-01-01

    Aortic valve stenosis is the most common type of valvular heart disease in the USA and Europe. Aortic valve stenosis is considered similar to atherosclerotic disease. Some studies have evaluated statins for aortic valve stenosis. To evaluate the effectiveness and safety of statins in aortic valve stenosis. Search methods: We searched the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, LILACS - IBECS, Web of Science and CINAHL Plus. These databases were searched from their inception to 24 November 2015. We also searched trials in registers for ongoing trials. We used no language restrictions.Selection criteria: Randomized controlled clinical trials (RCTs) comparing statins alone or in association with other systemic drugs to reduce cholesterol levels versus placebo or usual care. Data collection and analysis: Primary outcomes were severity of aortic valve stenosis (evaluated by echocardiographic criteria: mean pressure gradient, valve area and aortic jet velocity), freedom from valve replacement and death from cardiovascular cause. Secondary outcomes were hospitalization for any reason, overall mortality, adverse events and patient quality of life.Two review authors independently selected trials for inclusion, extracted data and assessed the risk of bias. The GRADE methodology was employed to assess the quality of result findings and the GRADE profiler (GRADEPRO) was used to import data from Review Manager 5.3 to create a 'Summary of findings' table. We included four RCTs with 2360 participants comparing statins (1185 participants) with placebo (1175 participants). We found low-quality evidence for our primary outcome of severity of aortic valve stenosis, evaluated by mean pressure gradient (mean difference (MD) -0.54, 95% confidence interval (CI) -1.88 to 0.80; participants = 1935; studies = 2), valve area (MD -0.07, 95% CI -0.28 to 0.14; participants = 127; studies = 2), and aortic jet velocity (MD -0.06, 95% CI -0.26 to 0

  16. Pleiotropic effects of statins

    Directory of Open Access Journals (Sweden)

    Narasaraju Kavalipati

    2015-01-01

    Full Text Available Statins or 3-hydroxy-methylglutaryl coenzyme A (HMG CoA reductase inhibitors not only prevents the synthesis of cholesterol biosynthesis but also inhibits the synthesis of essential isoprenoid intermediates such as farnesyl pyrophosphate, geranylgeranyl pyrophosphate, isopentanyl adenosine, dolichols and polyisoprenoid side chains of ubiquinone, heme A, and nuclear lamins. These isoprenoid intermediates are required for activation of various intracellular/signaling proteins- small guanosine triphosphate bound protein Ras and Ras-like proteins like Rho, Rab, Rac, Ral, or Rap which plays an indispensible role in multiple cellular processes. Reduction of circulating isoprenoids intermediates as a result of HMG CoA reductase inhibition by statins prevents activation of these signalling proteins. Hence, the multiple effects of statins such as antiinflammatory effects, antioxidant effects, antiproliferative and immunomodulatory effects, plaque stability, normalization of sympathetic outflow, and prevention of platelet aggregation are due to reduction of circulating isoprenoids and hence inactivation of signalling proteins. These multiple lipid-independent effects of statins termed as statin pleiotropy would potentially open floodgates for research in multiple treatment domains catching attentions of researchers and clinician across the globe.

  17. Statins use and risk of new-onset diabetes in hypertensive patients: a population-based retrospective cohort study in Yinzhou district, Ningbo city, People’s Republic of China

    Directory of Open Access Journals (Sweden)

    Li H

    2018-05-01

    Full Text Available Hailong Li,1 Hongbo Lin,2 Houyu Zhao,1 Yang Xu,1 Yinchu Cheng,1 Peng Shen,2 Siyan Zhan1 1Department of Epidemiology and Bio-statistics, School of Public Health, Peking University Health Science Centre, Beijing, People’s Republic of China; 2Department of Chronic Diseases and Health Promotion, Yinzhou District Center for Disease Control and Prevention, Ningbo, People’s Republic of China Background: Reports have suggested that statin use is associated with an increased incidence of type 2 diabetes mellitus (T2DM. Guidelines suggested that statins should be prescribed in hypertensive patients for primary prevention. However, there were very few studies on the risk of T2DM associated with statin use among patients with hypertension in mainland People’s Republic of China. Purpose: To determine the association between statin use and new-onset diabetes mellitus among patients with hypertension in mainland People’s Republic of China. Patients and methods: We performed a retrospective cohort study of hypertensive patients using the Yinzhou regional health care database from January 1, 2010, to August 31, 2016. Patients aged 30–90 years old without T2DM were eligible for inclusion. We identified new statin initiators and nonusers by using prescription records of inpatients and outpatients. Multivariate Cox model and propensity score methods were used to adjust potential confounders, including age, sex, body mass index, comorbidities, lifestyle characteristics, and baseline antihypertensive drug use. The risk of incident T2DM among statin initiators compared to nonusers was estimated by the Cox proportional hazards model. Propensity scores for statin use were then developed using logistic regression, statin initiators were matched 1:1 with nonusers according to propensity scores with the nearest neighbor matching method within 0.2 caliper width, and Cox regression was again conducted. Results: Among 67,993 patients (21,551 statin initiators; 46

  18. Statin treatment and mortality in community-dwelling frail older patients with diabetes mellitus : A retrospective observational study

    NARCIS (Netherlands)

    A. Pilotto (Alberto); F. Panza (Francesco); Copetti, M. (Massimiliano); Simonato, M. (Matteo); D. Sancarlo; P. Gallina (Pietro); T.E. Strandberg (Timo); A.J. Cruz-Jentoft (A.); Daragjati, J. (Julia); L. Ferrucci (Luigi); A. Fontana (Andrea); S. Maggi; F.U.S. Mattace Raso (Francesco); M. Paccalin; Polidori, M.C. (Maria Cristina); Schulz, R.-J. (Ralf-Joachim); E. Topinkova; G. Trifirò (Gianluca); A.-K. Welmer

    2015-01-01

    textabstractBackground: Older adults are often excluded from clinical trials. Decision making for administration of statins to older patients with diabetes mellitus (DM) is under debate, particularly in frail older patients with comorbidity and high mortality risk. We tested the hypothesis that

  19. Effect of statin use on mobility disability and its prevention in at-risk older adults: the LIFE study

    Science.gov (United States)

    BACKGROUND: HMG-CoA reductase inhibitors (statins) are among the most commonly prescribed classes of medications. Although their cardiovascular benefits and myalgia risks are well documented, their effects on older adults initiating an exercise training program are less understood. METHODS: 1,635 s...

  20. Statin Use and Cognitive Function : Population-Based Observational Study with Long-Term Follow-Up

    NARCIS (Netherlands)

    Joosten, Hanneke; Visser, Sipke T.; van Eersel, Marlise E.; Gansevoort, Ron T.; Bilo, Henk J. G.; Slaets, Joris P.; Izaks, Gerbrand J.

    2014-01-01

    We aimed to evaluate the association between statin use and cognitive function. Cognitive function was measured with the Ruff Figural Fluency Test (RFFT; worst score, 0; best score, 175 points) and the Visual Association Test (VAT; low performance, 0-10; high performance, 11-12 points) in an

  1. Efficacy, safety and tolerability of ongoing statin plus ezetimibe versus doubling the ongoing statin dose in hypercholesterolemic Taiwanese patients: an open-label, randomized clinical trial

    Directory of Open Access Journals (Sweden)

    Yu Chih-Chieh

    2012-05-01

    Full Text Available Abstract Background Reducing low-density lipoprotein cholesterol (LDL-C is associated with reduced risk for major coronary events. Despite statin efficacy, a considerable proportion of statin-treated hypercholesterolemic patients fail to reach therapeutic LDL-C targets as defined by guidelines. This study compared the efficacy of ezetimibe added to ongoing statins with doubling the dose of ongoing statin in a population of Taiwanese patients with hypercholesterolemia. Methods This was a randomized, open-label, parallel-group comparison study of ezetimibe 10 mg added to ongoing statin compared with doubling the dose of ongoing statin. Adult Taiwanese hypercholesterolemic patients not at optimal LDL-C levels with previous statin treatment were randomized (N = 83 to ongoing statin + ezetimibe (simvastatin, atorvastatin or pravastatin + ezetimibe at doses of 20/10, 10/10 or 20/10 mg or doubling the dose of ongoing statin (simvastatin 40 mg, atorvastatin 20 mg or pravastatin 40 mg for 8 weeks. Percent change in total cholesterol, LDL-C, high-density lipoprotein cholesterol (HDL-C and triglycerides, and specified safety parameters were assessed at 4 and 8 weeks. Results At 8 weeks, patients treated with statin + ezetimibe experienced significantly greater reductions compared with doubling the statin dose in LDL-C (26.2% vs 17.9%, p = 0.0026 and total cholesterol (20.8% vs 12.2%, p = 0.0003. Percentage of patients achieving treatment goal was greater for statin + ezetimibe (58.6% vs doubling statin (41.2%, but the difference was not statistically significant (p = 0.1675. The safety and tolerability profiles were similar between treatments. Conclusion Ezetimibe added to ongoing statin therapy resulted in significantly greater lipid-lowering compared with doubling the dose of statin in Taiwanese patients with hypercholesterolemia. Studies to assess clinical outcome benefit are ongoing. Trial registration Registered at ClinicalTrials.gov: NCT00652327

  2. Data warehousing as a basis for web-based documentation of data mining and analysis.

    Science.gov (United States)

    Karlsson, J; Eklund, P; Hallgren, C G; Sjödin, J G

    1999-01-01

    In this paper we present a case study for data warehousing intended to support data mining and analysis. We also describe a prototype for data retrieval. Further we discuss some technical issues related to a particular choice of a patient record environment.

  3. Application of Learning Analytics Using Clustering Data Mining for Students' Disposition Analysis

    Science.gov (United States)

    Bharara, Sanyam; Sabitha, Sai; Bansal, Abhay

    2018-01-01

    Learning Analytics (LA) is an emerging field in which sophisticated analytic tools are used to improve learning and education. It draws from, and is closely tied to, a series of other fields of study like business intelligence, web analytics, academic analytics, educational data mining, and action analytics. The main objective of this research…

  4. Identifying Engineering Students' English Sentence Reading Comprehension Errors: Applying a Data Mining Technique

    Science.gov (United States)

    Tsai, Yea-Ru; Ouyang, Chen-Sen; Chang, Yukon

    2016-01-01

    The purpose of this study is to propose a diagnostic approach to identify engineering students' English reading comprehension errors. Student data were collected during the process of reading texts of English for science and technology on a web-based cumulative sentence analysis system. For the analysis, the association-rule, data mining technique…

  5. Building a Bridge or Digging a Pipeline? Clinical Data Mining in Evidence-Informed Knowledge Building

    Science.gov (United States)

    Epstein, Irwin

    2015-01-01

    Challenging the "bridge metaphor" theme of this conference, this article contends that current practice-research integration strategies are more like research-to-practice "pipelines." The purpose of this article is to demonstrate the potential of clinical data-mining studies conducted by practitioners, practitioner-oriented PhD…

  6. Data-Mining Techniques in Detecting Factors Linked to Academic Achievement

    Science.gov (United States)

    Martínez Abad, Fernando; Chaparro Caso López, Alicia A.

    2017-01-01

    In light of the emergence of statistical analysis techniques based on data mining in education sciences, and the potential they offer to detect non-trivial information in large databases, this paper presents a procedure used to detect factors linked to academic achievement in large-scale assessments. The study is based on a non-experimental,…

  7. Geospatiotemporal data mining in an early warning system for forest threats in the United States

    Science.gov (United States)

    F.M. Hoffman; R.T. Mills; J. Kumar; S.S. Vulli; W.W. Hargrove

    2010-01-01

    We investigate the potential of geospatiotemporal data mining of multi-year land surface phenology data (250 m Normalized Difference Vegetation Index (NDVI) values derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) in this study) for the conterminous United States as part of an early warning system to identify threats to forest ecosystems. Cluster...

  8. Exploring Student Characteristics of Retention That Lead to Graduation in Higher Education Using Data Mining Models

    Science.gov (United States)

    Raju, Dheeraj; Schumacker, Randall

    2015-01-01

    The study used earliest available student data from a flagship university in the southeast United States to build data mining models like logistic regression with different variable selection methods, decision trees, and neural networks to explore important student characteristics associated with retention leading to graduation. The decision tree…

  9. Antiatherosclerotic effects of long-term maximally intensive statin therapy after acute coronary syndrome: insights from Study of Coronary Atheroma by Intravascular Ultrasound: Effect of Rosuvastatin Versus Atorvastatin.

    Science.gov (United States)

    Puri, Rishi; Nissen, Steven E; Shao, Mingyuan; Ballantyne, Christie M; Barter, Philip J; Chapman, M John; Erbel, Raimund; Libby, Peter; Raichlen, Joel S; Uno, Kiyoko; Kataoka, Yu; Nicholls, Stephen J

    2014-11-01

    Patients with acute coronary syndromes (ACS) display diffuse coronary atheroma instability and heightened risk of early and late recurrent coronary events. We compared the long-term antiatherosclerotic efficacy of high-intensity statins in patients with ACS when compared with stable disease. Study of Coronary Atheroma by Intravascular Ultrasound: Effect of Rosuvastatin Versus Atorvastatin (SATURN) used serial intravascular ultrasound measures of coronary atheroma volume in patients treated with rosuvastatin 40 mg or atorvastatin 80 mg for 24 months. The overall effect of high-intensity statins on the change in coronary percent atheroma volume and major adverse cardiovascular events (death/nonfatal myocardial infarction/coronary revascularization) were evaluated in this post hoc analysis. When compared with non-ACS patients (n=678), patients with ACS (n=361) were younger, actively smoking, and have had a previous myocardial infarction (all P<0.001). At baseline, patients with ACS exhibited lower high-density lipoprotein cholesterol (43.5±11 versus 45.8±11 mg/dL; P=0.002), a higher apolipoprotein B: apolipoprotein A-1 ratio (0.90±0.24 versus 0.83±0.24; P<0.001) and greater percent atheroma volume (37.3±8.5% versus 35.9±8.1%; P=0.01) when compared with non-ACS patients. Despite similar achieved levels of lipid and inflammatory markers after high-intensity statin therapy, patients with ACS demonstrated greater percent atheroma volume regression than non-ACS patients (-1.46±0.14 versus -0.89±0.13; P=0.003). After propensity-weighted multivariable adjustment, baseline percent atheroma volume (P<0.001) and an ACS clinical presentation (P=0.02) independently associated with plaque regression. The 24-month major adverse cardiovascular events-free survival was similar between patients with ACS and non-ACS (90.6 versus 92.9%; P=0.25). Long-term high-intensity statin therapy caused greater plaque regression and comparable major adverse cardiovascular events rates in

  10. Spatial data mining of pipeline data provides new wave of O and M capital cost optimization opportunities

    Energy Technology Data Exchange (ETDEWEB)

    Richardson, D. [QM4 Engineering Ltd., Calgary, AB (Canada)

    2010-07-01

    This paper discussed the cost optimization benefits of spatial data mining in upstream oil and gas pipeline operations. The data mining method was used to enhance the characterization and management of internal corrosion risk and to optimize pipeline corrosion inhibition, as well as to identify pipeline network hydraulic bottlenecks. The data mining method formed part of a quality-based pipeline integrity management program. Results of the data mining study highlighted trends in well operational data and historical pipeline failure events. Use of the methodology resulted in significant savings. It was demonstrated that the key to a successful pipeline management model is a complete inventory characterization and determination of failure susceptibility profiles through the application of rigorous data standards. 4 tabs., 8 figs.

  11. Event metadata records as a testbed for scalable data mining

    International Nuclear Information System (INIS)

    Gemmeren, P van; Malon, D

    2010-01-01

    At a data rate of 200 hertz, event metadata records ('TAGs,' in ATLAS parlance) provide fertile grounds for development and evaluation of tools for scalable data mining. It is easy, of course, to apply HEP-specific selection or classification rules to event records and to label such an exercise 'data mining,' but our interest is different. Advanced statistical methods and tools such as classification, association rule mining, and cluster analysis are common outside the high energy physics community. These tools can prove useful, not for discovery physics, but for learning about our data, our detector, and our software. A fixed and relatively simple schema makes TAG export to other storage technologies such as HDF5 straightforward. This simplifies the task of exploiting very-large-scale parallel platforms such as Argonne National Laboratory's BlueGene/P, currently the largest supercomputer in the world for open science, in the development of scalable tools for data mining. Using a domain-neutral scientific data format may also enable us to take advantage of existing data mining components from other communities. There is, further, a substantial literature on the topic of one-pass algorithms and stream mining techniques, and such tools may be inserted naturally at various points in the event data processing and distribution chain. This paper describes early experience with event metadata records from ATLAS simulation and commissioning as a testbed for scalable data mining tool development and evaluation.

  12. Data mining in healthcare: decision making and precision

    Directory of Open Access Journals (Sweden)

    Ionuţ ŢĂRANU

    2016-05-01

    Full Text Available The trend of application of data mining in healthcare today is increased because the health sector is rich with information and data mining has become a necessity. Healthcare organizations generate and collect large volumes of information to a daily basis. Use of information technology enables automation of data mining and knowledge that help bring some interesting patterns which means eliminating manual tasks and easy data extraction directly from electronic records, electronic transfer system that will secure medical records, save lives and reduce the cost of medical services as well as enabling early detection of infectious diseases on the basis of advanced data collection. Data mining can enable healthcare organizations to anticipate trends in the patient's medical condition and behaviour proved by analysis of prospects different and by making connections between seemingly unrelated information. The raw data from healthcare organizations are voluminous and heterogeneous. It needs to be collected and stored in organized form and their integration allows the formation unite medical information system. Data mining in health offers unlimited possibilities for analyzing different data models less visible or hidden to common analysis techniques. These patterns can be used by healthcare practitioners to make forecasts, put diagnoses, and set treatments for patients in healthcare organizations.

  13. Successful reintroduction of statin therapy after statin-associated rhabdomyolysis.

    Science.gov (United States)

    Simons, Janet E; Holbrook, Anne M; Don-Wauchope, Andrew C

    2015-01-01

    The case report demonstrates the successful use of an alternative statin after a statin-related episode of rhabdomyolysis. Statin-associated rhabdomyolysis is a serious adverse event with a very low incidence and is considered the most severe of the muscle-related side effects of the statins. Rechallenge with statins is not a recommended practice after rhabdomyolysis. The patient experienced a myocardial infarct 1 y after the episode of rhabdomyolysis. He used alternative lipid-lowering therapy for 2 y. His low-density lipoprotein cholesterol was not meeting typical secondary prevention targets. An alternative statin was introduced and the patient has been followed for 4 years without recurrence of the rhabdomyolysis. This case suggests it may be time to reconsider the accepted practice of permanently avoiding statin therapy after rhabdomyolysis. Copyright © 2015 National Lipid Association. Published by Elsevier Inc. All rights reserved.

  14. INTEGRATED ASSESSMENT OF STATIN-ASSOCIATED MUSCLE DAMAGE PREDICTORS IN PATIENTS WITH ISCHEMIC HEART DISEASE

    OpenAIRE

    V. I. Petrov; O. N. Smuseva; Yu. V. Solovkina

    2013-01-01

    Aim. To assess the risk factors of statin-associated muscle damage in patient with ischemic heart disease.Material and methods. 258 patients with ischemic heart disease treated with statin were included into the study. Total plasma creatine kinase levels were measured and SLCO1B1*5 genotyping was performed. Relationship between statin therapy and adverse events was evaluated by Naranjo algorithm.Results. Patients with muscle symptoms received statins significantly longer (48.8 vs 11.9 months,...

  15. Educational Data Mining Application for Estimating Students Performance in Weka Environment

    Science.gov (United States)

    Gowri, G. Shiyamala; Thulasiram, Ramasamy; Amit Baburao, Mahindra

    2017-11-01

    Educational data mining (EDM) is a multi-disciplinary research area that examines artificial intelligence, statistical modeling and data mining with the data generated from an educational institution. EDM utilizes computational ways to deal with explicate educational information keeping in mind the end goal to examine educational inquiries. To make a country stand unique among the other nations of the world, the education system has to undergo a major transition by redesigning its framework. The concealed patterns and data from various information repositories can be extracted by adopting the techniques of data mining. In order to summarize the performance of students with their credentials, we scrutinize the exploitation of data mining in the field of academics. Apriori algorithmic procedure is extensively applied to the database of students for a wider classification based on various categorizes. K-means procedure is applied to the same set of databases in order to accumulate them into a specific category. Apriori algorithm deals with mining the rules in order to extract patterns that are similar along with their associations in relation to various set of records. The records can be extracted from academic information repositories. The parameters used in this study gives more importance to psychological traits than academic features. The undesirable student conduct can be clearly witnessed if we make use of information mining frameworks. Thus, the algorithms efficiently prove to profile the students in any educational environment. The ultimate objective of the study is to suspect if a student is prone to violence or not.

  16. Data Mining Approaches for Landslide Susceptibility Mapping in Umyeonsan, Seoul, South Korea

    Directory of Open Access Journals (Sweden)

    Sunmin Lee

    2017-07-01

    Full Text Available The application of data mining models has become increasingly popular in recent years in assessments of a variety of natural hazards such as landslides and floods. Data mining techniques are useful for understanding the relationships between events and their influencing variables. Because landslides are influenced by a combination of factors including geomorphological and meteorological factors, data mining techniques are helpful in elucidating the mechanisms by which these complex factors affect landslide events. In this study, spatial data mining approaches based on data on landslide locations in the geographic information system environment were investigated. The topographical factors of slope, aspect, curvature, topographic wetness index, stream power index, slope length factor, standardized height, valley depth, and downslope distance gradient were determined using topographical maps. Additional soil and forest variables using information obtained from national soil and forest maps were also investigated. A total of 17 variables affecting the frequency of landslide occurrence were selected to construct a spatial database, and support vector machine (SVM and artificial neural network (ANN models were applied to predict landslide susceptibility from the selected factors. In the SVM model, linear, polynomial, radial base function, and sigmoid kernels were applied in sequence; the model yielded 72.41%, 72.83%, 77.17% and 72.79% accuracy, respectively. The ANN model yielded a validity accuracy of 78.41%. The results of this study are useful in guiding effective strategies for the prevention and management of landslides in urban areas.

  17. Profiling Oman education data using data mining approach

    Science.gov (United States)

    Alawi, Sultan Juma Sultan; Shaharanee, Izwan Nizal Mohd; Jamil, Jastini Mohd

    2017-10-01

    Nowadays, with a large amount of data generated by many application services in different learning fields has led to the new challenges in education field. Education portal is an important system that leads to a better development of education field. This research paper presents an innovative data mining techniques to understand and summarizes the information of Oman's education data generated from the Ministry of Education Oman "Educational Portal". This research embarks into performing student profiling of the Oman student database. This study utilized the k-means clustering technique to determine the students' profiles. An amount of 42484-student records from Sultanate of Oman has been extracted for this study. The findings of this study show the practicality of clustering technique to investigating student's profiles. Allowing for a better understanding of student's behavior and their academic performance. Oman Education Portal contain a large amounts of user activity and interaction data. Analyses of this large data can be meaningful for educator to improve the student performance level and recognize students who needed additional attention.

  18. Postoperative atrial fibrillation in patients on statins undergoing ...

    African Journals Online (AJOL)

    Introduction: The efficacy of perioperative statin therapy in decreasing postoperative morbidity in patients undergoing valve replacements and repairs is unknown. The aim of our study was to determine whether or not the literature supports the hypothesis that statins decrease postoperative atrial fibrillation (AF), and hence ...

  19. The case for statin therapy in chronic heart failure

    NARCIS (Netherlands)

    van der Harst, Pim; Boehm, Michael; van Gilst, Wiek H.; van Veldhuisen, Dirk J.

    Both primary and secondary prevention studies have provided a wealth of evidence that statin therapy effectively reduces cardiovascular events. However, this general statement on the efficacy and safety of statin treatment has not been validated in patients with chronic heart failure (CHF).

  20. Outcomes of educational interventions in type 2 diabetes: WEKA data-mining analysis.

    Science.gov (United States)

    Sigurdardottir, Arun K; Jonsdottir, Helga; Benediktsson, Rafn

    2007-07-01

    To analyze which factors contribute to improvement in glycemic control in educational interventions in type 2 diabetes reported in randomized controlled trials (RCT) published in 2001-2005. Papers were extracted from Medline and Scopus using educational intervention and adults with type 2 diabetes as keywords. Inclusion criteria were RCT design. Data were analyzed with a data-mining program. Of 464 titles extracted, 21 articles reporting 18 studies met the inclusion criteria. Data mining showed that for initial glycosylated hemoglobin (HbA1c) level education intervention achieved a small change in HbA1c level, or from +0.1 to -0.7%. For initial HbA1c > or = 8.0%, a significant drop in HbA1c level of 0.8-2.5% was found. Data mining indicated that duration, educational content and intensity of education did not predict changes in HbA1c levels. Initial HbA1c level is the single most important factor affecting improvements in glycemic control in response to patient education. Data mining is an appropriate and sufficiently sensitive method to analyze outcomes of educational interventions. Diversity in conceptualization of interventions and diversity of instruments used for outcome measurements could have hampered actual discovery of effective educational practices. Participation in educational interventions generally seems to benefit people with type 2 diabetes. Use of standardized instruments is encouraged as it gives better opportunities to identify conclusive results with consequent development of clinical guidelines.

  1. Evaluation of Documentation Patterns of Trainees and Supervising Physicians Using Data Mining.

    Science.gov (United States)

    Madhavan, Ramesh; Tang, Chi; Bhattacharya, Pratik; Delly, Fadi; Basha, Maysaa M

    2014-09-01

    The electronic health record (EHR) includes a rich data set that may offer opportunities for data mining and natural language processing to answer questions about quality of care, key aspects of resident education, or attributes of the residents' learning environment. We used data obtained from the EHR to report on inpatient documentation practices of residents and attending physicians at a large academic medical center. We conducted a retrospective observational study of deidentified patient notes entered over 7 consecutive months by a multispecialty university physician group at an urban hospital. A novel automated data mining technology was used to extract patient note-related variables. A sample of 26 802 consecutive patient notes was analyzed using the data mining and modeling tool Healthcare Smartgrid. Residents entered most of the notes (33%, 8178 of 24 787) between noon and 4 pm and 31% (7718 of 24 787) of notes between 8 am and noon. Attending physicians placed notes about teaching attestations within 24 hours in only 73% (17 843 of 24 443) of the records. Surgical residents were more likely to place notes before noon (P Data related to patient note entry was successfully used to objectively measure current work flow of resident physicians and their supervising faculty, and the findings have implications for physician oversight of residents' clinical work. We were able to demonstrate the utility of a data mining model as an assessment tool in graduate medical education.

  2. Quality of research results in agro-economy by data mining

    Directory of Open Access Journals (Sweden)

    Vukelić Gordana

    2015-01-01

    Full Text Available Data Mining (DM through data in agroeconomy is a scientific method that enables researchers not to go through set research scenarioes that are predetermined assumptions and hypotheses on the basis of insignificant atributes. On the contrary, by data mining detection of these atributes is made possible, in general, those hiden facts that enable setting a hypothesis. The DM method does this by an iterative way, including key atributes and factors and their influence on the quality of agro-resources. The research was conducted on a random sample, by analyzing the quality of eggs. The research subject is the posibility of classifying and predicting significant variablesatributes that determine the level of egg quality. The research starts from the use of Data Mining, as an area of machine studies, which significantly helps researchers in optimizing research. The applied methodology during research includes analyticalsintetic procedures and methods of Data Mining, with a special focus on using Supervised linear discrimination analysis and the Decision Tree. The results indicate significant posibilities of using DM as an additional analytical procedure in performing agroresearch and it can be concluded that it contributes to an improvement in effectiveness and validity of process in performing these researches.

  3. Comparative analysis of data mining techniques for business data

    Science.gov (United States)

    Jamil, Jastini Mohd; Shaharanee, Izwan Nizal Mohd

    2014-12-01

    Data mining is the process of employing one or more computer learning techniques to automatically analyze and extract knowledge from data contained within a database. Companies are using this tool to further understand their customers, to design targeted sales and marketing campaigns, to predict what product customers will buy and the frequency of purchase, and to spot trends in customer preferences that can lead to new product development. In this paper, we conduct a systematic approach to explore several of data mining techniques in business application. The experimental result reveals that all data mining techniques accomplish their goals perfectly, but each of the technique has its own characteristics and specification that demonstrate their accuracy, proficiency and preference.

  4. The First International Conference on Soft Computing and Data Mining

    CERN Document Server

    Ghazali, Rozaida; Deris, Mustafa

    2014-01-01

    This book constitutes the refereed proceedings of the First International Conference on Soft Computing and Data Mining, SCDM 2014, held in Universiti Tun Hussein Onn Malaysia, in June 16th-18th, 2014. The 65 revised full papers presented in this book were carefully reviewed and selected from 145 submissions, and organized into two main topical sections; Data Mining and Soft Computing. The goal of this book is to provide both theoretical concepts and, especially, practical techniques on these exciting fields of soft computing and data mining, ready to be applied in real-world applications. The exchanges of views pertaining future research directions to be taken in this field and the resultant dissemination of the latest research findings makes this work of immense value to all those having an interest in the topics covered.    

  5. Data Mining and Data Fusion for Enhanced Decision Support

    Energy Technology Data Exchange (ETDEWEB)

    Khan, Shiraj [ORNL; Ganguly, Auroop R [ORNL; Gupta, Amar [University of Arizona

    2008-01-01

    The process of Data Mining converts information to knowledge by utilizing tools from the disciplines of computational statistics, database technologies, machine learning, signal processing, nonlinear dynamics, process modeling, simulation, and allied disciplines. Data Mining allows business problems to be analyzed from diverse perspectives, including dimensionality reduction, correlation and co-occurrence, clustering and classification, regression and forecasting, anomaly detection, and change analysis. The predictive insights generated from Data Mining can be further utilized through real-time analysis and decision sciences, as well as through human-driven analysis based on management by exceptions or by objectives, to generate actionable knowledge. The tools that enable the transformation of raw data to actionable predictive insights are collectively referred as Decision Support tools. This chapter presents a new formalization of the decision process, leading to a new Decision Superiority model, partially motivated by the Joint Directors of Laboratories (JDL) Data Fusion Model. In addition, it examines the growing importance of Data Fusion concepts.

  6. Data mining in soft computing framework: a survey.

    Science.gov (United States)

    Mitra, S; Pal, S K; Mitra, P

    2002-01-01

    The present article provides a survey of the available literature on data mining using soft computing. A categorization has been provided based on the different soft computing tools and their hybridizations used, the data mining function implemented, and the preference criterion selected by the model. The utility of the different soft computing methodologies is highlighted. Generally fuzzy sets are suitable for handling the issues related to understandability of patterns, incomplete/noisy data, mixed media information and human interaction, and can provide approximate solutions faster. Neural networks are nonparametric, robust, and exhibit good learning and generalization capabilities in data-rich environments. Genetic algorithms provide efficient search algorithms to select a model, from mixed media data, based on some preference criterion/objective function. Rough sets are suitable for handling different types of uncertainty in data. Some challenges to data mining and the application of soft computing methodologies are indicated. An extensive bibliography is also included.

  7. Randomized algorithms in automatic control and data mining

    CERN Document Server

    Granichin, Oleg; Toledano-Kitai, Dvora

    2015-01-01

    In the fields of data mining and control, the huge amount of unstructured data and the presence of uncertainty in system descriptions have always been critical issues. The book Randomized Algorithms in Automatic Control and Data Mining introduces the readers to the fundamentals of randomized algorithm applications in data mining (especially clustering) and in automatic control synthesis. The methods proposed in this book guarantee that the computational complexity of classical algorithms and the conservativeness of standard robust control techniques will be reduced. It is shown that when a problem requires "brute force" in selecting among options, algorithms based on random selection of alternatives offer good results with certain probability for a restricted time and significantly reduce the volume of operations.

  8. Clinical diabetes research using data mining: a Canadian perspective.

    Science.gov (United States)

    Shah, Baiju R; Lipscombe, Lorraine L

    2015-06-01

    With the advent of the digitization of large amounts of information and the computer power capable of analyzing this volume of information, data mining is increasingly being applied to medical research. Datasets created for administration of the healthcare system provide a wealth of information from different healthcare sectors, and Canadian provinces' single-payer universal healthcare systems mean that data are more comprehensive and complete in this country than in many other jurisdictions. The increasing ability to also link clinical information, such as electronic medical records, laboratory test results and disease registries, has broadened the types of data available for analysis. Data-mining methods have been used in many different areas of diabetes clinical research, including classic epidemiology, effectiveness research, population health and health services research. Although methodologic challenges and privacy concerns remain important barriers to using these techniques, data mining remains a powerful tool for clinical research. Copyright © 2015 Canadian Diabetes Association. Published by Elsevier Inc. All rights reserved.

  9. Prediction of Thyroid Disease Using Data Mining Techniques

    Directory of Open Access Journals (Sweden)

    Irina Ioniţă

    2016-08-01

    Full Text Available Recently, thyroid diseases are more and more spread worldwide. In Romania, for example, one of eight women suffer from hypothyroidism, hyperthyroidism or thyroid cancer. Various research studies estimate that about 30% of Romanians are diagnosed with endemic goiter. The factors that affect the thyroid function are: stress, infection, trauma, toxins, low-calorie diet, certain medication etc. It is very important to prevent such diseases rather than cure them, because the majority of treatments consist in long term medication or in chirurgical intervention. The current study refers to the thyroid disease classification in two of the most common thyroid dysfunctions (hyperthyroidism and hypothyroidism among the population. The authors analyzed and compared four classification models: Naive Bayes, Decision Tree, Multilayer Perceptron and Radial Basis Function Network. The results indicate a significant accuracy for all the classification models mentioned above, the best classification rate being that of the Decision Tree model. The data set used to build and to validate the classifier was provided by the UCI machine learning repository and by a website with Romanian data. The framework for building and testing the classification models was KNIME Analytics Platform and Weka, two data mining software.

  10. Systematic Review of Data Mining Applications in Patient-Centered Mobile-Based Information Systems.

    Science.gov (United States)

    Fallah, Mina; Niakan Kalhori, Sharareh R

    2017-10-01

    Smartphones represent a promising technology for patient-centered healthcare. It is claimed that data mining techniques have improved mobile apps to address patients' needs at subgroup and individual levels. This study reviewed the current literature regarding data mining applications in patient-centered mobile-based information systems. We systematically searched PubMed, Scopus, and Web of Science for original studies reported from 2014 to 2016. After screening 226 records at the title/abstract level, the full texts of 92 relevant papers were retrieved and checked against inclusion criteria. Finally, 30 papers were included in this study and reviewed. Data mining techniques have been reported in development of mobile health apps for three main purposes: data analysis for follow-up and monitoring, early diagnosis and detection for screening purpose, classification/prediction of outcomes, and risk calculation (n = 27); data collection (n = 3); and provision of recommendations (n = 2). The most accurate and frequently applied data mining method was support vector machine; however, decision tree has shown superior performance to enhance mobile apps applied for patients' self-management. Embedded data-mining-based feature in mobile apps, such as case detection, prediction/classification, risk estimation, or collection of patient data, particularly during self-management, would save, apply, and analyze patient data during and after care. More intelligent methods, such as artificial neural networks, fuzzy logic, and genetic algorithms, and even the hybrid methods may result in more patients-centered recommendations, providing education, guidance, alerts, and awareness of personalized output.

  11. Combining complex networks and data mining: Why and how

    Science.gov (United States)

    Zanin, M.; Papo, D.; Sousa, P. A.; Menasalvas, E.; Nicchi, A.; Kubik, E.; Boccaletti, S.

    2016-05-01

    The increasing power of computer technology does not dispense with the need to extract meaningful information out of data sets of ever growing size, and indeed typically exacerbates the complexity of this task. To tackle this general problem, two methods have emerged, at chronologically different times, that are now commonly used in the scientific community: data mining and complex network theory. Not only do complex network analysis and data mining share the same general goal, that of extracting information from complex systems to ultimately create a new compact quantifiable representation, but they also often address similar problems too. In the face of that, a surprisingly low number of researchers turn out to resort to both methodologies. One may then be tempted to conclude that these two fields are either largely redundant or totally antithetic. The starting point of this review is that this state of affairs should be put down to contingent rather than conceptual differences, and that these two fields can in fact advantageously be used in a synergistic manner. An overview of both fields is first provided, some fundamental concepts of which are illustrated. A variety of contexts in which complex network theory and data mining have been used in a synergistic manner are then presented. Contexts in which the appropriate integration of complex network metrics can lead to improved classification rates with respect to classical data mining algorithms and, conversely, contexts in which data mining can be used to tackle important issues in complex network theory applications are illustrated. Finally, ways to achieve a tighter integration between complex networks and data mining, and open lines of research are discussed.

  12. International Conference on Computational Intelligence in Data Mining

    CERN Document Server

    Mohapatra, Durga

    2017-01-01

    The book presents high quality papers presented at the International Conference on Computational Intelligence in Data Mining (ICCIDM 2016) organized by School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha, India during December 10 – 11, 2016. The book disseminates the knowledge about innovative, active research directions in the field of data mining, machine and computational intelligence, along with current issues and applications of related topics. The volume aims to explicate and address the difficulties and challenges that of seamless integration of the two core disciplines of computer science. .

  13. Advances in machine learning and data mining for astronomy

    CERN Document Server

    Way, Michael J

    2012-01-01

    Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book's introductory part provides context to issues in the astronomical sciences that are also important to health

  14. Advanced Data Mining of Leukemia Cells Micro-Arrays

    OpenAIRE

    Richard S. Segall; Ryan M. Pierce

    2009-01-01

    This paper provides continuation and extensions of previous research by Segall and Pierce (2009a) that discussed data mining for micro-array databases of Leukemia cells for primarily self-organized maps (SOM). As Segall and Pierce (2009a) and Segall and Pierce (2009b) the results of applying data mining are shown and discussed for the data categories of microarray databases of HL60, Jurkat, NB4 and U937 Leukemia cells that are also described in this article. First, a background section is pro...

  15. 4th International conference on Knowledge Discovery and Data Mining

    CERN Document Server

    Knowledge Discovery and Data Mining

    2012-01-01

    The volume includes a set of selected papers extended and revised from the 4th International conference on Knowledge Discovery and Data Mining, March 1-2, 2011, Macau, Chin.   This Volume is to provide a forum for researchers, educators, engineers, and government officials involved in the general areas of knowledge discovery and data mining and learning to disseminate their latest research results and exchange views on the future research directions of these fields. 108 high-quality papers are included in the volume.

  16. Data mining with SPSS modeler theory, exercises and solutions

    CERN Document Server

    Wendler, Tilo

    2016-01-01

    Introducing the IBM SPSS Modeler, this book guides readers through data mining processes and presents relevant statistical methods. There is a special focus on step-by-step tutorials and well-documented examples that help demystify complex mathematical algorithms and computer programs. The variety of exercises and solutions as well as an accompanying website with data sets and SPSS Modeler streams are particularly valuable. While intended for students, the simplicity of the Modeler makes the book useful for anyone wishing to learn about basic and more advanced data mining, and put this knowledge into practice.

  17. Spatio-Temporal Data Mining for Location-Based Services

    DEFF Research Database (Denmark)

    Gidofalvi, Gyozo

    . The objectives of the presented thesis are three-fold. First, to extend popular data mining methods to the spatio-temporal domain. Second, to demonstrate the usefulness of the extended methods and the derived knowledge in promising LBS examples. Finally, to eliminate privacy concerns in connection with spatio......-temporal data mining by devising systems for privacy-preserving location data collection and mining.......Location-Based Services (LBS) are continuously gaining popularity. Innovative LBSes integrate knowledge about the users into the service. Such knowledge can be derived by analyzing the location data of users. Such data contain two unique dimensions, space and time, which need to be analyzed...

  18. Statin drug-drug interactions in a Romanian community pharmacy.

    Science.gov (United States)

    Badiu, Raluca; Bucsa, Camelia; Mogosan, Cristina; Dumitrascu, Dan

    2016-01-01

    Statins are frequently prescribed for patients with dyslipidemia and have a well-established safety profile. However, when associated with interacting dugs, the risk of adverse effects, especially muscular toxicity, is increased. The objective of this study was to identify, characterize and quantify the prevalence of the potential drug-drug interactions (pDDIs) of statins in reimbursed prescriptions from a community pharmacy in Bucharest. We analyzed the reimbursed prescriptions including statins collected during one month in a community pharmacy. The online program Medscape Drug Interaction Checker was used for checking the drug interactions and their classification based on severity: Serious - Use alternative, Significant - Monitor closely and Minor. 132 prescriptions pertaining to 125 patients were included in the analysis. Our study showed that 25% of the patients who were prescribed statins were exposed to pDDIs: 37 Serious and Significant interactions in 31 of the statins prescriptions. The statins involved were atorvastatin, simvastatin and rosuvastatin. Statin pDDIs have a high prevalence and patients should be monitored closely in order to prevent the development of adverse effects that result from statin interactions.

  19. How to take statins

    Science.gov (United States)

    ... allergies. You are taking other medicines. You have diabetes. You have liver disease. You should not take statins if you ... with your provider about the possible risks for: Liver damage Severe ... High blood sugar, or type 2 diabetes Memory loss Confusion

  20. Statins and polyneuropathy revisited

    DEFF Research Database (Denmark)

    Svendsen, Toke de Koning; Hansen, Peter Nørregaard; García-Rodríguez, Luis Alberto

    2017-01-01

    "); current use was further classified into long-term use (5+ years) and high or low intensity use. We used conditional logistic regression to calculate odds ratios (ORs) with 95% confidence intervals (CIs) to examine associations between polyneuropathy and statin use. RESULTS: We included 370 validated cases...

  1. Viewpoint: Personalizing Statin Therapy

    Directory of Open Access Journals (Sweden)

    Shlomo Keidar

    2013-04-01

    Full Text Available Cardiovascular disease (CVD, associated with vascular atherosclerosis, is the major cause of death in Western societies. Current risk estimation tools, such as Framingham Risk Score (FRS, based on evaluation of multiple standard risk factors, are limited in assessment of individual risk. The majority (about 70% of the general population is classified as low FRS where the individual risk for CVD is often underestimated but, on the other hand, cholesterol lowering with statin is often excessively administered. Adverse effects of statin therapy, such as muscle pain, affect a large proportion of the treated patients and have a significant influence on their quality of life. Coronary artery calcification (CAC, as assessed by computed tomography, carotid artery intima-media thickness (CIMT, and especially presence of plaques as assessed by B-mode ultrasound are directly correlated with increased risk for cardiovascular events and provide accurate and relevant information for individual risk assessment. Absence of vascular pathology as assessed by these imaging methods has a very high negative predictive value and therefore could be used as a method to reduce significantly the number of subjects who, in our opinion, would not benefit from statins and only suffer from their side-effects. In summary, we suggest that in very-low-risk subjects, with the exception of subjects with low FRS with a family history of coronary artery disease (CAD at young age, if vascular imaging shows no CAC or normal CIMT without plaques, statin treatment need not be administered.

  2. Statins: Evidence for effectiveness

    African Journals Online (AJOL)

    multiple sclerosis,9 and offer added benefit to men with erectile dysfunction.10 Amid this hype and against a backdrop of more the a billion people potentially taking statins,11 the obvious question is whether or not current ..... communications: a narrative review and clinical considerations for older adults. American Journal of ...

  3. Statin Resistance and Export

    DEFF Research Database (Denmark)

    Ley, Ana

    Statins are inhibitors of 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR), the key enzyme in the mevalonate pathway that leads to the synthesis of cholesterol and ergosterol in animal and fungal cells, respectively. Their extensiveuse in treatment and prevention of cardiovascular diseases...

  4. A novel Neuro-fuzzy classification technique for data mining

    Directory of Open Access Journals (Sweden)

    Soumadip Ghosh

    2014-11-01

    Full Text Available In our study, we proposed a novel Neuro-fuzzy classification technique for data mining. The inputs to the Neuro-fuzzy classification system were fuzzified by applying generalized bell-shaped membership function. The proposed method utilized a fuzzification matrix in which the input patterns were associated with a degree of membership to different classes. Based on the value of degree of membership a pattern would be attributed to a specific category or class. We applied our method to ten benchmark data sets from the UCI machine learning repository for classification. Our objective was to analyze the proposed method and, therefore compare its performance with two powerful supervised classification algorithms Radial Basis Function Neural Network (RBFNN and Adaptive Neuro-fuzzy Inference System (ANFIS. We assessed the performance of these classification methods in terms of different performance measures such as accuracy, root-mean-square error, kappa statistic, true positive rate, false positive rate, precision, recall, and f-measure. In every aspect the proposed method proved to be superior to RBFNN and ANFIS algorithms.

  5. Application of Data Mining Algorithm to Recipient of Motorcycle Installment

    Directory of Open Access Journals (Sweden)

    Harry Dhika

    2015-12-01

    Full Text Available The study was conducted in the subsidiaries that provide services of finance related to the purchase of a motorcycle on credit. At the time of applying, consumers enter their personal data. Based on the personal data, it will be known whether the consumer credit data is approved or rejected. From 224 consumer data obtained, it is known that the number of consumers whose applications are approved is 87% or about 217 consumers and consumers whose application is rejected is 16% or as much as 6 consumers. Acceptance of motorcycle financing on credit by using the method of applying the algorithm through CRIS-P DM is the industry standard in the processing of data mining. The algorithm used in the decision making is the algorithm C4.5. The results obtained previously, the level of accuracy is measured with the Confusion Matrix and Receiver Operating characteristic (ROC. Evaluation of the Confusion Matrix is intended to seek the value of accuracy, precision value, and the value of recall data. While the Receiver Operating Characteristic (ROC is used to find data tables and comparison Area Under Curve (AUC.

  6. An Integrative data mining approach to identifying Adverse ...

    Science.gov (United States)

    The Adverse Outcome Pathway (AOP) framework is a tool for making biological connections and summarizing key information across different levels of biological organization to connect biological perturbations at the molecular level to adverse outcomes for an individual or population. Computational approaches to explore and determine these connections can accelerate the assembly of AOPs. By leveraging the wealth of publicly available data covering chemical effects on biological systems, computationally-predicted AOPs (cpAOPs) were assembled via data mining of high-throughput screening (HTS) in vitro data, in vivo data and other disease phenotype information. Frequent Itemset Mining (FIM) was used to find associations between the gene targets of ToxCast HTS assays and disease data from Comparative Toxicogenomics Database (CTD) by using the chemicals as the common aggregators between datasets. The method was also used to map gene expression data to disease data from CTD. A cpAOP network was defined by considering genes and diseases as nodes and FIM associations as edges. This network contained 18,283 gene to disease associations for the ToxCast data and 110,253 for CTD gene expression. Two case studies show the value of the cpAOP network by extracting subnetworks focused either on fatty liver disease or the Aryl Hydrocarbon Receptor (AHR). The subnetwork surrounding fatty liver disease included many genes known to play a role in this disease. When querying the cpAOP

  7. Visualizing data mining results with the Brede tools

    Directory of Open Access Journals (Sweden)

    Finn A Nielsen

    2009-07-01

    Full Text Available A few neuroinformatics databases now exist that record results from neuroimaging studies in the form of brain coordinates in stereotaxic space. The Brede Toolbox was originally developed to extract, analyze and visualize data from one of them --- the BrainMap database. Since then the Brede Toolbox has expanded and now includes its own database with coordinates along with ontologies for brain regions and functions: The Brede Database. With Brede Toolbox and Database combined we setup automated workflows for extraction of data, mass meta-analytic data mining and visualizations. Most of the Web presence of the Brede Database is established by a single script executing a workflow involving these steps together with a final generation of Web pages with embedded visualizations and links to interactive three-dimensional models in the Virtual Reality Modeling Language. Apart from the Brede tools I briefly review alternate visualization tools and methods for Internet-based visualization and information visualization as well as portals for visualization tools.

  8. Simulation of California's Major Reservoirs Outflow Using Data Mining Technique

    Science.gov (United States)

    Yang, T.; Gao, X.; Sorooshian, S.

    2014-12-01

    The reservoir's outflow is controlled by reservoir operators, which is different from the upstream inflow. The outflow is more important than the reservoir's inflow for the downstream water users. In order to simulate the complicated reservoir operation and extract the outflow decision making patterns for California's 12 major reservoirs, we build a data-driven, computer-based ("artificial intelligent") reservoir decision making tool, using decision regression and classification tree approach. This is a well-developed statistical and graphical modeling methodology in the field of data mining. A shuffled cross validation approach is also employed to extract the outflow decision making patterns and rules based on the selected decision variables (inflow amount, precipitation, timing, water type year etc.). To show the accuracy of the model, a verification study is carried out comparing the model-generated outflow decisions ("artificial intelligent" decisions) with that made by reservoir operators (human decisions). The simulation results show that the machine-generated outflow decisions are very similar to the real reservoir operators' decisions. This conclusion is based on statistical evaluations using the Nash-Sutcliffe test. The proposed model is able to detect the most influential variables and their weights when the reservoir operators make an outflow decision. While the proposed approach was firstly applied and tested on California's 12 major reservoirs, the method is universally adaptable to other reservoir systems.

  9. Muscle-related side-effects of statins: from mechanisms to evidence-based solutions.

    Science.gov (United States)

    Taylor, Beth A; Thompson, Paul D

    2015-06-01

    This article highlights the recent findings regarding statin-associated muscle side effects, including mechanisms and treatment as well as the need for more comprehensive clinical trials in statin myalgia. Statin myalgia is difficult to diagnose and treat, as major clinical trials have not routinely assessed muscle side-effects, there are few clinically relevant biomarkers and assessment tools for the symptoms, many apparent statin-related muscle symptoms may be nonspecific and related to other drugs or health conditions, and prevalence estimates vary widely. Data thus suggest that only 30-50% of patients with self-reported statin myalgia actually experience muscle pain on statins during blinded, placebo-controlled trials. In addition, evidence to date involving mechanisms underlying statin myalgia and its range of symptoms and presentations supports the hypothesis that there are multiple, interactive and potentially additive mechanisms underlying statin-associated muscle side-effects. There are likely multiple and interactive mechanisms underlying statin myalgia, and recent studies have produced equivocal data regarding prevalence of statin-associated muscle side-effects, contributing factors and effectiveness of common interventions. Therefore, more clinical trials on statin myalgia are critical to the field, as are systematic resources for quantifying, predicting and reporting statin-associated muscle side-effects.

  10. Statin Intake Is Associated With Decreased Insulin Sensitivity During Cardiac Surgery

    Science.gov (United States)

    Sato, Hiroaki; Carvalho, George; Sato, Tamaki; Hatzakorzian, Roupen; Lattermann, Ralph; Codere-Maruyama, Takumi; Matsukawa, Takashi; Schricker, Thomas

    2012-01-01

    OBJECTIVE Surgical trauma impairs intraoperative insulin sensitivity and is associated with postoperative adverse events. Recently, preprocedural statin therapy is recommended for patients with coronary artery disease. However, statin therapy is reported to increase insulin resistance and the risk of new-onset diabetes. Thus, we investigated the association between preoperative statin therapy and intraoperative insulin sensitivity in nondiabetic, dyslipidemic patients undergoing coronary artery bypass grafting. RESEARCH DESIGN AND METHODS In this prospective, nonrandomized trial, patients taking lipophilic statins were assigned to the statin group and hypercholesterolemic patients not receiving any statins were allocated to the control group. Insulin sensitivity was assessed by the hyperinsulinemic-normoglycemic clamp technique during surgery. The mean, SD of blood glucose, and the coefficient of variation (CV) after surgery were calculated for each patient. The association between statin use and intraoperative insulin sensitivity was tested by multiple regression analysis. RESULTS We studied 120 patients. In both groups, insulin sensitivity gradually decreased during surgery with values being on average ∼20% lower in the statin than in the control group. In the statin group, the mean blood glucose in the intensive care unit was higher than in the control group (153 ± 20 vs. 140 ± 20 mg/dL; P statin group (SD, P statin use was independently associated with intraoperative insulin sensitivity (β = −0.16; P = 0.03). CONCLUSIONS Preoperative use of lipophilic statins is associated with increased insulin resistance during cardiac surgery in nondiabetic, dyslipidemic patients. PMID:22829524

  11. Statin and Atrial Fibrilation: When does it work?

    Science.gov (United States)

    Fauchier, Laurent; Clementy, Nicolas; Pierre, Bertrand; Babuty, Dominique

    2012-01-01

    In the recent years, some clinical and experimental studies have suggested that the use of statins may protect against atrial fibrillation (AF). A relation between inflammation and the development of AF has been described, and the potent anti-inflammatory and antioxidant properties of statins may make them effective in preventing the development of AF. A global analysis of the literature suggests that the use of statins is associated with a decreased risk of incidence or recurrence of AF in some cases. However, this beneficial effect is not seen for all types of AF in all the patients. The use of statins seems associated 1) with a lack of benefit in primary prevention of AF, 2) with a significant but heterogeneous decreased risk of recurrence of AF in secondary prevention, and 3) with a very significant and homogeneous reduction for the risk of post operative AF. An intensive lipid lowering statin regimen does not provide greater protection against AF. Patients with coronary heart disease are curr ently treated with statins in most cases, and this may not have an impact on their treatment. In contrast, it remains to determine more accurately if statins may bring a significant benefit for some AF patients without any type of established atherosclerotic disease or with a low risk of atherogenesis. Since it remains uncertain whether the suppression of AF in these patients is beyond doubt beneficial, prescribing statins for this purpose alone should not be recommended at the present time.

  12. Statin-associated muscle symptoms: impact on statin therapy—European Atherosclerosis Society Consensus Panel Statement on Assessment, Aetiology and Management

    Science.gov (United States)

    Stroes, Erik S.; Thompson, Paul D.; Corsini, Alberto; Vladutiu, Georgirene D.; Raal, Frederick J.; Ray, Kausik K.; Roden, Michael; Stein, Evan; Tokgözoğlu, Lale; Nordestgaard, Børge G.; Bruckert, Eric; De Backer, Guy; Krauss, Ronald M.; Laufs, Ulrich; Santos, Raul D.; Hegele, Robert A.; Hovingh, G. Kees; Leiter, Lawrence A.; Mach, Francois; März, Winfried; Newman, Connie B.; Wiklund, Olov; Jacobson, Terry A.; Catapano, Alberico L.; Chapman, M. John; Ginsberg, Henry N.; Stroes, Erik; Thompson, Paul D.; Corsini, Alberto; Vladutiu, Georgirene D.; Raal, Frederick J.; Ray, Kausik K.; Roden, Michael; Stein, Evan; Tokgözoğlu, Lale; Nordestgaard, Børge G.; Bruckert, Eric; Krauss, Ronald M.; Laufs, Ulrich; Santos, Raul D.; März, Winfried; Newman, Connie B.; John Chapman, M.; Ginsberg, Henry N.; John Chapman, M.; Ginsberg, Henry N.; de Backer, Guy; Catapano, Alberico L.; Hegele, Robert A.; Kees Hovingh, G.; Jacobson, Terry A.; Leiter, Lawrence; Mach, Francois; Wiklund, Olov

    2015-01-01

    Statin-associated muscle symptoms (SAMS) are one of the principal reasons for statin non-adherence and/or discontinuation, contributing to adverse cardiovascular outcomes. This European Atherosclerosis Society (EAS) Consensus Panel overviews current understanding of the pathophysiology of statin-associated myopathy, and provides guidance for diagnosis and management of SAMS. Statin-associated myopathy, with significant elevation of serum creatine kinase (CK), is a rare but serious side effect of statins, affecting 1 per 1000 to 1 per 10 000 people on standard statin doses. Statin-associated muscle symptoms cover a broader range of clinical presentations, usually with normal or minimally elevated CK levels, with a prevalence of 7–29% in registries and observational studies. Preclinical studies show that statins decrease mitochondrial function, attenuate energy production, and alter muscle protein degradation, thereby providing a potential link between statins and muscle symptoms; controlled mechanistic and genetic studies in humans are necessary to further understanding. The Panel proposes to identify SAMS by symptoms typical of statin myalgia (i.e. muscle pain or aching) and their temporal association with discontinuation and response to repetitive statin re-challenge. In people with SAMS, the Panel recommends the use of a maximally tolerated statin dose combined with non-statin lipid-lowering therapies to attain recommended low-density lipoprotein cholesterol targets. The Panel recommends a structured work-up to identify individuals with clinically relevant SAMS generally to at least three different statins, so that they can be offered therapeutic regimens to satisfactorily address their cardiovascular risk. Further research into the underlying pathophysiological mechanisms may offer future therapeutic potential. PMID:25694464

  13. Statin-associated muscle symptoms: impact on statin therapy-European Atherosclerosis Society Consensus Panel Statement on Assessment, Aetiology and Management.

    Science.gov (United States)

    Stroes, Erik S; Thompson, Paul D; Corsini, Alberto; Vladutiu, Georgirene D; Raal, Frederick J; Ray, Kausik K; Roden, Michael; Stein, Evan; Tokgözoğlu, Lale; Nordestgaard, Børge G; Bruckert, Eric; De Backer, Guy; Krauss, Ronald M; Laufs, Ulrich; Santos, Raul D; Hegele, Robert A; Hovingh, G Kees; Leiter, Lawrence A; Mach, Francois; März, Winfried; Newman, Connie B; Wiklund, Olov; Jacobson, Terry A; Catapano, Alberico L; Chapman, M John; Ginsberg, Henry N

    2015-05-01

    Statin-associated muscle symptoms (SAMS) are one of the principal reasons for statin non-adherence and/or discontinuation, contributing to adverse cardiovascular outcomes. This European Atherosclerosis Society (EAS) Consensus Panel overviews current understanding of the pathophysiology of statin-associated myopathy, and provides guidance for diagnosis and management of SAMS. Statin-associated myopathy, with significant elevation of serum creatine kinase (CK), is a rare but serious side effect of statins, affecting 1 per 1000 to 1 per 10 000 people on standard statin doses. Statin-associated muscle symptoms cover a broader range of clinical presentations, usually with normal or minimally elevated CK levels, with a prevalence of 7-29% in registries and observational studies. Preclinical studies show that statins decrease mitochondrial function, attenuate energy production, and alter muscle protein degradation, thereby providing a potential link between statins and muscle symptoms; controlled mechanistic and genetic studies in humans are necessary to further understanding. The Panel proposes to identify SAMS by symptoms typical of statin myalgia (i.e. muscle pain or aching) and their temporal association with discontinuation and response to repetitive statin re-challenge. In people with SAMS, the Panel recommends the use of a maximally tolerated statin dose combined with non-statin lipid-lowering therapies to attain recommended low-density lipoprotein cholesterol targets. The Panel recommends a structured work-up to identify individuals with clinically relevant SAMS generally to at least three different statins, so that they can be offered therapeutic regimens to satisfactorily address their cardiovascular risk. Further research into the underlying pathophysiological mechanisms may offer future therapeutic potential. © The Author 2015. Published by Oxford University Press on behalf of the European Society of Cardiology.

  14. The effect of coenzyme Q10 in statin myopathy.

    Science.gov (United States)

    Zlatohlavek, Lukas; Vrablik, Michal; Grauova, Barbora; Motykova, Eva; Ceska, Richard

    2012-01-01

    Statins significantly reduce CV morbidity and mortality. Unfortunately, one of the side effects of statins is myopathy, for which statins cannot be administered in sufficient doses or administered at all. The aim of this study was to demonstrate the effect of coenzyme Q10 in patients with statin myopathy. Twenty eight patients aged 60.6±10.7 years were monitored (18 women and 10 men) and treated with different types and doses of statin. Muscle weakness and pain was monitored using a scale of one to ten, on which patients expressed the degree of their inconvenience. Examination of muscle problems was performed prior to administration of CQ10 and after 3 and 6 months of dosing. Statistical analysis was performed using Friedman test, Annova and Students t-test. Pain decreased on average by 53.8% (pmuscle weakness by 44.4% (pmuscle pain and sensitivity statistically significantly decreased.

  15. Efficacy and safety of statins and exercise combination therapy compared to statin monotherapy in patients with dyslipidaemia: A systematic review and meta-analysis.

    Science.gov (United States)

    Gui, Ya-Jun; Liao, Cai-Xiu; Liu, Qiong; Guo, Yuan; Yang, Tao; Chen, Jing-Yuan; Wang, Ya-Ting; Hu, Jia-Hui; Xu, Dan-Yan

    2017-06-01

    Background Statin treatment in association with physical exercise can substantially reduce mortality in dyslipidaemic individuals. However, the available data to compare the efficacy and safety of statins and exercise combination therapy with statin monotherapy are limited. Design Systematic review and meta-analysis. Methods We systematically searched PubMed, Embase and the Cochrane Library from database inception until December 2016. We included randomised and non-randomised studies that compared the efficacy and safety of statins and exercise combination therapy with statin monotherapy in patients with dyslipidaemia. Standardised mean differences were calculated and pooled by means of fixed effects models. The risk of bias and heterogeneity among trials was also assessed. Seven articles were assessed in terms of the efficacy of therapy and 13 from the viewpoint of therapeutic safety. Results In terms of efficacy, statins and exercise combination decreased the incidence of diabetes mellitus, improved insulin sensitivity and inflammation, but caused no change in lipid profile compared to statins alone. In terms of safety, statins and exercise combination increased peak oxygen uptake (standardised mean difference 1.01, 95% confidence interval 0.46 to 1.57) compared to statins alone. In contrast to statin-induced myopathy, chronic exercise training prior to statin treatment could counteract statin-induced adverse effects in skeletal muscle. Conclusion Statins and exercise combination therapy is more effective than statin monotherapy in terms of insulin sensitivity, inflammation and exercise capacity. The small number of studies warrants the need for more randomised controlled trials evaluating the efficacy and safety of combination therapy.

  16. Modeling and Data Mining in Blogosphere

    CERN Document Server

    Agarwal, Nitin

    2009-01-01

    This book offers a comprehensive overview of the various concepts and research issues about blogs or weblogs. It introduces techniques and approaches, tools and applications, and evaluation methodologies with examples and case studies. Blogs allow people to express their thoughts, voice their opinions, and share their experiences and ideas. Blogs also facilitate interactions among individuals creating a network with unique characteristics. Through the interactions individuals experience a sense of community. We elaborate on approaches that extract communities and cluster blogs based on informa

  17. Statin-Associated Side Effects.

    Science.gov (United States)

    Thompson, Paul D; Panza, Gregory; Zaleski, Amanda; Taylor, Beth

    2016-05-24

    Hydroxy-methyl-glutaryl-coenzyme A (HMG-CoA) reductase inhibitors or statins are well tolerated, but associated with various statin-associated symptoms (SAS), including statin-associated muscle symptoms (SAMS), diabetes mellitus (DM), and central nervous system complaints. These are "statin-associated symptoms" because they are rare in clinical trials, making their causative relationship to statins unclear. SAS are, nevertheless, important because they prompt dose reduction or discontinuation of these life-saving mediations. SAMS is the most frequent SAS, and mild myalgia may affect 5% to 10% of statin users. Clinically important muscle symptoms, including rhabdomyolysis and statin-induced necrotizing autoimmune myopathy (SINAM), are rare. Antibodies against HMG-CoA reductase apparently provoke SINAM. Good evidence links statins to DM, but evidence linking statins to other SAS is largely anecdotal. Management of SAS requires making the possible diagnosis, altering or discontinuing the statin treatment, and using alternative lipid-lowering therapy. Copyright © 2016 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

  18. Data mining and the human genome

    Energy Technology Data Exchange (ETDEWEB)

    Abarbanel, Henry [The MITRE Corporation, McLean, VA (US). JASON Program Office; Callan, Curtis [The MITRE Corporation, McLean, VA (US). JASON Program Office; Dally, William [The MITRE Corporation, McLean, VA (US). JASON Program Office; Dyson, Freeman [The MITRE Corporation, McLean, VA (US). JASON Program Office; Hwa, Terence [The MITRE Corporation, McLean, VA (US). JASON Program Office; Koonin, Steven [The MITRE Corporation, McLean, VA (US). JASON Program Office; Levine, Herbert [The MITRE Corporation, McLean, VA (US). JASON Program Office; Rothaus, Oscar [The MITRE Corporation, McLean, VA (US). JASON Program Office; Schwitters, Roy [The MITRE Corporation, McLean, VA (US). JASON Program Office; Stubbs, Christopher [The MITRE Corporation, McLean, VA (US). JASON Program Office; Weinberger, Peter [The MITRE Corporation, McLean, VA (US). JASON Program Office

    2000-01-07

    As genomics research moves from an era of data acquisition to one of both acquisition and interpretation, new methods are required for organizing and prioritizing the data. These methods would allow an initial level of data analysis to be carried out before committing resources to a particular genetic locus. This JASON study sought to delineate the main problems that must be faced in bioinformatics and to identify information technologies that can help to overcome those problems. While the current influx of data greatly exceeds what biologists have experienced in the past, other scientific disciplines and the commercial sector have been handling much larger datasets for many years. Powerful datamining techniques have been developed in other fields that, with appropriate modification, could be applied to the biological sciences.

  19. Advances in learning analytics and educational data mining

    NARCIS (Netherlands)

    Vahdat, Mehrnoosh; Ghio, A; Oneto, L.; Anguita, D.; Funk, M.; Rauterberg, G.W.M.

    2015-01-01

    The growing interest in recent years towards Learning An- alytics (LA) and Educational Data Mining (EDM) has enabled novel ap- proaches and advancements in educational settings. The wide variety of research and practice in this context has enforced important possibilities and applications from

  20. Data Mining for Education Decision Support: A Review

    Directory of Open Access Journals (Sweden)

    Suhirman Suhirman

    2014-12-01

    Full Text Available Management of higher education must continue to evaluate on an ongoing basis in order to improve the quality of institutions. This will be able to do the necessary evaluation of various data, information, and knowledge of both internal and external institutions. They plan to use more efficiently the collected data, develop tools so that to collect and direct management information, in order to support managerial decision making. The collected data could be utilized to evaluate quality, perform analyses and diagnoses, evaluate dependability to the standards and practices of curricula and syllabi, and suggest alternatives in decision processes. Data minings to support decision making are well suited methods to provide decision support in the education environments, by generating and presenting relevant information and knowledge towards quality improvement of education processes. In educational domain, this information is very useful since it can be used as a base for investigating and enhancing the current educational standards and managements. In this paper, a review on data mining for academic decision support in education field is presented. The details of this paper will review on recent data mining in educational field and outlines future researches in educational data mining.