Dunyo, S K; Afari, E A; Koram, K A; Ahorlu, C K; Abubakar, I; Nkrumah, F K
A study was conducted in 1997 to compare the accuracy of presumptive diagnosis of malaria in children aged 1-9 years performed by caretakers of the children to that of health centre staff in 2 ecological zones in southern Ghana. Similar symptoms were reported in the children at home and at the health centre. In the home setting, symptoms were reported the same day that they occurred, 77.6% of the children with a report of fever were febrile (axillary temperature > or = 37.5 degrees C) and 64.7% of the reports of malaria were parasitologically confirmed. In the health centre, the median duration of symptoms before a child was seen was 3 days (range 1-14 days), 58.5% of the children with a report of fever were febrile and 62.6% of the clinically diagnosed cases were parasitologically confirmed. In the 2 settings almost all the infections were due to Plasmodium falciparum. Parasite density was 3 times higher in the health centre cases compared to the home-diagnosed cases. Early and appropriate treatment of malaria detected in children by caretakers may prevent complications that arise as a result of persistence of symptoms and attainment of high parasitaemic levels.
Sellors, J; Howard, M; Pickard, L; Jang, D; Mahony, J; Chernesky, M
To test the recommendation from the Canadian guidelines for sexually transmitted diseases (STDs) that mucopurulent endocervical discharge and 10 or more polymorphonuclear leukocytes (PMNs) per high-power field of a Gram-stained endocervical smear or, when Gram staining is not possible, the presence of endocervical discharge and one of edema, erythema or induced mucosal bleeding of the cervix can be considered diagnostic for chlamydial cervicitis. A total of 596 consecutive women attending 2 family planning clinics for routine care underwent vaginal speculum examination and were tested for Chlamydia trachomatis and Neisseria gonorrhoeae. PMN counts from Gram-stained endocervical smears and the presence or absence of putative indicators of chlamydial infection were recorded. The prevalence of chlamydial cervicitis was 6.2% (37/596), and no women tested positive for N. gonorrhoeae. Presumptive diagnosis of chlamydial cervicitis based on the guidelines criteria of mucopurulent endocervical discharge and 10 or more PMN per high-power microscopic field had a sensitivity and specificity of 18.9% and 97.0% respectively, a positive predictive value of 29.2% and a positive likelihood ratio (LR) of 6.2 (p = 0.003). Presumptive diagnosis based on endocervical discharge with edema, erythema or induced mucosal bleeding of the cervix had a sensitivity and specificity of 43.2% and 80.0% respectively, a positive predictive value of 12.5% and a positive LR of 2.2 (p = 0.002). In the presence of bacterial vaginosis or vaginitis, the LR for the criteria of mucopurulent endocervical discharge and 10 or more PMN per high-power field was 5.4 (p = 0.04), whereas the LR was 4.3 (p = 0.10) if bacterial vaginosis and vaginitis were absent. In this setting, the practice of making a presumptive diagnosis of chlamydial cervicitis on the basis of the criteria given in the Canadian STD guidelines was not supported.
Sriskandarajah, Srishamanthi; Carter-Storch, Rasmus; Frydkjær-Olsen, Ulrik; Mogensen, Christian Backer
In Denmark, patients referred from the general practitioner (GP) to the emergency department (ED) can be referred with either specific symptoms or with a presumptive diagnosis. The aim of the present study was to evaluate the diagnostic accuracy for various presumptive diagnoses made by the GP in a population acutely referred to an ED. This was a retrospective cohort study of all registered acute referrals for admission to Kolding ED in 2010. Eight presumptive diagnoses were selected for further studies: meningitis, acute coronary syndrome (ACS), pulmonary embolism, pneumonia, pancreatitis, deep venous thrombosis (DVT), pyelonephritis and intestinal obstruction. The presumptive diagnoses were compared with the final diagnosis on discharge. Sensitivity, specificity, predictive values and likelihood ratios were calculated. A total of 8,841 patients were enrolled. The highest and lowest sensitivities were seen for DVT (90%) and meningitis (36%), respectively; and the highest and lowest values for specificity were observed for meningitis (99%) and ACS (30%), respectively. The positive predictive value had a wide range with the lowest value for ACS (9%) and the highest for pneumonia (59%). For pyelonephritis, meningitis and pancreatitis, the likelihood ratio of a positive test was above 10. The likelihood ratio of a negative test was above 0.1 for all diagnoses. Patients referred with the presumptive diagnoses pyelonephritis, meningitis and pancreatitis had a high likelihood of having the disease in question. It is important not to discard any of the included presumptive diagnoses even if the GPs fail to suggest them on admission. none. none.
Self-diagnosis of influenza, yellow fever, typhoid, and pneumonia was also common. Conclusion: Self-diagnosis and presumptive treatment with antimalarial drugs and other antibiotic medications that are readily available without a prescription may compromise health outcomes for febrile adults and children. Key words: ...
First, 50 children with malaria-pneumonia symptom overlap were consecutively enrolled and treated presumptively with antibiotics and antimalarials irrespective of malaria test result (control arm).Then, another 50 eligible children were enrolled and treated with antibiotics with/out antimalarials based on rapid diagnostic test ...
Background: Making a diagnosis of HIV infection in children aged less than 18 months remains a challenge in low resource settings like Zambia due to the limited availability of gold standard testing with HIV DNA PCR. Clinicians in rural areas have to depend on clinical diagnosis to start HAART asthey wait for the dry blood ...
Sriskandarajah, Srishamanthi; Carter-Storch, Rasmus; Frydkjær-Olsen, Ulrik
by the GP in a population acutely referred to an ED. METHODS: This was a retrospective cohort study of all registered acute referrals for admission to Kolding ED in 2010. Eight presumptive diagnoses were selected for further studies: meningitis, acute coronary syndrome (ACS), pulmonary embolism, pneumonia......, pancreatitis, deep venous thrombosis (DVT), pyelonephritis and intestinal obstruction. The presumptive diagnoses were compared with the final diagnosis on discharge. Sensitivity, specificity, predictive values and likelihood ratios were calculated. RESULTS: A total of 8,841 patients were enrolled. The highest...... for pneumonia (59%). For pyelonephritis, meningitis and pancreatitis, the likelihood ratio of a positive test was above 10. The likelihood ratio of a negative test was above 0.1 for all diagnoses. CONCLUSIONS: Patients referred with the presumptive diagnoses pyelonephritis, meningitis and pancreatitis had...
Shewade, Hemant Deepak; Kokane, Arun M; Singh, Akash Ranjan; Verma, Manoj; Parmar, Malik; Chauhan, Ashish; Chahar, Sanjay Singh; Tiwari, Manoj; Khan, Sheeba Naz; Gupta, Vivek; Tripathy, Jaya Prasad; Nagar, Mukesh; Singh, Sanjai Kumar; Mehra, Pradeep Kumar; Kumar, Ajay Mv
Pre-diagnosis attrition needs to be addressed urgently if we are to make progress in improving MDR-TB case detection and achieve universal access to MDR-TB care. We report the pre-diagnosis attrition, along with factors associated, and turnaround times related to the diagnostic pathway among patient with presumptive MDR-TB in Bhopal district, central India (2014). Study was conducted under the Revised National Tuberculosis Control Programme setting. It was a retrospective cohort study involving record review of all registered TB cases in Bhopal district that met the presumptive MDR-TB criteria (eligible for DST) in 2014. In quarter 1, Line Probe Assay (LPA) was used if sample was smear/culture positive. Quarter 2 onwards, LPA and Cartridge-based Nucleic Acid Amplification Test (CbNAAT) was used for smear positive and smear negative samples respectively. Pre-diagnosis attrition was defined as failure to undergo DST among patients with presumptive MDR-TB (as defined by the programme). Of 770 patients eligible for DST, 311 underwent DST and 20 patients were diagnosed as having MDR-TB. Pre-diagnosis attrition was 60% (459/770). Among those with pre-diagnosis attrition, 91% (417/459) were not identified as 'presumptive MDR-TB' by the programme. TAT [median (IQR)] to undergo DST after eligibility was 4 (0, 10) days. Attrition was more than 40% across all subgroups. Age more than 64 years; those from a medical college; those eligible in quarter 1; patients with presumptive criteria 'previously treated - recurrent TB', 'treatment after loss-to-follow-up' and 'previously treated-others'; and patients with extra-pulmonary TB were independent risk factors for not undergoing DST. High pre-diagnosis attrition was contributed by failure to identify and refer patients. Attrition reduced modestly with time and one factor that might have contributed to this was introduction of CbNAAT in quarter 2 of 2014. General health system strengthening which includes improvement in
Hermann, G.; Gilbert, M.
A case has been presented of a 72-year-old man on whom an excretory urogram showed the incidental findings of two soft tissue masses in the abdomen containing considerable deposits of calcium. The history was interesting in that the patient was classic hemophiliac with Factor VIII level less than 1%, who first developed symptoms and signs of multiple hemarthroses affecting the knees, ankles, elbows, and shoulders at the age of nine years. Secondary hemophilic arthropathy followed, particularly advanced in the right elbow. Total knee replacements were performed within the last 10 years. A mass within the muscles of the right chest wall, superficial to the ribs, was surgically removed. The abdominal masses in this case were studied with CT and showed considerable calcification with a fibrous wall. Surgical removal of pseudotumors is usually undertaken following diagnosis because the natural history includes continuous enlargement and destruction of the adjacent tissues. Because of the age of the patient and the significant cardiac history, it was considered inappropriate to undertake surgery for the masses in the abdomen which were considered presumptively to be pseudotumors. The clinical, radiological, and pathological aspects of pseudotumor of hemophilia were reviewed. In this case, besides the masses in the abdomen, hemophilic arthropathy of an elbow was illustrated and a soft tissue mass in the right chest wall was demonstrated radiologically and the pathological specimen shown after surgical excision. (orig.)
Hoorfar, Jeffrey; Baggesen, Dorte Lau; Porting, P.H.
The purpose of the present study was to investigate the application of ready-to-go Salmonella PCR tests, based on dry chemistry, for final identification of rough presumptive Salmonella isolates. The results were compared with two different biotyping methods performed at two different laboratorie...... (serogroup D). In conclusion, rough presumptive Salmonella isolates can be conveniently confirmed to the serogroup-revel, using the pre-mixed PCR tests. The system can be easily implemented in accredited laboratories with limited experience in molecular biology....
Full Text Available Diagnosis of pulmonary tuberculosis (PTB in children is challenging due to difficulties in obtaining good quality sputum specimens as well as the paucibacillary nature of disease. Globally a large proportion of pediatric tuberculosis (TB cases are diagnosed based only on clinical findings. Xpert MTB/RIF, a highly sensitive and specific rapid tool, offers a promising solution in addressing these challenges. This study presents the results from pediatric groups taking part in a large demonstration study wherein Xpert MTB/RIF testing replaced smear microscopy for all presumptive PTB cases in public health facilities across India.The study covered a population of 8.8 million across 18 programmatic sub-district level tuberculosis units (TU, with one Xpert MTB/RIF platform established at each study TU. Pediatric presumptive PTB cases (both TB and Drug Resistant TB (DR-TB accessing any public health facilities in study area were prospectively enrolled and tested on Xpert MTB/RIF following a standardized diagnostic algorithm.4,600 pediatric presumptive pulmonary TB cases were enrolled. 590 (12.8%, CI 11.8-13.8 pediatric PTB were diagnosed. Overall 10.4% (CI 9.5-11.2 of presumptive PTB cases had positive results by Xpert MTB/RIF, compared with 4.8% (CI 4.2-5.4 who had smear-positive results. Upfront Xpert MTB/RIF testing of presumptive PTB and presumptive DR-TB cases resulted in diagnosis of 79 and 12 rifampicin resistance cases, respectively. Positive predictive value (PPV for rifampicin resistance detection was high (98%, CI 90.1-99.9, with no statistically significant variation with respect to past history of treatment.Upfront access to Xpert MTB/RIF testing in pediatric presumptive PTB cases was associated with a two-fold increase in bacteriologically-confirmed PTB, and increased detection of rifampicin-resistant TB cases under routine operational conditions across India. These results suggest that routine Xpert MTB/RIF testing is a promising
Tawiah, Theresa; Hansen, Kristian Schultz; Baiden, Frank
(ACT) in all suspected malaria patients. The use of malaria rapid diagnostic tests (mRDTs) would make it possible for prescribers to diagnose malaria at point-of-care and better target the use of antimalarials. Therefore, a cost-effectiveness analysis was performed on the introduction of m......RDTs for management of malaria in under-five children in a high transmission area in Ghana where presumptive diagnosis was the norm in public health centres. Methods: A cluster-randomised controlled trial where thirty-two health centres were randomised into test-based diagnosis of malaria using mRDTs (intervention...
Cakirer, Sinan [Department of Radiology, Neuroradiology Section, Istanbul Sisli Etfal Hospital, 81120 Istanbul (Turkey)
The aim of this study was to present our experience in MRI diagnosis of 23 patients with the clinical findings suggesting Tolosa-Hunt syndrome (THS). Cranial MRI studies of the patients with a clinical history of at least one episode of unilateral or bilateral orbital and periorbital pain, and associated paresis of one or more of third to sixth cranial nerves, were performed on a 1.5-T MRI scanner. Whereas 5 patients had the diagnosis of THS, paracavernous meningiomas in 4 patients, pituitary macroadenomas with cavernous sinus infiltration in 3 patients, Meckel's cave neurinoma in 1 patient, and suprasellar epidermoid in 1 patient were surgically proven MRI findings. Other pathological MRI findings were leptomeningeal metastases in 3 patients, granulomatous pachymeningitis sequelae in 2 patients, and aneurysm with compression on cavernous sinus in 1 patient. Three patients had normal MRI findings. The incidence of radiologically proven diagnosis of THS among the patients with the clinical findings suggesting THS seemed to be low in our study. In conclusion, MRI is the most valuable imaging technique to distinguish THS from other THS-like entities, and permits a precise assessment, management, and therapeutic planning of the underlying pathological conditions. (orig.)
Muñoz, Mario; Ramirez, Pedro T; Echeverri, Carolina; Alvarez, Luis Guillermo; Palomino, Maria Alejandra; Pareja, Luis René
To report the clinical presentation and oncologic outcomes of a series of patients who presented with an abdominal or pelvic mass and were diagnosed with a gastrointestinal stromal tumor (GIST). Data were obtained on all patients who presented with an abdominal or pelvic mass between September 2007 and June 2010 and who were ultimately diagnosed with a GIST. The patients' medical records were reviewed. A literature review was also conducted. Six patients were identified who met the inclusion criteria. All six patients had a tumor in the intestinal tract arising from the small bowel. The mean tumor size was 12 cm (range, 6 to 22 cm). A complete resection was achieved in five of the six patients. There were no intraoperative complications; one patient had a postoperative complication. Two patients were treated with imatinib after surgery. The mean follow-up time was 32 months (range, 0.3 to 40 months). At the last follow-up, five of the six patients were without any evidence of disease. One patient died of an unrelated hepatic encephalopathy. The incidence in our institution is 3%. GISTs are uncommon; however, they should be considered in the differential diagnosis of patients presenting with an abdominal or pelvic mass.
Full Text Available Background & Objectives: Considering new pandemic attack with new emerging (H1N1 influenza virus, the study was designed for evaluating clinical and epidemiologic characteristics of patients in Imam Khomeini hospital, admitted with presumptive diagnosis of H1N1 influenza . In all of the patients clinical and paraclinical findings and outcome (including mortality rate and definitive diagnosis were evaluated. Bed occupancy rate in infectious disease ward and ICU and also mean days of admission were calculated. Methods: This is a cross sectional study . All 118 patients with acute respiratory symptoms and possible diagnosis of emerging H1N1 influenza that had been admitted at least 24 hours in hospital from 20 October to 1 February 2009 were enrolled in the study. Data collection was done based on questionnaires, with a team other than researchers. The questionnaire included demographic data , clinical symptoms , laboratory findings , radiographic manifestations and outcome of patients. Data analysis was performed with SPSS software version16. Results: A total of 118 patients were studied: 71 patients ( 60.2% were female and 47 patients ( 39.8% were male. Mean ( ± SD age of admitted patients was 33.81 ± 15.64 years old. The most of admitted patients were in age range of 15 to 30 years old. The most common findings in CXR were bilateral respiratory consolidations and the most common symptoms were fever, weakness and fatigue. About 12.7% of patients had diarrhea. Leukopenia (WBC 10000 occurred respectively in 4.58% and 33.2% of cases . Nine patients (7% were admitted in ICU. I n 21 patients (18% RT-PCR test results were positive and three of these patients had been admitted in ICU. In patients admitted in ICU while their diagnosis was confirmed, mortality was 33%. 48.3% of patients had at least one predisposing medical condition . Total admission days were 577 days, consisting 519 days in infectious disease ward and 58 days in ICU. Average of
João Luiz Cioglia Pereira Diniz
Full Text Available Mucocutaneous Leishmaniasis (ML can lead to serious sequela; however, early diagnosis can prevent complications. AIM: To evaluate clinical markers for the early diagnosis of ML. MATERIALS AND METHODS: A series study of 21 cases of ML, which were evaluated through clinical interview, nasal endoscopy, biopsy and the Montenegro test. RESULTS: A skin scar and previous diagnosis of cutaneous leishmaniasis (CL were reported in 8(38% patients, and 13(62% of them denied having had previous CL and had no scar. Nasal/oral symptom onset until the ML diagnosis varied from 5 months to 20 years, mean value of 6 years. In the Montenegro test, the average size of the papule was 14.5 mm, which did not correlate with disease duration (p=0.87. The nose was the most often involved site and the extension of the injured mucosa did not correlate with disease duration. The parasite was found in 2 (9.52% biopsy specimens. CONCLUSIONS: ML diagnosis was late. Finding the parasite in the mucosa, cutaneous scar and/or previous diagnosis of CL were not clinical markers for ML. ML diagnosis must be based on the Montenegro test, chronic nasal and/or oral discharge and histological findings ruling out other granulomatous diseases.A leishmaniose cutâneo-mucosa (LM pode deixar sequelas graves. O diagnóstico precoce evita complicações. OBJETIVO: Avaliar marcadores clínicos para o diagnóstico precoce da LM. MATERIAL E MÉTODO: Estudo de série de 21 casos avaliados com diagnóstico confirmado de LM por meio de entrevista, endoscopia nasal, biópsia e teste cutâneo de Montenegro. RESULTADOS: A cicatriz cutânea ou história de leishmaniose cutânea foram observadas em 8 (38% pacientes e 13(62% negaram terem tido forma cutânea e não tinham cicatriz. O início dos sintomas nasais/orais até a definição do diagnóstico variou de 5 meses a 20 anos, média de 6 anos. No teste de Montenegro, o tamanho médio da pápula foi de 14,5mm e não se correlacionou com a duração da
Massenet, Denis; Couteaux, Clément; Goarant, Cyrille
Leptospirosis is a common condition in Wallis and Futuna, and the definitive diagnosis needs to be established urgently at the first patient consultation, which is usually one to two days after the onset of clinical signs. As a diagnostic aid, a composite index was established based on data from 338 patients seen by the Wallis and Futuna admissions services between 2008 and 2015. The data taken into account include: age and sex of the patient, their home island, the consultation period and the results of leukocytes, platelets, CRP, creatinine and GGT tests combined with 2 major clinical signs, headache and conjunctival suffusion. Then 5 threshold limits were defined for this index, which indicates from very low risk to almost certain biologically confirmed leptospirosis. Other febrile diseases responsible for thrombocytopenia are not found in Wallis and Futuna, which probably explains the good statistical qualities of this index with a value of area under the curve equal to 0.821.
Guerra, Alice; Luppi, Barbara; Parisi, Francesco
This paper is about the incentive effects of legal presumptions. We analyze three interrelated effects of legal presumptions in a tort setting: (1) incentives to invest in evidence technology; (2) incentives to invest in care-type precautions; and (3) incentives to mitigate excessive activity lev...
Niemann, Hans Henrik
Different aspects of fault detection and fault isolation in closed-loop systems are considered. It is shown that using the standard setup known from feedback control, it is possible to formulate fault diagnosis problems based on a performance index in this general standard setup. It is also shown...
Validez del diagnóstico presuntivo de leishmaniasis cutánea realizado por mediadores comunitarios en Colombia The validity of a presumptive diagnosis of cutaneous leishmaniasis performed by community health workers in Colombia
captación activa de casos en la comunidad.OBJECTIVE: To validate a method for the presumptive diagnosis of cutaneous leishmaniasis based on the observation of clinical-epidemiological criteria, carried out by community health workers in three endemic municipalities in Santander, a department in northeastern Colombia. METHODS: This evaluation study of diagnostic technologies was based on a cross-sectional sampling of suspected cases of cutaneous leishmaniasis in the municipalities of Rionegro, El Playón, and Landázuri. After being trained, the community health workers carried out the presumptive diagnoses of cutaneous leishmaniasis between October 2004 and November 2005. At the time of diagnosis, the health workers also collected samples for confirmatory diagnosis through Field's stain method, culturing, and polymerase chain reaction. Four criteria were used to assess the validity of the presumptive diagnoses carried out by the health workers: sensitivity, specificity, positive predictive value, and negative predictive value. Replicability among the health workers was estimated through their observed level of agreement. RESULTS: According to the laboratory tests, of the 196 patients studied, 33 (16.8% were negative and 163 (83.2% were positive. For all the levels of certainty of the clinical diagnosis, the sensitivity was between 52% and 98% (k(1, 0 = 39.0% and the specificity between 9% and 55% (k(0, 0 = 14.0%. The area under the receiver operating characteristic curve was 56.5% (95% confidence interval: 45.6% to 67.4%. The proportion of positive agreement and of negative agreement was 86.3% and 43.5%, respectively. CONCLUSIONS: The sensitivity of the presumptive diagnosis carried out by the health workers surpasses that of the parasitological diagnostic methods generally used in the three endemic areas, but its specificity is much lower. Even though this approach is not useful as a diagnostic test for cutaneous leishmaniasis or as a criterion for deciding to proceed
Niemann, Hans Henrik; Poulsen, Niels Kjølstad
Fault detection and isolation, (FDI) of parametric faults in dynamic systems will be considered in this paper. An active fault diagnosis (AFD) approach is applied. The fault diagnosis will be investigated with respect to different information levels from the external inputs to the systems....... These inputs are disturbance inputs, reference inputs and auxilary inputs. The diagnosis of the system is derived by an evaluation of the signature from the inputs in the residual outputs. The changes of the signatures form the external inputs are used for detection and isolation of the parametric faults....
The Department of Veterans Affairs (VA) is amending its adjudication regulations to establish a presumption of service connection for osteoporosis for former Prisoners of War (POWs) who were detained or interned for at least 30 days and whose osteoporosis is at least 10 percent disabling. The amendment implements a decision by the Secretary to establish such a presumption based on scientific studies. VA is additionally amending its adjudication regulations to establish a presumption of service connection for osteoporosis for POWs who were detained or interned for any period of time, have a diagnosis of posttraumatic stress disorder (PTSD), and whose osteoporosis is at least 10 percent disabling. This amendment reflects statutory provisions of the Veterans' Benefits Improvement Act of 2008.
Baiden, Frank; Bruce, Jane; Webster, Jayne; Tivura, Mathilda; Delmini, Rupert; Amengo-Etego, Seeba; Owusu-Agyei, Seth; Chandramohan, Daniel
Malaria-endemic countries in sub-Saharan Africa are shifting from the presumptive approach that is based on clinical judgement (CJ) to the test-based approach that is based on confirmation through test with rapid diagnostic tests (RDT). It has been suggested that the loss of the prophylactic effect of presumptive-administered ACT in children who do not have malaria will result in increase in their risk of malaria and anaemia. We undertook a cluster-randomized controlled trial to compare the effects of the presumptive approach using clinical judgment (CJ-arm) and the test-based approach using RDTs (RDT-arm in a high-transmission setting in Ghana. A total of 3046 eligible children (1527 in the RDT arm and 1519 in the CJ- arm) living around 32 health centres were enrolled. Nearly half were female (48.7%) and 47.8% were below the age of 12 months as at enrolment. Over 24-months, the incidence of all episodes of malaria following the first febrile illness was 0.64 (95% CI 0.49-0.82) and 0.76 (0.63-0.93) per child per year in the RDT and CJ arms respectively (adjusted rate ratio 1.13 (0.82-1.55). After the first episode of febrile illness, the incidence of severe anaemia was the same in both arms (0.11 per child per year) and that of moderate anaemia was 0.16 (0.13-0.21) vs. 0.17 (0.14-0.21) per child year respectively. The incidence of severe febrile illness was 0.15 (0.09, 0.24) in the RDT arm compared to 0.17 (0.11, 0.28) per child per year respectively. The proportion of fever cases receiving ACT was lower in the RDT arm (72% vs 81%; p = 0.02). The test-based approach to the management of malaria did not increase the incidence of malaria or anaemia among under-five children in this setting. ClinicalTrials.gov NCT00832754.
Full Text Available OBJECTIVE: To determine the sensitivity and specificity of a Computer Aided Diagnosis (CAD program for scoring chest x-rays (CXRs of presumptive tuberculosis (TB patients compared to Xpert MTB/RIF (Xpert. METHOD: Consecutive presumptive TB patients with a cough of any duration were offered digital CXR, and opt out HIV testing. CXRs were electronically scored as normal (CAD score ≤ 60 or abnormal (CAD score > 60 using a CAD program. All patients regardless of CAD score were requested to submit a spot sputum sample for testing with Xpert and a spot and morning sample for testing with LED Fluorescence Microscopy-(FM. RESULTS: Of 350 patients with evaluable data, 291 (83.1% had an abnormal CXR score by CAD. The sensitivity, specificity, positive predictive value (PPV and negative predictive value (NPV of CXR compared to Xpert were 100% (95%CI 96.2-100, 23.2% (95%CI 18.2-28.9, 33.0% (95%CI 27.6-38.7 and 100% (95% 93.9-100, respectively. The area under the receiver operator curve (AUC for CAD was 0.71 (95%CI 0.66-0.77. CXR abnormality correlated with smear grade (r = 0.30, p<0.0001 and with Xpert CT(r = 0.37, p<0.0001. CONCLUSIONS: To our knowledge this is the first time that a CAD program for TB has been successfully tested in a real world setting. The study shows that the CAD program had high sensitivity but low specificity and PPV. The use of CAD with digital CXR has the potential to increase the use and availability of chest radiography in screening for TB where trained human resources are scarce.
Casazza, Krista; Fontaine, Kevin R.; Astrup, Arne; Birch, Leann L.; Brown, Andrew W.; Bohan Brown, Michelle M.; Durant, Nefertiti; Dutton, Gareth; Foster, E. Michael; Heymsfield, Steven B.; McIver, Kerry; Mehta, Tapan; Menachemi, Nir; Newby, P.K.; Pate, Russell; Rolls, Barbara J.; Sen, Bisakha; Smith, Daniel L.; Thomas, Diana M.; Allison, David B.
BACKGROUND Many beliefs about obesity persist in the absence of supporting scientific evidence (presumptions); some persist despite contradicting evidence (myths). The promulgation of unsupported beliefs may yield poorly informed policy decisions, inaccurate clinical and public health recommendations, and an unproductive allocation of research resources and may divert attention away from useful, evidence-based information. METHODS Using Internet searches of popular media and scientific literature, we identified, reviewed, and classified obesity-related myths and presumptions. We also examined facts that are well supported by evidence, with an emphasis on those that have practical implications for public health, policy, or clinical recommendations. RESULTS We identified seven obesity-related myths concerning the effects of small sustained increases in energy intake or expenditure, establishment of realistic goals for weight loss, rapid weight loss, weight-loss readiness, physical-education classes, breast-feeding, and energy expended during sexual activity. We also identified six presumptions about the purported effects of regularly eating breakfast, early childhood experiences, eating fruits and vegetables, weight cycling, snacking, and the built (i.e., human-made) environment. Finally, we identified nine evidence-supported facts that are relevant for the formulation of sound public health, policy, or clinical recommendations. CONCLUSIONS False and scientifically unsupported beliefs about obesity are pervasive in both scientific literature and the popular press. (Funded by the National Institutes of Health.) PMID:23363498
A report of Avian encephalomyelitis outbreak in two flocks of adult Japanese quail is presented. High mortalities, tremor, ataxia and lateral recumbency were the prominent clinical signs observed. Absence of gross pathology and microscopic lesions of gliosis, neuronal degeneration, meningitis, congested blood vessel with ...
... Acquisition Regulation Supplement; Presumption of Development at Private Expense AGENCY: Defense Acquisition... the presumption of development at private expense for major systems; and section 815(a)(2) of the NDAA... Expense--Technical Data The validation of asserted restrictions on technical data is based on statutory...
Groot, T.L.C.M.; Quadackers, L.M.; Wright, A.
Although skepticism is widely viewed as essential to audit quality, there is a debate about what form is optimal. The two prevailing perspectives that have surfaced are "neutrality" and "presumptive doubt." With neutrality, auditors neither believe nor disbelieve client management. With presumptive
Ubaldo O. Martín
the larvae migrate through the capillaries, taking up residence in different tissues. Clinical manifestations are associated with mechanical and/or reaction damage caused by these parasites larvae. Clinical diagnosis is difficult. The method applied in this work is the demonstration of antibodies against the helminth in the blood of children, target host population of this parasitic disease. An ELISA test was performed using T. canis larval excretory-secretory products as antigen. A total of 100 children presumptively diagnosed of toxocariasis that had been derived from different services of the Regional Children’s Hospital for complementary studies, were included in the analysis. The test detected two different populations: infected (59 and non-infected (41. The statistical analysis showed a non significant association between infection and sex (p = 0.279. Infected subjects tended to be older than the non infected (p = 0.009. Eosinophilia was detected in 100% of seropositive children and in 85.2% of the seronegative. There was no significant association between infection and leucocytosis ( = 0.950. The association of these two parameters was significantly higher among infected patients (R = 0.918. Respiratory symptoms and signs were more frequently detected in the positive population (p = 0.05. Dogs tenancy was as frequent among infected as in the non infected homes (p = 0.53. According to these results, prevention, early diagnosis and opportune treatment for toxocariasis should be considered as prioritary health activities in this region.
Zhang, Nannan; Wu, Lifeng; Yang, Jing; Guan, Yong
The bearing is the key component of rotating machinery, and its performance directly determines the reliability and safety of the system. Data-based bearing fault diagnosis has become a research hotspot. Naive Bayes (NB), which is based on independent presumption, is widely used in fault diagnosis. However, the bearing data are not completely independent, which reduces the performance of NB algorithms. In order to solve this problem, we propose a NB bearing fault diagnosis method based on enhanced independence of data. The method deals with data vector from two aspects: the attribute feature and the sample dimension. After processing, the classification limitation of NB is reduced by the independence hypothesis. First, we extract the statistical characteristics of the original signal of the bearings effectively. Then, the Decision Tree algorithm is used to select the important features of the time domain signal, and the low correlation features is selected. Next, the Selective Support Vector Machine (SSVM) is used to prune the dimension data and remove redundant vectors. Finally, we use NB to diagnose the fault with the low correlation data. The experimental results show that the independent enhancement of data is effective for bearing fault diagnosis. PMID:29401730
Zhang, Nannan; Wu, Lifeng; Yang, Jing; Guan, Yong
The bearing is the key component of rotating machinery, and its performance directly determines the reliability and safety of the system. Data-based bearing fault diagnosis has become a research hotspot. Naive Bayes (NB), which is based on independent presumption, is widely used in fault diagnosis. However, the bearing data are not completely independent, which reduces the performance of NB algorithms. In order to solve this problem, we propose a NB bearing fault diagnosis method based on enhanced independence of data. The method deals with data vector from two aspects: the attribute feature and the sample dimension. After processing, the classification limitation of NB is reduced by the independence hypothesis. First, we extract the statistical characteristics of the original signal of the bearings effectively. Then, the Decision Tree algorithm is used to select the important features of the time domain signal, and the low correlation features is selected. Next, the Selective Support Vector Machine (SSVM) is used to prune the dimension data and remove redundant vectors. Finally, we use NB to diagnose the fault with the low correlation data. The experimental results show that the independent enhancement of data is effective for bearing fault diagnosis.
Casazza, Krista; Fontaine, Kevin R; Astrup, Arne
Many beliefs about obesity persist in the absence of supporting scientific evidence (presumptions); some persist despite contradicting evidence (myths). The promulgation of unsupported beliefs may yield poorly informed policy decisions, inaccurate clinical and public health recommendations...
U.S. Department of Health & Human Services — Health care providers and Head Start programs can play a major role in finding and enrolling uninsured children through presumptive eligibility. States can authorize...
... conformity with the Department's present enforcement policy, is in compliance with those provisions of the FCPA. Such a presumption may be rebutted by a preponderance of the evidence. In considering the...
Dengue fever is a common tropical infection. This acute febrile illness can be a deadly infection in cases of severe manifestation, causing dengue hemorrhagic shock. In this brief article, I will summarize and discuss the diagnosis and treatment of this disease. For diagnosis of dengue, most tropical doctors make use of presumptive diagnosis; however, the definite diagnosis should be based on immunodiagnosis or viral study. Focusing on treatment, symptomatic and supportive treatment is the main therapeutic approach. The role of antiviral drugs in the treatment of dengue fever has been limited, but is currently widely studied.
Model-based diagnosis is an area of abductive inference that uses a system model, together with observations about system behavior, to isolate sets of faulty components (diagnoses) that explain the observed behavior, according to some minimality criterion. This thesis presents greedy approximation
Full Text Available The purpose of this article is to develop a new method for corporate diagnosis (CD. To this end, a method is developed for the diagnosis process of project-based companies. The article presents a case study in a large company where data have been collected through focus groups. Project delay, high project cost, and low profitability are examples of project deficiency in project-based companies. Such issues have made managers pay special attention to find effective solutions to improve them. Prominent factors are inappropriate strategy, structure, system, human resource management, and PMBOK(Project Management Body of Knowledge processes. Thus, CD and analysis is an important task in improvement of corporate performance. The CD model that is developed in this article could be used for project-based companies. The proposed method can be used for CD in any project-based company. This article provides an emphatic application of CD as a prerequisite for restructuring in project-based companies.
Pang, Bo; Zhang, David; Li, Naimin; Wang, Kuanquan
Tongue diagnosis is an important diagnostic method in traditional Chinese medicine (TCM). However, due to its qualitative, subjective and experience-based nature, traditional tongue diagnosis has a very limited-application in clinical medicine. Moreover, traditional tongue diagnosis is always concerned with the identification of syndromes rather than with the connection between tongue abnormal appearances and diseases. This is not well understood in Western medicine, thus greatly obstruct its wider use in the world. In this paper, we present a novel computerized tongue inspection method aiming to address these problems. First, two kinds of quantitative features, chromatic and textural measures, are extracted from tongue images by using popular digital image processing techniques. Then, Bayesian networks are employed to model the relationship between these quantitative features and diseases. The effectiveness of the method is tested on a group of 455 patients affected by 13 common diseases as well as other 70 healthy volunteers, and the diagnostic results predicted by the previously trained Bayesian network classifiers are reported.
Simanjuntak, Herbert P.; Pereyra, Pedro
We analyze different examples to show that the so-called generalized Hartman effect is an erroneous presumption. The results obtained for electron tunneling and transmission of electromagnetic waves through superlattices and Bragg gratings show clearly the resonant character of the phase time behavior where a generalized Hartman effect is expected. A reinterpretation of the experimental results in double Bragg gratings is proposed. (author)
Mar 2, 2010 ... diagnostic test in the management of children with this overlap, but this has not been evaluated. Therefore, the objective of this study was to compare the clinical outcome of presumptive versus malaria rapid diagnostic test - ... the rain forest belt of Nigeria, an area with perennial malaria trans mission.
Three hundred and fifteen blood samples were obtained from presumptively diagnosed typhoid patients who were referred for Widal Serological test at four diagnostic centres. The blood samples were subjected to bacteriological investigations. Salmonella and non-Salmonella organisms isolated were identified according ...
...) NATIONAL OCEANIC AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE OIL POLLUTION ACT REGULATIONS... assessment of damages to natural resources made by a Federal, State, or Indian trustee in accordance with this part shall have the force and effect of a rebuttable presumption on behalf of the trustee in any...
Immunodiagnostic confirmation of hydatid disease in patients with a presumptive diagnosis of infection Confirmación inmunodiagnóstica de la hidatidosis en pacientes con diagnóstico presuntivo de la infeccion
V. M. Varela-diaz
Full Text Available Information obtained from the routine application of hydatid immunodiagnostic techniques in different clinical situations over a seven-year period is presented. The Immunoelectrophoresis test was used until it was replaced by the simpler, more sensitive and equally specific arc 5 double diffusion (DD5 test. Examination of sera from 1,888 patients with signs and/or symptoms compatible with hydatid disease revealed that the presurgical confirmation of Echinococcus granulosus infection is only obtained by detection of anti-antigen 5 antibodies. The latter were not found in 1,539 presumptive hydatidosis patients whose definitive diagnoses corresponded to other disease conditions. However, false positive latex agglutination test results were obtained in two cases. In all patients whose preoperative serum showed three or more uncharacteristic bands in the absence of anti-antigen 5 antibodies, hydatid cysts were found sur gically. DD5 testing of a fluid sample collected by puncture established its hydatid etiology. Post-operative monitoring of hydatidosis patients demonstrated that persistence of DD5-positivity two years after surgery established the presence of other cysts. Further evidence was obtained in patients with hydatid cysts in intrathoracic, abdominal or other locations associating cyst membrane integrity, antigen release and immunodiagnostic test positivity.Se presenta la información obtenida de la aplicación de las técnicas inmunodiagnósticas para hidatidosis en diferentes situaciones clínicas durante un período de 7 años. Se empleó la prueba de inmunoelectroforesis hasta que se la sustituyó por la prueba de doble difusión arco 5 (DD5, igualmente específica pero de mayor sensibilidad y sencillez. El examen de sueros de 1 888 pacientes con signos y/o sintomas compatibles con la hidatidosis reveló que la confirmación prequirúrgica de la infeccion por Echinococcus granulosus sólo se obtiene mediante la detección de anticuerpos
Dislich, Mathias; Wohlsein, Peter; Croukamp, Anna Sophie; Neumann, Ulrich
Snake bites represent a serious public health risk in many regions of the globe, especially in tropical areas. Clinical signs and postmortem changes are well described in human and other mammalian species. However, detailed case reports about venomous snake attacks in avian species are limited. This report describes presumptive fatal envenomations in three psittacines caused by pit vipers in a Brazilian zoo. In one case, a Brazilian lancehead (Bothrops moojeni) was captured in the aviary. In all three cases the dermis around the suspected snake bite area exhibited hemorrhages and edema. Histologically, degeneration and necrosis of subcutaneous musculature were observed. Lung, heart, and kidneys displayed focal hemorrhages. The local changes are similar to those described for mammalian patients including humans. However, except for the parenchymatous hemorrhages, additional external and internal gross and histopathological lesions were missing. After ruling out other causes, such as aggression and dicoumarinic intoxication, the presumptive diagnosis of snake envenomation was made. The smaller size and variabilities of pathophysiological effects of the venom in parrots might explain the different lesion patterns observed, compared with mammals. Possibly, the birds may have reacted differently to envenomation by pit vipers and died before the venom could cause macroscopic and histological changes often observed in mammals.
Phuong, Hoang L.; Nga, Tran T. T.; Giao, Phan T.; Hung, Le Q.; Binh, Tran Q.; Nam, Nguyen V.; Nagelkerke, Nico; de Vries, Peter J.
ABSTRACT: BACKGROUND: Fever is a common reason for attending primary health facilities in Vietnam. Response of health care providers to patients with fever commonly consists of making a presumptive diagnosis and proposing corresponding treatment. In Vietnam, where malaria was brought under control,
Amy T. Dao
Full Text Available Auscultation is used to evaluate heart health, and can indicate when it’s needed to refer a patient to a cardiologist. Advanced phonocardiograph (PCG signal processing algorithms are developed to assist the physician in the initial diagnosis but they are primarily designed and demonstrated with research quality equipment. Therefore, there is a need to demonstrate the applicability of those techniques with consumer grade instrument. Furthermore, routine monitoring would benefit from a wireless PCG sensor that allows continuous monitoring of cardiac signals of patients in physical activity, e.g., treadmill or weight exercise. In this work, a low-cost portable and wireless healthcare monitoring system based on PCG signal is implemented to validate and evaluate the most advanced algorithms. Off-the-shelf electronics and a notebook PC are used with MATLAB codes to record and analyze PCG signals which are collected with a notebook computer in tethered and wireless mode. Physiological parameters based on the S1 and S2 signals and MATLAB codes are demonstrated. While the prototype is based on MATLAB, the later is not an absolute requirement.
The results are described of an investigation of techniques for using continuous simulation models as basis for reasoning about physical systems, with emphasis on the diagnosis of system faults. It is assumed that a continuous simulation model of the properly operating system is available. Malfunctions are diagnosed by posing the question: how can we make the model behave like that. The adjustments that must be made to the model to produce the observed behavior usually provide definitive clues to the nature of the malfunction. A novel application of Dijkstra's weakest precondition predicate transformer is used to derive the preconditions for producing the required model behavior. To minimize the size of the search space, an envisionment generator based on interval mathematics was developed. In addition to its intended application, the ability to generate qualitative state spaces automatically from quantitative simulations proved to be a fruitful avenue of investigation in its own right. Implementations of the Dijkstra transform and the envisionment generator are reproduced in the Appendix.
Chanda-Kapata, Pascalina; Kapata, Nathan; Masiye, Felix; Maboshe, Mwendaweli; Klinkenberg, Eveline; Cobelens, Frank; Grobusch, Martin P
Tuberculosis (TB) prevalence surveys offer a unique opportunity to study health seeking behaviour at the population level because they identify individuals with symptoms that should ideally prompt a health consultation. To assess the health-seeking behaviour among individuals who were presumptive TB cases in a national population based TB prevalence survey. A cross sectional survey was conducted between 2013 and 2014 among 66 survey clusters in Zambia. Clusters were census supervisory areas (CSAs). Participants (presumptive TB cases) were individuals aged 15 years and above; having either cough, fever or chest pain for 2 weeks or more; and/or having an abnormal or inconclusive chest x-ray image. All survey participants were interviewed about symptoms and had a chest X-ray taken. An in-depth interview was conducted to collect information on health seeking behaviour and previous TB treatment. Of the 6,708 participants, the majority reported at least a history of chest pain (3,426; 51.1%) followed by cough (2,405; 35.9%), and fever (1,030; 15.4%) for two weeks or more. Only 34.9% (2,340) had sought care for their symptoms, mainly (92%) at government health facilities. Of those who sought care, 13.9% (326) and 12.1% (283) had chest x-ray and sputum examinations, respectively. Those ever treated for TB were 9.6% (644); while 1.7% (114) was currently on treatment. The average time (in weeks) from onset of symptoms to first care-seeking was 3 for the presumptive TB cases. Males, urban dwellers and individuals in the highest wealth quintile were less likely to seek care for their symptoms. The likelihood of having ever been treated for TB was highest among males, urban dwellers; respondents aged 35-64 years, individuals in the highest wealth quintile, or HIV positive. Some presumptive TB patients delay care-seeking for their symptoms. The health system misses opportunities to diagnose TB among those who seek care. Improving health-seeking behaviour among males, urban
Full Text Available In Mexico, increasing demands for public safety coupled with the need for a more effective criminal justice system resulted in the security and justice constitutional reform of 2008. The outcome was a constitutional framework with provisions based on the highest standards of human rights on the one hand, and on the other, exceptional measures that restrict rights in an attempt to improve public safety. Unfortunately, the crime rate and incidence of unreported crime have changed little. When public safety is demanded, a clear, rational and concrete response is required. Limiting the alternatives to pre-trial detention or increasing penalties is rarely the appropriate response. This paper focuses on pre-trial detention and non-custodial measures supported by the new criminal justice system, how they relate to the principle of the presumption of innocence and the tension between this and the punitive demands for increased imprisonment. In addition, this study discusses a technical solution, found in pre-trial services, which seeks to balance the presumption of innocence and the right to personal liberty with public safety.
Full Text Available Current guidelines recommend that all fever episodes in African children be treated presumptively with antimalarial drugs. But declining malarial transmission in parts of sub-Saharan Africa, declining proportions of fevers due to malaria, and the availability of rapid diagnostic tests mean it may be time for this policy to change. This debate examines whether enough evidence exists to support abandoning presumptive treatment and whether African health systems have the capacity to support a shift toward laboratory-confirmed rather than presumptive diagnosis and treatment of malaria in children under five.
Poulsen, Niels Kjølstad; Niemann, Hans Henrik
The focus of this paper is on stochastic change detection applied in connection with active fault diagnosis (AFD). An auxiliary input signal is applied in AFD. This signal injection in the system will in general allow us to obtain a fast change detection/isolation by considering the output...
Sellors, J; Howard, M; Pickard, L; Jang, D; Mahony, J; Chernesky, M
OBJECTIVE: To test the recommendation from the Canadian guidelines for sexually transmitted diseases (STDs) that mucopurulent endocervical discharge and 10 or more polymorphonuclear leukocytes (PMNs) per high-power field of a Gram-stained endocervical smear or, when Gram staining is not possible, the presence of endocervical discharge and one of edema, erythema or induced mucosal bleeding of the cervix can be considered diagnostic for chlamydial cervicitis. METHODS: A total of 596 consecutive...
Kraft, S.L.; Mussman, J.M.; Smith, T.; Biller, D.S.; Hoskinson, J.J.
three-year-old male Boxer dog had hyperesthesia, symmetrical epaxial, gluteal and hind limb muscular atrophy and rear limb ataxia. Neurological deficits included decreased conscious proprioception of the left hind limb, decreased withdrawal and increased patellar reflexes of both hind limbs. The dog had a urinary tract infection with positive culture for Staphylococcus intermedius. On survey radiography of the lumbosacral spine there was active bone proliferation spanning the L7 S1 intervertebral disc space with an epidural filling defect at the ventral aspect of the vertebral canal on epidurography. On magnetic resonance imaging (MRI), findings were similar to those described for human diskospondylitis including altered signal intensity and nonuniform contrast enhancement of the L7-S1 intervertebral disc, adjacent vertebral end plates and epidural and sublumbar soft tissues. Although skeletal radiography is usually sufficient to reach a diagnosis of discospondylitis, MRI of this patient made it possible to reach a presumptive diagnosis of discospondylitis prior to development of definitive radiographic abnormalities
Tamilselvan, Prasanna; Wang, Pingfeng
Effective health diagnosis provides multifarious benefits such as improved safety, improved reliability and reduced costs for operation and maintenance of complex engineered systems. This paper presents a novel multi-sensor health diagnosis method using deep belief network (DBN). DBN has recently become a popular approach in machine learning for its promised advantages such as fast inference and the ability to encode richer and higher order network structures. The DBN employs a hierarchical structure with multiple stacked restricted Boltzmann machines and works through a layer by layer successive learning process. The proposed multi-sensor health diagnosis methodology using DBN based state classification can be structured in three consecutive stages: first, defining health states and preprocessing sensory data for DBN training and testing; second, developing DBN based classification models for diagnosis of predefined health states; third, validating DBN classification models with testing sensory dataset. Health diagnosis using DBN based health state classification technique is compared with four existing diagnosis techniques. Benchmark classification problems and two engineering health diagnosis applications: aircraft engine health diagnosis and electric power transformer health diagnosis are employed to demonstrate the efficacy of the proposed approach
... 20 Employees' Benefits 2 2010-04-01 2010-04-01 false Presumption relating to respirable disease... Pneumoconiosis § 410.462 Presumption relating to respirable disease. (a) Even though the existence of... was employed for 10 years or more in the Nation's coal mines and died from a respirable disease, it...
Montes-Bayon, M.; Del Castillo, M.E.; Sanz-Medel, A.
Full text: Glycosylation is the enzymatic process that links saccharides to produce glycans, either free or attached to proteins. This is an enzyme-directed site-specific process, as opposed to the chemical reaction of glycation which is the result of addition of a sugar molecule to a protein or lipid molecule without the controlling action of an enzyme. Both protein modifications, however, can be used as clinical biomarkers for a variety of disorders including chronic alcoholism, diabetes or congenital disorders of glycosylation. The potential of ICPMS as a tool in the diagnosis of such diseases will be illustrated in the presentation. (author)
Zhou Yong; Zhang Li
Diagnosis tasks in nuclear power plants characterized as high-dynamic uncertainties are complex reasoning tasks. Diagnosis errors are the main causes for the error of commission. Firstly, based on mental model theory and perception/action cycle theory, a cognitive model for analyzing operators' diagnosis tasks is proposed. Then, the model is used to investigate a trip event which occurred at crystal river nuclear power plant. The application demonstrates typical cognitive bias and mistakes which operators may make when performing diagnosis tasks. They mainly include the strong confirmation tendency, difficulty to produce complete hypothesis sets, group mindset, non-systematic errors in hypothesis testing, and etc. (authors)
Li, Ze; Huang, Hong-Xing; Zheng, Ye-Lu; Wang, Zhou-Yuan
Along with the popularity of computer and rapid development of information technology, how to increase the accuracy of the agricultural diagnosis becomes a difficult problem of popularizing the agricultural expert system. Analyzing existing research, baseing on the knowledge acquisition technology of rough set theory, towards great sample data, we put forward a intelligent diagnosis model. Extract rough set decision table from the samples property, use decision table to categorize the inference relation, acquire property rules related to inference diagnosis, through the means of rough set knowledge reasoning algorithm to realize intelligent diagnosis. Finally, we validate this diagnosis model by experiments. Introduce the rough set theory to provide the agricultural expert system of great sample data a effective diagnosis model.
Niemann, Hans Henrik
Detection and isolation of parametric faults in closed-loop systems will be considered in this paper. A major problem is that a feedback controller will in general reduce the effects from variations in the systems including parametric faults on the controlled output from the system. Parametric...... faults can be detected and isolated using active methods, where an auxiliary input is applied. Using active methods for the diagnosis of parametric faults in closed-loop systems, the amplitude of the applied auxiliary input need to be increased to be able to detect and isolate the faults in a reasonable......-parameterization (after Youla, Jabr, Bongiorno and Kucera) for the controller, it is possible to modify the feedback controller with a minor effect on the closed-loop performance in the fault-free case and at the same time optimize the detection and isolation in a faulty case. Controller modification in connection...
Cai, Baoping; Liu, Yonghong; Huang, Lei; Hu, Song; Xue, Haitao; Wang, Jiaxing
The paper proposes a Bayesian-network-based real-time fault diagnosis methodology of M-shaped subsea jumper. Finite element models of a typical M-shaped subsea jumper system are built to get the data for diagnosis. Netica is Bayesian-network -based software and is used to construct diagnosis models of the jumper in two main loading conditions which are falling objects and seabed moving. The results show that the accuracy of falling objects diagnosis model with four faults is 100%, and the accuracy of seabed moving diagnosis model with two faults is also 100%. Combine the two models into one and the accuracy of combined model is 96.59%. The effectiveness of the proposed method is validated.
Niemann, Hans Henrik
An active fault diagnosis (AFD) method will be considered in this paper in connection with a Fault Tolerant Control (FTC) architecture based on the YJBK parameterization of all stabilizing controllers. The architecture consists of a fault diagnosis (FD) part and a controller reconfiguration (CR......) part. The FTC architecture can be applied for additive faults, parametric faults, and for system structural changes. Only parametric faults will be considered in this paper. The main focus in this paper is on the use of the new approach of active fault diagnosis in connection with FTC. The active fault...... diagnosis approach is based on including an auxiliary input in the system. A fault signature matrix is introduced in connection with AFD, given as the transfer function from the auxiliary input to the residual output. This can be considered as a generalization of the passive fault diagnosis case, where...
Rehman, H.; Hafizullah, M.; Shah, S.T.; Khan, S.B.; Hadi, A.; Ahmad, F.; Shah, I.; Gul, A.M.
Objective: To know the sensitivity of polymerase chain reaction (PCR) in pericardial fluid and response to antituberculous treatment (ATT) in PCR positive patients who were presumptively diagnosed and treated as tuberculous pericardial effusion. Methodology: This was a descriptive cross sectional study carried out from June 1, 2009 to 31 May 2010 at Cardiology Department, Lady Reading Hospital, Peshawar. Patients with presumptive diagnosis and receiving treatment for tuberculous pericardial effusion were included. Pericardial fluid sample was aspirated under fluoroscopy for the routine work up. The specimens were subjected to PCR detection of mycobacterium tuberculous DNA. Results: During 12 month study period, a total of 54 patients with large pericardial effusion presented to Cardiology department, Lady Reading Hospital, Peshawar. Of them, 46 patients fulfilled the criteria for presumptive diagnosis of tuberculous pericardial effusion. PCR for mycobacterium tuberculous DNA in pericardial fluid was positive in 45.7%(21). Patients were followed for three months. In PCR positive group, 01 patient while in PCR negative group 3 patients were lost to follow up. Among PCR positive patients 17(85%) while in PCR negative group 11(47.82%) patient responded to ATT both clinically and echo-cardio graphically. We found that patients who were PCR positive responded better to therapy than those who were PCR negative and this finding was statistically significant (p=0.035). Conclusion: PCR, with all its limitations, is potentially a useful diagnostic test in patients with presumptively diagnosed tuberculous pericardial effusion. A PCR positive patient responds better to therapy as compared to PCR negative patient. (author)
Abstract. Health care data diagnosis is a significant task that needs to be exe- cuted precisely, which requires much experience and domain-knowledge. Traditional symptoms-based disease diagnosis may perhaps lead to false presumptions. In recent times, Associative Classification (AC), the combination of association ...
National Aeronautics and Space Administration — We present in this article a case study of the probabilistic approach to model-based diagnosis. Here, the diagnosed system is a real-world electrical power system,...
The potential for the use of possibility in the qualitative model-based diagnosis of spacecraft systems is described. The first sections of the paper briefly introduce the Model-Based Diagnostic (MBD) approach to spacecraft fault diagnosis; Qualitative Modeling (QM) methodologies; and the concepts of possibilistic modeling in the context of Generalized Information Theory (GIT). Then the necessary conditions for the applicability of possibilistic methods to qualitative MBD, and a number of potential directions for such an application, are described.
Pinhata, Juliana Maira Watanabe; Felippe, Isis Moreira; Gallo, Juliana Failde; Chimara, Erica; Ferrazoli, Lucilaine; de Oliveira, Rosangela Siqueira
We evaluated the microscopic and macroscopic characteristics of mycobacteria growth indicator tube (MGIT) cultures for the presumptive identification of the Mycobacterium tuberculosis complex (MTBC) and assessed the reliability of this strategy for correctly directing isolates to drug susceptibility testing (DST) or species identification. A total of 1526 isolates of mycobacteria received at the Instituto Adolfo Lutz were prospectively subjected to presumptive identification by the observation of growth characteristics along with cord formation detection via microscopy. The presumptive identification showed a sensitivity, specificity and accuracy of 98.8, 92.5 and 97.9 %, respectively. Macroscopic analysis of MTBC isolates that would have been erroneously classified as non-tuberculous mycobacteria based solely on microscopic morphology enabled us to direct them rapidly to DST, representing a substantial gain to patients. In conclusion, the growth characteristics of mycobacteria in MGIT, when considered along with cord formation, increased the reliability of the presumptive identification, which has a great impact on the laboratory budget and turnaround times.
Zhang, Yingfeng; Liu, Sichao; Zhu, Zhenfei; Si, Shubin
According to the analysis of the challenges faced by the current public health circumstances such as the sharp increase in elderly patients, limited medical personnel, resources and technology, the agent-based intelligent medical diagnosis system for patients (AIMDS) is proposed in this research. Based on advanced sensing technology and professional medical knowledge, the AIMDS can output the appropriate medical prescriptions and food prohibition when the physical signs and symptoms of the patient are inputted. Three core modules are designed include sensing module, intuition-based fuzzy set theory/medical diagnosis module, and medical knowledge module. The result shows that the optimized prescription can reach the desired level, with great curative effect for patient disease, through a case study simulation. The presented AIMDS can integrate sensor technique and intelligent medical diagnosis methods to make an accurate diagnosis, resulting in three-type of optimized descriptions for patient selection.
Full Text Available Under the trend of increasing installed capacity of wind power, the intelligent fault diagnosis of wind turbine is of great significance to the safe and efficient operation of wind farms. Based on the knowledge of fault diagnosis of wind turbines, this paper builds expert system diagnostic knowledge base by using confidence production rules and expert system self-learning method. In Visual Studio 2013 platform, C # language is selected and ADO.NET technology is used to access the database. Development of Fault Diagnosis Expert System for Wind Turbine. The purpose of this paper is to realize on-line diagnosis of wind turbine fault through human-computer interaction, and to improve the diagnostic capability of the system through the continuous improvement of the knowledge base.
Liana Preto Webber
Full Text Available Paracoccidioidomycosis (PCM is a deep mycosis with primary lung manifestations that may present cutaneous and oral lesions. Oral lesions mimic other infectious diseases or even squamous cell carcinoma, clinically and microscopically. Sometimes, the dentist is the first to detect the disease, because lung lesions are asymptomatic, or even misdiagnosed. An unusual case of PCM with 5 months of evolution presenting pulmonary, oral, and cutaneous lesions that was diagnosed by the dentist based on oral lesions is presented and discussed.
Jordi Ferrer Beltrán
Full Text Available The article aims to address the multiple faces that the presumption of innocence incorporates in modern legal systems from a critical perspective. In this sense, an analytical methodology seeks to demonstrate that some of these faces overlap with other legal rights and institutes, which, far from increasing the guarantees of citizens, leads to confusion and lack of controllability of judicial decisions. Thus, it is defended the conceptual and practical convenience of thinking the presumption of innocence avoiding overlaps with other legal rights or concepts, as standards of proof or burden of proof rules. Hence the reference to a minimalist and guarantistic conception of the presumption of innocence and, and, as will be seen, the defense of the presumption of innocence as a second order rule whose application would make sense in contexts of uncertainty about the satisfaction of the standard of proof.
Xu, Feixiang; Liu, Xinhui; Chen, Wei; Zhou, Chen; Cao, Bingwei
This paper proposes an ontology-based fault diagnosis method which overcomes the difficulty of understanding complex fault diagnosis knowledge of loaders and offers a universal approach for fault diagnosis of all loaders. This method contains the following components: (1) An ontology-based fault diagnosis model is proposed to achieve the integrating, sharing and reusing of fault diagnosis knowledge for loaders; (2) combined with ontology, CBR (case-based reasoning) is introduced to realize effective and accurate fault diagnoses following four steps (feature selection, case-retrieval, case-matching and case-updating); and (3) in order to cover the shortages of the CBR method due to the lack of concerned cases, ontology based RBR (rule-based reasoning) is put forward through building SWRL (Semantic Web Rule Language) rules. An application program is also developed to implement the above methods to assist in finding the fault causes, fault locations and maintenance measures of loaders. In addition, the program is validated through analyzing a case study.
Nucleic acid analysis is one of the most important disease diagnostic approaches in medical practice, and has been commonly used in cancer biomarker detection, bacterial speciation and many other fields in laboratory. Currently, the application of powerful research methods for genetic analysis, including the polymerase chain reaction (PCR), DNA sequencing, and gene expression profiling using fluorescence microarrays, are not widely used in hospitals and extended-care units due to high-cost, long detection times, and extensive sample preparation. Bioassays, especially chip-based electrochemical sensors, may be suitable for the next generation of rapid, sensitive, and multiplexed detection tools. Herein, we report three different microelectrode platforms with capabilities enabled by nano- and microtechnology: nanoelectrode ensembles (NEEs), nanostructured microelectrodes (NMEs), and hierarchical nanostructured microelectrodes (HNMEs), all of which are able to directly detect unpurified RNA in clinical samples without enzymatic amplification. Biomarkers that are cancer and infectious disease relevant to clinical medicine were chosen to be the targets. Markers were successfully detected with clinically-relevant sensitivity. Using peptide nucleic acids (PNAs) as probes and an electrocatalytic reporter system, NEEs were able to detect prostate cancer-related gene fusions in tumor tissue samples with 100 ng of RNA. The development of NMEs improved the sensitivity of the assay further to 10 aM of DNA target, and multiplexed detection of RNA sequences of different prostate cancer-related gene fusion types was achieved on the chip-based NMEs platform. An HNMEs chip integrated with a bacterial lysis device was able to detect as few as 25 cfu bacteria in 30 minutes and monitor the detection in real time. Bacterial detection could also be performed in neat urine samples. The development of these versatile clinical diagnostic tools could be extended to the detection of various
Ferry de Jong
Full Text Available This article’s primary aim is to highlight the essentially critical potential of the presumption of innocence, as well as the need for this critical potential to be duly recognized. It is argued that the essential meaning of the presumption of innocence is best understood when approached from what is referred to as its counterfactual status. As a first step, the different values and functions that are attributed to the presumption of innocence in contemporary legal literature are discussed, in order to provide an outline of the central ideas it contains or is supposed to contain. Subsequently, the concept of ‘counterfactuality’ is introduced and it is argued that a counterfactual perspective can further clarify the nature of the presumption of innocence. Next, a number of fundamental shifts in society and criminal justice are discussed that affect the presumption of innocence and that lend a large measure of urgency to disclosing its essence and critical potential. The conclusion argues that today’s threats to the presumption of innocence are of a fundamental nature, and that attempts to preserve the principle’s efficacy should focus on the value attached to its counterfactual and critical nature.
This book presents a bond graph model-based approach to fault diagnosis in mechatronic systems appropriately represented by a hybrid model. The book begins by giving a survey of the fundamentals of fault diagnosis and failure prognosis, then recalls state-of-art developments referring to latest publications, and goes on to discuss various bond graph representations of hybrid system models, equations formulation for switched systems, and simulation of their dynamic behavior. The structured text: • focuses on bond graph model-based fault detection and isolation in hybrid systems; • addresses isolation of multiple parametric faults in hybrid systems; • considers system mode identification; • provides a number of elaborated case studies that consider fault scenarios for switched power electronic systems commonly used in a variety of applications; and • indicates that bond graph modelling can also be used for failure prognosis. In order to facilitate the understanding of fault diagnosis and the presented...
Brillman, J; Valeriano, J; Adatepe, M H
The differential diagnosis of multiple cranial nerve palsies in patients with cancer includes meningeal infections, meningeal carcinomatosis, and skull base metastases. In distinguishing these, spinal fluid analysis and skull base tomography should be helpful in most cases. In circumstances when results of skull base tomography are negative, radionuclide bone scans can demonstrate metastatic disease in the base of the skull, and it should be obtained in all patients who are highly suspicious for having skull base metastasis with negative skull base tomography, including computed tomography (CT).
Full Text Available We report a case of isolated homonymous hemianopsia due to presumptive cerebral tubercular abscess as the initial manifestation of human immunodeficiency virus (HIV infection. A 30-year-old man presented to our outpatient department with sudden loss of visibility in his left visual field. He had no other systemic symptoms. Perimetry showed left-sided incongruous homonymous hemianopsia denser above the horizontal meridian. Magnetic resonance imaging revealed irregular well-marginated lobulated lesions right temporo-occipital cerebral hemisphere and left high fronto-parietal cerebral hemisphere suggestive of brain tubercular abscess. Serological tests for HIV were reactive, and the patient was started only on anti-tubercular drugs with the presumptive diagnosis of cerebral tubercular abscess. Therapeutic response confirmed the diagnosis. Atypical ophthalmic manifestations may be the initial presenting feature in patients with HIV infection. This highlights the need for increased index of suspicion for HIV infection in young patients with atypical ophthalmic manifestations.
Mishra, Swagat; Khilar, Pabitra Mohan
Distributed Embedded Systems have significant applications in automobile industry as steer-by-wire, fly-by-wire and brake-by-wire systems. In this paper, we provide a general framework for fault detection in a distributed embedded real time system. We use heartbeat monitoring, check pointing and model based redundancy to design a scalable framework that takes care of task scheduling, temperature control and diagnosis of faulty nodes in a distributed embedded system. This helps in diagnosis and shutting down of faulty actuators before the system becomes unsafe. The framework is designed and tested using a new simulation model consisting of virtual nodes working on a message passing system.
Ma, Shangjun; Cheng, Bo; Shang, Zhaowei; Liu, Geng
This paper proposes an algorithm for fault diagnosis of rotating machinery to overcome the shortcomings of classical techniques which are noise sensitive in feature extraction and time consuming for training. Based on the scattering transform and the least squares recursive projection twin support vector machine (LSPTSVM), the method has the advantages of high efficiency and insensitivity for noise signal. Using the energy of the scattering coefficients in each sub-band, the features of the vibration signals are obtained. Then, an LSPTSVM classifier is used for fault diagnosis. The new method is compared with other common methods including the proximal support vector machine, the standard support vector machine and multi-scale theory by using fault data for two systems, a motor bearing and a gear box. The results show that the new method proposed in this study is more effective for fault diagnosis of rotating machinery.
Full Text Available Fault diagnosis is essentially a kind of pattern recognition. The measured signal samples usually distribute on nonlinear low-dimensional manifolds embedded in the high-dimensional signal space, so how to implement feature extraction, dimensionality reduction and improve recognition performance is a crucial task. In this paper a novel machinery fault diagnosis approach based on a statistical locally linear embedding (S-LLE algorithm which is an extension of LLE by exploiting the fault class label information is proposed. The fault diagnosis approach first extracts the intrinsic manifold features from the high-dimensional feature vectors which are obtained from vibration signals that feature extraction by time-domain, frequency-domain and empirical mode decomposition (EMD, and then translates the complex mode space into a salient low-dimensional feature space by the manifold learning algorithm S-LLE, which outperforms other feature reduction methods such as PCA, LDA and LLE. Finally in the feature reduction space pattern classification and fault diagnosis by classifier are carried out easily and rapidly. Rolling bearing fault signals are used to validate the proposed fault diagnosis approach. The results indicate that the proposed approach obviously improves the classification performance of fault pattern recognition and outperforms the other traditional approaches.
Yuan, Kaijuan; Xiao, Fuyuan; Fei, Liguo; Kang, Bingyi; Deng, Yong
Sensor data fusion plays an important role in fault diagnosis. Dempster-Shafer (D-R) evidence theory is widely used in fault diagnosis, since it is efficient to combine evidence from different sensors. However, under the situation where the evidence highly conflicts, it may obtain a counterintuitive result. To address the issue, a new method is proposed in this paper. Not only the statistic sensor reliability, but also the dynamic sensor reliability are taken into consideration. The evidence distance function and the belief entropy are combined to obtain the dynamic reliability of each sensor report. A weighted averaging method is adopted to modify the conflict evidence by assigning different weights to evidence according to sensor reliability. The proposed method has better performance in conflict management and fault diagnosis due to the fact that the information volume of each sensor report is taken into consideration. An application in fault diagnosis based on sensor fusion is illustrated to show the efficiency of the proposed method. The results show that the proposed method improves the accuracy of fault diagnosis from 81.19% to 89.48% compared to the existing methods.
Wang, Xiang; Zheng, Yuan; Zhao, Zhenzhou; Wang, Jinping
Fault diagnosis is essentially a kind of pattern recognition. The measured signal samples usually distribute on nonlinear low-dimensional manifolds embedded in the high-dimensional signal space, so how to implement feature extraction, dimensionality reduction and improve recognition performance is a crucial task. In this paper a novel machinery fault diagnosis approach based on a statistical locally linear embedding (S-LLE) algorithm which is an extension of LLE by exploiting the fault class label information is proposed. The fault diagnosis approach first extracts the intrinsic manifold features from the high-dimensional feature vectors which are obtained from vibration signals that feature extraction by time-domain, frequency-domain and empirical mode decomposition (EMD), and then translates the complex mode space into a salient low-dimensional feature space by the manifold learning algorithm S-LLE, which outperforms other feature reduction methods such as PCA, LDA and LLE. Finally in the feature reduction space pattern classification and fault diagnosis by classifier are carried out easily and rapidly. Rolling bearing fault signals are used to validate the proposed fault diagnosis approach. The results indicate that the proposed approach obviously improves the classification performance of fault pattern recognition and outperforms the other traditional approaches.
Karamouzis, Stamos T.; Feyock, Stefan
The work described in this paper has as its goal the integration of a number of reasoning techniques into a unified intelligent information system that will aid flight crews with malfunction diagnosis and prognostication. One of these approaches involves using the extensive archive of information contained in aircraft accident reports along with various models of the aircraft as the basis for case-based reasoning about malfunctions. Case-based reasoning draws conclusions on the basis of similarities between the present situation and prior experience. We maintain that the ability of a CBR program to reason about physical systems is significantly enhanced by the addition to the CBR program of various models. This paper describes the diagnostic concepts implemented in a prototypical case based reasoner that operates in the domain of in-flight fault diagnosis, the various models used in conjunction with the reasoner's CBR component, and results from a preliminary evaluation.
Escobet, T.; de Lira, S.; Puig, V.; Quevedo, J. [Automatic Control Department (ESAII), Universitat Politecnica de Catalunya (UPC), Rambla Sant Nebridi 10, 08222 Terrassa (Spain); Feroldi, D.; Riera, J.; Serra, M. [Institut de Robotica i Informatica Industrial (IRI), Consejo Superior de Investigaciones Cientificas (CSIC), Universitat Politecnica de Catalunya (UPC) Parc Tecnologic de Barcelona, Edifici U, Carrer Llorens i Artigas, 4-6, Planta 2, 08028 Barcelona (Spain)
In this work, a model-based fault diagnosis methodology for PEM fuel cell systems is presented. The methodology is based on computing residuals, indicators that are obtained comparing measured inputs and outputs with analytical relationships, which are obtained by system modelling. The innovation of this methodology is based on the characterization of the relative residual fault sensitivity. To illustrate the results, a non-linear fuel cell simulator proposed in the literature is used, with modifications, to include a set of fault scenarios proposed in this work. Finally, it is presented the diagnosis results corresponding to these fault scenarios. It is remarkable that with this methodology it is possible to diagnose and isolate all the faults in the proposed set in contrast with other well known methodologies which use the binary signature matrix of analytical residuals and faults. (author)
Tawde, S N; Puschner, B; Albin, T; Stump, S; Poppenga, R H
A 4-year-old, 37 kg, male German shepherd developed hyperthermia, tachycardia, and agitation following consumption of ground meat found in the backyard of its owner. When presented to a veterinary clinic, plasma ethylene glycol (EG) testing was positive, and the dog was given ethanol and lactated Ringer's solution intravenously. Approximately 11 h postexposure the dog died. Among tissues submitted for toxicological analysis, urine was negative for EG, ground meat was negative for certain drugs of abuse, and gastric contents were negative for zinc/aluminum phosphide and metaldehyde. Analysis of gastric contents by gas chromatography-mass spectrometry confirmed the presence of caffeine. Caffeine concentration in the ground meat was estimated at 1 %. Caffeine is a methylxanthine alkaloid with a reported canine oral median lethal dose (MLD(50)) of 140 mg/kg (range 120-200 mg/kg). A commercially available 200-mg tablet formulation of caffeine was considered to be a possible source but this was not confirmed. By conservative estimates, the dog would need to ingest approximately 500-550 g of the meat to reach the MLD(50). Acute intoxication affects the cardiovascular, pulmonary, neurologic, gastrointestinal, and metabolic systems. Although no tablet remnants were observed in the bait, tablets could have been crushed and/or dissolved. Other potential caffeine sources include guarana, brewed and concentrated coffee, and caffeine-containing beverages. Based on the history, clinical signs, and the detection of caffeine in the gastric contents and meat, a presumptive diagnosis of malicious caffeine poisoning was made. A suggested treatment regimen for caffeine intoxication in dogs is described. While few cases of accidental ingestion of caffeine by dogs have been described, the intentional use of a concentrated caffeine source to cause mortality in a dog has not been previously reported.
Full Text Available This study proposes a novel fault diagnosis method that is based on empirical wavelet transform (EWT and kernel density estimation classifier (KDEC, which can well diagnose fault type of the rolling element bearings. With the proposed fault diagnosis method, the vibration signal of rolling element bearing was firstly decomposed into a series of F modes by EWT, and the root mean square, kurtosis, and skewness of the F modes were computed and combined into the feature vector. According to the characteristics of kernel density estimation, a classifier based on kernel density estimation and mutual information was proposed. Then, the feature vectors were input into the KDEC for training and testing. The experimental results indicated that the proposed method can effectively identify three different operative conditions of rolling element bearings, and the accuracy rates was higher than support vector machine (SVM classifier and back-propagation (BP neural network classifier.
Shi Qingfeng; Yan Junming; Zhang Yanhong
The paper considered the vibration signals of rotating equipment as cyclo stationary signals through analyzing the features of this kind of signals. Based on the analytic method of cyclic spectrum density, the paper pointed out that the impact frequency could be extracted effectively with the help of scanning cyclic frequency domain. The validity of the method of cyclic spectrum density is proved by simulating signals and the method is applied to the diagnosis of rolling bearings. (authors)
Satoh, Hitoshi; Niki, Noboru; Eguchi, Kenji; Moriyama, Noriyuki; Ohmatsu, Hironobu; Masuda, Hideo; Machida, Suguru
Mass screening based on multi-helical CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. To overcome this problem, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images, a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification and a vertebra body analysis algorithm for quantitative evaluation of osteoporosis likelihood by using helical CT scanner for the lung cancer mass screening. The function to observe suspicious shadow in detail are provided in computer-aided diagnosis workstation with these screening algorithms. We also have developed the telemedicine network by using Web medical image conference system with the security improvement of images transmission, Biometric fingerprint authentication system and Biometric face authentication system. Biometric face authentication used on site of telemedicine makes "Encryption of file" and Success in login" effective. As a result, patients' private information is protected. Based on these diagnostic assistance methods, we have developed a new computer-aided workstation and a new telemedicine network that can display suspected lesions three-dimensionally in a short time. The results of this study indicate that our radiological information system without film by using computer-aided diagnosis workstation and our telemedicine network system can increase diagnostic speed, diagnostic accuracy and security improvement of medical information.
Arbaiy, Nureize; Sulaiman, Shafiza Eliza; Hassan, Norlida; Afizah Afip, Zehan
The role and importance of healthcare systems to improve quality of life and social welfare in a society have been well recognized. Attention should be given to raise awareness and implementing appropriate measures to improve health care. Therefore, a computer based system is developed to serve as an alternative for people to self-diagnose their health status based on given symptoms. This strategy should be emphasized so that people can utilize the information correctly as a reference to enjoy healthier life. Hence, a Web-based Community Center for Healthcare Diagnosis system is developed based on expert system technique. Expert system reasoning technique is employed in the system to enable information about treatment and prevention of the diseases based on given symptoms. At present, three diseases are included which are arthritis, thalassemia and pneumococcal. Sets of rule and fact are managed in the knowledge based system. Web based technology is used as a platform to disseminate the information to users in order for them to optimize the information appropriately. This system will benefit people who wish to increase health awareness and seek expert knowledge on the diseases by performing self-diagnosis for early disease detection.
Batwala, Vincent; Magnussen, Pascal; Nuwaha, Fred
-based diagnosis for uncomplicated malaria in rural health centres (HCs) was investigated with a view to recommending measures for scaling up the policy. METHODS: Thirty HCs were randomized to implement parasite-based diagnosis based on rapid diagnostic tests [RDTs] (n=10), blood microscopy (n=10) and presumptive...... diagnosis (control arm) (n=10). Feasibility was assessed by comparing the proportion of patients who received parasite-based diagnosis; with a positive malaria parasite-based diagnosis who received artemether-lumefantrine (AL); with a negative malaria parasite-based diagnosis who received AL; and patient...... waiting time. Clinicaltrials.gov: NCT00565071. RESULTS: 102,087 outpatients were enrolled. Patients were more likely to be tested in the RDT 44,565 (96.6%) than in microscopy arm 19,545 (60.9%) [RR: 1.59]. RDTs reduced patient waiting time compared to microscopy and were more convenient to health workers...
York, Sloane; Lichtenberg, E Steve
Disseminated intravascular coagulation (DIC) is a serious and relatively uncommon complication of induced or spontaneous abortion or delivery. Occasionally, it has been reported in the absence of predisposing conditions. Little information in the literature describing idiopathic DIC or the treatment of patients with DIC exists. From 2002 through 2008, 24 cases of presumptive idiopathic DIC occurred following dilation and evacuation (D&E) abortion between 13 5/7 and 23 6/7 weeks' estimated gestational age at a Midwestern ambulatory surgical center. The characteristics of each patient, their pregnancies and surgical experiences were examined and compared with a temporally matched control group of D&E patients. We explored whether the index cases had a predominance of any historical, clinical or reproductive characteristics compared with controls matched for demographic and reproductive landmarks. Overall incidence of presumptive idiopathic DIC was 1.8 per 1000 D&E cases. Compared with matched controls, there was a greater likelihood of DIC with more advanced gestation (p=.009); no case of DIC was under 17 weeks' gestational age. Increased bleeding occurred at a mean time of 153 min after completion of surgery (range, 55-491 min; median, 131 min). Nineteen of 24 cases were successfully treated at the surgical center after receiving 6 to 8 units of fresh-frozen plasma (FFP); 5 cases were transferred to a hospital for further treatment. The abnormal bleeding of presumptive DIC typically begins to appear within 2 h after uncomplicated D&E and is more likely to occur at 17 weeks' estimated gestational age and more. With rapid diagnosis and treatment, most patients were able to be treated in an outpatient setting with up to 6 to 8 units of FFP and rehydration. Copyright © 2012 Elsevier Inc. All rights reserved.
Background: Intermittent presumptive treatment of malaria in pregnancy (IPTp) is one of the recommended interventions by World Health Organization to mitigate the impact of malaria in pregnancy in malaria stable transmission zones such as Sub Saharan regions so as to ensure the best outcome for both the mother and ...
Chanda-Kapata, Pascalina; Kapata, Nathan; Masiye, Felix; Maboshe, Mwendaweli; Klinkenberg, Eveline; Cobelens, Frank; Grobusch, Martin P.
Tuberculosis (TB) prevalence surveys offer a unique opportunity to study health seeking behaviour at the population level because they identify individuals with symptoms that should ideally prompt a health consultation. To assess the health-seeking behaviour among individuals who were presumptive TB
“Although presumption is not evidence and has no weight as such, it does make a ..... intelligence to appreciate the difficulties of the subject-matter has approached .... facts exists. This envisages the existence of another fact or aggregate of facts, called the presumed fact or fact(s) that must be assumed in the absence of.
... 47 Telecommunication 4 2010-10-01 2010-10-01 false Presumption of no effective competition. 76.906... competition. In the absence of a demonstration to the contrary, cable systems are presumed not to be subject to effective competition. ...
Akbarzadeh-T, Mohammad-R; Moshtagh-Khorasani, Majid
Aphasia diagnosis is a particularly challenging medical diagnostic task due to the linguistic uncertainty and vagueness, inconsistencies in the definition of aphasic syndromes, large number of measurements with imprecision, natural diversity and subjectivity in test objects as well as in opinions of experts who diagnose the disease. To efficiently address this diagnostic process, a hierarchical fuzzy rule-based structure is proposed here that considers the effect of different features of aphasia by statistical analysis in its construction. This approach can be efficient for diagnosis of aphasia and possibly other medical diagnostic applications due to its fuzzy and hierarchical reasoning construction. Initially, the symptoms of the disease which each consists of different features are analyzed statistically. The measured statistical parameters from the training set are then used to define membership functions and the fuzzy rules. The resulting two-layered fuzzy rule-based system is then compared with a back propagating feed-forward neural network for diagnosis of four Aphasia types: Anomic, Broca, Global and Wernicke. In order to reduce the number of required inputs, the technique is applied and compared on both comprehensive and spontaneous speech tests. Statistical t-test analysis confirms that the proposed approach uses fewer Aphasia features while also presenting a significant improvement in terms of accuracy.
Full Text Available Meckel-Gruber syndrome (MGS is a rare lethal congenital malformation affecting 1 in 13,250-140,000 live births. The classical diagnostic triad comprises multicystic dysplastic kidneys, occipital encephalocele, and postaxial polydactyly. It can variably be associated with other malformations such as cleft lip and palate, pulmonary hypoplasia, hepatic fibrosis, and anomalies of central nervous system. A 20 weeks fetus was diagnosed as MGS with classical features along with many other congenital abnormalities such as microcephaly, microphthalmia, hypertelorism, cleft lip and palate, neonatal teeth, and the right side club foot which were detected only after doing autopsy. This case is reported because of its rarity emphasizing the importance of neonatal autopsy in every case of fetal death, especially where the antenatal diagnosis has not been made previously. A systematic approach to accurate diagnosis of MGS based on autopsy will be described here which can allow recurrence risk counseling and proper management in future pregnancies.
A. O. Adejumo
Full Text Available Abstract Background Engagement of communities and civil society organizations is a critical part of the Post-2015 End TB Strategy. Since 2007, many models of community referral have been implemented to boost TB case detection in Nigeria. Yet clear insights into the comparative TB yield from particular approaches have been limited. Methods We compared four models of active case finding in three Nigerian states. Data on presumptive TB case referral by community workers (CWs, TB diagnoses among referred clients, active case finding model characteristics, and CWs compensation details for 2012 were obtained from implementers and CWs via interviews and log book review. Self-reported performance data were triangulated against routine surveillance data to assess concordance. Analysis focused on assessing the predictors of presumptive TB referral. Results CWs referred 4–22 % of presumptive TB clients tested, and 4–24 % of the total TB cases detected. The annual median referral per CW ranged widely among the models from 1 to 48 clients, with an overall average of 13.4 referrals per CW. The highest median referrals (48 per CW/yr and mean TB diagnoses (7.1/yr per CW (H =70.850, p < 0.001 was obtained by the model with training supervision, and $80/quarterly payments (Comprehensive Quotas-Oriented model. The model with irregularly supervised, trained, and compensated CWs contributed the least to TB case detection with a median of 13 referrals per CW/yr and mean of 0.53 TB diagnoses per CW/yr. Hours spent weekly on presumptive TB referral made the strongest unique contribution (Beta = 0.514, p < 0.001 to explaining presumptive TB referral after controlling for other variables. Conclusion All community based TB case-finding projects studied referred a relative low number of symptomatic individuals. The study shows that incentivized referral, appropriate selection of CWs, supportive supervision, leveraged treatment support roles, and a
Moussa, Husseiny Sh; Bayoumi, Faten Sayed; Mohamed, Ahmed Mohamed Ali
Gene Xpert(GX) is a novel real time polymerase chain reaction (RT-PCR) assay which was endorsed by the World Health Organization (WHO) in 2011 for tuberculosis (TB) diagnosis and susceptibility to refampicin(RIF). To evaluate GX for direct diagnosis of TB in stool samples from children with suspected pulmonary Tuberculosis (PTB). Children older than one year and younger than 16 years with presumptive PTB were enrolled and classified to five clinical categories based on clinical, radiological, and laboratory findings: confirmed TB, probable TB, possible TB, Unlikely TB, and not TB. Two stool samples were collected from each child and tested for the presence of Mycobacterium tuberculosis (MTB) by GX and the obtained results were compared to Lowenstien-Jensen (LJ) culture as a gold standard. In total, 115 children were enrolled. 36 had been confirmed with TB, 61 probably TB, 10 possible TB, 5 unlikely TB, and 3 not TB. GX had a sensitivity of 83.33 and 80.56 % and specificity of 98.73 and 99.36 % by patients and samples respectively. GX was positive in 83.3% of confirmed TB as well as 1.6 and 0.8% of probable TB cases by patients and samples respectively. GX provided timely results with quit acceptable sensitivity and good specificity compared to LJ culture. In this study, sensitivity calculations take into account only children with confirmed TB. GX could not detect TB in children with probable TB, so it should not be used alone for TB diagnosis. Further studies for GX stool protocol optimization and assessment is required. © 2016 by the Association of Clinical Scientists, Inc.
Cameron, Anne P; Heidelbaugh, Joel J; Jimbo, Masahito
Urinary incontinence is a common problem in both men and women. This review article addresses its prevalence, risk factors, cost, the various types of incontinence, as well as how to diagnose them. The US Preventive Services Task Force, the Cochrane Database of Systematic Reviews, and PubMed were reviewed for articles focusing on urinary incontinence. Incontinence is a common problem with a high societal cost. It is frequently underreported by patients so it is appropriate for primary-care providers to screen all women and older men during visits. A thorough history and physical examination combined with easy office-based tests can often yield a clear diagnosis and rule out other transient illnesses contributing to the incontinence. Specialist referral is occasionally needed in specific situations before embarking on a treatment plan.
McKenzie, R K; Gibson, I R; Ritmeester, A
Three weanling Thoroughbred fillies were presented during autumn with depression, muscle rigidity and, in one case, colic symptoms and cardiovascular shock. All fillies had abnormal physical examinations that included elevated heart rates and respiratory rates coupled with muscle rigidity through the back and rump. Biochemistry revealed markedly elevated creatinine kinase and aspartate aminotransferase which indicated a myopathy. All three horses were diagnosed with presumptive equine atypical myopathy. The horses received supportive therapy as per the literature available at the time regarding this condition; two responded to supportive therapy and survived, and one was euthanased due to a rapid deterioration in clinical status. Following post mortem of one case, histology of the trapezius muscle demonstrated an acute, severe myofibre degeneration. Atypical myopathy and a very similar disorder termed seasonal pasture myopathy in North America are potentially fatal, pasture-related syndromes that have been described in Europe and America but have not been previously described in New Zealand. This report describes three presumptive cases of this unique syndrome in New Zealand for the first time; it outlines the characteristics of the condition; and includes recently published information regarding diagnosis and treatment.
Kevin C. Tseng
Full Text Available This paper presents an expert diagnosis system based on cloud computing. It classifies a user’s fitness level based on supervised machine learning techniques. This system is able to learn and make customized diagnoses according to the user’s physiological data, such as age, gender, and body mass index (BMI. In addition, an elastic algorithm based on Poisson distribution is presented to allocate computation resources dynamically. It predicts the required resources in the future according to the exponential moving average of past observations. The experimental results show that Naïve Bayes is the best classifier with the highest accuracy (90.8% and that the elastic algorithm is able to capture tightly the trend of requests generated from the Internet and thus assign corresponding computation resources to ensure the quality of service.
Hu, Zhikun; Chen, Zhiwen; Gui, Weihua; Jiang, Bin
In this paper, an adaptive fault detection scheme based on a recursive principal component analysis (PCA) is proposed to deal with the problem of false alarm due to normal process changes in real process. Our further study is also dedicated to develop a fault isolation approach based on Generalized Likelihood Ratio (GLR) test and Singular Value Decomposition (SVD) which is one of general techniques of PCA, on which the off-set and scaling fault can be easily isolated with explicit off-set fault direction and scaling fault classification. The identification of off-set and scaling fault is also applied. The complete scheme of PCA-based fault diagnosis procedure is proposed. The proposed scheme is first applied to Imperial Smelting Process, and the results show that the proposed strategies can be able to mitigate false alarms and isolate faults efficiently. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.
Patton, R.J.; Chen, J.; Nielsen, S.B.
This paper provides a review of model-based fault diagnosis techniques. Starting from basic principles, the properties.......This paper provides a review of model-based fault diagnosis techniques. Starting from basic principles, the properties....
Sagir, Abdu Masanawa; Sathasivam, Saratha
The prediction of breast cancer in women who have no signs or symptoms of the disease as well as survivability after undergone certain surgery has been a challenging problem for medical researchers. The decision about presence or absence of diseases depends on the physician's intuition, experience and skill for comparing current indicators with previous one than on knowledge rich data hidden in a database. This measure is a very crucial and challenging task. The goal is to predict patient condition by using an adaptive neuro fuzzy inference system (ANFIS) pre-processed by grid partitioning. To achieve an accurate diagnosis at this complex stage of symptom analysis, the physician may need efficient diagnosis system. A framework describes methodology for designing and evaluation of classification performances of two discrete ANFIS systems of hybrid learning algorithms least square estimates with Modified Levenberg-Marquardt and Gradient descent algorithms that can be used by physicians to accelerate diagnosis process. The proposed method's performance was evaluated based on training and test datasets with mammographic mass and Haberman's survival Datasets obtained from benchmarked datasets of University of California at Irvine's (UCI) machine learning repository. The robustness of the performance measuring total accuracy, sensitivity and specificity is examined. In comparison, the proposed method achieves superior performance when compared to conventional ANFIS based gradient descent algorithm and some related existing methods. The software used for the implementation is MATLAB R2014a (version 8.3) and executed in PC Intel Pentium IV E7400 processor with 2.80 GHz speed and 2.0 GB of RAM.
Murphy, Donald R; Hurwitz, Eric L
Abstract Background Neck pain (NP) is a common cause of disability. Accurate and efficacious methods of diagnosis and treatment have been elusive. A diagnosis-based clinical decision guide (DBCDG; previously referred to as a diagnosis-based clinical decision rule) has been proposed which attempts to provide the clinician with a systematic, evidence-based guide in applying the biopsychosocial model of care. The approach is based on three questions of diagnosis. The purpose of this study is to ...
Murphy, Donald R; Hurwitz, Eric L
Abstract Background Low back pain (LBP) is common and costly. Development of accurate and efficacious methods of diagnosis and treatment has been identified as a research priority. A diagnosis-based clinical decision guide (DBCDG; previously referred to as a diagnosis-based clinical decision rule) has been proposed which attempts to provide the clinician with a systematic, evidence-based means to apply the biopsychosocial model of care. The approach is based on three questions of diagnosis. T...
Full Text Available A new rolling bearing fault diagnosis approach based on multiscale permutation entropy (MPE, Laplacian score (LS, and support vector machines (SVMs is proposed in this paper. Permutation entropy (PE was recently proposed and defined to measure the randomicity and detect dynamical changes of time series. However, for the complexity of mechanical systems, the randomicity and dynamic changes of the vibration signal will exist in different scales. Thus, the definition of MPE is introduced and employed to extract the nonlinear fault characteristics from the bearing vibration signal in different scales. Besides, the SVM is utilized to accomplish the fault feature classification to fulfill diagnostic procedure automatically. Meanwhile, in order to avoid a high dimension of features, the Laplacian score (LS is used to refine the feature vector by ranking the features according to their importance and correlations with the main fault information. Finally, the rolling bearing fault diagnosis method based on MPE, LS, and SVM is proposed and applied to the experimental data. The experimental data analysis results indicate that the proposed method could identify the fault categories effectively.
Binh Tran Q
Full Text Available Abstract Background Fever is a common reason for attending primary health facilities in Vietnam. Response of health care providers to patients with fever commonly consists of making a presumptive diagnosis and proposing corresponding treatment. In Vietnam, where malaria was brought under control, viral infections, notably dengue, are the main causes of undifferentiated fever but they are often misdiagnosed and inappropriately treated with antibiotics. This study investigate if educating primary health center (PHC staff or introducing rapid diagnostic tests (RDTs improve diagnostic resolution and accuracy for acute undifferentiated fever (AUF and reduce prescription of antibiotics and costs for patients. Methods In a PHC randomized intervention study in southern Vietnam, the presumptive diagnoses for AUF patients were recorded and confirmed by serology on paired (acute and convalescence sera. After one year, PHCs were randomized to four intervention arms: training on infectious diseases (A, the provision of RDTs (B, the combination (AB and control (C. The intervention lasted from 2002 until 2006. Results The frequency of the non-etiologic diagnosis "undifferentiated fever" decreased in group AB, and - with some delay- also in group B. The diagnosis "dengue" increased in group AB, but only temporarily, although dengue was the most common cause of fever. A correct diagnosis for dengue initially increased in groups AB and B but only for AB this was sustained. Antibiotics prescriptions increased in group C. During intervention it initially declined in AB with a tendency to increase afterwards; in B it gradually declined. There was a substantial increase of patients' costs in B. Conclusions The introduction of RDTs for infectious diseases such as dengue, through free market principles, does improve the quality of the diagnosis and decreases the prescription of antibiotics at the PHC level. However, the effect is more sustainable in combination with
Phuong, Hoang L; Nga, Tran T T; Giao, Phan T; Hung, Le Q; Binh, Tran Q; Nam, Nguyen V; Nagelkerke, Nico; de Vries, Peter J
Fever is a common reason for attending primary health facilities in Vietnam. Response of health care providers to patients with fever commonly consists of making a presumptive diagnosis and proposing corresponding treatment. In Vietnam, where malaria was brought under control, viral infections, notably dengue, are the main causes of undifferentiated fever but they are often misdiagnosed and inappropriately treated with antibiotics.This study investigate if educating primary health center (PHC) staff or introducing rapid diagnostic tests (RDTs) improve diagnostic resolution and accuracy for acute undifferentiated fever (AUF) and reduce prescription of antibiotics and costs for patients. In a PHC randomized intervention study in southern Vietnam, the presumptive diagnoses for AUF patients were recorded and confirmed by serology on paired (acute and convalescence) sera. After one year, PHCs were randomized to four intervention arms: training on infectious diseases (A), the provision of RDTs (B), the combination (AB) and control (C). The intervention lasted from 2002 until 2006. The frequency of the non-etiologic diagnosis "undifferentiated fever" decreased in group AB, and - with some delay- also in group B. The diagnosis "dengue" increased in group AB, but only temporarily, although dengue was the most common cause of fever. A correct diagnosis for dengue initially increased in groups AB and B but only for AB this was sustained. Antibiotics prescriptions increased in group C. During intervention it initially declined in AB with a tendency to increase afterwards; in B it gradually declined. There was a substantial increase of patients' costs in B. The introduction of RDTs for infectious diseases such as dengue, through free market principles, does improve the quality of the diagnosis and decreases the prescription of antibiotics at the PHC level. However, the effect is more sustainable in combination with training; without it RDTs lead to an excess of costs.
Peng, Yingwei; Taylor, Jeremy M G
Model diagnosis, an important issue in statistical modeling, has not yet been addressed adequately for cure models. We focus on mixture cure models in this work and propose some residual-based methods to examine the fit of the mixture cure model, particularly the fit of the latency part of the mixture cure model. The new methods extend the classical residual-based methods to the mixture cure model. Numerical work shows that the proposed methods are capable of detecting lack-of-fit of a mixture cure model, particularly in the latency part, such as outliers, improper covariate functional form, or nonproportionality in hazards if the proportional hazards assumption is employed in the latency part. The methods are illustrated with two real data sets that were previously analyzed with mixture cure models. © 2016, The International Biometric Society.
Leka, Stavroula; Van Wassenhove, Wim; Jain, Aditya Kailash
This paper tackles a much debated and often misunderstood issue in the modern world of work, psychosocial risks. Although the prevalence and impact of psychosocial risks is now widely acknowledged as a priority in health and safety in Europe, there remains resistance by key stakeholders in prioritizing psychosocial risk management both in business and policy making. This paper explores why this is still the case by discussing three presumptions in relation to the current state of the art in t...
National Aeronautics and Space Administration — Diagnosis and prognosis are necessary tasks for system reconfiguration and fault-adaptive control in complex systems. Diagnosis consists of detec- tion, isolation...
Full Text Available The relationship between fault phenomenon and fault cause is always nonlinear, which influences the accuracy of fault location. And neural network is effective in dealing with nonlinear problem. In order to improve the efficiency of uncertain fault diagnosis based on neural network, a neural network fault diagnosis method based on rule base is put forward. At first, the structure of BP neural network is built and the learning rule is given. Then, the rule base is built by fuzzy theory. An improved fuzzy neural construction model is designed, in which the calculated methods of node function and membership function are also given. Simulation results confirm the effectiveness of this method.
Qin, B; Sun, G D; Zhang L Y; Wang J G; HU, J
For the fault classification model based on extreme learning machine (ELM), the diagnosis accuracy and stability of rolling bearing is greatly influenced by a critical parameter, which is the number of nodes in hidden layer of ELM. An adaptive adjustment strategy is proposed based on vibrational mode decomposition, permutation entropy, and nuclear kernel extreme learning machine to determine the tunable parameter. First, the vibration signals are measured and then decomposed into different fault feature models based on variation mode decomposition. Then, fault feature of each model is formed to a high dimensional feature vector set based on permutation entropy. Second, the ELM output function is expressed by the inner product of Gauss kernel function to adaptively determine the number of hidden layer nodes. Finally, the high dimension feature vector set is used as the input to establish the kernel ELM rolling bearing fault classification model, and the classification and identification of different fault states of rolling bearings are carried out. In comparison with the fault classification methods based on support vector machine and ELM, the experimental results show that the proposed method has higher classification accuracy and better generalization ability. (paper)
Full Text Available Abstract Background Progress in automated image analysis, virtual microscopy, hospital information systems, and interdisciplinary data exchange require image standards to be applied in tissue-based diagnosis. Aims To describe the theoretical background, practical experiences and comparable solutions in other medical fields to promote image standards applicable for diagnostic pathology. Theory and experiences Images used in tissue-based diagnosis present with pathology – specific characteristics. It seems appropriate to discuss their characteristics and potential standardization in relation to the levels of hierarchy in which they appear. All levels can be divided into legal, medical, and technological properties. Standards applied to the first level include regulations or aims to be fulfilled. In legal properties, they have to regulate features of privacy, image documentation, transmission, and presentation; in medical properties, features of disease – image combination, human – diagnostics, automated information extraction, archive retrieval and access; and in technological properties features of image acquisition, display, formats, transfer speed, safety, and system dynamics. The next lower second level has to implement the prescriptions of the upper one, i.e. describe how they are implemented. Legal aspects should demand secure encryption for privacy of all patient related data, image archives that include all images used for diagnostics for a period of 10 years at minimum, accurate annotations of dates and viewing, and precise hardware and software information. Medical aspects should demand standardized patients' files such as DICOM 3 or HL 7 including history and previous examinations, information of image display hardware and software, of image resolution and fields of view, of relation between sizes of biological objects and image sizes, and of access to archives and retrieval. Technological aspects should deal with image
Full Text Available Faults behaviors of automotive engine in running-up stage are shown a multidimensional pattern that evolves as a function of time (called dynamic patterns. It is necessary to identify the type of fault during early running stages of automotive engine for the selection of appropriate operator actions to prevent a more severe situation. In this situation, the Faults diagnosis method based on continuous HMM is proposed. Feature vectors of main FFT spectrum component are extracted from vibration signals and looked up as observation vectors of HMM. Several HMMs which substitute the types of faults in automotive engine vibration system are modeled. Decision-making for faults classification is performed. The results of experiment are shown the proposed method is executable and effective.
Parkash, Om; Hanim Shueb, Rafidah
Dengue is an arthropod-borne viral disease caused by four antigenically different serotypes of dengue virus. This disease is considered as a major public health concern around the world. Currently, there is no licensed vaccine or antiviral drug available for the prevention and treatment of dengue disease. Moreover, clinical features of dengue are indistinguishable from other infectious diseases such as malaria, chikungunya, rickettsia and leptospira. Therefore, prompt and accurate laboratory diagnostic test is urgently required for disease confirmation and patient triage. The traditional diagnostic techniques for the dengue virus are viral detection in cell culture, serological testing, and RNA amplification using reverse transcriptase PCR. This paper discusses the conventional laboratory methods used for the diagnosis of dengue during the acute and convalescent phase and highlights the advantages and limitations of these routine laboratory tests. Subsequently, the biosensor based assays developed using various transducers for the detection of dengue are also reviewed. PMID:26492265
Parkash, Om; Shueb, Rafidah Hanim
Dengue is an arthropod-borne viral disease caused by four antigenically different serotypes of dengue virus. This disease is considered as a major public health concern around the world. Currently, there is no licensed vaccine or antiviral drug available for the prevention and treatment of dengue disease. Moreover, clinical features of dengue are indistinguishable from other infectious diseases such as malaria, chikungunya, rickettsia and leptospira. Therefore, prompt and accurate laboratory diagnostic test is urgently required for disease confirmation and patient triage. The traditional diagnostic techniques for the dengue virus are viral detection in cell culture, serological testing, and RNA amplification using reverse transcriptase PCR. This paper discusses the conventional laboratory methods used for the diagnosis of dengue during the acute and convalescent phase and highlights the advantages and limitations of these routine laboratory tests. Subsequently, the biosensor based assays developed using various transducers for the detection of dengue are also reviewed.
Liao, L.Y.; Tang, H.C.; Chen, S.S.
An accident diagnostic system is developed as an attempt to provide a useful aid for the operators of an experimental loop or a nuclear power plant in the case of emergency condition. Because the current practices in the system diagnosis are not satisfactory, there is an increasing demand on the establishment of various operator decision support systems. The knowledge based system is a new and promising technique which can be used to fulfill this demand. With the capability of automatic reasoning and by incorporating the information about system status, the knowledge based system can simulate the process of human thinking and serve as a good decision support system. This knowledge based decision support system can be helpful for both a fast, violent accident and a slowly developed accident. Specifically, a fast diagnostic report can be provided for a fast and violent accident of which time is the main concern and a complete diagnostic report can be provided for a slowly developed accident of which complexity is the main concern. Such a knowledge based decision support system also provides many other equally important advantages, such as the elimination of human error, the automatic validation of signal readings, the establishment of human error, the automatic validation of signal readings, and the establishment of a simulation environment
acquired infection in critically ill children. The increasing incidence of infections by antibiotic-resistant pathogens adds significantly to the cost of hospital care and to the length of hospital stays. Besides clinical prerequisites for presumptive diagnosis ...
Moshtagh-Khorasani, Majid; Akbarzadeh-T, Mohammad-R; Jahangiri, Nader; Khoobdel, Mehdi
Aphasia diagnosis is particularly challenging due to the linguistic uncertainty and vagueness, inconsistencies in the definition of aphasic syndromes, large number of measurements with imprecision, natural diversity and subjectivity in test objects as well as in opinions of experts who diagnose the disease. Fuzzy probability is proposed here as the basic framework for handling the uncertainties in medical diagnosis and particularly aphasia diagnosis. To efficiently construct this fuzzy probabilistic mapping, statistical analysis is performed that constructs input membership functions as well as determines an effective set of input features. Considering the high sensitivity of performance measures to different distribution of testing/training sets, a statistical t-test of significance is applied to compare fuzzy approach results with NN results as well as author's earlier work using fuzzy logic. The proposed fuzzy probability estimator approach clearly provides better diagnosis for both classes of data sets. Specifically, for the first and second type of fuzzy probability classifiers, i.e. spontaneous speech and comprehensive model, P-values are 2.24E-08 and 0.0059, respectively, strongly rejecting the null hypothesis. THE TECHNIQUE IS APPLIED AND COMPARED ON BOTH COMPREHENSIVE AND SPONTANEOUS SPEECH TEST DATA FOR DIAGNOSIS OF FOUR APHASIA TYPES: Anomic, Broca, Global and Wernicke. Statistical analysis confirms that the proposed approach can significantly improve accuracy using fewer Aphasia features.
Doecke, James D; Laws, Simon M; Faux, Noel G; Wilson, William; Burnham, Samantha C; Lam, Chiou-Peng; Mondal, Alinda; Bedo, Justin; Bush, Ashley I; Brown, Belinda; De Ruyck, Karl; Ellis, Kathryn A; Fowler, Christopher; Gupta, Veer B; Head, Richard; Macaulay, S Lance; Pertile, Kelly; Rowe, Christopher C; Rembach, Alan; Rodrigues, Mark; Rumble, Rebecca; Szoeke, Cassandra; Taddei, Kevin; Taddei, Tania; Trounson, Brett; Ames, David; Masters, Colin L; Martins, Ralph N
To identify plasma biomarkers for the diagnosis of Alzheimer disease (AD). Baseline plasma screening of 151 multiplexed analytes combined with targeted biomarker and clinical pathology data. General community-based, prospective, longitudinal study of aging. A total of 754 healthy individuals serving as controls and 207 participants with AD from the Australian Imaging Biomarker and Lifestyle study (AIBL) cohort with identified biomarkers that were validated in 58 healthy controls and 112 individuals with AD from the Alzheimer Disease Neuroimaging Initiative (ADNI) cohort. A biomarker panel was identified that included markers significantly increased (cortisol, pancreatic polypeptide, insulinlike growth factor binding protein 2, β(2) microglobulin, vascular cell adhesion molecule 1, carcinoembryonic antigen, matrix metalloprotein 2, CD40, macrophage inflammatory protein 1α, superoxide dismutase, and homocysteine) and decreased (apolipoprotein E, epidermal growth factor receptor, hemoglobin, calcium, zinc, interleukin 17, and albumin) in AD. Cross-validated accuracy measures from the AIBL cohort reached a mean (SD) of 85% (3.0%) for sensitivity and specificity and 93% (3.0) for the area under the receiver operating characteristic curve. A second validation using the ADNI cohort attained accuracy measures of 80% (3.0%) for sensitivity and specificity and 85% (3.0) for area under the receiver operating characteristic curve. This study identified a panel of plasma biomarkers that distinguish individuals with AD from cognitively healthy control subjects with high sensitivity and specificity. Cross-validation within the AIBL cohort and further validation within the ADNI cohort provides strong evidence that the identified biomarkers are important for AD diagnosis.
Majid Moshtagh Khorasani
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Deng, Xiaogang; Tian, Xuemin; Chen, Sheng; Harris, Chris J
Many industrial processes contain both linear and nonlinear parts, and kernel principal component analysis (KPCA), widely used in nonlinear process monitoring, may not offer the most effective means for dealing with these nonlinear processes. This paper proposes a new hybrid linear-nonlinear statistical modeling approach for nonlinear process monitoring by closely integrating linear principal component analysis (PCA) and nonlinear KPCA using a serial model structure, which we refer to as serial PCA (SPCA). Specifically, PCA is first applied to extract PCs as linear features, and to decompose the data into the PC subspace and residual subspace (RS). Then, KPCA is performed in the RS to extract the nonlinear PCs as nonlinear features. Two monitoring statistics are constructed for fault detection, based on both the linear and nonlinear features extracted by the proposed SPCA. To effectively perform fault identification after a fault is detected, an SPCA similarity factor method is built for fault recognition, which fuses both the linear and nonlinear features. Unlike PCA and KPCA, the proposed method takes into account both linear and nonlinear PCs simultaneously, and therefore, it can better exploit the underlying process's structure to enhance fault diagnosis performance. Two case studies involving a simulated nonlinear process and the benchmark Tennessee Eastman process demonstrate that the proposed SPCA approach is more effective than the existing state-of-the-art approach based on KPCA alone, in terms of nonlinear process fault detection and identification.
SP-100 space nuclear reactor system development is a joint effort by the Department of Energy, the Department of Defense and the National Aeronautics and Space Administration. The system is designed to operate in isolation for many years, and is possibly subject to little or no remote maintenance. This dissertation proposes a knowledge based diagnostic system which, in principle, can diagnose the faults which can either cause reactor shutdown or lead to another serious problem. This framework in general can be applied to the fully specified system if detailed design information becomes available. The set of faults considered herein is identified based on heuristic knowledge about the system operation. The suitable approach to diagnostic problem solving is proposed after investigating the most prevalent methodologies in Artificial Intelligence as well as the causal analysis of the system. Deep causal knowledge modeling based on digraph, fault-tree or logic flowgraph methodology would present a need for some knowledge representation to handle the time dependent system behavior. A proposed qualitative temporal knowledge modeling methodology, using rules with specified time delay among the process variables, has been proposed and is used to develop the diagnostic sufficient rule set. The rule set has been modified by using a time zone approach to have a robust system design. The sufficient rule set is transformed to a sufficient and necessary one by searching the whole knowledge base. Qualitative data analysis is proposed in analyzing the measured data if in a real time situation. An expert system shell - Intelligence Compiler is used to develop the prototype system. Frames are used for the process variables. Forward chaining rules are used in monitoring and backward chaining rules are used in diagnosis
Andrew C. Elton
Full Text Available Salmonella meningitis is a rare manifestation of meningitis typically presenting in neonates and the elderly. This infection typically associates with foodborne outbreaks in developing nations and AIDS-endemic regions. We report a case of a 19-year-old male presenting with altered mental status after 3-day absence from work at a Wisconsin tourist area. He was febrile, tachycardic, and tachypneic with a GCS of 8. The patient was intubated and a presumptive diagnosis of meningitis was made. Treatment was initiated with ceftriaxone, vancomycin, acyclovir, dexamethasone, and fluid resuscitation. A lumbar puncture showed cloudy CSF with Gram negative rods. He was admitted to the ICU. CSF culture confirmed Salmonella enterica subsp. I (enterica Enteritidis (A. Based on this finding, a 4th-generation HIV antibody/p24 antigen test was sent. When this returned positive, a CD4 count was obtained and showed 3 cells/mm3, confirming AIDS. The patient ultimately received 38 days of ceftriaxone, was placed on elvitegravir, cobicistat, emtricitabine, and tenofovir alafenamide (Genvoya for HIV/AIDS, and was discharged neurologically intact after a 44-day admission.
Feldman, A.B.; Pietersma, J.; Van Gemund, A.J.C.
Model-Based Reasoning (MBR) over qualitative models of complex, real-world systems has proven succesful for automated fault diagnosis, control, and repair. Expressing a system under diagnosis in a formal model and infering a diagnosis given observations are both challenging problems. In this paper
Gawkrodger, David J; Ormerod, Anthony D; Shaw, Lindsay; Mauri-Sole, Inma; Whitton, Maxine E; Watts, M Jane; Anstey, Alex V; Ingham, Jane; Young, Katharine
Vitiligo is a common disease that causes a great degree of psychological distress. In its classical forms it is easily recognised and diagnosed. This review provides an evidence based outline of the management of vitiligo, particularly with the non-specialist in mind. Treatments for vitiligo are generally unsatisfactory. The initial approach to a patient who is thought to have vitiligo is to make a definite diagnosis, offer psychological support, and suggest supportive treatments such as the use of camouflage cosmetics and sunscreens, or in some cases after discussion the option of no treatment. Active therapies open to the non-specialist, after an explanation of potential side effects, include the topical use of potent or highly potent steroids or calcineurin inhibitors for a defined period of time (usually 2 months), following which an assessment is made to establish whether or not there has been a response. Patients whose condition is difficult to diagnose, unresponsive to straightforward treatments, or is causing psychological distress, are usually referred to a dermatologist. Specialist dermatology units have at their disposal phototherapy, either narrow band ultraviolet B or in some cases photochemotherapy, which is the most effective treatment presently available and can be considered for symmetrical types of vitiligo. Depigmenting treatments and possibly surgical approaches may be appropriate for vitiligo in selected cases. There is no evidence that presently available systemic treatments are helpful and safe in vitiligo. There is a need for further research into the causes of vitiligo, and into discovering better treatments.
Kumar, M S; Ghosh, S; Nayak, S; Das, A P
Urinary tract infections (UTIs) are potentially life threatening infections that are associated with high rates of incidence, recurrence and mortality. UTIs are characterized by several chronic infections which may lead to lethal consequences if left undiagnosed and untreated. The uropathogens are consistent across the globe. The most prevalent uropathogenic gram negative bacteria are Escherichia coli, Proteus mirabilis, Pseudomonas aeruginosa, Klebsiella pneumonia. Early detection and precise diagnosis of these infections will play a pivotal role in health care, pharmacological and biomedical sectors. A number of detection methods are available but their performances are not upto the mark. Therefore a more rapid, selective and highly sensitive technique for the detection and quantification of uropathogen levels in extremely minute concentrations need of the time. This review brings all the major concerns of UTI at one's doorstep such as clinical costs and incidence rate, several diagnostic approaches along with their advantages and disadvantages. Paying attention to detection approaches with emphasizing biosensor based recent developments in the quest for new diagnostics for UTI and the need for more sophisticated techniques in terms of selectivity and sensitivity is discussed. Copyright © 2016 Elsevier B.V. All rights reserved.
Thiesen, Flavia Valladão; Noto, Ana Regina; Barros, Helena M T
Toluene is the main substance contained in products used as inhalants. The frequent abuse of toluene-based inhalants requires the definition of a simple laboratory parameter that allows acute exposure assessment. This study aimed at defining urinary hippuric acid (UHA) levels related to intentional exposure to toluene, and to correlate them to blood toluene concentration (BT). BT and UHA levels were measured in 65 homeless adolescents of Porto Alegre, Brazil. Toluene was detected in 91.9% of the investigated population, who presented BT levels from 0.5 to 83.7 microg/mL. There was good correlation between UHA and BT concentrations (r = 0.78), and in homeless adolescents, UHA levels higher than 3.0 g/g creatinine indicate intentional exposure to toluene. The determination of UHA concentrations can be used as a screening method for the detection of intentional exposure to toluene, but its diagnosis must include BT toluene dosage, as well as circumstantial and clinical evidence.
Full Text Available This paper proposes a generalized distance measure and its similarity measures between single valued neutrosophic multisets (SVNMs. Then, the similarity measures are applied to a medical diagnosis problem with incomplete, indeterminate and inconsistent information. This diagnosis method can deal with the diagnosis problem with indeterminate and inconsistent information which cannot be handled by the diagnosis method based on intuitionistic fuzzy multisets (IFMs.
Full Text Available Abstract Background A general theory of sampling and its application in tissue based diagnosis is presented. Sampling is defined as extraction of information from certain limited spaces and its transformation into a statement or measure that is valid for the entire (reference space. The procedure should be reproducible in time and space, i.e. give the same results when applied under similar circumstances. Sampling includes two different aspects, the procedure of sample selection and the efficiency of its performance. The practical performance of sample selection focuses on search for localization of specific compartments within the basic space, and search for presence of specific compartments. Methods When a sampling procedure is applied in diagnostic processes two different procedures can be distinguished: I the evaluation of a diagnostic significance of a certain object, which is the probability that the object can be grouped into a certain diagnosis, and II the probability to detect these basic units. Sampling can be performed without or with external knowledge, such as size of searched objects, neighbourhood conditions, spatial distribution of objects, etc. If the sample size is much larger than the object size, the application of a translation invariant transformation results in Kriege's formula, which is widely used in search for ores. Usually, sampling is performed in a series of area (space selections of identical size. The size can be defined in relation to the reference space or according to interspatial relationship. The first method is called random sampling, the second stratified sampling. Results Random sampling does not require knowledge about the reference space, and is used to estimate the number and size of objects. Estimated features include area (volume fraction, numerical, boundary and surface densities. Stratified sampling requires the knowledge of objects (and their features and evaluates spatial features in relation to
Mansourypoor, Fatemeh; Asadi, Shahrokh
The early diagnosis of disease is critical to preventing the occurrence of severe complications. Diabetes is a serious health problem. A variety of methods have been developed for diagnosing diabetes. The majority of these methods have been developed in a black-box manner, which cannot be used to explain the inference and diagnosis procedure. Therefore, it is essential to develop methods with high accuracy and interpretability. In this study, a Reinforcement Learning-based Evolutionary Fuzzy Rule-Based System (RLEFRBS) is developed for diabetes diagnosis. The proposed model involves the building of a Rule Base (RB) and rule optimization. The initial RB is constructed using numerical data without initial rules; after learning the rules, redundant rules are eliminated based on the confidence measure. Next, redundant conditions in the antecedent parts are pruned to yield simpler rules with higher interpretability. Finally, an appropriate subset of the rules is selected using a Genetic Algorithm (GA), and the RB is constructed. Evolutionary tuning of the membership functions and weight adjusting using Reinforcement Learning (RL) are used to improve the performance of RLEFRBS. Moreover, to deal with uncovered instances, it makes use of an efficient rule stretching method. The performance of RLEFRBS was examined using two common datasets: Pima Indian Diabetes (PID) and BioSat Diabetes Dataset (BDD). The experimental results show that the proposed model provides a more compact, interpretable and accurate RB that can be considered to be a promising alternative for diagnosis of diabetes. Copyright © 2017 Elsevier Ltd. All rights reserved.
Nguchu, Benedictor A.; Li, Li
The study takes advantage of several new breakthroughs in computer vision technology to develop a new mid-irisbiomedical platform that processes iris image for early detection of heart-disease. Guaranteeing early detection of heart disease provides a possibility of having non-surgical treatment as suggested by biomedical researchers and associated institutions. However, our observation discovered that, a clinical practicable solution which could be both sensible and specific for early detection is still lacking. Due to this, the rate of majority vulnerable to death is highly increasing. The delayed diagnostic procedures, inefficiency, and complications of available methods are the other reasons for this catastrophe. Therefore, this research proposes the novel IFB (Iris Features Based) method for diagnosis of premature, and early stage heart disease. The method incorporates computer vision and iridology to obtain a robust, non-contact, nonradioactive, and cost-effective diagnostic tool. The method analyzes abnormal inherent weakness in tissues, change in color and patterns, of a specific region of iris that responds to impulses of heart organ as per Bernard Jensen-iris Chart. The changes in iris infer the presence of degenerative abnormalities in heart organ. These changes are precisely detected and analyzed by IFB method that includes, tensor-based-gradient(TBG), multi orientations gabor filters(GF), textural oriented features(TOF), and speed-up robust features(SURF). Kernel and Multi class oriented support vector machines classifiers are used for classifying normal and pathological iris features. Experimental results demonstrated that the proposed method, not only has better diagnostic performance, but also provides an insight for early detection of other diseases.
Fijany, A.; Vatan, F.; Barrett, A.; James, M.; Williams, C.; Mackey, R.
Systematic methods of general diagnosis exist in literature, but they all suffer from two major drawbacks that severely limit their practical applications. In this paper, we propose a two-fold approach to overcome these limitations.
Yuan, Yixuan; Wang, Jiaole; Li, Baopu; Meng, Max Q-H
Ulcer is one of the most common symptoms of many serious diseases in the human digestive tract. Especially for the ulcers in the small bowel where other procedures cannot adequately visualize, wireless capsule endoscopy (WCE) is increasingly being used in the diagnosis and clinical management. Because WCE generates large amount of images from the whole process of inspection, computer-aided detection of ulcer is considered an indispensable relief to clinicians. In this paper, a two-staged fully automated computer-aided detection system is proposed to detect ulcer from WCE images. In the first stage, we propose an effective saliency detection method based on multi-level superpixel representation to outline the ulcer candidates. To find the perceptually and semantically meaningful salient regions, we first segment the image into multi-level superpixel segmentations. Each level corresponds to different initial region sizes of the superpixels. Then we evaluate the corresponding saliency according to the color and texture features in superpixel region of each level. In the end, we fuse the saliency maps from all levels together to obtain the final saliency map. In the second stage, we apply the obtained saliency map to better encode the image features for the ulcer image recognition tasks. Because the ulcer mainly corresponds to the saliency region, we propose a saliency max-pooling method integrated with the Locality-constrained Linear Coding (LLC) method to characterize the images. Experiment results achieve promising 92.65% accuracy and 94.12% sensitivity, validating the effectiveness of the proposed method. Moreover, the comparison results show that our detection system outperforms the state-of-the-art methods on the ulcer classification task.
National Aeronautics and Space Administration — Model-based diagnosis enables efficient and safe operation of engineered systems. In this paper, we describe two algorithms based on a qualitative event-based fault...
Measurement of radiocesium concentration in trees using cumulative gamma radiation dose rate detection systems - A simple presumption for radiocesium concentration in living woods using glass-badge based gamma radiation dose rate detection system
Yoshihara, T.; Hashida, S.N. [Plant Molecular Biology, Laboratory of Environmental Science, Central Research Institute of Electric Power Industry (CRIEPI), 1646 Abiko, Chiba 270-1194 (Japan); Kawachi, N.; Suzui, N.; Yin, Y.G.; Fujimaki, S. [Radiotracer Imaging Gr., Quantum Beam Science Center, Japan Atomic Energy Agency (JAEA), 1233 Watanuki, Takasaki, Gunma 370-1292 (Japan); Nagao, Y.; Yamaguchi, M. [Takasaki Advanced Radiation Research Institute, Japan Atomic Energy Agency (JAEA), 1233 Watanuki, Takasaki, Gunma 370-1292 (Japan)
Radiocesium from the severe accident at the Fukushima Dai-ichi Nuclear Power Plant on 11 March 2011 contaminates large areas. After this, a doubt for forest products, especially of mushroom, is indelible at the areas. Pruned woody parts and litters are containing a considerable amount of radiocesium, and generates a problem at incineration and composting. These mean that more attentive survey for each subject is expected; however, the present survey system is highly laborious/expensive and/or non-effective for this purpose. On the other hand, we can see a glass-badge based gamma radiation dose rate detection system. This system always utilized to detect a personal cumulative radiation dose, and thus, it is not suitable to separate a radiation from a specific object. However, if we can separate a radiation from a specific object and relate it with the own radiocesium concentration, it would enable us to presume the specific concentration with just an easy monitoring but without a destruction of the target nature and a complicated process including sampling, pre-treatment, and detection. Here, we present the concept of the measurement and results of the trials. First, we set glass-badges (type FS, Chiyoda Technol Corp., Japan) on a part of bough (approximately 10 cm in diameter) of Japanese flowering cherry trees (Prunus x yedoensis cv. Somei-Yoshino) with four different settings: A, a direct setting without any shield; B, a setting with an aluminum shield between bough and the glass-badge; C, a setting with a lead shield between bough and the glass-badge; D, a setting with a lead shield covering the glass-badge to shut the radiation from the surrounding but from bough. The deduction between the amount of each setting should separate a specific radiation of the bough from unlimited radiation from the surrounding. Even if the hourly dose rate is not enough to count the difference, a moderate cumulative dose would clear the difference. In fact, results demonstrated a
This book offers first a short introduction to advanced supervision, fault detection and diagnosis methods. It then describes model-based methods of fault detection and diagnosis for the main components of gasoline and diesel engines, such as the intake system, fuel supply, fuel injection, combustion process, turbocharger, exhaust system and exhaust gas aftertreatment. Additionally, model-based fault diagnosis of electrical motors, electric, pneumatic and hydraulic actuators and fault-tolerant systems is treated. In general series production sensors are used. It includes abundant experimental results showing the detection and diagnosis quality of implemented faults. Written for automotive engineers in practice, it is also of interest to graduate students of mechanical and electrical engineering and computer science. The Content Introduction.- I SUPERVISION, FAULT DETECTION AND DIAGNOSIS METHODS.- Supervision, Fault-Detection and Fault-Diagnosis Methods - a short Introduction.- II DIAGNOSIS OF INTERNAL COMBUST...
Mauricio Iglesias, Miguel; Vangsgaard, Anna Katrine; Gernaey, Krist
Diagnosis and control modules based on fuzzy set theory were tested for novel bioreactor monitoring and control. Two independent modules were used jointly to carry out first the diagnosis of the state of the system and then use transfer this information to control the reactor. The separation...... in diagnosis and control allowed a more intuitive design of the membership functions and the production rules. Hence, the resulting diagnosis-control module is simple to tune, update and maintain while providing a good control performance. In particular the diagnosis-control system was designed for a complete...
Waikar, S; Pathak, A; Ghule, V; Kapoor, A; Sagili, K; Babu, E R; Chadha, S
Setting: Sputum smear microscopy, the primary diagnostic tool used for diagnosis of tuberculosis (TB) in India's Revised National TB Control Programme (RNTCP), has low sensitivity, resulting in a significant number of TB cases reported as sputum-negative. As the revised guidelines pose challenges in implementation, sputum-negative presumptive TB (SNPT) patients are subjected to 2 weeks of antibiotics, followed by chest X-ray (CXR), resulting in significant loss to care among these cases. Objective: To determine whether reducing delays in CXR would yield additional TB cases and reduce initial loss to follow-up for diagnosis among SNPT cases. Methods: In an ongoing intervention in five districts of Maharashtra, SNPT patients were offered upfront CXR. Results: Of 119 male and 116 female SNPT patients with a mean age of 45 years who were tested by CXR, 32 (14%) were reported with CXR suggestive of TB. Administering upfront CXR in SNPT patients yielded twice as many additional cases, doubling the proportion of cases detected among all those tested as against administering CXR 2 weeks after smear examination. Conclusion: Our interventional study showed that the yield of TB cases was significantly greater when upfront CXR examination was undertaken without waiting for a 2-week antibiotic trial.
... 20 Employees' Benefits 2 2010-04-01 2010-04-01 false Irrebuttable presumption of death due to... FEDERAL COAL MINE HEALTH AND SAFETY ACT OF 1969, TITLE IV-BLACK LUNG BENEFITS (1969- ) Total Disability or Death Due to Pneumoconiosis § 410.458 Irrebuttable presumption of death due to pneumoconiosis—survivor's...
Abstract. This cross-sectional study evaluated knowledge and acceptability of prenatal diagnosis among 500 pregnant women at the University College Hospital, Ibadan. Most participants were aged 25-34 years , self-employed, Muslim, monogamy, secondary school leavers, on income of < ₦10,000.00 (US$ 67.00)/month.
Poulsen, Niels Kjølstad; Niemann, Hans Henrik
The focus in this paper is on stochastic change detection applied in connection with active fault diagnosis (AFD). An auxiliary input signal is applied in AFD. This signal injection in the system will in general allow to obtain a fast change detection/isolation by considering the output or an error...
In this paper, a Mobile Phone Diagnosis Support System is presented as an extension to jCOLIBRI which accepts a problem and reasons with cases to provide a solution related to a new given problem. Experimental evaluation using some set of problems shows that the developed system predicts the solution that is ...
Two of the many factors which may affect the accuracy of pregnancy diagnosis using milk progesterone levels are day of sampling and number of samples taken per cow. These two aspects were analysed using information obtained from progesterone profiles encompassing 359 pregnancy tests. Where a single sample was ...
AJRH Managing Editor
Determinants of acceptability were age, educational attainment, marital status and religion. Being married significantly affected knowledge scores, while tertiary education, being divorced, unskilled and self-employed positively influenced attitude towards prenatal diagnosis. (Afr J Reprod Health 2014; 18: 127-. 132).
Jin, Bo; Wu, Han; Xu, Jiahui; Yan, Jianwei; Ding, Yao; Wang, Z Irene; Guo, Yi; Wang, Zhongjin; Shen, Chunhong; Chen, Zhong; Ding, Meiping; Wang, Shuang
This study aimed to determine the accuracy of seizure diagnosis by semiological analysis and to assess the factors that affect diagnostic reliability. A total of 150 video clips of seizures from 50 patients (each with three seizures of the same type) were observed by eight epileptologists, 12 neurologists, and 20 physicians (internists). The videos included 37 series of epileptic seizures, eight series of physiologic nonepileptic events (PNEEs), and five series of psychogenic nonepileptic seizures (PNESs). After observing each video, the doctors chose the diagnosis of epileptic seizures or nonepileptic events for the patient; if the latter was chosen, they further chose the diagnosis of PNESs or PNEEs. The overall diagnostic accuracy rate for epileptic seizures and nonepileptic events increased from 0.614 to 0.660 after observations of all three seizures (p semiological diagnosis of seizures is greatly affected by the seizure type as well as the doctor's experience. Although the overall reliability is limited, it can be improved by observing more seizures. Copyright © 2014 Elsevier Inc. All rights reserved.
Pramono, Renard Xaviero Adhi; Imtiaz, Syed Anas; Rodriguez-Villegas, Esther
Pertussis is a contagious respiratory disease which mainly affects young children and can be fatal if left untreated. The World Health Organization estimates 16 million pertussis cases annually worldwide resulting in over 200,000 deaths. It is prevalent mainly in developing countries where it is difficult to diagnose due to the lack of healthcare facilities and medical professionals. Hence, a low-cost, quick and easily accessible solution is needed to provide pertussis diagnosis in such areas to contain an outbreak. In this paper we present an algorithm for automated diagnosis of pertussis using audio signals by analyzing cough and whoop sounds. The algorithm consists of three main blocks to perform automatic cough detection, cough classification and whooping sound detection. Each of these extract relevant features from the audio signal and subsequently classify them using a logistic regression model. The output from these blocks is collated to provide a pertussis likelihood diagnosis. The performance of the proposed algorithm is evaluated using audio recordings from 38 patients. The algorithm is able to diagnose all pertussis successfully from all audio recordings without any false diagnosis. It can also automatically detect individual cough sounds with 92% accuracy and PPV of 97%. The low complexity of the proposed algorithm coupled with its high accuracy demonstrates that it can be readily deployed using smartphones and can be extremely useful for quick identification or early screening of pertussis and for infection outbreaks control. PMID:27583523
Angelo Pasquale Giannuzzi
Full Text Available A ten-year-old neutered female mixed breed dog was referred for pale mucous membrane and acute onset of right prosencephalic clinical signs. Brain magnetic resonance imaging was suggestive for right middle cerebral artery ischemic stroke. Based on cell blood count, serum biochemistry and serologic tests and flow cytometric detection of anti-platelets and anti-red blood cells antibodies, a diagnosis of immunomediated haemolytic anemia associated with thrombocytopenia of suspected immunomediated origin was done. Immunosuppresive therapy with prednisone was started and the dog clinically recovered. Two months later complete normalization of CBC and serum biochemistry was documented. The dog remained stable for 7 months without therapy; then she relapsed. CBC revealed mild regenerative anemia with spherocytosis and thrombocytopenia. A conclusive Evans’ syndrome diagnosis was done and prednisone and cyclosporine treatment led to normalization of physical and CBC parameters. The dog is still alive at the time the paper submitted. Possible thrombotic etiopathogenetic mechanisms are illustrated in the paper and the authors suggest introducing Evans’ syndrome in the differential diagnosis list for brain ischemic stroke in dogs.
Full Text Available The actuality of this article is determined by the necessity of implementing fiscal simplicity for increasing tax compliance through fiscal education of small business representatives. In many developing and transition countries, micro and small enterprises are the most rapidly growing business segment. Tax compliance attitude within this sector varies significantly because high conformation costs and difficult formalization procedures can determine many small enterprises to operate in the informal economy. Thus tax regulation of small enterprises is crucial in the process of small entrepreneurs fiscal education and tax simplification of SMEs in many countries becomes one of the most efficient instruments. The main research methods were systemic analysis and logic synthesis. The main results obtained in article, as a result of research, are identification, analysis and systematization of foreign countries’ practices in implementing presumptive tax design and elaboration of some recommendations on fiscal simplicity.
Mullin, C M; Arkans, M A; Sammarco, C D; Vail, D M; Britton, B M; Vickery, K R; Risbon, R E; Lachowicz, J; Burgess, K E; Manley, C A; Clifford, C A
Sixty-four dogs were treated with single-agent doxorubicin (DOX) for presumptive cardiac hemangiosarcoma (cHSA). The objective response rate (CR + PR) was 41%, and the biologic response rate (CR + PR + SD), or clinical benefit, was 68%. The median progression-free survival (PFS) for treated dogs was 66 days. The median survival time (MST) for this group was 116 days and was significantly improved compared to a MST of 12 days for untreated control dogs (P = 0.0001). Biologic response was significantly associated with improved PFS (P < 0.0001) and OS (P < 0.0001). Univariate analysis identified larger tumour size as a variable negatively associated with PFS. The high rate of clinical benefit and improved MST suggest that DOX has activity in canine cHSA. © 2014 John Wiley & Sons Ltd.
Fenn, Joe; Drees, Randi; Volk, Holger A; De Decker, Steven
OBJECTIVE To compare clinical signs and outcomes between dogs with presumptive ischemic myelopathy and dogs with presumptive acute noncompressive nucleus pulposus extrusion (ANNPE). DESIGN Retrospective study. ANIMALS 51 dogs with ischemic myelopathy and 42 dogs with ANNPE examined at 1 referral hospital. PROCEDURES Medical records and MRI sequences were reviewed for dogs with a presumptive antemortem diagnosis of ischemic myelopathy or ANNPE. Information regarding signalment, clinical signs at initial examination, and short-term outcome was retrospectively retrieved from patient records. Long-term outcome information was obtained by telephone communication with referring or primary-care veterinarians and owners. RESULTS Compared with the hospital population, English Staffordshire Bull Terriers and Border Collies were overrepresented in the ischemic myelopathy and ANNPE groups, respectively. Dogs with ANNPE were significantly older at disease onset and were more likely to have a history of vocalization at onset of clinical signs, have spinal hyperesthesia during initial examination, have a lesion at C1-C5 spinal cord segments, and be ambulatory at hospital discharge, compared with dogs with ischemic myelopathy. Dogs with ischemic myelopathy were more likely to have a lesion at L4-S3 spinal cord segments and have long-term fecal incontinence, compared with dogs with ANNPE. However, long-term quality of life and outcome did not differ between dogs with ischemic myelopathy and dogs with ANNPE. CONCLUSIONS AND CLINICAL RELEVANCE Results revealed differences in clinical signs at initial examination between dogs with ischemic myelopathy and dogs with ANNPE that may aid clinicians in differentiating the 2 conditions.
Full Text Available It is essential to monitor and to diagnose faults in rotating machinery with a high thrust–weight ratio and complex structure for a variety of industrial applications, for which reliable signal measurements are required. However, the measured values consist of the true values of the parameters, the inertia of measurements, random errors and systematic errors. Such signals cannot reflect the true performance state and the health state of rotating machinery accurately. High-quality, steady-state measurements are necessary for most current diagnostic methods. Unfortunately, it is hard to obtain these kinds of measurements for most rotating machinery. Diagnosis based on transient performance is a useful tool that can potentially solve this problem. A model-based fault diagnosis method for gas turbines based on transient performance is proposed in this paper. The fault diagnosis consists of a dynamic simulation model, a diagnostic scheme, and an optimization algorithm. A high-accuracy, nonlinear, dynamic gas turbine model using a modular modeling method is presented that involves thermophysical properties, a component characteristic chart, and system inertial. The startup process is simulated using this model. The consistency between the simulation results and the field operation data shows the validity of the model and the advantages of transient accumulated deviation. In addition, a diagnostic scheme is designed to fulfill this process. Finally, cuckoo search is selected to solve the optimization problem in fault diagnosis. Comparative diagnostic results for a gas turbine before and after washing indicate the improved effectiveness and accuracy of the proposed method of using data from transient processes, compared with traditional methods using data from the steady state.
Zhao, Zhen; Zhang, Jun; Sun, Yigang; Liu, Zhexu
Fault diagnosis and safety analysis of an aero-engine have attracted more and more attention in modern society, whose safety directly affects the flight safety of an aircraft. In this paper, the problem concerning sensor fault diagnosis is investigated for an aero-engine during the whole flight process. Considering that the aero-engine is always working in different status through the whole flight process, a flight status division-based sensor fault diagnosis method is presented to improve fault diagnosis precision for the aero-engine. First, aero-engine status is partitioned according to normal sensor data during the whole flight process through the clustering algorithm. Based on that, a diagnosis model is built for each status using the principal component analysis algorithm. Finally, the sensors are monitored using the built diagnosis models by identifying the aero-engine status. The simulation result illustrates the effectiveness of the proposed method.
Huang, Sijia; Chong, Nicole; Lewis, Nathan
diagnosis. We applied this method to predict breast cancer occurrence, in combination with correlation feature selection (CFS) and classification methods. Results: The resulting all-stage and early-stage diagnosis models are highly accurate in two sets of testing blood samples, with average AUCs (Area Under.......993. Moreover, important metabolic pathways, such as taurine and hypotaurine metabolism and the alanine, aspartate, and glutamate pathway, are revealed as critical biological pathways for early diagnosis of breast cancer. Conclusions: We have successfully developed a new type of pathway-based model to study...... metabolomics data for disease diagnosis. Applying this method to blood-based breast cancer metabolomics data, we have discovered crucial metabolic pathway signatures for breast cancer diagnosis, especially early diagnosis. Further, this modeling approach may be generalized to other omics data types for disease...
Kanegane, Hirokazu; Hoshino, Akihiro; Okano, Tsubasa; Yasumi, Takahiro; Wada, Taizo; Takada, Hidetoshi; Okada, Satoshi; Yamashita, Motoi; Yeh, Tzu-Wen; Nishikomori, Ryuta; Takagi, Masatoshi; Imai, Kohsuke; Ochs, Hans D; Morio, Tomohiro
Primary immunodeficiencies (PIDs) are a heterogeneous group of inherited diseases of the immune system. The definite diagnosis of PID is ascertained by genetic analysis; however, this takes time and is costly. Flow cytometry provides a rapid and highly sensitive tool for diagnosis of PIDs. Flow cytometry can evaluate specific cell populations and subpopulations, cell surface, intracellular and intranuclear proteins, biologic effects associated with specific immune defects, and certain functional immune characteristics, each being useful for the diagnosis and evaluation of PIDs. Flow cytometry effectively identifies major forms of PIDs, including severe combined immunodeficiency, X-linked agammaglobulinemia, hyper IgM syndromes, Wiskott-Aldrich syndrome, X-linked lymphoproliferative syndrome, familial hemophagocytic lymphohistiocytosis, autoimmune lymphoproliferative syndrome, IPEX syndrome, CTLA 4 haploinsufficiency and LRBA deficiency, IRAK4 and MyD88 deficiencies, Mendelian susceptibility to mycobacterial disease, chronic mucocuneous candidiasis, and chronic granulomatous disease. While genetic analysis is the definitive approach to establish specific diagnoses of PIDs, flow cytometry provides a tool to effectively evaluate patients with PIDs at relatively low cost. Copyright © 2017 Japanese Society of Allergology. Production and hosting by Elsevier B.V. All rights reserved.
Yan, Gaoliang; Hu, Hailong; Zhao, Xiang; Zhang, Lin; Qu, Zehui; Li, Yantao
Artificial intelligence (AI) is a hotspot in computer science research nowadays. To apply AI technology in all industries has been the developing direction for researchers. Major depressive disorder (MDD) is a common disease of serious mental disorders. The World Health Organization (WHO) reports that MDD is projected to become the second most common cause of death and disability by 2020. At present, the way of MDD diagnosis is single. Applying AI technology to MDD diagnosis and pathophysiological research will speed up the MDD research and improve the efficiency of MDD diagnosis. In this study, we select the higher degree of brain network functional connectivity by statistical methods. And our experiments show that the average accuracy of Logistic Regression (LR) classifier using feature filtering reaches 88.48%. Compared with other classification methods, both the efficiency and accuracy of this method are improved, which will greatly improve the process of MDD diagnose. In these experiments, we also define the brain regions associated with MDD, which plays a vital role in MDD pathophysiological research.
Wolfe, Frederick; Fitzcharles, Mary-Ann; Goldenberg, Don L; Häuser, Winfried; Katz, Robert L; Mease, Philip J; Russell, Anthony S; Jon Russell, I; Walitt, Brian
The American College of Rheumatology (ACR) 2010 preliminary fibromyalgia diagnostic criteria require symptom ascertainment by physicians. The 2011 survey or research modified ACR criteria use only patient self-report. We compared physician-based (MD) (2010) and patient-based (PT) (2011) criteria and criteria components to determine the degree of agreement between criteria methodology. We studied prospectively collected, previously unreported rheumatology practice data from 514 patients and 30 physicians in the ACR 2010 study. We evaluated the widespread pain index, polysymptomatic distress (PSD) scale, tender point count (TPC), and fibromyalgia diagnosis using 2010 and 2011 rules. Bland-Altman 95% limits of agreement (LOA), kappa statistic, Lin's concordance coefficient, and the area under the receiver operating curve (ROC) were used to measure agreement and discrimination. MD and PT diagnostic agreement was substantial (83.4%, κ = 0.67). PSD scores differed slightly (12.3 MD, 12.8 PT; P = 0.213). LOA for PSD were -8.5 and 7.7, with bias of -0.42. The TPC was strongly associated with both the MD (r = 0.779) and PT PSD scales (r = 0.702). There was good agreement in MD and PT fibromyalgia diagnosis and other measures among rheumatology patients. Low bias scores indicate consistent results for physician and patient measures, but large values for LOA indicate many widely discordant pairs. There is acceptable agreement in diagnosis and PSD for research, but insufficient agreement for clinical decisions and diagnosis. We suggest adjudication of symptom data by patients and physicians, as recommended by the 2010 ACR criteria. © 2016, American College of Rheumatology.
... establishing presumptions of service connection for former prisoners of war. 1.18 Section 1.18 Pensions... Guidelines for establishing presumptions of service connection for former prisoners of war. (a) Purpose. The Secretary of Veterans Affairs will establish presumptions of service connection for former prisoners of war...
Zhao, Yue; Wang, Nian; Cui, Xiaoyu
In the field of medicine, quickly and accurately determining whether the patient is malignant or benign is the key to treatment. In this paper, K-Nearest Neighbor, Linear Discriminant Analysis, Logistic Regression were applied to predict the classification of thyroid,Her-2,PR,ER,Ki67,metastasis and lymph nodes in breast cancer, in order to recognize the benign and malignant breast tumors and achieve the purpose of aided diagnosis of breast cancer. The results showed that the highest classification accuracy of LDA was 88.56%, while the classification effect of KNN and Logistic Regression were better than that of LDA, the best accuracy reached 96.30%.
Full Text Available Deep Autoencoder has the powerful ability to learn features from large number of unlabeled samples and a small number of labeled samples. In this work, we have improved the network structure of the general deep autoencoder and applied it to the disease auxiliary diagnosis. We have achieved a network by entering the specific indicators and predicting whether suffering from liver disease, the network using real physical examination data for training and verification. Compared with the traditional semi-supervised machine learning algorithm, deep autoencoder will get higher accuracy.
Kim, Sewoong; Cho, Dongrae; Kim, Jihun; Kim, Manjae; Youn, Sangyeon; Jang, Jae Eun; Je, Minkyu; Lee, Dong Hun; Lee, Boreom; Farkas, Daniel L; Hwang, Jae Youn
We investigate the potential of mobile smartphone-based multispectral imaging for the quantitative diagnosis and management of skin lesions. Recently, various mobile devices such as a smartphone have emerged as healthcare tools. They have been applied for the early diagnosis of nonmalignant and malignant skin diseases. Particularly, when they are combined with an advanced optical imaging technique such as multispectral imaging and analysis, it would be beneficial for the early diagnosis of such skin diseases and for further quantitative prognosis monitoring after treatment at home. Thus, we demonstrate here the development of a smartphone-based multispectral imaging system with high portability and its potential for mobile skin diagnosis. The results suggest that smartphone-based multispectral imaging and analysis has great potential as a healthcare tool for quantitative mobile skin diagnosis.
Daigle, Matthew; Roychoudhury, Indranil
We describe a diagnosis algorithm entered into the Second International Diagnostic Competition. We focus on the first diagnostic problem of the industrial track of the competition in which a diagnosis algorithm must detect, isolate, and identify faults in an electrical power distribution testbed and provide corresponding recovery recommendations. The diagnosis algorithm embodies a model-based approach, centered around qualitative event-based fault isolation. Faults produce deviations in measured values from model-predicted values. The sequence of these deviations is matched to those predicted by the model in order to isolate faults. We augment this approach with model-based fault identification, which determines fault parameters and helps to further isolate faults. We describe the diagnosis approach, provide diagnosis results from running the algorithm on provided example scenarios, and discuss the issues faced, and lessons learned, from implementing the approach
Duyar, A.; Guo, T.-H.; Merrill, W.; Musgrave, J.
In a previous study, Guo, Merrill and Duyar, 1990, reported a conceptual development of a fault detection and diagnosis system for actuation faults of the Space Shuttle main engine. This study, which is a continuation of the previous work, implements the developed fault detection and diagnosis scheme for the real time actuation fault diagnosis of the Space Shuttle Main Engine. The scheme will be used as an integral part of an intelligent control system demonstration experiment at NASA Lewis. The diagnosis system utilizes a model based method with real time identification and hypothesis testing for actuation, sensor, and performance degradation faults.
Mauricio Iglesias, Miguel; Vangsgaard, Anna Katrine; Gernaey, Krist
Diagnosis and control modules based on fuzzy set theory were tested for novel bioreactor monitoring and control. Two independent modules were used jointly to carry out first the diagnosis of the state of the system and then use transfer this information to control the reactor. The separation in d...... autotrophic nitrogen removal process. The whole module is evaluated by dynamic simulation....
Schermer, T.R.; Robberts, B.; Crockett, A.J.; Thoonen, B.P.; Lucas, A.; Grootens, J.; Smeele, I.J.; Thamrin, C.; Reddel, H.K.
Clinical guidelines indicate that a chronic obstructive pulmonary disease (COPD) diagnosis is made from a single spirometry test. However, long-term stability of diagnosis based on forced expiratory volume in 1 s over forced vital capacity (FEV1/FVC) ratio has not been reported. In primary care
Schermer, T.R.J.; Robberts, B.; Crockett, A.J.; Thoonen, B.P.A.; Lucas, A.; Grootens, J.; Smeele, I.J.; Thamrin, C.; Reddel, H.K.
Clinical guidelines indicate that a chronic obstructive pulmonary disease (COPD) diagnosis is made from a single spirometry test. However, long-term stability of diagnosis based on forced expiratory volume in 1 s over forced vital capacity (FEV1/FVC) ratio has not been reported. In primary care
Wang, Lu; Xiong, Qirong; Xiao, Fei; Duan, Hongwei
Cancer is a leading cause of death in the world. Increasing evidence has demonstrated that early diagnosis holds the key towards effective treatment outcome. Cancer biomarkers are extensively used in oncology for cancer diagnosis and prognosis. Electrochemical sensors play key roles in current laboratory and clinical analysis of diverse chemical and biological targets. Recent development of functional nanomaterials offers new possibilities of improving the performance of electrochemical sensors. In particular, 2D nanomaterials have stimulated intense research due to their unique array of structural and chemical properties. The 2D materials of interest cover broadly across graphene, graphene derivatives (i.e., graphene oxide and reduced graphene oxide), and graphene-like nanomaterials (i.e., 2D layered transition metal dichalcogenides, graphite carbon nitride and boron nitride nanomaterials). In this review, we summarize recent advances in the synthesis of 2D nanomaterials and their applications in electrochemical biosensing of cancer biomarkers (nucleic acids, proteins and some small molecules), and present a personal perspective on the future direction of this area. Copyright © 2016 Elsevier B.V. All rights reserved.
Pang, Bin; Tang, Guiji; Tian, Tian; Zhou, Chong
When rolling bearing failure occurs, vibration signals generally contain different signal components, such as impulsive fault feature signals, background noise and harmonic interference signals. One of the most challenging aspects of rolling bearing fault diagnosis is how to inhibit noise and harmonic interference signals, while enhancing impulsive fault feature signals. This paper presents a novel bearing fault diagnosis method, namely an improved Hilbert time-time (IHTT) transform, by combining a Hilbert time-time (HTT) transform with principal component analysis (PCA). Firstly, the HTT transform was performed on vibration signals to derive a HTT transform matrix. Then, PCA was employed to de-noise the HTT transform matrix in order to improve the robustness of the HTT transform. Finally, the diagonal time series of the de-noised HTT transform matrix was extracted as the enhanced impulsive fault feature signal and the contained fault characteristic information was identified through further analyses of amplitude and envelope spectrums. Both simulated and experimental analyses validated the superiority of the presented method for detecting bearing failures.
Flow cytometry can evaluate specific cell populations and subpopulations, cell surface, intracellular and intranuclear proteins, biologic effects associated with specific immune defects, and certain functional immune characteristics, each being useful for the diagnosis and evaluation of PIDs. Flow cytometry effectively identifies major forms of PIDs, including severe combined immunodeficiency, X-linked agammaglobulinemia, hyper IgM syndromes, Wiskott-Aldrich syndrome, X-linked lymphoproliferative syndrome, familial hemophagocytic lymphohistiocytosis, autoimmune lymphoproliferative syndrome, IPEX syndrome, CTLA 4 haploinsufficiency and LRBA deficiency, IRAK4 and MyD88 deficiencies, Mendelian susceptibility to mycobacterial disease, chronic mucocuneous candidiasis, and chronic granulomatous disease. While genetic analysis is the definitive approach to establish specific diagnoses of PIDs, flow cytometry provides a tool to effectively evaluate patients with PIDs at relatively low cost.
Fohona S. Coulibaly
Full Text Available Lectins are carbohydrate recognizing proteins originating from diverse origins in nature, including animals, plants, viruses, bacteria and fungus. Due to their exceptional glycan recognition property, they have found many applications in analytical chemistry, biotechnology and surface chemistry. This manuscript explores the current use of lectins for cancer diagnosis and therapy. Moreover, novel drug delivery strategies aiming at improving lectin’s stability, reducing their undesired toxicity and controlling their non-specific binding interactions are discussed. We also explore the nanotechnology application of lectins for cancer targeting and imaging. Although many investigations are being conducted in the field of lectinology, there is still a limited clinical translation of the major findings reported due to lectins stability and toxicity concerns. Therefore, new investigations of safe and effective drug delivery system strategies for lectins are warranted in order to take full advantage of these proteins.
Cao, Yiqin; Cen, Zhao-Hui; Wei, Jiao-Long
This paper presents a novel fault diagnosis software (called FDSAC-SPICE) based on SPICE simulator for analog circuits. Four important techniques in AFDS-SPICE, including visual user-interface(VUI), component modeling and fault modeling (CMFM), fault injection and fault simulation (FIFS), fault dictionary and fault diagnosis (FDFD), greatly increase design-for-test and diagnosis efficiency of analog circuit by building a fault modeling-injection-simulationdiagnosis environment to get prior fault knowledge of target circuit. AFDS-SPICE also generates accurate fault coverage statistics that are tied to the circuit specifications. With employing a dictionary diagnosis method based on node-signalcharacters and regular BPNN algorithm, more accurate and effective diagnosis results are available for analog circuit with tolerance.
Busbait, Monther I.
We study the depth of decision trees for diagnosis of constant faults in read-once contact networks over finite bases. This includes diagnosis of 0-1 faults, 0 faults and 1 faults. For any finite basis, we prove a linear upper bound on the minimum depth of decision tree for diagnosis of constant faults depending on the number of edges in a contact network over that basis. Also, we obtain asymptotic bounds on the depth of decision trees for diagnosis of each type of constant faults depending on the number of edges in contact networks in the worst case per basis. We study the set of indecomposable contact networks with up to 10 edges and obtain sharp coefficients for the linear upper bound for diagnosis of constant faults in contact networks over bases of these indecomposable contact networks. We use a set of algorithms, including one that we create, to obtain the sharp coefficients.
Full Text Available Intelligent knowledge system is an important knowledge base for internal medicine diagnosis. Intelligent diagnosis of the knowledge base can be realized by establishing appropriate expert models to assist diagnosis and treatment. By building the hierarchical model of internal diseases, this paper established an internal medicine diagnostic system assisted by intelligence knowledge base with the mathematical model of analytic hierarchy. The hierarchical model is able to summarize characteristics of diseases and quantize the determinant criterion of diseases. The weighted value of a possible disease can be obtained through the judgment of physicians on the weight of factors of the criterion layer and the compared calculation of database. It is concluded that the analytic hierarchy model can realize the auxiliary diagnosis function of intelligence knowledge base and the weight of a disease providing diagnostic reference for physicians.
National Aeronautics and Space Administration — We describe two model-based diagnosis algo- rithms entered into the Third International Diag- nostic Competition. We focus on the first diag- nostic problem of the...
Full Text Available Mechanical equipment is the heart of industry. For this reason, mechanical fault diagnosis has drawn considerable attention. In terms of the rich information hidden in fault vibration signals, the processing and analysis techniques of vibration signals have become a crucial research issue in the field of mechanical fault diagnosis. Based on the theory of sparse decomposition, Selesnick proposed a novel nonlinear signal processing method: resonance-based sparse signal decomposition (RSSD. Since being put forward, RSSD has become widely recognized, and many RSSD-based methods have been developed to guide mechanical fault diagnosis. This paper attempts to summarize and review the theoretical developments and application advances of RSSD in mechanical fault diagnosis, and to provide a more comprehensive reference for those interested in RSSD and mechanical fault diagnosis. Followed by a brief introduction of RSSD’s theoretical foundation, based on different optimization directions, applications of RSSD in mechanical fault diagnosis are categorized into five aspects: original RSSD, parameter optimized RSSD, subband optimized RSSD, integrated optimized RSSD, and RSSD combined with other methods. On this basis, outstanding issues in current RSSD study are also pointed out, as well as corresponding instructional solutions. We hope this review will provide an insightful reference for researchers and readers who are interested in RSSD and mechanical fault diagnosis.
Huang, Wentao; Sun, Hongjian; Wang, Weijie
Mechanical equipment is the heart of industry. For this reason, mechanical fault diagnosis has drawn considerable attention. In terms of the rich information hidden in fault vibration signals, the processing and analysis techniques of vibration signals have become a crucial research issue in the field of mechanical fault diagnosis. Based on the theory of sparse decomposition, Selesnick proposed a novel nonlinear signal processing method: resonance-based sparse signal decomposition (RSSD). Since being put forward, RSSD has become widely recognized, and many RSSD-based methods have been developed to guide mechanical fault diagnosis. This paper attempts to summarize and review the theoretical developments and application advances of RSSD in mechanical fault diagnosis, and to provide a more comprehensive reference for those interested in RSSD and mechanical fault diagnosis. Followed by a brief introduction of RSSD's theoretical foundation, based on different optimization directions, applications of RSSD in mechanical fault diagnosis are categorized into five aspects: original RSSD, parameter optimized RSSD, subband optimized RSSD, integrated optimized RSSD, and RSSD combined with other methods. On this basis, outstanding issues in current RSSD study are also pointed out, as well as corresponding instructional solutions. We hope this review will provide an insightful reference for researchers and readers who are interested in RSSD and mechanical fault diagnosis.
Cao, Yan; Sun, Fengru
In order to improve the troubleshooting efficiency and accuracy of the track circuit, track circuit fault diagnosis method was researched. Firstly, the least squares support vector machine was applied to design the multi-fault classifier of the track circuit, and then the measured track data as training samples was used to verify the feasibility of the methods. Finally, the results based on BP neural network fault diagnosis methods and the methods used in this paper were compared. Results shows that the track fault classifier based on least squares support vector machine can effectively achieve the five track circuit fault diagnosis with less computing time.
Elaraby, S.M.; Zaky, M.M.; Emara, M.M.; El-metwally, K.
Nuclear plant accidents can cause injuries to operators, public as well as environment. Hence, advanced fault diagnosis techniques for nuclear plants are necessary to early detect, isolate and diagnose faults and accidents. This paper presents a new technique for accidents diagnosis of nuclear plants based on artificial neural networks. A new training technique based on particle swarm optimization (PSO) has been investigated to train the neural network. Results show the effectiveness of the technique for neural network training to diagnose nuclear reactor accidents
AWARD NUMBER: W81XWH-15-1-0157 TITLE: Nanotechnology -Based Detection of Novel microRNAs for Early Diagnosis of Prostate Cancer PRINCIPAL...TITLE AND SUBTITLE Nanotechnology -Based Detection of Novel microRNAs for Early Diagnosis of Prostate Cancer 5a. CONTRACT NUMBER 5b. GRANT NUMBER...identify novel differentially expressed miRNAs in the body fluids (blood, urine, etc.) for an early detection of PCa. Advances in nanotechnology and
Our research investigates how observations can be categorized by integrating a qualitative physical model with experiential knowledge. Our domain is diagnosis of pathologic gait in humans, in which the observations are the gait motions, muscle activity during gait, and physical exam data, and the diagnostic hypotheses are the potential muscle weaknesses, muscle mistimings, and joint restrictions. Patients with underlying neurological disorders typically have several malfunctions. Among the problems that need to be faced are: the ambiguity of the observations, the ambiguity of the qualitative physical model, correspondence of the observations and hypotheses to the qualitative physical model, the inherent uncertainty of experiential knowledge, and the combinatorics involved in forming composite hypotheses. Our system divides the work so that the knowledge-based reasoning suggests which hypotheses appear more likely than others, the qualitative physical model is used to determine which hypotheses explain which observations, and another process combines these functionalities to construct a composite hypothesis based on explanatory power and plausibility. We speculate that the reasoning architecture of our system is generally applicable to complex domains in which a less-than-perfect physical model and less-than-perfect experiential knowledge need to be combined to perform diagnosis.
Nguyen, Jie C; De Smet, Arthur A; Graf, Ben K; Rosas, Humberto G
Magnetic resonance (MR) imaging is currently the modality of choice for detecting meniscal injuries and planning subsequent treatment. A thorough understanding of the imaging protocols, normal meniscal anatomy, surrounding anatomic structures, and anatomic variants and pitfalls is critical to ensure diagnostic accuracy and prevent unnecessary surgery. High-spatial-resolution imaging of the meniscus can be performed using fast spin-echo and three-dimensional MR imaging sequences. Normal anatomic structures that can mimic a tear include the meniscal ligament, meniscofemoral ligaments, popliteomeniscal fascicles, and meniscomeniscal ligament. Anatomic variants and pitfalls that can mimic a tear include discoid meniscus, meniscal flounce, a meniscal ossicle, and chondrocalcinosis. When a meniscal tear is identified, accurate description and classification of the tear pattern can guide the referring clinician in patient education and surgical planning. For example, longitudinal tears are often amenable to repair, whereas horizontal and radial tears may require partial meniscectomy. Tear patterns include horizontal, longitudinal, radial, root, complex, displaced, and bucket-handle tears. Occasionally, meniscal tears can be difficult to detect at imaging; however, secondary indirect signs, such as a parameniscal cyst, meniscal extrusion, or linear subchondral bone marrow edema, should increase the radiologist's suspicion for an underlying tear. Awareness of common diagnostic errors can ensure accurate diagnosis of meniscal tears. Online supplemental material is available for this article. ©RSNA, 2014.
Khan, M. Abdesh Shafiel Kafiey; Rahman, M. Azizur
In this chapter, a short review of conventional Fourier transforms and new wavelet based faults diagnostic and protection techniques for electric motors is presented. The new hybrid wavelet packet transform (WPT) and neural network (NN) based faults diagnostic algorithm is developed and implemented for electric motors. The proposed WPT and NN
Shen, Qikun; Shi, Peng
This book provides recent theoretical developments in and practical applications of fault diagnosis and fault tolerant control for complex dynamical systems, including uncertain systems, linear and nonlinear systems. Combining adaptive control technique with other control methodologies, it investigates the problems of fault diagnosis and fault tolerant control for uncertain dynamic systems with or without time delay. As such, the book provides readers a solid understanding of fault diagnosis and fault tolerant control based on adaptive control technology. Given its depth and breadth, it is well suited for undergraduate and graduate courses on linear system theory, nonlinear system theory, fault diagnosis and fault tolerant control techniques. Further, it can be used as a reference source for academic research on fault diagnosis and fault tolerant control, and for postgraduates in the field of control theory and engineering. .
Balfe, Andrew; O'Connor, Peter; McDonnell, Ciaran
This paper describes the development of a prototype Expert System-based Analysis and Diagnostic (ESAD) package for the Hotel and Catering Industry. This computerised tool aids the hospitality manager in methodically scrutinising the hotel unit and environment, combining key information with systematic reasoning. The system searches through its extensive knowledge base, investigating complicated relationships. The number of possibilities considered is increased which will broaden the depth and...
Costamagna, Paola; De Giorgi, Andrea; Gotelli, Alberto; Magistri, Loredana; Moser, Gabriele; Sciaccaluga, Emanuele; Trucco, Andrea
The success of distributed power generation by plants based on solid oxide fuel cells (SOFCs) is hindered by reliability problems that can be mitigated through an effective fault detection and isolation (FDI) system. However, the numerous operating conditions under which such plants can operate and the random size of the possible faults make identifying damaged plant components starting from the physical variables measured in the plant very difficult. In this context, we assess two classical FDI strategies (model-based with fault signature matrix and data-driven with statistical classification) and the combination of them. For this assessment, a quantitative model of the SOFC-based plant, which is able to simulate regular and faulty conditions, is used. Moreover, a hybrid approach based on the random forest (RF) classification method is introduced to address the discrimination of regular and faulty situations due to its practical advantages. Working with a common dataset, the FDI performances obtained using the aforementioned strategies, with different sets of monitored variables, are observed and compared. We conclude that the hybrid FDI strategy, realized by combining a model-based scheme with a statistical classifier, outperforms the other strategies. In addition, the inclusion of two physical variables that should be measured inside the SOFCs can significantly improve the FDI performance, despite the actual difficulty in performing such measurements.
Costamagna, Paola; De Giorgi, Andrea; Gotelli, Alberto; Magistri, Loredana; Moser, Gabriele; Sciaccaluga, Emanuele; Trucco, Andrea
The success of distributed power generation by plants based on solid oxide fuel cells (SOFCs) is hindered by reliability problems that can be mitigated through an effective fault detection and isolation (FDI) system. However, the numerous operating conditions under which such plants can operate and the random size of the possible faults make identifying damaged plant components starting from the physical variables measured in the plant very difficult. In this context, we assess two classical FDI strategies (model-based with fault signature matrix and data-driven with statistical classification) and the combination of them. For this assessment, a quantitative model of the SOFC-based plant, which is able to simulate regular and faulty conditions, is used. Moreover, a hybrid approach based on the random forest (RF) classification method is introduced to address the discrimination of regular and faulty situations due to its practical advantages. Working with a common dataset, the FDI performances obtained using the aforementioned strategies, with different sets of monitored variables, are observed and compared. We conclude that the hybrid FDI strategy, realized by combining a model-based scheme with a statistical classifier, outperforms the other strategies. In addition, the inclusion of two physical variables that should be measured inside the SOFCs can significantly improve the FDI performance, despite the actual difficulty in performing such measurements. PMID:27556472
Full Text Available The success of distributed power generation by plants based on solid oxide fuel cells (SOFCs is hindered by reliability problems that can be mitigated through an effective fault detection and isolation (FDI system. However, the numerous operating conditions under which such plants can operate and the random size of the possible faults make identifying damaged plant components starting from the physical variables measured in the plant very difficult. In this context, we assess two classical FDI strategies (model-based with fault signature matrix and data-driven with statistical classification and the combination of them. For this assessment, a quantitative model of the SOFC-based plant, which is able to simulate regular and faulty conditions, is used. Moreover, a hybrid approach based on the random forest (RF classification method is introduced to address the discrimination of regular and faulty situations due to its practical advantages. Working with a common dataset, the FDI performances obtained using the aforementioned strategies, with different sets of monitored variables, are observed and compared. We conclude that the hybrid FDI strategy, realized by combining a model-based scheme with a statistical classifier, outperforms the other strategies. In addition, the inclusion of two physical variables that should be measured inside the SOFCs can significantly improve the FDI performance, despite the actual difficulty in performing such measurements.
Full Text Available Stomach bleeding is a kind of gastrointestinal disease which can be diagnosed noninvasively by wireless capsule endoscopy (WCE. However, it requires much time for physicians to scan large amount of WCE images. Alternatively, computer-assisted bleeding localization systems are developed where color, edge, and intensity features are defined to distinguish lesions from normal tissues. This paper proposes a saliency-based localization system where three saliency maps are computed: phase congruency-based edge saliency map derived from Log-Gabor filter bands, intensity histogram-guided intensity saliency map, and red proportion-based saliency map. Fusing the three maps together, the proposed system can detect bleeding regions by thresholding the fused saliency map. Results demonstrate the accuracy of 98.97% for our system to mark bleeding regions.
Full Text Available Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, multiscale permutation entropy (MPE was introduced for feature extraction from faulty bearing vibration signals. After extracting feature vectors by MPE, the support vector machine (SVM was applied to automate the fault diagnosis procedure. Simulation results demonstrated that the proposed method is a very powerful algorithm for bearing fault diagnosis and has much better performance than the methods based on single scale permutation entropy (PE and multiscale entropy (MSE.
Rijsman, Lucas H; Monkelbaan, Jan F|info:eu-repo/dai/nl/344499383; Kusters, Johannes G|info:eu-repo/dai/nl/074307428
The implementation of Polymerase Chain Reaction (PCR) based diagnostics of intestinal protozoa have led to higher sensitivity and (subtype) specificity, more convenient sampling and the possibility for high-throughput screening. An increasing number of clinical laboratories use PCR for routine
Daigle, Matthew; Roychoudhurry, Indranil; Biswas, Gautam; Koutsoukos, Xenofon
Distributed fault diagnosis solutions are becoming necessary due to the complexity of modern engineering systems, and the advent of smart sensors and computing elements. This paper presents a novel event-based approach for distributed diagnosis of abrupt parametric faults in continuous systems, based on a qualitative abstraction of measurement deviations from the nominal behavior. We systematically derive dynamic fault signatures expressed as event-based fault models. We develop a distributed diagnoser design algorithm that uses these models for designing local event-based diagnosers based on global diagnosability analysis. The local diagnosers each generate globally correct diagnosis results locally, without a centralized coordinator, and by communicating a minimal number of measurements between themselves. The proposed approach is applied to a multi-tank system, and results demonstrate a marked improvement in scalability compared to a centralized approach.
Jose M. Bernal-de-Lázaro
Full Text Available This article summarizes the main contributions of the PhD thesis titled: "Application of learning techniques based on kernel methods for the fault diagnosis in Industrial processes". This thesis focuses on the analysis and design of fault diagnosis systems (DDF based on historical data. Specifically this thesis provides: (1 new criteria for adjustment of the kernel methods used to select features with a high discriminative capacity for the fault diagnosis tasks, (2 a proposed approach process monitoring using statistical techniques multivariate that incorporates a reinforced information concerning to the dynamics of the Hotelling's T2 and SPE statistics, whose combination with kernel methods improves the detection of small-magnitude faults; (3 an robustness index to compare the diagnosis classifiers performance taking into account their insensitivity to possible noise and disturbance on historical data.
Lee, S. C.; Lollar, Louis F.
The overall approach currently being taken in the development of AMPERES (Autonomously Managed Power System Extendable Real-time Expert System), a knowledge-based expert system for fault monitoring and diagnosis of space power systems, is discussed. The system architecture, knowledge representation, and fault monitoring and diagnosis strategy are examined. A 'component-centered' approach developed in this project is described. Critical issues requiring further study are identified.
La réalisation visée est un outil collaboratif d\\'enseignement pour les médecins du terrain et les étudiants en médecine au sein d\\'une organisation virtuelle. The emerging grid-based technologies are increasingly adopted to enhance education and provide better learning services. This is characterized all over the world, ...
Xia Hong; Zhang Nan; Du Xingfu
In this paper, two fault diagnostic casts were constructed using SVM theory for model fault such as cracks of steam generator heat transfer tubes and small break loss of coolant accident in nuclear power plant. One fault diagnostic cast was constructed based on least squares support vector machines using C++ programming language. The other was constructed based on traditionary support vector machines using Matlab7.0 program. The results in the Simulation Test have shown that the performance of the two models based on two kinds of SVMs both depends on the choice of nuclear function model and the parameters. In this study, after the suitable choice, the same diagnostic performance was obtained using two kinds of SVMs. The fault can be diagnosed exactly during the period between the third second until shutdown. In the first three seconds, the fault data were not yet shown or the data were fluctuant. The simulation results demonstrated that the methods could diagnose the fault phenomenon accurately under the circumstances of small example sizes, and the precision was very high. (authors)
Satoh, Hitoshi; Niki, Noboru; Eguchi, Kenji; Ohmatsu, Hironobu; Kakinuma, Ryutaru; Moriyama, Noriyuki
Mass screening based on multi-helical CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. Moreover, the doctor who diagnoses a medical image is insufficient in Japan. To overcome these problems, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images, a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification and a vertebra body analysis algorithm for quantitative evaluation of osteoporosis likelihood by using helical CT scanner for the lung cancer mass screening. The functions to observe suspicious shadow in detail are provided in computer-aided diagnosis workstation with these screening algorithms. We also have developed the telemedicine network by using Web medical image conference system with the security improvement of images transmission, Biometric fingerprint authentication system and Biometric face authentication system. Biometric face authentication used on site of telemedicine makes "Encryption of file" and "Success in login" effective. As a result, patients' private information is protected. We can share the screen of Web medical image conference system from two or more web conference terminals at the same time. An opinion can be exchanged mutually by using a camera and a microphone that are connected with workstation. Based on these diagnostic assistance methods, we have developed a new computer-aided workstation and a new telemedicine network that can display suspected lesions three-dimensionally in a short time. The results of this study indicate that our radiological information system without film by using computer-aided diagnosis workstation and our telemedicine network system can increase diagnostic speed, diagnostic accuracy and
Huang, Sijia; Chong, Nicole; Lewis, Nathan E; Jia, Wei; Xie, Guoxiang; Garmire, Lana X
More accurate diagnostic methods are pressingly needed to diagnose breast cancer, the most common malignant cancer in women worldwide. Blood-based metabolomics is a promising diagnostic method for breast cancer. However, many metabolic biomarkers are difficult to replicate among studies. We propose that higher-order functional representation of metabolomics data, such as pathway-based metabolomic features, can be used as robust biomarkers for breast cancer. Towards this, we have developed a new computational method that uses personalized pathway dysregulation scores for disease diagnosis. We applied this method to predict breast cancer occurrence, in combination with correlation feature selection (CFS) and classification methods. The resulting all-stage and early-stage diagnosis models are highly accurate in two sets of testing blood samples, with average AUCs (Area Under the Curve, a receiver operating characteristic curve) of 0.968 and 0.934, sensitivities of 0.946 and 0.954, and specificities of 0.934 and 0.918. These two metabolomics-based pathway models are further validated by RNA-Seq-based TCGA (The Cancer Genome Atlas) breast cancer data, with AUCs of 0.995 and 0.993. Moreover, important metabolic pathways, such as taurine and hypotaurine metabolism and the alanine, aspartate, and glutamate pathway, are revealed as critical biological pathways for early diagnosis of breast cancer. We have successfully developed a new type of pathway-based model to study metabolomics data for disease diagnosis. Applying this method to blood-based breast cancer metabolomics data, we have discovered crucial metabolic pathway signatures for breast cancer diagnosis, especially early diagnosis. Further, this modeling approach may be generalized to other omics data types for disease diagnosis.
Full Text Available The fault diagnosis process is essentially a class discrimination problem. However, traditional class discrimination methods such as SVM and ANN fail to capitalize the interactions among the feature variables. Variable predictive model-based class discrimination (VPMCD can adequately use the interactions. But the feature extraction and selection will greatly affect the accuracy and stability of VPMCD classifier. Aiming at the nonstationary characteristics of vibration signal from rotating machinery with local fault, singular value decomposition (SVD technique based local characteristic-scale decomposition (LCD was developed to extract the feature variables. Subsequently, combining artificial neural net (ANN and mean impact value (MIV, ANN-MIV as a kind of feature selection approach was proposed to select more suitable feature variables as input vector of VPMCD classifier. In the end of this paper, a novel fault diagnosis model based on LCD-SVD-ANN-MIV and VPMCD is proposed and proved by an experimental application for roller bearing fault diagnosis. The results show that the proposed method is effective and noise tolerant. And the comparative results demonstrate that the proposed method is superior to the other methods in diagnosis speed, diagnosis success rate, and diagnosis stability.
Wermuth, Lene; Lassen, Christina Funch; Himmerslev, Liselotte
Denmark has a long-standing tradition of maintaining one of the world's largest health science specialized register data bases as the National Hospital Register (NHR). To estimate the prevalence and incidence of diseases, the correctness of the diagnoses recorded is critical. Parkinson's disease...... (PD) is a neurodegenerative disorder and only 75-80% of patients with parkinsonism will have idiopathic PD (iPD). It is necessary to follow patients in order to determine if some of them will develop other neurodegenerative diseases and a one-time-only diagnostic code for iPD reported in the register...
Vardeh, Daniel; Mannion, Richard J; Woolf, Clifford J
The past few decades have witnessed a huge leap forward in our understanding of the mechanistic underpinnings of pain, in normal states where it helps protect from injury, and also in pathological states where pain evolves from a symptom reflecting tissue injury to become the disease itself. However, despite these scientific advances, chronic pain remains extremely challenging to manage clinically. Although the number of potential treatment targets has grown substantially and a strong case has been made for a mechanism-based and individualized approach to pain therapy, arguably clinicians are not much more advanced now than 20 years ago, in their capacity to either diagnose or effectively treat their patients. The gulf between pain research and pain management is as wide as ever. We are still currently unable to apply an evidence-based approach to chronic pain management that reflects mechanistic understanding, and instead, clinical practice remains an empirical and often unsatisfactory journey for patients, whose individual response to treatment cannot be predicted. In this article we take a common and difficult to treat pain condition, chronic low back pain, and use its presentation in clinical practice as a framework to highlight what is known about pathophysiological pain mechanisms and how we could potentially detect these to drive rational treatment choice. We discuss how present methods of assessment and management still fall well short, however, of any mechanism-based or precision medicine approach. Nevertheless, substantial improvements in chronic pain management could be possible if a more strategic and coordinated approach were to evolve, one designed to identify the specific mechanisms driving the presenting pain phenotype. We present an analysis of such an approach, highlighting the major problems in identifying mechanisms in patients, and develop a framework for a pain diagnostic ladder that may prove useful in the future, consisting of successive
The last few decades have witnessed a huge leap forward in our understanding of the mechanistic underpinnings of pain, both in normal states where it helps protect from injury, and in pathological states where pain evolves from a symptom reflecting tissue injury to become the disease itself. However, despite these scientific advances, chronic pain remains extremely challenging to manage clinically. While the number of potential treatment targets has grown substantially and a strong case has been made for a mechanism-based and individualized approach to pain therapy, arguably clinicians are not much more advanced now than 20 years ago, in their capacity to either diagnose or effectively treat their patients. The gulf between pain research and pain management is as wide as ever. We are still currently unable to apply an evidence-based approach to chronic pain management that reflects mechanistic understanding, and instead, clinical practice remains an empirical and often unsatisfactory journey for patients, whose individual response to treatment cannot be predicted. Here we take a common and difficult to treat pain condition, chronic low back pain, and use its presentation in clinical practice as a framework to highlight what is known about pathophysiological pain mechanisms and how we could potentially detect these to drive rational treatment choice. We discuss how present methods of assessment and management still fall well short, however, of any mechanism-based or precision-medicine approach. Nevertheless, substantial improvements in chronic pain management could be possible if a more strategic and coordinated approach were to evolve, one designed to identify the specific mechanisms driving the presenting pain phenotype. We present an analysis of such an approach, highlighting the major problems in identifying mechanisms in patients, and develop a framework for a pain diagnostic ladder that may prove useful in the future, consisting of successive identification of
Batwala, Vincent; Magnussen, Pascal; Hansen, Kristian Schultz
ABSTRACT: BACKGROUND: Current Uganda National Malaria treatment guidelines recommend parasitological confirmation either by microscopy or rapid diagnostic test (RDT) before treatment with artemether-lumefantrine (AL). However, the cost-effectiveness of these strategies has not been assessed...... departments were enrolled from March 2010 to February 2011. Of these, a random sample of 1,627 was selected to measure additional socio-economic characteristics. Costing was performed following the standard step-down cost allocation and the ingredients approach. Effectiveness was measured as the number...
Varela-Diaz, V.M.; Coltorti, E.A.
Information obtained from the routine application of hydatid immunodiagnostic techniques in different clinical situations over a seven-year period is presented. The immunoelectrophoresis test was used and was replaced by the arc 5 double diffusion (DD5) test. Examination of sera from 1.888 patients with signs and/or symptoms comparatible with hydatid disease revealed that the presurgical confirmation of Echinococcus granulosus infection is only obtained by detection of anti-antigen 5 antibodies. In all patients whose preoperative serum showed three or more uncharacteristic bands in the absence of anti-antigen 5 antibodies, hydatid cysts were found surgically. DD5 testing of a fluid sample collected by puncture estabilished its hydatid etiology. Post-operative monitoring of hydatidosis patients demonstrated that persistence of DD5-positivity two years after surgery established the presence of ther cysts.
Banica, Constantin; Dobrea, Dumitru
This paper describes a recent noise analysis of the neutronic signals provided by in-core flux detectors (ICFD) and ion chambers (IC). This analysis is part of on-going program developed for Unit 1 of the Cernavoda NPP, Romania, with the following main objectives: - prediction of detector failures based on pattern recognition; - determination of fast excursions from steady states; - detection of abnormal mechanical vibrations in the reactor core. The introduction presents briefly the reactor, the location of ICFD's and IC's. The second section presents the data acquisition systems and their capabilities. The paper continues with a brief presentation of the numerical methods used for analysis (section 3). The most significant results can be found in section 4, while section 5 concludes about useful information that can be obtained from the neutronic signals at high power steady-state operation. (authors)
Omar, Farag K.; Gaouda, A. M.
This paper proposes a novel wavelet-based technique for detecting and localizing gear tooth defects in a noisy environment. The proposed technique utilizes a dynamic windowing process while analyzing gearbox vibration signals in the wavelet domain. The gear vibration signal is processed through a dynamic Kaiser's window of varying parameters. The window size, shape, and sliding rate are modified towards increasing the similarity between the non-stationary vibration signal and the selected mother wavelet. The window parameters are continuously modified until they provide maximum wavelet coefficients localized at the defected tooth. The technique is applied on laboratory data corrupted with high noise level. The technique has shown accurate results in detecting and localizing gear tooth fracture with different damage severity.
Scarl, E.; McCall, K.
The Rodon model-based diagnosis shell was applied to a breadboard test-bed, modeling an automated power distribution system. The constraint-based modeling paradigm and diagnostic algorithm were found to adequately represent the selected set of test scenarios.
Bonnieres, P. de; Boutes, J.L.; Calas, M.A.; Para, S.
This paper presents a knowledge-based diagnosis system which can be a valuable aid in resolving malfunctions and failures encountered using the automatic hot-wire TIG weld cladding process. This knowledge-based system is currently under evaluation by welding operators at the Framatome heavy fabricating facility. Extension to other welding processes is being considered
Hsieh, Yi-Zeng; Su, Mu-Chun; Wang, Pa-Chun
One of the major bottlenecks in applying conventional neural networks to the medical field is that it is very difficult to interpret, in a physically meaningful way, because the learned knowledge is numerically encoded in the trained synaptic weights. In one of our previous works, we proposed a class of Hyper-Rectangular Composite Neural Networks (HRCNNs) of which synaptic weights can be interpreted as a set of crisp If-Then rules; however, a trained HRCNN may result in some ineffective If-Then rules which can only justify very few positive examples (i.e., poor generalization). This motivated us to propose a PSO-based Fuzzy Hyper-Rectangular Composite Neural Network (PFHRCNN) which applies particle swarm optimization (PSO) to trim the rules generated by a trained HRCNN while the recognition performance will not be degraded or even be improved. The performance of the proposed PFHRCNN is demonstrated on three benchmark medical databases including liver disorders data set, the breast cancer data set and the Parkinson's disease data set. Copyright © 2014 Elsevier Inc. All rights reserved.
Guerra, Juliana M; Freitas, Mariana F; Daniel, Alexandre Gt; Pellegrino, Arine; Cardoso, Natália C; Castro, Isac de; Onuchic, Luiz F; Cogliati, Bruno
Objectives The aim of this study was to establish ultrasound criteria for the diagnosis of autosomal dominant polycystic kidney disease (ADPKD) in Persian cats. Methods Eighty-two Persian cats were assessed using renal ultrasound and genotyped for the C→A transversion in exon 29 of PKD1. The animals were also submitted to hematological characterization, serum biochemistry analyses and urinalysis. Results Age, sex and neutering status did not differ between ADPKD (n = 12) and non-ADPKD (n = 70) cats. After integrated molecular genetics/ultrasonographic analysis, the presence of at least one renal cyst was sufficient to establish a diagnosis of ADPKD in animals up to 15 months of age. Two or more cysts were required for diagnosis in cats aged 16-32 months, and at least three cysts warranted diagnosis of ADPKD in animals aged 33-49 months. Finally, four or more cysts led to diagnosis in cats aged 50-66 months. Although cats with ADPKD exhibited higher serum calcium levels than non-affected cats, hematological, urinalysis and other biochemical parameters did not differ between the two groups. Conclusions and relevance Integrated analyses of imaging and molecular genetics data enabled, for the first time, the establishment of age-based ultrasonographic criteria for the diagnosis of ADPKD in Persian cats. The development of imaging criteria is particularly relevant and useful in the clinical setting given the current limitations to access and the cost of molecular genetics-based diagnostic tests.
Full Text Available At present, the solar photovoltaic system is extensively used. However, once a fault occurs, it is inspected manually, which is not economical. In order to remedy the defect of unavailable fault diagnosis at any irradiance and temperature in the literature with chaos synchronization based intelligent fault diagnosis for photovoltaic systems proposed by Hsieh et al., this study proposed a chaotic extension fault diagnosis method combined with error back propagation neural network to overcome this problem. It used the nn toolbox of matlab 2010 for simulation and comparison, measured current irradiance and temperature, and used the maximum power point tracking (MPPT for chaotic extraction of eigenvalue. The range of extension field was determined by neural network. Finally, the voltage eigenvalue obtained from current temperature and irradiance was used for the fault diagnosis. Comparing the diagnostic rates with the results by Hsieh et al., this scheme can obtain better diagnostic rates when the irradiances or the temperatures are changed.
Full Text Available Microwave tomography (MT based on stroke diagnosis is one of the alternative methods for determinations of the haemorrhagic, ischemic and stroke in brain nervous systems. It is focusing on the brain imaging, continuous monitoring, and preclinical applications. It provides cost effective system and able to use the rural and urban medical clinics that lack the necessary resources in effective stroke diagnosis during emerging applications in road accident and pre-ambulance clinical treatment. In the early works, the design of microstrip patch antennas (MPAs involved the implementation of MT system. Consequently, the MT system presented a few limitations since it required an efficient MPA design with appropriate parameters. Moreover, there were no specific diagnosis modules and body centric features in it. The present research proposes the MPA designs in the forms of diagnosis modules and implements it on the MT system.
Lebbe, Celeste; Becker, Jürgen C; Grob, Jean-Jacques
Merkel cell carcinoma (MCC) is a rare tumour of the skin of neuro-endocrine origin probably developing from neuronal mechanoreceptors. A collaborative group of multidisciplinary experts form the European Dermatology Forum (EDF), The European Association of Dermato-Oncology (EADO) and the European...... Organization of Research and Treatment of Cancer (EORTC) was formed to make recommendations on MCC diagnosis and management, based on a critical review of the literature, existing guidelines and expert's experience. Clinical features of the cutaneous/subcutaneous nodules hardly contribute to the diagnosis...... of MCC. The diagnosis is made by histopathology, and an incisional or excisional biopsy is mandatory. Immunohistochemical staining contributes to clarification of the diagnosis. Initial work-up comprises ultrasound of the loco-regional lymph nodes and total body scanning examinations. The primary tumour...
Full Text Available Abstract The term congenital neutropenia encompasses a family of neutropenic disorders, both permanent and intermittent, severe ( When neutropenia is detected, an attempt should be made to establish the etiology, distinguishing between acquired forms (the most frequent, including post viral neutropenia and auto immune neutropenia and congenital forms that may either be isolated or part of a complex genetic disease. Except for ethnic neutropenia, which is a frequent but mild congenital form, probably with polygenic inheritance, all other forms of congenital neutropenia are extremely rare and have monogenic inheritance, which may be X-linked or autosomal, recessive or dominant. About half the forms of congenital neutropenia with no extra-hematopoetic manifestations and normal adaptive immunity are due to neutrophil elastase (ELANE mutations. Some patients have severe permanent neutropenia and frequent infections early in life, while others have mild intermittent neutropenia. Congenital neutropenia may also be associated with a wide range of organ dysfunctions, as for example in Shwachman-Diamond syndrome (associated with pancreatic insufficiency and glycogen storage disease type Ib (associated with a glycogen storage syndrome. So far, the molecular bases of 12 neutropenic disorders have been identified. Treatment of severe chronic neutropenia should focus on prevention of infections. It includes antimicrobial prophylaxis, generally with trimethoprim-sulfamethoxazole, and also granulocyte-colony-stimulating factor (G-CSF. G-CSF has considerably improved these patients' outlook. It is usually well tolerated, but potential adverse effects include thrombocytopenia, glomerulonephritis, vasculitis and osteoporosis. Long-term treatment with G-CSF, especially at high doses, augments the spontaneous risk of leukemia in patients with congenital neutropenia.
Yoshikawa, S.; Saiki, A.; Ugolini, D.; Ozawa, K.
The main objective of this paper is to develop an advanced diagnosis system based on the artificial intelligence technique to monitor the operation and to improve the operational safety of nuclear power plants. Three different methods have been elaborated in this study: an artificial neural network local diagnosis (NN ds ) scheme that acting at the component level discriminates between normal and abnormal transients, a model-based diagnostic reasoning mechanism that combines a physical causal network model-based knowledge compiler (KC) that generates applicable diagnostic rules from widely accepted physical knowledge compiler (KC) that generates applicable diagnostic rules from widely accepted physical knowledge. Although the three methods have been developed and verified independently, they are highly correlated and, when connected together, form a effective and robust diagnosis and monitoring tool. (authors)
Satoh, Hitoshi; Niki, Noboru; Mori, Kiyoshi; Eguchi, Kenji; Kaneko, Masahiro; Kakinuma, Ryutarou; Moriyama, Noriyuki; Ohmatsu, Hironobu; Masuda, Hideo; Machida, Suguru; Sasagawa, Michizou
Multi-helical CT scanner advanced remarkably at the speed at which the chest CT images were acquired for mass screening. Mass screening based on multi-helical CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. To overcome this problem, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images and a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification. We also have developed electronic medical recording system and prototype internet system for the community health in two or more regions by using the Virtual Private Network router and Biometric fingerprint authentication system and Biometric face authentication system for safety of medical information. Based on these diagnostic assistance methods, we have now developed a new computer-aided workstation and database that can display suspected lesions three-dimensionally in a short time. This paper describes basic studies that have been conducted to evaluate this new system. The results of this study indicate that our computer-aided diagnosis workstation and network system can increase diagnostic speed, diagnostic accuracy and safety of medical information.
Reyes, Laura D; Stimpson, Cheryl D; Gupta, Kanika; Raghanti, Mary Ann; Hof, Patrick R; Reep, Roger L; Sherwood, Chet C
Within afrotherians, sirenians are unusual due to their aquatic lifestyle, large body size and relatively large lissencephalic brain. However, little is known about the neuron type distributions of the cerebral cortex in sirenians within the context of other afrotherians and aquatic mammals. The present study investigated two cortical regions, dorsolateral cortex area 1 (DL1) and cluster cortex area 2 (CL2), in the presumptive primary somatosensory cortex (S1) in Florida manatees (Trichechus manatus latirostris) to characterize cyto- and chemoarchitecture. The mean neuron density for both cortical regions was 35,617 neurons/mm(3) and fell within the 95% prediction intervals relative to brain mass based on a reference group of afrotherians and xenarthrans. Densities of inhibitory interneuron subtypes labeled against calcium-binding proteins and neuropeptide Y were relatively low compared to afrotherians and xenarthrans and also formed a small percentage of the overall population of inhibitory interneurons as revealed by GAD67 immunoreactivity. Nonphosphorylated neurofilament protein-immunoreactive (NPNFP-ir) neurons comprised a mean of 60% of neurons in layer V across DL1 and CL2. DL1 contained a higher percentage of NPNFP-ir neurons than CL2, although CL2 had a higher variety of morphological types. The mean percentage of NPNFP-ir neurons in the two regions of the presumptive S1 were low compared to other afrotherians and xenarthrans but were within the 95% prediction intervals relative to brain mass, and their morphologies were comparable to those found in other afrotherians and xenarthrans. Although this specific pattern of neuron types and densities sets the manatee apart from other afrotherians and xenarthrans, the manatee isocortex does not appear to be explicitly adapted for an aquatic habitat. Many of the features that are shared between manatees and cetaceans are also shared with a diverse array of terrestrial mammals and likely represent highly conserved
Kato, Hideki; Fujii, Shigehisa; Shirakawa, Seiji; Suzuki, Yusuke; Nishii, Yoshio
A presumption calculating formula of the X-ray spectrum generated from a molybdenum target X-ray tube is presented. The calculation procedure is to add an amount of characteristic X-ray photons that corresponds to the ratio of characteristic photons and bremsstrahlung photons to the bremsstrahlung spectrum obtained using semiempirical calculation. The bremsstrahlung spectrum was calculated by using a corrected Tucker's formula. The corrected content was a formula for calculating the self-absorption length in the target that originated in the difference of the incident angle to the target of the electron and the mass stopping power data. The measured spectrum was separated into the bremsstrahlung component and the characteristic photon component, and the ratio of the characteristic photons and bremsstrahlung photons was obtained. The regression was derived from the function of the tube voltage. Based on this calculation procedure, computer software was constructed that can calculate an X-ray spectrum in arbitrary exposure conditions. The X-ray spectrum obtained from this presumption calculating formula and the measured X-ray spectrum corresponded well. This formula is very useful for analyzing various problems related to mammography by means of Monte Carlo simulations.
Yuan, Xianfeng; Song, Mumin; Zhou, Fengyu; Chen, Zhumin; Li, Yan
The wheeled robots have been successfully applied in many aspects, such as industrial handling vehicles, and wheeled service robots. To improve the safety and reliability of wheeled robots, this paper presents a novel hybrid fault diagnosis framework based on Mittag-Leffler kernel (ML-kernel) support vector machine (SVM) and Dempster-Shafer (D-S) fusion. Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA) models for fault feature extraction. The fault feature vectors are then applied to train the probabilistic SVM (PSVM) classifiers that arrive at a preliminary fault diagnosis. To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well. The basic probability assignments (BPAs) are defined based on the preliminary fault diagnosis results and their confidence values. Eventually, the final fault diagnosis result is archived by the fusion of the BPAs. Experimental results show that the proposed framework not only is capable of detecting and identifying the faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods.
Full Text Available Fault diagnosis for rolling bearings has attracted increasing attention in recent years. However, few studies have focused on fault diagnosis for rolling bearings under variable conditions. This paper introduces a fault diagnosis method for rolling bearings under variable conditions based on visual cognition. The proposed method includes the following steps. First, the vibration signal data are transformed into a recurrence plot (RP, which is a two-dimensional image. Then, inspired by the visual invariance characteristic of the human visual system (HVS, we utilize speed up robust feature to extract fault features from the two-dimensional RP and generate a 64-dimensional feature vector, which is invariant to image translation, rotation, scaling variation, etc. Third, based on the manifold perception characteristic of HVS, isometric mapping, a manifold learning method that can reflect the intrinsic manifold embedded in the high-dimensional space, is employed to obtain a low-dimensional feature vector. Finally, a classical classification method, support vector machine, is utilized to realize fault diagnosis. Verification data were collected from Case Western Reserve University Bearing Data Center, and the experimental result indicates that the proposed fault diagnosis method based on visual cognition is highly effective for rolling bearings under variable conditions, thus providing a promising approach from the cognitive computing field.
presents a novel hybrid fault diagnosis framework based on Mittag-Leffler kernel (ML-kernel support vector machine (SVM and Dempster-Shafer (D-S fusion. Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA models for fault feature extraction. The fault feature vectors are then applied to train the probabilistic SVM (PSVM classifiers that arrive at a preliminary fault diagnosis. To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well. The basic probability assignments (BPAs are defined based on the preliminary fault diagnosis results and their confidence values. Eventually, the final fault diagnosis result is archived by the fusion of the BPAs. Experimental results show that the proposed framework not only is capable of detecting and identifying the faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods.
Cheng, Yujie; Zhou, Bo; Lu, Chen; Yang, Chao
Fault diagnosis for rolling bearings has attracted increasing attention in recent years. However, few studies have focused on fault diagnosis for rolling bearings under variable conditions. This paper introduces a fault diagnosis method for rolling bearings under variable conditions based on visual cognition. The proposed method includes the following steps. First, the vibration signal data are transformed into a recurrence plot (RP), which is a two-dimensional image. Then, inspired by the visual invariance characteristic of the human visual system (HVS), we utilize speed up robust feature to extract fault features from the two-dimensional RP and generate a 64-dimensional feature vector, which is invariant to image translation, rotation, scaling variation, etc. Third, based on the manifold perception characteristic of HVS, isometric mapping, a manifold learning method that can reflect the intrinsic manifold embedded in the high-dimensional space, is employed to obtain a low-dimensional feature vector. Finally, a classical classification method, support vector machine, is utilized to realize fault diagnosis. Verification data were collected from Case Western Reserve University Bearing Data Center, and the experimental result indicates that the proposed fault diagnosis method based on visual cognition is highly effective for rolling bearings under variable conditions, thus providing a promising approach from the cognitive computing field.
Full Text Available The condition diagnosis of rotating machinery depends largely on the feature analysis of vibration signals measured for the condition diagnosis. However, the signals measured from rotating machinery usually are nonstationary and nonlinear and contain noise. The useful fault features are hidden in the heavy background noise. In this paper, a novel fault diagnosis method for rotating machinery based on multiwavelet adaptive threshold denoising and mutation particle swarm optimization (MPSO is proposed. Geronimo, Hardin, and Massopust (GHM multiwavelet is employed for extracting weak fault features under background noise, and the method of adaptively selecting appropriate threshold for multiwavelet with energy ratio of multiwavelet coefficient is presented. The six nondimensional symptom parameters (SPs in the frequency domain are defined to reflect the features of the vibration signals measured in each state. Detection index (DI using statistical theory has been also defined to evaluate the sensitiveness of SP for condition diagnosis. MPSO algorithm with adaptive inertia weight adjustment and particle mutation is proposed for condition identification. MPSO algorithm effectively solves local optimum and premature convergence problems of conventional particle swarm optimization (PSO algorithm. It can provide a more accurate estimate on fault diagnosis. Practical examples of fault diagnosis for rolling element bearings are given to verify the effectiveness of the proposed method.
Yang Xinglin; Wang Huacen; Chen Nan; Dai Wenhua; Li Jin
High current linear induction accelerator (LIA) is a complicated experimental physics device. It is difficult to evaluate and predict its performance. this paper presents a method which combines wavelet packet transform and radial basis function (RBF) neural network to build fault diagnosis and performance evaluation in order to improve reliability of high current LIA. The signal characteristics vectors which are extracted based on energy parameters of wavelet packet transform can well present the temporal and steady features of pulsed power signal, and reduce data dimensions effectively. The fault diagnosis system for accelerating cell and the trend classification system for the beam current based on RBF networks can perform fault diagnosis and evaluation, and provide predictive information for precise maintenance of high current LIA. (authors)
The aim of this thesis is the design of a faults diagnosis-aiding system in a nuclear facility of the Cea. Actually the existing system allows the optimization of the production processes in regular operating conditions. Meanwhile during accidental events, the alarms, managed by threshold, are bringing no relevant information. To increase the reliability and the safety, the human operator needs a faults diagnosis-aiding system. The developed system, SECAPI, combines problem solving techniques and automatic learning techniques, that allow the diagnosis and the the simulation of various faults happening on nuclear facilities. Its reasoning principle uses case-based and rules-based techniques. SECAPI owns a learning module which reads out knowledge connected with faults. It can then simulate various faults, using the inductive logical computing. SECAPI has been applied on a radioactive tritium treatment operating channel, at the Cea with good results. (A.L.B.)
Full Text Available Rolling bearing plays an important role in rotating machinery and its working condition directly affects the equipment efficiency. While dozens of methods have been proposed for real-time bearing fault diagnosis and monitoring, the fault classification accuracy of existing algorithms is still not satisfactory. This work presents a novel algorithm fusion model based on principal component analysis and Dempster-Shafer evidence theory for rolling bearing fault diagnosis. It combines the advantages of the learning vector quantization (LVQ neural network model and the decision tree model. Experiments under three different spinning bearing speeds and two different crack sizes show that our fusion model has better performance and higher accuracy than either of the base classification models for rolling bearing fault diagnosis, which is achieved via synergic prediction from both types of models.
Bear, Robert A.
The ability to diagnose and manage acid-base disorders rapidly and effectively is essential to the care of critically ill patients. This article presents an approach to the diagnosis of pure and mixed acid-base disorders, metabolic or respiratory. The approach taken is based on using the law of mass-action equation as it applies to the bicarbonate buffer system (Henderson equation), using sub-classifications for diagnostic purposes of causes of metabolic acidosis and metabolic alkalosis, and ...
Cummings, F O; Rizzo, S A
The objective of this study was to compare hospitalisation duration, survival times, adverse events and cost of therapy in dogs with presumptive primary immune-mediated thrombocytopenia undergoing therapy with mycophenolate mofetil and corticosteroids versus cyclosporine and corticosteroids. A retrospective study of medical case records of dogs with presumed primary immune-mediated thrombocytopenia was conducted. Data collected included signalment, presenting complaints, haematologic and biochemical profiles, vector-borne disease testing, thoracic and abdominal radiographs, abdominal ultrasound, medications administered, duration of hospitalisation, 30- and 60-day survival, adverse events and cost of therapy. Variables were compared between dogs treated solely with mycophenolate mofetil and corticosteroids or cyclosporine and corticosteroids. A total of 55 dogs with primary immune-mediated thrombocytopenia were identified. Eighteen were excluded because multiple immunosuppressive medications were used during treatment. Hospitalisation times, 30-day survival and 60-day survival times were similar between both groups. Dogs in the mycophenolate mofetil/corticosteroid group experienced fewer adverse events than the cyclosporine/corticosteroid group. Therapy with mycophenolate mofetil was less expensive than that with cyclosporine. These results suggest that using the combination of mycophenolate mofetil and corticosteroids appears to be as effective as cyclosporine and corticosteroids in the treatment of presumed primary immune-mediated thrombocytopenia in dogs. Adverse events were less common and cost of therapy was lower in the mycophenolate mofetil group. Additional larger prospective, controlled, double-masked, outcome-based, multi-institutional studies are required to substantiate these preliminary findings. © 2017 British Small Animal Veterinary Association.
Full Text Available Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for
Dominique C. Leibbrandt
Full Text Available Background: Anterior knee pain (AKP or patellofemoral pain syndrome is common and may limit an individual’s ability to perform common activities of daily living such as stair climbing and prolonged sitting. The diagnosis is difficult as there are multiple definitions for this disorder and there are no accepted criteria for diagnosis. It is therefore most commonly a diagnosis that is made once other pathologies have been excluded. Objectives: The aim of this study was to create an evidence-based checklist for researchers and clinicians to use for the diagnosis of AKP. Methods: A systematic review was conducted in July 2016, and an evidence-based checklist was created based on the subjective and objective findings most commonly used to diagnose AKP. For the subjective factors, two or more of the systematic reviews needed to identify the factor as being important in the diagnosis of AKP. Results: Two systematic reviews, consisting of nine different diagnostic studies, were identified by our search methods. Diagnosis of AKP is based on the area of pain, age, duration of symptoms, common aggravating factors, manual palpation and exclusion of other pathologies. Of the functional tests, squatting demonstrated the highest sensitivity. Other useful tests include pain during stair climbing and prolonged sitting. The cluster of two out of three positive tests for squatting, isometric quadriceps contraction and palpation of the patella borders and the patella tilt test were also recommended as useful tests to include in the clinical assessment. Conclusion: A diagnostic checklist is useful as it provides a structured method for diagnosing AKP in a clinical setting. Research is needed to establish the causes of AKP as it is difficult to diagnose a condition with unknown aetiology.
Lu, Chen; Wang, Yang; Ragulskis, Minvydas; Cheng, Yujie
Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for rotating machinery.
BAVA Amadeo Javier
Full Text Available We have compared the searching of the presence of "honeycomb" structures by direct microscopy on wet mount preparations with the direct immunofluorescence (DIF for the diagnosis of Pneumocystis carinii pneumonia (PCP in 115 bronchoalveolar (BAL fluids. The samples belonged to 115 AIDS patients; 87 with presumptive diagnosis of PCP and 28 with presumptive diagnosis other than PCP. The obtained results were coincident in 114 out of 115 studied samples (27 were positive and 87 negative with both techniques. A higher percentage of positive results (32.18% among patients with presumptive diagnosis of PCP with respect to those with presumptive diagnosis other than PCP (3.57% was observed. One BAL fluid was positive only with DIF, showed scarce and isolated P. carinii elements and absence of typical "honeycomb" structures. The searching for "honeycomb" structures by direct microscopy on wet mount preparations could be considered as a cheap and rapid alternative for diagnosis of PCP when other techniques are not available or as screening test for DIF. This method showed a sensitivity close to DIF when it was applied to BAL fluids of AIDS patients with poor clinical condition and it was performed by an experienced microscopist.
patient' serum for salmonella antibodies is a rapid tool in the diagnosis of enteric fever, but can afford an indirect ... which various dilutions of patient's serum are mixed with drops of either O or H-antigen of Salm. Typhi .... biochemical characterization and sero-typing are essential for complete identification of salmonella.
Huang, Chenn-Jung; Liu, Ming-Chou; Chu, San-Shine; Cheng, Chih-Lun
This work proposes an intelligent learning diagnosis system that supports a Web-based thematic learning model, which aims to cultivate learners' ability of knowledge integration by giving the learners the opportunities to select the learning topics that they are interested, and gain knowledge on the specific topics by surfing on the Internet to…
Gholami, Mehdi; Schiøler, Henrik; Bak, Thomas
An active fault diagnosis approach for different kinds of faults is proposed. The input of the approach is designed off-line based on sensitivity analysis such that the maximum sensitivity for each individual system parameter is obtained. Using maximum sensitivity, results in a better precision...
Wilhelm, M.; Veltman, J.A.; Olshen, A.B.; Jain, A.N.; Moore, D.H.; Presti Jr, J.C.; Kovacs, G.; Waldman, F.M.
Array-based comparative genomic hybridization (CGH) uses multiple genomic clones arrayed on a slide to detect relative copy number of tumor DNA sequences. Application of array CGH to tumor specimens makes genetic diagnosis of cancers possible and may help to differentiate relevant subsets of tumors,
Murata, Yutaka; Wada, Mikio; Kawashima, Atsushi; Kagawa, Keizo
A 37-year-old woman presented with fever and rigor after experiencing respiratory symptoms the previous week that had resolved within a few days. On presentation, her neck was swollen along the right sternocleidomastoid muscle, and chest CT showed pulmonary septic embolisms. Lemierre's syndrome was strongly suspected based on the patient's medical history and physical findings. Further examination revealed venous thrombus, and Fusobacterium necrophorum was later isolated from blood cultures. Antibiotics for anaerobes were administered before a final diagnosis was made, and the patient's symptoms thereafter improved. A rapid diagnosis is essential, since Lemierre's syndrome can be fatal with a diagnostic delay.
Full Text Available Single-Stage Extreme Learning Machine (SS-ELM is presented to dispose of the mechanical fault diagnosis in this paper. Based on it, the traditional mapping type of extreme learning machine (ELM has been changed and the eigenvectors extracted from signal processing methods are directly regarded as outputs of the network’s hidden layer. Then the uncertainty that training data transformed from the input space to the ELM feature space with the ELM mapping and problem of the selection of the hidden nodes are avoided effectively. The experiment results of diesel engine fault diagnosis show good performance of the SS-ELM algorithm.
Pei, Di; Yue, Jianhai; Jiao, Jing
This paper presents a fault diagnosis method for rolling bearing based on information fusion. Acceleration sensors are arranged at different position to get bearing vibration data as diagnostic evidence. The Dempster-Shafer (D-S) evidence theory is used to fuse multi-sensor data to improve diagnostic accuracy. The efficiency of the proposed method is demonstrated by the high speed train transmission test bench. The results of experiment show that the proposed method in this paper improves the rolling bearing fault diagnosis accuracy compared with traditional signal analysis methods.
Full Text Available The fault detection and diagnosis information in process industry can be received, anytime and anywhere, based on bigdata and WeChat with mobile phone, which got rid of constraints that can only check Distributed Control System (DCS in the central control room or look over in office. Then, fault detection, diagnosis information sharing can be provided, and what’s more, fault detection alarm range, code and inform time can be personalized. The pressure of managers who worked on process industry can be release with the mobile information system.
Bosworth, Edward L., Jr.
The focus of this research is the investigation of data structures and associated search algorithms for automated fault diagnosis of complex systems such as the Hubble Space Telescope. Such data structures and algorithms will form the basis of a more sophisticated Knowledge Based Fault Diagnosis System. As a part of the research, several prototypes were written in VAXLISP and implemented on one of the VAX-11/780's at the Marshall Space Flight Center. This report describes and gives the rationale for both the data structures and algorithms selected. A brief discussion of a user interface is also included.
Wang, M; Hu, N Q; Qin, G J
In order to extract decision rules for fault diagnosis from incomplete historical test records for knowledge-based damage assessment of helicopter power train structure. A method that can directly extract the optimal generalized decision rules from incomplete information based on GrC was proposed. Based on semantic analysis of unknown attribute value, the granule was extended to handle incomplete information. Maximum characteristic granule (MCG) was defined based on characteristic relation, and MCG was used to construct the resolution function matrix. The optimal general decision rule was introduced, with the basic equivalent forms of propositional logic, the rules were extracted and reduction from incomplete information table. Combined with a fault diagnosis example of power train, the application approach of the method was present, and the validity of this method in knowledge acquisition was proved.
The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems. A part of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault diagnosis schemes as well as the importance of robustness. The book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. The book is also devoted to advanced schemes of description of neural model uncertainty. In particular, the methods of computation of neural networks uncertainty with robust parameter estimation are presented. Moreover, a novel approach for system identification with the state-space GMDH neural network is delivered. All the concepts described in this book are illustrated by both simple academic illustrative examples and practica...
Yin, Hong; Yang, Shuqiang; Zhu, Xiaoqian; Jin, Songchang; Wang, Xiang
The satellite fault diagnosis has an important role in enhancing the safety, reliability, and availability of the satellite system. However, the problem of enormous parameters and multiple faults makes a challenge to the satellite fault diagnosis. The interactions between parameters and misclassifications from multiple faults will increase the false alarm rate and the false negative rate. On the other hand, for each satellite fault, there is not enough fault data for training. To most of the classification algorithms, it will degrade the performance of model. In this paper, we proposed an improving SVM based on a hybrid voting mechanism (HVM-SVM) to deal with the problem of enormous parameters, multiple faults, and small samples. Many experimental results show that the accuracy of fault diagnosis using HVM-SVM is improved.
Long, Qi; Guo, Shuyong; Li, Qing; Sun, Yong; Li, Yi; Fan, Youping
To overcome those disadvantages that BP (Back Propagation) neural network and conventional Particle Swarm Optimization (PSO) converge at the global best particle repeatedly in early stage and is easy trapped in local optima and with low diagnosis accuracy when being applied in converter transformer fault diagnosis, we come up with the improved PSO-BP neural network to improve the accuracy rate. This algorithm improves the inertia weight Equation by using the attenuation strategy based on concave function to avoid the premature convergence of PSO algorithm and Time-Varying Acceleration Coefficient (TVAC) strategy was adopted to balance the local search and global search ability. At last the simulation results prove that the proposed approach has a better ability in optimizing BP neural network in terms of network output error, global searching performance and diagnosis accuracy.
Yang, Shuqiang; Zhu, Xiaoqian; Jin, Songchang; Wang, Xiang
The satellite fault diagnosis has an important role in enhancing the safety, reliability, and availability of the satellite system. However, the problem of enormous parameters and multiple faults makes a challenge to the satellite fault diagnosis. The interactions between parameters and misclassifications from multiple faults will increase the false alarm rate and the false negative rate. On the other hand, for each satellite fault, there is not enough fault data for training. To most of the classification algorithms, it will degrade the performance of model. In this paper, we proposed an improving SVM based on a hybrid voting mechanism (HVM-SVM) to deal with the problem of enormous parameters, multiple faults, and small samples. Many experimental results show that the accuracy of fault diagnosis using HVM-SVM is improved. PMID:25215324
Lo, Chun; Lynch, Jerome P.; Liu, Mingyan
Wireless sensors operating in harsh environments have the potential to be error-prone. This paper presents a distributive model-based diagnosis algorithm that identifies nonlinear sensor faults. The diagnosis algorithm has advantages over existing fault diagnosis methods such as centralized model-based and distributive model-free methods. An algorithm is presented for detecting common non-linearity faults without using reference sensors. The study introduces a model-based fault diagnosis framework that is implemented within a pair of wireless sensors. The detection of sensor nonlinearities is shown to be equivalent to solving the largest empty rectangle (LER) problem, given a set of features extracted from an analysis of sensor outputs. A low-complexity algorithm that gives an approximate solution to the LER problem is proposed for embedment in resource constrained wireless sensors. By solving the LER problem, sensors corrupted by non-linearity faults can be isolated and identified. Extensive analysis evaluates the performance of the proposed algorithm through simulation.
Full Text Available Aiming at the problem of online fault diagnosis for compensating capacitors of jointless track circuit, a dynamic time warping (DTW based diagnosis method is proposed in this paper. Different from the existing related works, this method only uses the ground indoor monitoring signals of track circuit to locate the faulty compensating capacitor, not depending on the shunt current of inspection train, which is an indispensable condition for existing methods. So, it can be used for online diagnosis of compensating capacitor, which has not yet been realized by existing methods. To overcome the key problem that track circuit cannot obtain the precise position of the train, the DTW method is used for the first time in this situation to recover the function relationship between receiver’s peak voltage and shunt position. The necessity, thinking, and procedure of the method are described in detail. Besides the classical DTW based method, two improved methods for improving classification quality and reducing computation complexity are proposed. Finally, the diagnosis experiments based on the simulation model of track circuit show the effectiveness of the proposed methods.
Kim, Yong Hee; Kim, Myung-Joon; Shin, Hyun Joo; Yoon, Haesung; Han, Seok Joo; Koh, Hong; Roh, Yun Ho; Lee, Mi-Jung
To evaluate MRI findings and to generate a decision tree model for diagnosis of biliary atresia (BA) in infants with jaundice. We retrospectively reviewed features of MRI and ultrasonography (US) performed in infants with jaundice between January 2009 and June 2016 under approval of the institutional review board, including the maximum diameter of periportal signal change on MRI (MR triangular cord thickness, MR-TCT) or US (US-TCT), visibility of common bile duct (CBD) and abnormality of gallbladder (GB). Hepatic subcapsular flow was reviewed on Doppler US. We performed conditional inference tree analysis using MRI findings to generate a decision tree model. A total of 208 infants were included, 112 in the BA group and 96 in the non-BA group. Mean age at the time of MRI was 58.7 ± 36.6 days. Visibility of CBD, abnormality of GB and MR-TCT were good discriminators for the diagnosis of BA and the MRI-based decision tree using these findings with MR-TCT cut-off 5.1 mm showed 97.3 % sensitivity, 94.8 % specificity and 96.2 % accuracy. MRI-based decision tree model reliably differentiates BA in infants with jaundice. MRI can be an objective imaging modality for the diagnosis of BA. • MRI-based decision tree model reliably differentiates biliary atresia in neonatal cholestasis. • Common bile duct, gallbladder and periportal signal changes are the discriminators. • MRI has comparable performance to ultrasonography for diagnosis of biliary atresia.
Liu, Zengkai; Liu, Yonghong; Shan, Hongkai; Cai, Baoping; Huang, Qing
This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information.
Hu, Baiqing; Wang, Boxiong; Li, An; Zhang, Mingzhao; Qin, Fangjun; Pan, Hua
With regard to the complex structure of the inertial navigation system and the low rate of fault detection with BITE (built-in test equipment), a fault diagnosis system for INS based on virtual technologies (virtual instrument and virtual equipment) is proposed in this paper. The hardware of the system is a PXI computer with highly stable performance and strong extensibility. In addition to the basic functions of digital multimeter, oscilloscope and cymometer, it can also measure the attitude of the ship in real-time, connect and control the measurement instruments with digital interface. The software is designed with the languages of Measurement Studio for VB, JAVA, and CULT3D. Using the extensively applied fault-tree reasoning and fault cases makes fault diagnosis. To suit the system to the diagnosis for various navigation electronic equipments, the modular design concept is adopted for the software programming. Knowledge of the expert system is digitally processed and the parameters of the system's interface and the expert diagnosis knowledge are stored in the database. The application shows that system is stable in operation, easy to use, quick and accurate in fault diagnosis.
Liu, Yong-Kuo, E-mail: LYK08@126.com [Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University, Harbin 150001 (China); Wu, Guo-Hua [Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University, Harbin 150001 (China); Institute of Nuclear Energy Technology, Tsinghua University, Beijing 100084 (China); Xie, Chun-Li [Traffic College, Northeast Forestry University, Harbin, 150040 (China); Duan, Zhi-Yong; Peng, Min-Jun; Li, Meng-Kun [Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University, Harbin 150001 (China)
Highlights: • “Rules matrix” is proposed for FDD. • “State matrix” is proposed to solve SDG online inference. • SDG inference and search method are combined for FDD. - Abstract: In order to solve SDG online fault diagnosis and inference, matrix diagnosis and inference methods are proposed for fault detection and diagnosis (FDD). Firstly, “rules matrix” based on SDG model is used for FDD. Secondly, “status matrix” is proposed to achieve SDG online inference. According to different diagnosis results, “status matrix” is applied for the depth-first search and the breadth-first search respectively to find the propagation paths of each fault. Finally, the SDG model of the secondary-loop system in pressurized water reactor (PWR) is built to verify the effectiveness of the proposed method. The simulation experiment results indicate that the “status matrix” used for online inference can be used to find the fault propagation paths and to explain the causes for fault. Therefore, it can be concluded that the proposed method is one of the fault diagnosis for nuclear power plants (NPPs), which can be used to facilitate the development of fault diagnostic system.
Jiang, Guo-Qian; Xie, Ping; Wang, Xiao; Chen, Meng; He, Qun
The performance of traditional vibration based fault diagnosis methods greatly depends on those handcrafted features extracted using signal processing algorithms, which require significant amounts of domain knowledge and human labor, and do not generalize well to new diagnosis domains. Recently, unsupervised representation learning provides an alternative promising solution to feature extraction in traditional fault diagnosis due to its superior learning ability from unlabeled data. Given that vibration signals usually contain multiple temporal structures, this paper proposes a multiscale representation learning (MSRL) framework to learn useful features directly from raw vibration signals, with the aim to capture rich and complementary fault pattern information at different scales. In our proposed approach, a coarse-grained procedure is first employed to obtain multiple scale signals from an original vibration signal. Then, sparse filtering, a newly developed unsupervised learning algorithm, is applied to automatically learn useful features from each scale signal, respectively, and then the learned features at each scale to be concatenated one by one to obtain multiscale representations. Finally, the multiscale representations are fed into a supervised classifier to achieve diagnosis results. Our proposed approach is evaluated using two different case studies: motor bearing and wind turbine gearbox fault diagnosis. Experimental results show that the proposed MSRL approach can take full advantages of the availability of unlabeled data to learn discriminative features and achieved better performance with higher accuracy and stability compared to the traditional approaches.
Full Text Available Roller bearing plays a significant role in industrial sectors. To improve the ability of roller bearing fault diagnosis under multi-rotating situation, this paper proposes a novel roller bearing fault characteristic: the Amplitude Modulation (AM based correntropy extracted from the Intrinsic Mode Functions (IMFs, which are decomposed by Fast Ensemble Empirical mode decomposition (FEEMD and employ Least Square Support Vector Machine (LSSVM to implement intelligent fault identification. Firstly, the roller bearing vibration acceleration signal is decomposed by FEEMD to extract IMFs. Secondly, IMF correntropy matrix (IMFCM as the fault feature matrix is calculated from the AM-correntropy model of the primary vibration signal and IMFs. Furthermore, depending on LSSVM, the fault identification results of the roller bearing are obtained. Through the bearing identification experiments in stationary rotating conditions, it was verified that IMFCM generates more stable and higher diagnosis accuracy than conventional fault features such as energy moment, fuzzy entropy, and spectral kurtosis. Additionally, it proves that IMFCM has more diagnosis robustness than conventional fault features under cross-mixed roller bearing operating conditions. The diagnosis accuracy was more than 84% for the cross-mixed operating condition, which is much higher than the traditional features. In conclusion, it was proven that FEEMD-IMFCM-LSSVM is a reliable technology for roller bearing fault diagnosis under the constant or multi-positioned operating conditions, and as such, it possesses potential prospects for a broad application of uses.
Liu, Yong-Kuo; Wu, Guo-Hua; Xie, Chun-Li; Duan, Zhi-Yong; Peng, Min-Jun; Li, Meng-Kun
Highlights: • “Rules matrix” is proposed for FDD. • “State matrix” is proposed to solve SDG online inference. • SDG inference and search method are combined for FDD. - Abstract: In order to solve SDG online fault diagnosis and inference, matrix diagnosis and inference methods are proposed for fault detection and diagnosis (FDD). Firstly, “rules matrix” based on SDG model is used for FDD. Secondly, “status matrix” is proposed to achieve SDG online inference. According to different diagnosis results, “status matrix” is applied for the depth-first search and the breadth-first search respectively to find the propagation paths of each fault. Finally, the SDG model of the secondary-loop system in pressurized water reactor (PWR) is built to verify the effectiveness of the proposed method. The simulation experiment results indicate that the “status matrix” used for online inference can be used to find the fault propagation paths and to explain the causes for fault. Therefore, it can be concluded that the proposed method is one of the fault diagnosis for nuclear power plants (NPPs), which can be used to facilitate the development of fault diagnostic system.
Busbait, Monther I.
We study the depth of decision trees for diagnosis of three types of constant faults in read-once contact networks over finite bases containing only indecomposable networks. For each basis and each type of faults, we obtain a linear upper bound on the minimum depth of decision trees depending on the number of edges in networks. For bases containing networks with at most 10 edges, we find sharp coefficients for linear bounds.
Busbait, Monther I.
We study the depth of decision trees for diagnosis of constant 0 and 1 faults in read-once contact networks over finite bases containing only indecomposable networks. For each basis, we obtain a linear upper bound on the minimum depth of decision trees depending on the number of edges in the networks. For bases containing networks with at most 10 edges we find coefficients for linear bounds which are close to sharp. © 2014 Elsevier B.V. All rights reserved.
Kwak, Nakwon; Han, Sung Koo; Yim, Jae-Joon [Seoul National University College of Medicine, Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, 101 Daehak-Ro, Jongno-Gu, Seoul (Korea, Republic of); Lee, Chang Hyun; Lee, Hyun-Ju [Seoul National University College of Medicine, Department of Radiology, and Institute of Radiation Medicine, Seoul (Korea, Republic of); Kang, Young Ae [Yonsei University College of Medicine, Division of Pulmonology, Department of Internal Medicine, Severance Hospital, Institute of Chest Diseases, Seoul (Korea, Republic of); Lee, Jae Ho [Seoul National University Bundang Hospital, Department of Internal Medicine, Seongnam, Gyeonggi-do (Korea, Republic of)
To elucidate the accuracy and inter-observer agreement of non-tuberculous mycobacterial lung disease (NTM-LD) diagnosis based on chest CT findings. Two chest radiologists and two pulmonologists interpreted chest CTs of 66 patients with NTM-LD, 33 with pulmonary tuberculosis and 33 with non-cystic fibrosis bronchiectasis. These observers selected one of these diagnoses for each case without knowing any clinical information except age and sex. Sensitivity and specificity were calculated according to degree of observer confidence. Inter-observer agreement was assessed using Fleiss' κ values. Multiple logistic regression was performed to elucidate which radiological features led to the correct diagnosis. The sensitivity of NTM-LD diagnosis was 56.4 % (95 % CI 47.9-64.7) and specificity 80.3 % (73.1-86.0). The specificity of NTM-LD diagnosis increased with confidence: 44.4 % (20.5-71.3) for possible, 77.4 % (67.4-85.0) for probable, 95.2 % (87.2-98.2) for definite (P < 0.001) diagnoses. Inter-observer agreement for NTM-LD diagnosis was moderate (κ = 0.453). Tree-in-bud pattern (adjusted odds ratio [aOR] 6.24, P < 0.001), consolidation (aOR 1.92, P = 0.036) and atelectasis (aOR 3.73, P < 0.001) were associated with correct NTM-LD diagnoses, whereas presence of pleural effusion (aOR 0.05, P < 0.001) led to false diagnoses. NTM-LD diagnosis based on chest CT findings is specific but not sensitive. (orig.)
Antunes, Viviane Baptista; Meirelles, Gustavo de Souza Portes; Jasinowodolinski, Dany; Verrastro, Carlos Gustavo Yuji; Torlai, Fabiola Goda
Objective: to determine the interobserver and intraobserver agreement in the diagnosis of interstitial lung diseases (ILDs) based on HRCT scans and the impact of observer expertise, clinical data and confidence level on such agreement. Methods: two thoracic radiologists and two general radiologists independently reviewed the HRCT images of 58 patients with ILDs on two distinct occasions: prior to and after the clinical anamnesis. The radiologists selected up to three diagnostic hypotheses for each patient and defined the confidence level for these hypotheses. One of the thoracic and one of the general radiologists re-evaluated the same images up to three months after the first readings. In the coefficient analyses, the kappa statistic was used. Results: the thoracic and general radiologists, respectively, agreed on at least one diagnosis for each patient in 91.4% and 82.8% of the patients. The thoracic radiologists agreed on the most likely diagnosis in 48.3% (κ = 0.42) and 62.1% (κ = 0.58) of the cases, respectively, prior to and after the clinical anamnesis; likewise, the general radiologists agreed on the most likely diagnosis in 37.9% (κ 0.32) and 36.2% (κ = 0.30) of the cases. For the thoracic radiologist, the intraobserver agreement on the most likely diagnosis was 0.73 and 0.63 prior to and after the clinical anamnesis, respectively. That for the general radiologist was 0.38 and 0.42.The thoracic radiologists presented almost perfect agreement for the diagnostic hypotheses defined with the high confidence level. Conclusions: Interobserver and intraobserver agreement in the diagnosis of ILDs based on HRCT scans ranged from fair to almost perfect and was influenced by radiologist expertise, clinical history and confidence level. (author)
Li, Chaoshun; Zhou, Jianzhong
Supervised learning method, like support vector machine (SVM), has been widely applied in diagnosing known faults, however this kind of method fails to work correctly when new or unknown fault occurs. Traditional unsupervised kernel clustering can be used for unknown fault diagnosis, but it could not make use of the historical classification information to improve diagnosis accuracy. In this paper, a semi-supervised kernel clustering model is designed to diagnose known and unknown faults. At first, a novel semi-supervised weighted kernel clustering algorithm based on gravitational search (SWKC-GS) is proposed for clustering of dataset composed of labeled and unlabeled fault samples. The clustering model of SWKC-GS is defined based on wrong classification rate of labeled samples and fuzzy clustering index on the whole dataset. Gravitational search algorithm (GSA) is used to solve the clustering model, while centers of clusters, feature weights and parameter of kernel function are selected as optimization variables. And then, new fault samples are identified and diagnosed by calculating the weighted kernel distance between them and the fault cluster centers. If the fault samples are unknown, they will be added in historical dataset and the SWKC-GS is used to partition the mixed dataset and update the clustering results for diagnosing new fault. In experiments, the proposed method has been applied in fault diagnosis for rotatory bearing, while SWKC-GS has been compared not only with traditional clustering methods, but also with SVM and neural network, for known fault diagnosis. In addition, the proposed method has also been applied in unknown fault diagnosis. The results have shown effectiveness of the proposed method in achieving expected diagnosis accuracy for both known and unknown faults of rotatory bearing. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.
Hurwitz Eric L
Full Text Available Abstract Background Spinal pain is a common and often disabling problem. The research on various treatments for spinal pain has, for the most part, suggested that while several interventions have demonstrated mild to moderate short-term benefit, no single treatment has a major impact on either pain or disability. There is great need for more accurate diagnosis in patients with spinal pain. In a previous paper, the theoretical model of a diagnosis-based clinical decision rule was presented. The approach is designed to provide the clinician with a strategy for arriving at a specific working diagnosis from which treatment decisions can be made. It is based on three questions of diagnosis. In the current paper, the literature on the reliability and validity of the assessment procedures that are included in the diagnosis-based clinical decision rule is presented. Methods The databases of Medline, Cinahl, Embase and MANTIS were searched for studies that evaluated the reliability and validity of clinic-based diagnostic procedures for patients with spinal pain that have relevance for questions 2 (which investigates characteristics of the pain source and 3 (which investigates perpetuating factors of the pain experience. In addition, the reference list of identified papers and authors' libraries were searched. Results A total of 1769 articles were retrieved, of which 138 were deemed relevant. Fifty-one studies related to reliability and 76 related to validity. One study evaluated both reliability and validity. Conclusion Regarding some aspects of the DBCDR, there are a number of studies that allow the clinician to have a reasonable degree of confidence in his or her findings. This is particularly true for centralization signs, neurodynamic signs and psychological perpetuating factors. There are other aspects of the DBCDR in which a lesser degree of confidence is warranted, and in which further research is needed.
Mandzuka, Mensur; Begic, Edin; Boskovic, Dusanka; Begic, Zijo; Masic, Izet
This paper presents mobile application implementing a decision support system for acid-base disorder diagnosis and treatment recommendation. The application was developed using the official integrated development environment for the Android platform (to maximize availability and minimize investments in specialized hardware) called Android Studio. The application identifies disorder, based on the blood gas analysis, evaluates whether the disorder has been compensated, and based on additional input related to electrolyte imbalance, provides recommendations for treatment. The application is a tool in the hands of the user, which provides assistance during acid-base disorders treatment. The application will assist the physician in clinical practice and is focused on the treatment in intensive care.
Lu, Chunhong; Zhu, Zhaomin; Gu, Xiaofeng
In this paper, we develop a novel feature selection algorithm based on the genetic algorithm (GA) using a specifically devised trace-based separability criterion. According to the scores of class separability and variable separability, this criterion measures the significance of feature subset, independent of any specific classification. In addition, a mutual information matrix between variables is used as features for classification, and no prior knowledge about the cardinality of feature subset is required. Experiments are performed by using a standard lung cancer dataset. The obtained solutions are verified with three different classifiers, including the support vector machine (SVM), the back-propagation neural network (BPNN), and the K-nearest neighbor (KNN), and compared with those obtained by the whole feature set, the F-score and the correlation-based feature selection methods. The comparison results show that the proposed intelligent system has a good diagnosis performance and can be used as a promising tool for lung cancer diagnosis.
Pravas Ranjan Sahoo
Full Text Available Surface plasmon resonance (SPR based biosensors are the most advanced and developed optical label-free biosensor technique used for powerful detection with vast applications in environmental protection, biotechnology, medical diagnostics, drug screening, food safety, and security as well in livestock sector. The livestock sector which contributes the largest economy of India, harbors many bacterial, viral, and fungal diseases impacting a great loss to the production and productive potential which is a major concern in both small and large ruminants. Hence, an accurate, sensitive, and rapid diagnosis is required for prevention of these above-mentioned diseases. SPR based biosensor assay may fulfill the above characteristics which lead to a greater platform for rapid diagnosis of different livestock diseases. Hence, this review may give a detail idea about the principle, recent development of SPR based biosensor techniques and its application in livestock sector.
Djemal, Ridha; AlSharabi, Khalil; Ibrahim, Sutrisno; Alsuwailem, Abdullah
Autism spectrum disorder (ASD) is a type of neurodevelopmental disorder with core impairments in the social relationships, communication, imagination, or flexibility of thought and restricted repertoire of activity and interest. In this work, a new computer aided diagnosis (CAD) of autism based on electroencephalography (EEG) signal analysis is investigated. The proposed method is based on discrete wavelet transform (DWT), entropy (En), and artificial neural network (ANN). DWT is used to decompose EEG signals into approximation and details coefficients to obtain EEG subbands. The feature vector is constructed by computing Shannon entropy values from each EEG subband. ANN classifies the corresponding EEG signal into normal or autistic based on the extracted features. The experimental results show the effectiveness of the proposed method for assisting autism diagnosis. A receiver operating characteristic (ROC) curve metric is used to quantify the performance of the proposed method. The proposed method obtained promising results tested using real dataset provided by King Abdulaziz Hospital, Jeddah, Saudi Arabia.
Antoni, Jérôme; Griffaton, Julien; André, Hugo; Avendaño-Valencia, Luis David; Bonnardot, Frédéric; Cardona-Morales, Oscar; Castellanos-Dominguez, German; Daga, Alessandro Paolo; Leclère, Quentin; Vicuña, Cristián Molina; Acuña, David Quezada; Ompusunggu, Agusmian Partogi; Sierra-Alonso, Edgar F.
This paper presents the content and outcomes of the Safran contest organized during the International Conference Surveillance 8, October 20-21, 2015, at the Roanne Institute of Technology, France. The contest dealt with the diagnosis of a civil aircraft engine based on vibration data measured in a transient operating mode and provided by Safran. Based on two independent exercises, the contest offered the possibility to benchmark current diagnostic methods on real data supplemented with several challenges. Outcomes of seven competing teams are reported and discussed. The object of the paper is twofold. It first aims at giving a picture of the current state-of-the-art in vibration-based diagnosis of rolling-element bearings in nonstationary operating conditions. Second, it aims at providing the scientific community with a benchmark and some baseline solutions. In this respect, the data used in the contest are made available as supplementary material.
Full Text Available This study helps to provide a second eye to the expert radiologists for the classification of manually extracted breast masses taken from 60 digital mammıgrams. These mammograms have been acquired from Istanbul University Faculty of Medicine Hospital and have 78 masses. The diagnosis is implemented with pre-processing by using feature extraction based Fast Wavelet Transform (FWT. Afterwards Adaptive Neuro-Fuzzy Inference System (ANFIS based fuzzy subtractive clustering and Support Vector Machines (SVM methods are used for the classification. It is a comparative study which uses these methods respectively. According to the results of the study, ANFIS based subtractive clustering produces ??% while SVM produces ??% accuracy in malignant-benign classification. The results demonstrate that the developed system could help the radiologists for a true diagnosis and decrease the number of the missing cancerous regions or unnecessary biopsies.
Venson, José Eduardo; Albiero Berni, Jean Carlo; Edmilson da Silva Maia, Carlos; Marques da Silva, Ana Maria; Cordeiro d'Ornellas, Marcos; Maciel, Anderson
In radiology diagnosis, medical images are most often visualized slice by slice. At the same time, the visualization based on 3D volumetric rendering of the data is considered useful and has increased its field of application. In this work, we present a case-based study with 16 medical specialists to assess the diagnostic effectiveness of a Virtual Reality interface in fracture identification over 3D volumetric reconstructions. We developed a VR volume viewer compatible with both the Oculus Rift and handheld-based head mounted displays (HMDs). We then performed user experiments to validate the approach in a diagnosis environment. In addition, we assessed the subjects' perception of the 3D reconstruction quality, ease of interaction and ergonomics, and also the users opinion on how VR applications can be useful in healthcare. Among other results, we have found a high level of effectiveness of the VR interface in identifying superficial fractures on head CTs.
Ben Rabah, N.; Saddem, R.; Ben Hmida, F.; Carre-Menetrier, V.; Tagina, M.
Diagnosis of Automated Production System (APS) is a decision-making process designed to detect, locate and identify a particular failure caused by the control law. In the literature, there are three major types of reasoning for industrial diagnosis: the first is model-based, the second is rule-based and the third is case-based. The common and major limitation of the first and the second reasonings is that they do not have automated learning ability. This paper presents an interactive and effective Case Based Decision Support System for online Diagnosis (CB-DSSD) of an APS. It offers a synergy between the Case Based Reasoning (CBR) and the Decision Support System (DSS) in order to support and assist Human Operator of Supervision (HOS) in his/her decision process. Indeed, the experimental evaluation performed on an Interactive Training System for PLC (ITS PLC) that allows the control of a Programmable Logic Controller (PLC), simulating sensors or/and actuators failures and validating the control algorithm through a real time interactive experience, showed the efficiency of our approach.
Full Text Available Nowadays the demand of power supply reliability has been strongly increased as the development within power industry grows rapidly. Nevertheless such large demand requires substantial power grid to sustain. Therefore power equipment’s running and testing data which contains vast information underpins online monitoring and fault diagnosis to finally achieve state maintenance. In this paper, an intelligent fault diagnosis model for power equipment based on case-based reasoning (IFDCBR will be proposed. The model intends to discover the potential rules of equipment fault by data mining. The intelligent model constructs a condition case base of equipment by analyzing the following four categories of data: online recording data, history data, basic test data, and environmental data. SVM regression analysis was also applied in mining the case base so as to further establish the equipment condition fingerprint. The running data of equipment can be diagnosed by such condition fingerprint to detect whether there is a fault or not. Finally, this paper verifies the intelligent model and three-ratio method based on a set of practical data. The resulting research demonstrates that this intelligent model is more effective and accurate in fault diagnosis.
Ben Rabah, N; Saddem, R; Carre-Menetrier, V; Ben Hmida, F; Tagina, M
Diagnosis of Automated Production System (APS) is a decision-making process designed to detect, locate and identify a particular failure caused by the control law. In the literature, there are three major types of reasoning for industrial diagnosis: the first is model-based, the second is rule-based and the third is case-based. The common and major limitation of the first and the second reasonings is that they do not have automated learning ability. This paper presents an interactive and effective Case Based Decision Support System for online Diagnosis (CB-DSSD) of an APS. It offers a synergy between the Case Based Reasoning (CBR) and the Decision Support System (DSS) in order to support and assist Human Operator of Supervision (HOS) in his/her decision process. Indeed, the experimental evaluation performed on an Interactive Training System for PLC (ITS PLC) that allows the control of a Programmable Logic Controller (PLC), simulating sensors or/and actuators failures and validating the control algorithm through a real time interactive experience, showed the efficiency of our approach. (paper)
Particle accelerators are generators that produce beams of charged particles, acquiring different energies, depending on the accelerator type. The MGC-20 cyclotron is a cyclic particle accelerator used for accelerating protons, deuterons, alpha particles, and helium-3 to different energies. Its applications include isotope production, nuclear reaction, and mass spectroscopy studies. It is a complicated machine, it consists of five main parts, the ion source, the deflector, the beam transport system, the concentric and harmonic coils, and the radio frequency system. The diagnosis of this device is a very complex task. it depends on the conditions of 27 indicators of the control panel of the device. The accurate diagnosis can lead to a high system reliability and save maintenance costs. so an expert system for the cyclotron fault diagnosis is necessary to be built. In this thesis , a hybrid expert system was developed for the fault diagnosis of the MGC-20 cyclotron. Two intelligent techniques, multilayer feed forward back propagation neural network and the rule based expert system, are integrated as a pre-processor loosely coupled model to build the proposed hybrid expert system. The architecture of the developed hybrid expert system consists of two levels. The first level is two feed forward back propagation neural networks, used for isolating the faulty part of the cyclotron. The second level is the rule based expert system, used for troubleshooting the faults inside the isolated faulty part. 4-6 tabs., 4-5 figs., 36 refs
Full Text Available Central nervous system (CNS tuberculosis, particularly tuberculous meningitis (TBM, is the severest form of Mycobacterium tuberculosis (M.Tb infection, causing death or severe neurological defects in more than half of those affected, in spite of recent advancements in available anti-tuberculosis treatment. The definitive diagnosis of CNS tuberculosis depends upon the detection of M.Tb bacilli in the cerebrospinal fluid (CSF. At present, the diagnosis of CNS tuberculosis remains a complex issue because the most widely used conventional “gold standard” based on bacteriological detection methods, such as direct smear and culture identification, cannot rapidly detect M.Tb in CSF specimens with sufficient sensitivity in the acute phase of TBM. Recently, instead of the conventional “gold standard”, the various molecular-based methods including nucleic acid amplification (NAA assay technique, particularly polymerase chain reaction (PCR assay, has emerged as a promising new method for the diagnosis of CNS tuberculosis because of its rapidity, sensitivity and specificity. In addition, the innovation of nested PCR assay technique is worthy of note given its contribution to improve the diagnosis of CNS tuberculosis. In this review, an overview of recent progress of the NAA methods, mainly highlighting the PCR assay technique, was presented.
Liu, Mingxia; Zhang, Jun; Adeli, Ehsan; Shen, Dinggang
In conventional Magnetic Resonance (MR) image based methods, two stages are often involved to capture brain structural information for disease diagnosis, i.e., 1) manually partitioning each MR image into a number of regions-of-interest (ROIs), and 2) extracting pre-defined features from each ROI for diagnosis with a certain classifier. However, these pre-defined features often limit the performance of the diagnosis, due to challenges in 1) defining the ROIs and 2) extracting effective disease-related features. In this paper, we propose a landmark-based deep multi-instance learning (LDMIL) framework for brain disease diagnosis. Specifically, we first adopt a data-driven learning approach to discover disease-related anatomical landmarks in the brain MR images, along with their nearby image patches. Then, our LDMIL framework learns an end-to-end MR image classifier for capturing both the local structural information conveyed by image patches located by landmarks and the global structural information derived from all detected landmarks. We have evaluated our proposed framework on 1526 subjects from three public datasets (i.e., ADNI-1, ADNI-2, and MIRIAD), and the experimental results show that our framework can achieve superior performance over state-of-the-art approaches. Copyright © 2017 Elsevier B.V. All rights reserved.
Full Text Available Alertness to dengue and making a timely diagnosis is extremely important in the treatment of dengue and containment of dengue epidemics. We evaluated the complementary role of clinical-practice-based laboratory data in facilitating suspicion/diagnosis of dengue. One hundred overall dengue (57 dengue fever [DF] and 43 dengue hemorrhagic fever [DHF] cases and another 100 nondengue cases (78 viral infections other than dengue, 6 bacterial sepsis, and 16 miscellaneous diseases were analyzed. We separately compared individual laboratory variables (platelet count [PC] , prothrombin time [PT], activated partial thromboplastin time [APTT], alanine aminotransferase [ALT], and aspartate aminotransferase [AST] and varied combined variables of DF and/or DHF cases with the corresponding ones of nondengue cases. The sensitivity, specificity, accuracy, positive predictive value (PPV, and negative predictive value (NPV in the diagnosis of DF and/or DHF were measured based on these laboratory variables. While trade-off between sensitivity and specificity, and/or suboptimal PPV/NPV was found at measurements using these variables, prolonged APTT + normal PT + PC < 100 × 109 cells/L had a favorable sensitivity, specificity, PPV, and NPV in diagnosis of DF and/or DHF. In conclusion, these data suggested that prolonged APTT + normal PT + PC < 100 × 109 cells/L is useful in evaluating the likelihood of DF and/or DHF.
Wang, Tianzhen; Qi, Jie; Xu, Hao; Wang, Yide; Liu, Lei; Gao, Diju
Thanks to reduced switch stress, high quality of load wave, easy packaging and good extensibility, the cascaded H-bridge multilevel inverter is widely used in wind power system. To guarantee stable operation of system, a new fault diagnosis method, based on Fast Fourier Transform (FFT), Relative Principle Component Analysis (RPCA) and Support Vector Machine (SVM), is proposed for H-bridge multilevel inverter. To avoid the influence of load variation on fault diagnosis, the output voltages of the inverter is chosen as the fault characteristic signals. To shorten the time of diagnosis and improve the diagnostic accuracy, the main features of the fault characteristic signals are extracted by FFT. To further reduce the training time of SVM, the feature vector is reduced based on RPCA that can get a lower dimensional feature space. The fault classifier is constructed via SVM. An experimental prototype of the inverter is built to test the proposed method. Compared to other fault diagnosis methods, the experimental results demonstrate the high accuracy and efficiency of the proposed method. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.
Jabez Christopher, J; Khanna Nehemiah, H; Kannan, A
Allergic Rhinitis is a universal common disease, especially in populated cities and urban areas. Diagnosis and treatment of Allergic Rhinitis will improve the quality of life of allergic patients. Though skin tests remain the gold standard test for diagnosis of allergic disorders, clinical experts are required for accurate interpretation of test outcomes. This work presents a clinical decision support system (CDSS) to assist junior clinicians in the diagnosis of Allergic Rhinitis. Intradermal Skin tests were performed on patients who had plausible allergic symptoms. Based on patient׳s history, 40 clinically relevant allergens were tested. 872 patients who had allergic symptoms were considered for this study. The rule based classification approach and the clinical test results were used to develop and validate the CDSS. Clinical relevance of the CDSS was compared with the Score for Allergic Rhinitis (SFAR). Tests were conducted for junior clinicians to assess their diagnostic capability in the absence of an expert. The class based Association rule generation approach provides a concise set of rules that is further validated by clinical experts. The interpretations of the experts are considered as the gold standard. The CDSS diagnoses the presence or absence of rhinitis with an accuracy of 88.31%. The allergy specialist and the junior clinicians prefer the rule based approach for its comprehendible knowledge model. The Clinical Decision Support Systems with rule based classification approach assists junior doctors and clinicians in the diagnosis of Allergic Rhinitis to make reliable decisions based on the reports of intradermal skin tests. Copyright © 2015 Elsevier Ltd. All rights reserved.
... 20 Employees' Benefits 2 2010-04-01 2010-04-01 false Death due to pneumoconiosis, including... COAL MINE HEALTH AND SAFETY ACT OF 1969, TITLE IV-BLACK LUNG BENEFITS (1969- ) Total Disability or Death Due to Pneumoconiosis § 410.450 Death due to pneumoconiosis, including statutory presumption...
... FEDERAL COAL MINE HEALTH AND SAFETY ACT OF 1969, TITLE IV-BLACK LUNG BENEFITS (1969- ) Total Disability or Death Due to Pneumoconiosis § 410.418 Irrebuttable presumption of total disability due to pneumoconiosis... miner was totally disabled due to pneumoconiosis at the time of his death, if he is suffering or...
Examines the conflict between individualism and communitarianism (and its implications for rhetorical theory) by examining the historical debate between liberals and conservatives during and after the French Revolution. Shows how Richard Whately's notions of "presumption" and "burden of proof" (further developed by Mill and…
The administration of the criminal justice system tries to strike a balance between the search for truth and the fairness of the process. To this end, the law should protect individual rights and impose various legal burdens on the state. One such tool is the principle of the presumption of innocence until proven guilty. This is a ...
Galetta, Antonella; de Hert, Paul; Wright, D.; Kreissl, R.
This contribution focuses on the impact of surveillance on the rule of law, due process and the presumption of innocence, key values and principles of a democratic order. It illustrates how they are implemented and enforced in contemporary surveillance societies, while referring to European law and
Given advances in the science of fertility preservation and the link between fertility choices and wellbeing, it is time to reframe our ethical thinking around fertility preservation procedures for children and young people with cancer. The current framing of fertility preservation as a possible offer may no longer be universally appropriate. There is an increasingly pressing need to discuss the ethics of failing to preserve fertility, particularly for patient groups for whom established techniques exist. I argue that the starting point for deliberating about a particular patient should be a rebuttable presumption that fertility preservation ought to be attempted. Consideration of the harms applicable to that specific patient may then override this presumption. I outline the benefits of attempting fertility preservation; these justify a presumption in favour of the treatment. I then discuss the potential harms associated with fertility preservation procedures, which may justify failing to attempt fertility preservation in an individual patient's particular case. Moving from a framework of offer to one of rebuttable presumption in favour of fertility preservation would have significant implications for medical practice, healthcare organizations and the state. © 2015 John Wiley & Sons Ltd.
...; Presumptive Eligibility for Psychosis and Other Mental Illness AGENCY: Department of Veterans Affairs. ACTION... psychosis within specified time periods and for Persian Gulf War veterans who developed a mental illness other than psychosis within two years after service and within two years after the end of the Persian...
...; Presumptive Eligibility for Psychosis and Other Mental Illness AGENCY: Department of Veterans Affairs. ACTION... care eligibility for veterans of certain wars and conflicts who developed psychosis within specified time periods and for Persian Gulf War veterans who developed a mental illness other than psychosis...
... during the Vietnam War. After careful review of the IOM report, the Secretary determines that the... served in deep-water naval vessels off the coast of Vietnam during the Vietnam War are referred to as... DEPARTMENT OF VETERANS AFFAIRS Presumption of Exposure to Herbicides for Blue Water Navy Vietnam...
... 47 Telecommunication 3 2010-10-01 2010-10-01 false Presumption of acceptability for deployment of an advanced services loop technology. 51.230 Section 51.230 Telecommunication FEDERAL COMMUNICATIONS... following circumstances, where the technology: (1) Complies with existing industry standards; or (2) Is...
Pellegrini-Masini, Alessandra; Bentz, Amy I; Johns, Imogen C; Parsons, Corrina S; Beech, Jill; Whitlock, Robert H; Flaminio, M Julia B F
Three adult horses were evaluated for signs of musculoskeletal pain, dullness, ataxia, and seizures. A diagnosis of bacterial meningitis was made on the basis of results of CSF analysis. Because primary bacterial meningitis is so rare in adult horses without any history of generalized sepsis or trauma, immune function testing was pursued. Flow cytometric phenotyping of peripheral blood lymphocytes was performed, and proliferation of peripheral blood lymphocytes in response to concanavalin A, phytohemagglutinin, pokeweed mitogen, and lipopolysaccharide was determined. Serum IgA, IgM, and IgG concentrations were measured by means of radial immunodiffusion, and serum concentrations of IgG isotypes were assessed with a capture antibody ELISA. Serum tetanus antibody concentrations were measured before and 1 month after tetanus toxoid administration. Phagocytosis and oxidative burst activity of isolated peripheral blood phagocytes were evaluated by means of simultaneous flow cytometric analysis. Persistent B-cell lymphopenia, hypogammaglobulinemia, and abnormal in vitro responses to mitogens were detected in all 3 horses, and a diagnosis of common variable immunodeficiency was made.
Full Text Available At present, the fault signals of surface to air missile equipment are hard to collect and the accuracy of fault diagnosis is very low. To solve the above problems, based on the superiority of wavelet transformation on processing non-stationary signals and the advantage of SVM on pattern classification, this paper proposes a fault diagnosis model and takes the typical analog circuit diagnosis of one power distribution system as an example to verify the fault diagnosis model based on Wavelet Transformation and SVM. The simulation results show that the model is able to achieve fault diagnosis based on a small amount of training samples, which improves the accuracy of fault diagnosis.
Murphy Donald R
Full Text Available Abstract Background Neck pain (NP is a common cause of disability. Accurate and efficacious methods of diagnosis and treatment have been elusive. A diagnosis-based clinical decision guide (DBCDG; previously referred to as a diagnosis-based clinical decision rule has been proposed which attempts to provide the clinician with a systematic, evidence-based guide in applying the biopsychosocial model of care. The approach is based on three questions of diagnosis. The purpose of this study is to present the prevalence of findings using the DBCDG in consecutive patients with NP. Methods Demographic, diagnostic and baseline outcome measure data were gathered on a cohort of NP patients examined by one of three examiners trained in the application of the DBCDG. Results Data were gathered on 95 patients. Signs of visceral disease or potentially serious illness were found in 1%. Centralization signs were found in 27%, segmental pain provocation signs were found in 69% and radicular signs were found in 19%. Clinically relevant myofascial signs were found in 22%. Dynamic instability was found in 40%, oculomotor dysfunction in 11.6%, fear beliefs in 31.6%, central pain hypersensitivity in 4%, passive coping in 5% and depression in 2%. Conclusion The DBCDG can be applied in a busy private practice environment. Further studies are needed to investigate clinically relevant means to identify central pain hypersensitivity, oculomotor dysfunction, poor coping and depression, correlations and patterns among the diagnostic components of the DBCDG as well as inter-examiner reliability, validity and efficacy of treatment based on the DBCDG.
Murphy Donald R
Full Text Available Abstract Background Low back pain (LBP is common and costly. Development of accurate and efficacious methods of diagnosis and treatment has been identified as a research priority. A diagnosis-based clinical decision guide (DBCDG; previously referred to as a diagnosis-based clinical decision rule has been proposed which attempts to provide the clinician with a systematic, evidence-based means to apply the biopsychosocial model of care. The approach is based on three questions of diagnosis. The purpose of this study is to present the prevalence of findings using the DBCDG in consecutive patients with LBP. Methods Demographic, diagnostic and baseline outcome measure data were gathered on a cohort of LBP patients examined by one of three examiners trained in the application of the DBCDG. Results Data were gathered on 264 patients. Signs of visceral disease or potentially serious illness were found in 2.7%. Centralization signs were found in 41%, lumbar and sacroiliac segmental signs in 23% and 27%, respectively and radicular signs were found in 24%. Clinically relevant myofascial signs were diagnosed in 10%. Dynamic instability was diagnosed in 63%, fear beliefs in 40%, central pain hypersensitivity in 5%, passive coping in 3% and depression in 3%. Conclusion The DBCDG can be applied in a busy private practice environment. Further studies are needed to investigate clinically relevant means to identify central pain hypersensitivity, poor coping and depression, correlations and patterns among the diagnostic components of the DBCDG as well as inter-examiner reliability and efficacy of treatment based on the DBCDG.
Background Neck pain (NP) is a common cause of disability. Accurate and efficacious methods of diagnosis and treatment have been elusive. A diagnosis-based clinical decision guide (DBCDG; previously referred to as a diagnosis-based clinical decision rule) has been proposed which attempts to provide the clinician with a systematic, evidence-based guide in applying the biopsychosocial model of care. The approach is based on three questions of diagnosis. The purpose of this study is to present the prevalence of findings using the DBCDG in consecutive patients with NP. Methods Demographic, diagnostic and baseline outcome measure data were gathered on a cohort of NP patients examined by one of three examiners trained in the application of the DBCDG. Results Data were gathered on 95 patients. Signs of visceral disease or potentially serious illness were found in 1%. Centralization signs were found in 27%, segmental pain provocation signs were found in 69% and radicular signs were found in 19%. Clinically relevant myofascial signs were found in 22%. Dynamic instability was found in 40%, oculomotor dysfunction in 11.6%, fear beliefs in 31.6%, central pain hypersensitivity in 4%, passive coping in 5% and depression in 2%. Conclusion The DBCDG can be applied in a busy private practice environment. Further studies are needed to investigate clinically relevant means to identify central pain hypersensitivity, oculomotor dysfunction, poor coping and depression, correlations and patterns among the diagnostic components of the DBCDG as well as inter-examiner reliability, validity and efficacy of treatment based on the DBCDG. PMID:21871119
Han, Zhongyi; Wei, Benzheng; Leung, Stephanie; Nachum, Ilanit Ben; Laidley, David; Li, Shuo
Pathogenesis-based diagnosis is a key step to prevent and control lumbar neural foraminal stenosis (LNFS). It conducts both early diagnosis and comprehensive assessment by drawing crucial pathological links between pathogenic factors and LNFS. Automated pathogenesis-based diagnosis would simultaneously localize and grade multiple spinal organs (neural foramina, vertebrae, intervertebral discs) to diagnose LNFS and discover pathogenic factors. The automated way facilitates planning optimal therapeutic schedules and relieving clinicians from laborious workloads. However, no successful work has been achieved yet due to its extreme challenges since 1) multiple targets: each lumbar spine has at least 17 target organs, 2) multiple scales: each type of target organ has structural complexity and various scales across subjects, and 3) multiple tasks, i.e., simultaneous localization and diagnosis of all lumbar organs, are extremely difficult than individual tasks. To address these huge challenges, we propose a deep multiscale multitask learning network (DMML-Net) integrating a multiscale multi-output learning and a multitask regression learning into a fully convolutional network. 1) DMML-Net merges semantic representations to reinforce the salience of numerous target organs. 2) DMML-Net extends multiscale convolutional layers as multiple output layers to boost the scale-invariance for various organs. 3) DMML-Net joins a multitask regression module and a multitask loss module to prompt the mutual benefit between tasks. Extensive experimental results demonstrate that DMML-Net achieves high performance (0.845 mean average precision) on T1/T2-weighted MRI scans from 200 subjects. This endows our method an efficient tool for clinical LNFS diagnosis.
Round, A R; Wilkinson, S J; Hall, C J; Rogers, K D; Glatter, O; Wess, T; Ellis, I O
Breast tissue collected from tumour samples and normal tissue from bi-lateral mastectomy procedures were examined using small angle x-ray scattering. Previous work has indicated that breast tissue disease diagnosis could be performed using small angle x-ray scattering (SAXS) from a synchrotron radiation source. The technique would be more useful to health services if it could be made to work using a conventional x-ray source. Consistent and reliable differences in x-ray scatter distributions were observed between samples from normal and tumour tissue samples using the laboratory based 'SAXSess' system. Albeit from a small number of samples, a sensitivity of 100% was obtained. This result encourages us to pursue the implementation of SAXS as a laboratory based diagnosis technique
Round, A. R.; Wilkinson, S. J.; Hall, C. J.; Rogers, K. D.; Glatter, O.; Wess, T.; Ellis, I. O.
Breast tissue collected from tumour samples and normal tissue from bi-lateral mastectomy procedures were examined using small angle x-ray scattering. Previous work has indicated that breast tissue disease diagnosis could be performed using small angle x-ray scattering (SAXS) from a synchrotron radiation source. The technique would be more useful to health services if it could be made to work using a conventional x-ray source. Consistent and reliable differences in x-ray scatter distributions were observed between samples from normal and tumour tissue samples using the laboratory based 'SAXSess' system. Albeit from a small number of samples, a sensitivity of 100% was obtained. This result encourages us to pursue the implementation of SAXS as a laboratory based diagnosis technique.
Round, A R [Daresbury Laboratories, Warrington, WA4 4AD (United Kingdom); Wilkinson, S J [Daresbury Laboratories, Warrington, WA4 4AD (United Kingdom); Hall, C J [Daresbury Laboratories, Warrington, WA4 4AD (United Kingdom); Rogers, K D [Department of Materials and Medical Sciences, Cranfield University, Swindon, SN6 8LA (United Kingdom); Glatter, O [Department of Chemistry, University of Graz (Austria); Wess, T [School of Optometry and Vision Sciences, Cardiff University, Cardiff CF10 3NB, Wales (United Kingdom); Ellis, I O [Nottingham City Hospital, Nottingham (United Kingdom)
Breast tissue collected from tumour samples and normal tissue from bi-lateral mastectomy procedures were examined using small angle x-ray scattering. Previous work has indicated that breast tissue disease diagnosis could be performed using small angle x-ray scattering (SAXS) from a synchrotron radiation source. The technique would be more useful to health services if it could be made to work using a conventional x-ray source. Consistent and reliable differences in x-ray scatter distributions were observed between samples from normal and tumour tissue samples using the laboratory based 'SAXSess' system. Albeit from a small number of samples, a sensitivity of 100% was obtained. This result encourages us to pursue the implementation of SAXS as a laboratory based diagnosis technique.
Zamanian, Amir Hosein [Southern Methodist University, Dallas (United States); Ohadi, Abdolreza [Amirkabir University of Technology (Tehran Polytechnic), Tehran (Iran, Islamic Republic of)
Low-dimensional relevant feature sets are ideal to avoid extra data mining for classification. The current work investigates the feasibility of utilizing energies of vibration signals in optimal frequency bands as features for machine fault diagnosis application. Energies in different frequency bands were derived based on Parseval's theorem. The optimal feature sets were extracted by optimization of the related frequency bands using genetic algorithm and a Modified distance function (MDF). The frequency bands and the number of bands were optimized based on the MDF. The MDF is designed to a) maximize the distance between centers of classes, b) minimize the dispersion of features in each class separately, and c) minimize dimension of extracted feature sets. The experimental signals in two different gearboxes were used to demonstrate the efficiency of the presented technique. The results show the effectiveness of the presented technique in gear fault diagnosis application.
Garbe, Claus; Peris, Ketty; Hauschild, Axel
and Treatment of Cancer was formed to make recommendations on CM diagnosis and treatment, based on systematic literature reviews and the experts' experience. Diagnosis is made clinically using dermoscopy and staging is based upon the AJCC system. CMs are excised with 1-2 cm safety margins. Sentinel lymph node...... dissection is routinely offered as a staging procedure in patients with tumours >1 mm in thickness, although there is as yet no clear survival benefit for this approach. Interferon-α treatment may be offered to patients with stage II and III melanoma as an adjuvant therapy, as this treatment increases....... For first-line treatment particularly in BRAF wild-type patients, immunotherapy with PD-1 antibodies alone or in combination with CTLA-4 antibodies should be considered. BRAF inhibitors like dabrafenib and vemurafenib in combination with the MEK inhibitors trametinib and cobimetinib for BRAF mutated...
Full Text Available Aiming at fault diagnosis for tilting-pad journal bearing with fluid support developed recently, a new method based on singular value decomposition (SVD and local mean decomposition (LMD is proposed. First, the phase space reconstruction of Hankel matrix and SVD method are used as pre-filter process unit to reduce the random noises in the original signal. Then the purified signal is decomposed by LMD into a series of production functions (PFs. Based on PFs, time frequency map and marginal spectrum can be obtained for fault diagnosis. Finally, this method is applied to numerical simulation and practical experiment data. The results show that the proposed method can effectively detect fault features of tilting-pad journal bearing.
Bron, Esther E.; Smits, Marion; van der Flier, Wiesje M.
Abstract Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform...... on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare......, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans...
Collmann, Jeff R.; Lin, Jyh-Shyan; Freedman, Matthew T.; Wu, Chris Y.; Hayes, Wendelin S.; Mun, Seong K.
A design-based approach to ethical analysis examines how computer scientists, physicians and patients make and justify choices in designing, using and reacting to computer-aided diagnosis (CADx) systems. The basic hypothesis of this research is that values are embedded in CADx systems during all phases of their development, not just retrospectively imposed on them. This paper concentrates on the work of computer scientists and physicians as they attempt to resolve central technical questions in designing clinically functional CADx systems for lung cancer and breast cancer diagnosis. The work of Lo, Chan, Freedman, Lin, Wu and their colleagues provides the initial data on which this study is based. As these researchers seek to increase the rate of true positive classifications of detected abnormalities in chest radiographs and mammograms, they explore dimensions of the fundamental ethical principal of beneficence. The training of CADx systems demonstrates the key ethical dilemmas inherent in their current design.
Full Text Available The objective of the study was to isolate potential Bacillus cereus sensu lato (B. cereus s.l. from a range of farm environments. Samples of tap water, milking equipment rinse water, milk sediment filter, grass, soil and bulk tank milk were collected from 63 farms. In addition, milk liners were swabbed at the start and the end of milking, and swabs were taken from cows’ teats prior to milking. The samples were plated on mannitol egg yolk polymyxin agar (MYP and presumptive B. cereus s.l. colonies were isolated and stored in nutrient broth with 20% glycerol and frozen at -80 °C. These isolates were then plated on chromogenic medium (BACARA and colonies identified as presumptive B. cereus s.l. on this medium were subjected to 16S ribosomal RNA (rRNA sequencing. Of the 507 isolates presumed to be B. cereus s.l. on the basis of growth on MYP, only 177 showed growth typical of B. cereus s.l. on BACARA agar. The use of 16S rRNA sequencing to identify isolates that grew on BACARA confirmed that the majority of isolates belonged to B. cereus s.l. A total of 81 of the 98 isolates sequenced were tentatively identified as presumptive B. cereus s.l. Pulsed-field gel electrophoresis was carried out on milk and soil isolates from seven farms that were identified as having presumptive B. cereus s.l. No pulsotype was shared by isolates from soil and milk on the same farm. Presumptive B. cereus s.l. was widely distributed within the dairy farm environment.
Full Text Available Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary machines. It can reduce economical losses by eliminating unexpected downtime in industry due to failure of rotary machines. Though widely investigated in the past couple of decades, continued advancement is still desirable to improve upon existing fault diagnosis techniques. Vibration acceleration signals collected from machine bearings exhibit nonstationary behavior due to variable working conditions and multiple fault severities. In the current work, a two-layered bearing fault diagnosis scheme is proposed for the identification of fault pattern and crack size for a given fault type. A hybrid feature pool is used in combination with sparse stacked autoencoder (SAE-based deep neural networks (DNNs to perform effective diagnosis of bearing faults of multiple severities. The hybrid feature pool can extract more discriminating information from the raw vibration signals, to overcome the nonstationary behavior of the signals caused by multiple crack sizes. More discriminating information helps the subsequent classifier to effectively classify data into the respective classes. The results indicate that the proposed scheme provides satisfactory performance in diagnosing bearing defects of multiple severities. Moreover, the results also demonstrate that the proposed model outperforms other state-of-the-art algorithms, i.e., support vector machines (SVMs and backpropagation neural networks (BPNNs.
Full Text Available Polyvinyl chloride (PVC polymerizing production process is a typical complex controlled object, with complexity features, such as nonlinear, multivariable, strong coupling, and large time-delay. Aiming at the real-time fault diagnosis and optimized monitoring requirements of the large-scale key polymerization equipment of PVC production process, a real-time fault diagnosis strategy is proposed based on rough sets theory with the improved discernibility matrix and BP neural networks. The improved discernibility matrix is adopted to reduct the attributes of rough sets in order to decrease the input dimensionality of fault characteristics effectively. Levenberg-Marquardt BP neural network is trained to diagnose the polymerize faults according to the reducted decision table, which realizes the nonlinear mapping from fault symptom set to polymerize fault set. Simulation experiments are carried out combining with the industry history datum to show the effectiveness of the proposed rough set neural networks fault diagnosis method. The proposed strategy greatly increased the accuracy rate and efficiency of the polymerization fault diagnosis system.
Full Text Available In the rolling bearing fault diagnosis, the vibration signal of single sensor is usually nonstationary and noisy, which contains very little useful information, and impacts the accuracy of fault diagnosis. In order to solve the problem, this paper presents a novel fault diagnosis method using multivibration signals and deep belief network (DBN. By utilizing the DBN’s learning ability, the proposed method can adaptively fuse multifeature data and identify various bearing faults. Firstly, multiple vibration signals are acquainted from various fault bearings. Secondly, some time-domain characteristics are extracted from original signals of each individual sensor. Finally, the features data of all sensors are put into the DBN and generate an appropriate classifier to complete fault diagnosis. In order to demonstrate the effectiveness of multivibration signals, experiments are carried out on the individual sensor with the same conditions and procedure. At the same time, the method is compared with SVM, KNN, and BPNN methods. The results show that the DBN-based method is able to not only adaptively fuse multisensor data, but also obtain higher identification accuracy than other methods.
Jing, Ya-Bing; Liu, Chang-Wen; Bi, Feng-Rong; Bi, Xiao-Yang; Wang, Xia; Shao, Kang
Numerous vibration-based techniques are rarely used in diesel engines fault diagnosis in a direct way, due to the surface vibration signals of diesel engines with the complex non-stationary and nonlinear time-varying features. To investigate the fault diagnosis of diesel engines, fractal correlation dimension, wavelet energy and entropy as features reflecting the diesel engine fault fractal and energy characteristics are extracted from the decomposed signals through analyzing vibration acceleration signals derived from the cylinder head in seven different states of valve train. An intelligent fault detector FastICA-SVM is applied for diesel engine fault diagnosis and classification. The results demonstrate that FastICA-SVM achieves higher classification accuracy and makes better generalization performance in small samples recognition. Besides, the fractal correlation dimension and wavelet energy and entropy as the special features of diesel engine vibration signal are considered as input vectors of classifier FastICA-SVM and could produce the excellent classification results. The proposed methodology improves the accuracy of feature extraction and the fault diagnosis of diesel engines.
Full Text Available The reliability of battery fault diagnosis depends on an accurate estimation of the state of charge and battery characterizing parameters. This paper presents a fault diagnosis method based on an adaptive unscented Kalman filter to diagnose the parameter bias faults for a Li-ion battery in real time. The first-order equivalent circuit model and relationship between the open circuit voltage and state of charge are established to describe the characteristics of the Li-ion battery. The parameters in the equivalent circuit model are treated as system state variables to set up a joint state and parameter space equation. The algorithm for fault diagnosis is designed according to the estimated parameters. Two types of fault of the Li-ion battery, including internal ohmic resistance fault and diffusion resistance faults, are studied as a case to validate the effectiveness of the algorithm. The experimental results show that the proposed approach in this paper has effective tracking ability, better estimation accuracy, and reliable diagnosis for Li-ion batteries.
Sohaib, Muhammad; Kim, Cheol-Hong; Kim, Jong-Myon
Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary machines. It can reduce economical losses by eliminating unexpected downtime in industry due to failure of rotary machines. Though widely investigated in the past couple of decades, continued advancement is still desirable to improve upon existing fault diagnosis techniques. Vibration acceleration signals collected from machine bearings exhibit nonstationary behavior due to variable working conditions and multiple fault severities. In the current work, a two-layered bearing fault diagnosis scheme is proposed for the identification of fault pattern and crack size for a given fault type. A hybrid feature pool is used in combination with sparse stacked autoencoder (SAE)-based deep neural networks (DNNs) to perform effective diagnosis of bearing faults of multiple severities. The hybrid feature pool can extract more discriminating information from the raw vibration signals, to overcome the nonstationary behavior of the signals caused by multiple crack sizes. More discriminating information helps the subsequent classifier to effectively classify data into the respective classes. The results indicate that the proposed scheme provides satisfactory performance in diagnosing bearing defects of multiple severities. Moreover, the results also demonstrate that the proposed model outperforms other state-of-the-art algorithms, i.e., support vector machines (SVMs) and backpropagation neural networks (BPNNs).
Momoh, J. A.; Zhang, Z. Z.
A new technique of knowledge-based and integrated monitoring and diagnosis (KBIMD) to deal with abnormalities and incipient or potential failures in autonomous power systems is presented. The KBIMD conception is discussed as a new function of autonomous power system automation. Available diagnostic modelling, system structure, principles and strategies are suggested. In order to verify the feasibility of the KBIMD, a preliminary prototype expert system is designed to simulate the KBIMD function in a main electric network of the autonomous power system.
form of sleep -disordered breathing in SCI is nocturnal hypoventilation (NH) due to respiratory muscle paralysis and/or reduced ventilatory drive...DATES COVERED 2011 – 29 2012 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER W81XWH-11-1-0826 Home-Based Diagnosis and Management of Sleep ...with spinal cord injury (SCI) commonly have sleep -disordered breathing due to obstructive sleep apnea (OSA) and/or nocturnal hypoventilation (NH) due to
Petri net is an important tool for discrete event dynamic systems modeling and analysis. And it has great ability to handle concurrent phenomena and non-deterministic phenomena. Currently Petri nets used in wind turbine fault diagnosis have not participated in the actual system. This article will combine the existing fuzzy Petri net algorithms; build wind turbine control system simulation based on Siemens S7-1200 PLC, while making matlab gui interface for migration of the system to different platforms.
In this article, the diagnosis of a three cell converter is developed. The hybrid nature of the system represented by the presence of continuous and discrete dynamics is taken into account in the control design. The idea is based on using a hybrid control and an observer-type sliding mode to generate residuals from the observation errors of the system. The simulation results are presented at the end to illustrate the performance of the proposed approach. © 2013 FEI STU.
Kolakkadan Mubeen Hasaf
Full Text Available Malaria is one of the most pervasive parasitic diseases ever known to mankind affecting nearly 300 million people every year. The need for rapid diagnosis of malaria in tropical and subtropical malaria endemic areas is on the rise. In this study we evaluated the usefulness of hematology autoanalyzers, Sysmex XE-2100 & XT-2000i in the presumptive diagnosis of malaria. Our study shows that abnormalities in WBC/BASO scattergram when combined with presence of thrombocytopenia had a high sensitivity and positive predictive value in the presumptive diagnosis of malaria.
Full Text Available Hydraulic piston pumps are commonly used in aircraft. In order to improve the viability of aircraft and energy efficiency, intelligent variable pressure pump systems have been used in aircraft hydraulic systems more and more widely. Efficient fault diagnosis plays an important role in improving the reliability and performance of hydraulic systems. In this paper, a fault diagnosis method of an intelligent hydraulic pump system (IHPS based on a nonlinear unknown input observer (NUIO is proposed. Different from factors of a full-order Luenberger-type unknown input observer, nonlinear factors of the IHPS are considered in the NUIO. Firstly, a new type of intelligent pump is presented, the mathematical model of which is established to describe the IHPS. Taking into account the real-time requirements of the IHPS and the special structure of the pump, the mechanism of the intelligent pump and failure modes are analyzed and two typical failure modes are obtained. Furthermore, a NUIO of the IHPS is performed based on the output pressure and swashplate angle signals. With the residual error signals produced by the NUIO, online intelligent pump failure occurring in real-time can be detected. Lastly, through analysis and simulation, it is confirmed that this diagnostic method could accurately diagnose and isolate those typical failure modes of the nonlinear IHPS. The method proposed in this paper is of great significance in improving the reliability of the IHPS. Keywords: Fault diagnosis, Hydraulic piston pump, Model-based, Nonlinear unknown input observer (NUIO, Residual error
Schermer, Tjard R; Robberts, Bas; Crockett, Alan J; Thoonen, Bart P; Lucas, Annelies; Grootens, Joke; Smeele, Ivo J; Thamrin, Cindy; Reddel, Helen K
Clinical guidelines indicate that a chronic obstructive pulmonary disease (COPD) diagnosis is made from a single spirometry test. However, long-term stability of diagnosis based on forced expiratory volume in 1 s over forced vital capacity (FEV 1 /FVC) ratio has not been reported. In primary care subjects at risk for COPD, we investigated shifts in diagnostic category (obstructed/non-obstructed). The data were from symptomatic 40+ years (ex-)smokers referred for diagnostic spirometry, with three spirometry tests, each 12±2 months apart. The obstruction was based on post-bronchodilator FEV 1 /FVC smokers or SABA users at year 1. Change from non-obstructed to obstructed was more likely for males, older subjects, current smokers and patients with lower baseline FEV 1 % predicted, and less likely for those with higher baseline BMI. Up to one-third of symptomatic (ex-)smokers with baseline obstruction on diagnostic spirometry had shifted to non-obstructed when routinely re-tested after 1 or 2 years. Given the implications for patients and health systems of a diagnosis of COPD, it should not be based on a single spirometry test.
Devaney, D.; Livesley, P.; Shaw, D.
With indications for computerised imaging expanding, elastofibroma dorsi will probably be seen more frequently. This report describes an elastofibroma presenting in an 11-year-old girl and its appearance by magnetic resonance imaging. Presumptive diagnosis by magnetic resonance imaging may prevent unnecessary radical surgery. (orig.)
Devaney, D. [Dept. of Histopathology, Hospital for Sick Children, London (United Kingdom); Livesley, P. [Dept. of Orthopaedics, Hospital for Sick Children, London (United Kingdom); Shaw, D. [Dept. of Paediatric Radiology, Hospital for Sick Children, London (United Kingdom)
With indications for computerised imaging expanding, elastofibroma dorsi will probably be seen more frequently. This report describes an elastofibroma presenting in an 11-year-old girl and its appearance by magnetic resonance imaging. Presumptive diagnosis by magnetic resonance imaging may prevent unnecessary radical surgery. (orig.)
Xue, Xiaoming; Zhou, Jianzhong
To make further improvement in the diagnosis accuracy and efficiency, a mixed-domain state features data based hybrid fault diagnosis approach, which systematically blends both the statistical analysis approach and the artificial intelligence technology, is proposed in this work for rolling element bearings. For simplifying the fault diagnosis problems, the execution of the proposed method is divided into three steps, i.e., fault preliminary detection, fault type recognition and fault degree identification. In the first step, a preliminary judgment about the health status of the equipment can be evaluated by the statistical analysis method based on the permutation entropy theory. If fault exists, the following two processes based on the artificial intelligence approach are performed to further recognize the fault type and then identify the fault degree. For the two subsequent steps, mixed-domain state features containing time-domain, frequency-domain and multi-scale features are extracted to represent the fault peculiarity under different working conditions. As a powerful time-frequency analysis method, the fast EEMD method was employed to obtain multi-scale features. Furthermore, due to the information redundancy and the submergence of original feature space, a novel manifold learning method (modified LGPCA) is introduced to realize the low-dimensional representations for high-dimensional feature space. Finally, two cases with 12 working conditions respectively have been employed to evaluate the performance of the proposed method, where vibration signals were measured from an experimental bench of rolling element bearing. The analysis results showed the effectiveness and the superiority of the proposed method of which the diagnosis thought is more suitable for practical application. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.
Lei Wang; Chunmei Xu; Lijun Diao; Jie Chen; Ruichang Qiu; Peizhen Wang
For online open circuit fault diagnosis of the traction converter in rail transit vehicles, conventional approaches depend heavily on component parameters and circuit layouts. For better universality and less parameter sensitivity during the diagnosis, this paper proposes a novel topology analysis approach to diagnose switching device open circuit failures. During the diagnosis, the topology is analyzed with fault reasoning mechanism, which is based on object-oriented Petri net (OOCPN). The O...
In July 2009, the Medical Advisory Secretariat (MAS) began work on Non-Invasive Cardiac Imaging Technologies for the Diagnosis of Coronary Artery Disease (CAD), an evidence-based review of the literature surrounding different cardiac imaging modalities to ensure that appropriate technologies are accessed by patients suspected of having CAD. This project came about when the Health Services Branch at the Ministry of Health and Long-Term Care asked MAS to provide an evidentiary platform on effectiveness and cost-effectiveness of non-invasive cardiac imaging modalities.After an initial review of the strategy and consultation with experts, MAS identified five key non-invasive cardiac imaging technologies for the diagnosis of CAD. Evidence-based analyses have been prepared for each of these five imaging modalities: cardiac magnetic resonance imaging, single photon emission computed tomography, 64-slice computed tomographic angiography, stress echocardiography, and stress echocardiography with contrast. For each technology, an economic analysis was also completed (where appropriate). A summary decision analytic model was then developed to encapsulate the data from each of these reports (available on the OHTAC and MAS website).The Non-Invasive Cardiac Imaging Technologies for the Diagnosis of Coronary Artery Disease series is made up of the following reports, which can be publicly accessed at the MAS website at: www.health.gov.on.ca/mas">www.health.gov.on.ca/mas or at www.health.gov.on.ca/english/providers/program/mas/mas_about.htmlSINGLE PHOTON EMISSION COMPUTED TOMOGRAPHY FOR THE DIAGNOSIS OF CORONARY ARTERY DISEASE: An Evidence-Based AnalysisSTRESS ECHOCARDIOGRAPHY FOR THE DIAGNOSIS OF CORONARY ARTERY DISEASE: An Evidence-Based AnalysisSTRESS ECHOCARDIOGRAPHY WITH CONTRAST FOR THE DIAGNOSIS OF CORONARY ARTERY DISEASE: An Evidence-Based Analysis64-Slice Computed Tomographic Angiography for the Diagnosis of Coronary Artery Disease: An Evidence-Based Analysis
Fatima Zohra Benkaddour
Full Text Available In spunlace nonwovens industry, the maintenance task is very complex, it requires experts and operators collaboration. In this paper, we propose a new approach integrating an agent- based modelling with case-based reasoning that utilizes similarity measures and preferences module. The main purpose of our study is to compare and evaluate the most suitable similarity measure for our case. Furthermore, operators that are usually geographically dispersed, have to collaborate and negotiate to achieve mutual agreements, especially when their proposals (diagnosis lead to a conflicting situation. The experimentation shows that the suggested agent-based approach is very interesting and efficient for operators and experts who collaborate in INOTIS enterprise.
He, Qingbo; Wang, Jun; Hu, Fei; Kong, Fanrang
The diagnosis of train bearing defects plays a significant role to maintain the safety of railway transport. Among various defect detection techniques, acoustic diagnosis is capable of detecting incipient defects of a train bearing as well as being suitable for wayside monitoring. However, the wayside acoustic signal will be corrupted by the Doppler effect and surrounding heavy noise. This paper proposes a solution to overcome these two difficulties in wayside acoustic diagnosis. In the solution, a dynamically resampling method is firstly presented to reduce the Doppler effect, and then an adaptive stochastic resonance (ASR) method is proposed to enhance the defective characteristic frequency automatically by the aid of noise. The resampling method is based on a frequency variation curve extracted from the time-frequency distribution (TFD) of an acoustic signal by dynamically minimizing the local cost functions. For the ASR method, the genetic algorithm is introduced to adaptively select the optimal parameter of the multiscale noise tuning (MST)-based stochastic resonance (SR) method. The proposed wayside acoustic diagnostic scheme combines signal resampling and information enhancement, and thus is expected to be effective in wayside defective bearing detection. The experimental study verifies the effectiveness of the proposed solution.
Full Text Available The gearbox is one of the most important parts of mechanical equipment and plays a significant role in many industrial applications. A fault diagnostic of rotating machinery has attracted attention for its significance in preventing catastrophic accidents and beneficially guaranteeing sufficient maintenance. In recent years, fault diagnosis has developed in the direction of multidisciplinary integration. This work addresses a fault diagnosis method based on an image processing method for a gearbox, which overcomes the limitations of manual feature selection. Differing from the analysis method in a one-dimensional space, the computing method in the field of image processing in a 2-dimensional space is applied to accomplish autoextraction and fault diagnosis of a gearbox. The image-processing-based diagnostic flow consists of the following steps: first, the vibration signal after noise reduction by wavelet denoising and signal demodulation by Hilbert transform is transformed into an image by bispectrum analysis. Then, speeded up robustness feature (SURF is applied to automatically extract the image feature points of the bispectrum contour map, and the feature dimension is reduced by principal component analysis (PCA. Finally, an extreme learning machine (ELM is introduced to identify the fault types of the gearbox. From the experimental results, the proposed method appears to be able to accurately diagnose and identify different types of faults of the gearbox.
Kohigashi, Satoru; Nakamae, Koji; Fujioka, Hiromu
We develop the image based computer assisted diagnosis system for benign paroxysmal positional vertigo (BPPV) that consists of the balance control system simulator, the 3D eye movement simulator, and the extraction method of nystagmus response directly from an eye movement image sequence. In the system, the causes and conditions of BPPV are estimated by searching the database for record matching with the nystagmus response for the observed eye image sequence of the patient with BPPV. The database includes the nystagmus responses for simulated eye movement sequences. The eye movement velocity is obtained by using the balance control system simulator that allows us to simulate BPPV under various conditions such as canalithiasis, cupulolithiasis, number of otoconia, otoconium size, and so on. Then the eye movement image sequence is displayed on the CRT by the 3D eye movement simulator. The nystagmus responses are extracted from the image sequence by the proposed method and are stored in the database. In order to enhance the diagnosis accuracy, the nystagmus response for a newly simulated sequence is matched with that for the observed sequence. From the matched simulation conditions, the causes and conditions of BPPV are estimated. We apply our image based computer assisted diagnosis system to two real eye movement image sequences for patients with BPPV to show its validity.
Full Text Available The present study was designed to estimate the prevalence of Giardia infection in preschool- and school-aged children living in an endemic area. Fecal samples from 573 children were processed by zinc sulfate centrifugal flotation, centrifugal sedimentation (using a commercial device for fecal concentration - TF-Test kit® and polymerase chain reaction (PCR-based methods. Of the stool samples assessed, 277 (48.3% were positive for intestinal parasites and/or commensal protozoa. Centrifugal flotation presented the highest diagnostic sensitivity for Giardia infections. The kappa index revealed that both coproparasitological techniques closely agreed on the Giardia diagnosis (86% versus satisfactory (72% and poor (35% concordances for commensal protozoan and helminth infections, respectively. Concerning Giardia molecular diagnosis, from the 71 microscopy-positive samples, specific amplification of gdh and tpi fragments was noted in 68 (95.7% and 64 (90% samples, respectively. Amplification of gdh and tpi genes was observed, respectively, in 95.7% and 90% of microscopy-positive Giardia samples. For 144 microscopy-negative samples, gdh and tpi gene amplification products were obtained from 8.3% and 35.9% samples, respectively. The agreement between these genes was about 40%. The centrifuge-flotation based method was the most suitable means of Giardia diagnosis assessed in the present study by combining accuracy and low cost.
Full Text Available The battery critical functions such as State-of-Charge (SoC and State-of-Health (SoH estimations, over-current, and over-/under-voltage protections mainly depend on current and voltage sensor measurements. Therefore, it is imperative to develop a reliable sensor fault diagnosis scheme to guarantee the battery performance, safety and life. This paper presents a systematic model-based fault diagnosis scheme for a battery cell to detect current or voltage sensor faults. The battery model is developed based on the equivalent circuit technique. For the diagnostic scheme implementation, the extended Kalman filter (EKF is used to estimate the terminal voltage of battery cell, and the residual carrying fault information is then generated by comparing the measured and estimated voltage. Further, the residual is evaluated by a statistical inference method that determines the presence of a fault. To highlight the importance of battery sensor fault diagnosis, the effects of sensors faults on battery SoC estimation and possible influences are analyzed. Finally, the effectiveness of the proposed diagnostic scheme is experimentally validated, and the results show that the current or voltage sensor fault can be accurately detected.
Full Text Available Dopaminergic degeneration is a pathologic hallmark of Parkinson's disease (PD, which can be assessed by dopamine transporter imaging such as FP-CIT SPECT. Until now, imaging has been routinely interpreted by human though it can show interobserver variability and result in inconsistent diagnosis. In this study, we developed a deep learning-based FP-CIT SPECT interpretation system to refine the imaging diagnosis of Parkinson's disease. This system trained by SPECT images of PD patients and normal controls shows high classification accuracy comparable with the experts' evaluation referring quantification results. Its high accuracy was validated in an independent cohort composed of patients with PD and nonparkinsonian tremor. In addition, we showed that some patients clinically diagnosed as PD who have scans without evidence of dopaminergic deficit (SWEDD, an atypical subgroup of PD, could be reclassified by our automated system. Our results suggested that the deep learning-based model could accurately interpret FP-CIT SPECT and overcome variability of human evaluation. It could help imaging diagnosis of patients with uncertain Parkinsonism and provide objective patient group classification, particularly for SWEDD, in further clinical studies.
Sardi, Hector Eloy Sanchez; Escobet, Teressa; Puig, Vicenc
This paper proposes a model-based fault diagnosis (FD) approach for wind turbines and its application to a realistic wind turbine FD benchmark. The proposed FD approach combines the use of analytical redundancy relations (ARRs) and interval observers. Interval observers consider an unknown...... turbine and noise/parameter uncertainty bounds. Fault isolation is based on considering a set of ARRs obtained from the structural analysis of the wind turbine model and a fault signature matrix that considers the relation of ARRs and faults. The proposed FD approach has been validated on a 5-MW wind...
Ajay M.V. Kumar
Full Text Available For certain subgroups within people living with the human immunodeficiency virus (HIV [active tuberculosis (TB, pregnant women, children <5 years old, and serodiscordant couples], the World Health Organization recommends antiretroviral therapy (ART irrespective of CD4 count. Another subgroup which has received increased attention is “HIV-infected presumptive TB patients without TB”. In this study, we assess the proportion of HIV-infected presumptive TB patients eligible for ART in Karnataka State (population 60 million, India. This was a cross-sectional analysis of data of HIV-infected presumptive TB patients diagnosed in May 2015 abstracted from national TB and HIV program records. Of 42,585 presumptive TB patients, 28,964 (68% were tested for HIV and 2262 (8% were HIV positive. Of the latter, 377 (17% had active TB. Of 1885 “presumptive TB patients without active TB”, 1100 (58% were already receiving ART. Of the remaining 785 who were not receiving ART, 617 (79% were assessed for ART eligibility and of those, 548 (89% were eligible for ART. About 90% of “HIV-infected presumptive TB patients without TB” were eligible for ART. This evidence supports a public health approach of starting all “HIV-infected presumptive TB patients without TB” on ART irrespective of CD4 count in line with global thinking about ‘test and treat’.
Full Text Available One of the concerns people have is how to get the diagnosis online without privacy being jeopardized. In this paper, we propose a privacy-preserving intelligent medical diagnosis system (IMDS, which can efficiently solve the problem. In IMDS, users submit their health examination parameters to the server in a protected form; this submitting process is based on Paillier cryptosystem and will not reveal any information about their data. And then the server retrieves the most likely disease (or multiple diseases from the database and returns it to the users. In the above search process, we use the oblivious keyword search (OKS as a basic framework, which makes the server maintain the computational ability but cannot learn any personal information over the data of users. Besides, this paper also provides a preprocessing method for data stored in the server, to make our protocol more efficient.
Full Text Available Nonlinear characteristics are ubiquitous in the vibration signals produced by rolling element bearings. Fractal dimensions are effective tools to illustrate nonlinearity. This paper proposes a new approach based on Multiscale General Fractal Dimensions (MGFDs to realize fault diagnosis of rolling element bearings, which are robust to the effects of variation in operating conditions. The vibration signals of bearing are analyzed to extract the general fractal dimensions in multiscales, which are in turn utilized to construct a feature space to identify fault pattern. Finally, bearing faults are revealed by pattern recognition. Case studies are carried out to evaluate the validity and accuracy of the approach. It is verified that this approach is effective for fault diagnosis of rolling element bearings under various operating conditions via experiment and data analysis.
Li, Peng; Wen, Bo; Li, Hengheng; Li, Jin
In the event of a grid failure, the network message data (SMV, GOOSE) contains all the information about the primary and secondary devices. The amount of information data is large, and it is difficult for the personnel to make a quick and accurate judgment about the fault type from the massive data, affecting fault handling and safe grid operation. In this paper, modeling is unified based on network message data about the primary equipment. The comprehensive data analysis between multiple substations can be achieved by acquiring alarm information, network message information and wave recording information through network message analysis devices; meanwhile, the longitudinal differential principle is applied to fault diagnosis, which can realize accurate fault diagnosis of power grid, and provide data basis for accident treatment.
Gao, Chen; Zhou, Yuqing; Ren, Yan
This paper focused on the damage diagnosis for NC machine tools and put forward a damage diagnosis method based on hybrid Stationary subspace analysis (SSA), for improving the accuracy and visibility of damage identification. First, the observed single sensor signal was reconstructed to multi-dimensional signals by the phase space reconstruction technique, as the inputs of SSA. SSA method was introduced to separate the reconstructed data into stationary components and non-stationary components without the need for independency and prior information of the origin signals. Subsequently, the selected non-stationary components were analysed for training LS-SVM (Least Squares Support Vector Machine) classifier model, in which several statistic parameters in the time and frequency domains were exacted as the sample of LS-SVM. An empirical analysis in NC milling machine tools is developed, and the result shows high accuracy of the proposed approach.
Full Text Available Autonomous Underwater Vehicles (AUVs generally work in complex marine environments. Any fault in AUVs may cause significant losses. Thus, system reliability and automatic fault diagnosis are important. To address the actuator failure of AUVs, a fault diagnosis method based on the Gaussian particle filter is proposed in this study. Six free-space motion equation mathematical models are established in accordance with the actuator configuration of AUVs. The value of the control (moment loss parameter is adopted on the basis of these models to represent underwater vehicle malfunction, and an actuator failure model is established. An improved Gaussian particle filtering algorithm is proposed and is used to estimate the AUV failure model and motion state. Bayes algorithm is employed to perform robot fault detection. The sliding window method is adopted for fault magnitude estimation. The feasibility and validity of the proposed method are verified through simulation experiments and experimental data.
Full Text Available Fault diagnosis is an important research direction in modern industry. In this paper, a new fault diagnosis method based on multi-sensor data fusion is proposed, in which the Dempster–Shafer (D–S evidence theory is employed to model the uncertainty. Firstly, Gaussian types of fault models and test models are established by observations of sensors. After the models are determined, the intersection area between test model and fault models is transformed into a set of BPAs (basic probability assignments, and a weighted average combination method is used to combine the obtained BPAs. Finally, through some given decision making rules, diagnostic results can be obtained. The proposed method in this paper is tested by the Iris data set and actual measurement data of the motor rotor, which verifies the effectiveness of the proposed method.
Chen, Jin; Wu, Pei; Xu, Kai
Most combine harvesters have not be equipped with online fault diagnosis system. A fault information acquisition and diagnosis system of the Combine Harvester based on LabVIEW is designed, researched and developed. Using ARM development board, by collecting many sensors' signals, this system can achieve real-time measurement, collection, displaying and analysis of different parts of combine harvesters. It can also realize detection online of forward velocity, roller speed, engine temperature, etc. Meanwhile the system can judge the fault location. A new database function is added so that we can search the remedial measures to solve the faults and also we can add new faults to the database. So it is easy to take precautions against before the combine harvester breaking down then take measures to service the harvester.
Omland, Lars Haukali; Legarth, Rebecca Asbjørn; Ahlström, Magnus Glindvad
diseases, such as most urologic, nephrologic, rheumatologic, and endocrine disorders were generally associated with a low FYRHD. CONCLUSION: Our study identified a large number of indicator diseases associated with a FYRHD >0.1%. These data can be used as a tool for planning targeted HIV screening programs.......BACKGROUND: It has been suggested that targeted human immunodeficiency virus (HIV) testing programs are cost-effective in populations with an HIV prevalence >0.1%. Several indicator diseases are known to be associated with increased risk of HIV infection, but estimates of HIV frequency in persons...... with relevant indicator diseases are nonexistent. METHODS: In a nationwide population-based cohort study encompassing all Danish residents aged 20-60 years during 1994-2013, we estimated the 5-year risk of an HIV diagnosis (FYRHD) after a first-time diagnosis of 147 prespecified potential indicator diseases...
Full Text Available This paper proposes a new approach combining autoregressive (AR model and fuzzy cluster analysis for bearing fault diagnosis and degradation assessment. AR model is an effective approach to extract the fault feature, and is generally applied to stationary signals. However, the fault vibration signals of a roller bearing are non-stationary and non-Gaussian. Aiming at this problem, the set of parameters of the AR model is estimated based on higher-order cumulants. Consequently, the AR parameters are taken as the feature vectors, and fuzzy cluster analysis is applied to perform classification and pattern recognition. Experiments analysis results show that the proposed method can be used to identify various types and severities of fault bearings. This study is significant for non-stationary and non-Gaussian signal analysis, fault diagnosis and degradation assessment.
Full Text Available It is reported a case of an eight-year-old Yorkshire Terrier dog, with a history of prolonged use of prednisone in a dosage of 1mg/kg of body weight each 24 hours during two years. The helical computed tomography revealed hepatomegaly associated to a hyperattenuation of the parenchyma, with a radiodensity value of 82.55 Hounsfield units (HU. The spleen presented a mean radiodensity of 57.17HU, and a radiodensity difference of 25.38HU was observed between the two organs. Based on the history and findings of imaging technique, it was determined the presumptive diagnosis of steroidal hepatopathy compatible with accumulation of hepatic glycogen. It was concluded that computed tomography enabled the characterization of hepatic injury and the presumed diagnosis of steroidal hepatopathy
Oliveira, D.C; Costa, L.A.V.S.; Lopes, B.F.; Lanis, A.B.; Borlini, D.C.; Costa, F.S.
It is reported a case of an eight-year-old Yorkshire Terrier dog, with a history of prolonged use of prednisone in a dosage of 1mg/kg of body weight each 24 hours during two years. The helical computed tomography revealed hepatomegaly associated to a hyper attenuation of the parenchyma, with a radiodensity value of 82.55 Hounsfield units (HU). The spleen presented a mean radiodensity of 57.17HU, and a radiodensity difference of 25.38HU was observed between the two organs. Based on the history and findings of imaging technique, it was determined the presumptive diagnosis of steroidal hepatopathy compatible with accumulation of hepatic glycogen. It was concluded that computed tomography enabled the characterization of hepatic injury and the presumed diagnosis of steroidal hepatopathy. (author)
Oliveira, D.C; Costa, L.A.V.S.; Lopes, B.F.; Lanis, A.B.; Borlini, D.C.; Costa, F.S., E-mail: firstname.lastname@example.org [Universidade Federal do Espirito Santo (UFES), Vitroria, ES (Brazil). Dept. de Medicina Veterinaria; Maia Junior, J.A. [Centro de Escolas de Formacao de Tecnicos em Radiologia, Vila Velha, ES (Brazil)
It is reported a case of an eight-year-old Yorkshire Terrier dog, with a history of prolonged use of prednisone in a dosage of 1mg/kg of body weight each 24 hours during two years. The helical computed tomography revealed hepatomegaly associated to a hyper attenuation of the parenchyma, with a radiodensity value of 82.55 Hounsfield units (HU). The spleen presented a mean radiodensity of 57.17HU, and a radiodensity difference of 25.38HU was observed between the two organs. Based on the history and findings of imaging technique, it was determined the presumptive diagnosis of steroidal hepatopathy compatible with accumulation of hepatic glycogen. It was concluded that computed tomography enabled the characterization of hepatic injury and the presumed diagnosis of steroidal hepatopathy. (author)
Kim, Jung Taek; Park, Jae Chang; Lee, Jung Woon; Kim, Kyung Youn; Lee, In Soo; Kim, Bong Seok; Kang, Sook In
It is important to note that an effective means to assure the reliability and security for the nuclear power plant is to detect and diagnose the faults (failures) as soon and as accurately as possible. The objective of the project is to develop model-based fault detection and diagnosis algorithm for the pressurized water reactor and evaluate the performance of the developed algorithm. The scope of the work can be classified into two categories. The one is state-space model-based FDD algorithm based on the interacting multiple model (IMM) algorithm. The other is input-output model-based FDD algorithm based on the ART neural network. Extensive computer simulations are carried out to evaluate the performance in terms of speed and accuracy
Groot, Noortje; de Graeff, Nienke; Avcin, Tadej; Bader-Meunier, Brigitte; Dolezalova, Pavla; Feldman, Brian; Kenet, Gili; Koné-Paut, Isabelle; Lahdenne, Pekka; Marks, Stephen D; McCann, Liza; Pilkington, Clarissa A; Ravelli, Angelo; van Royen-Kerkhof, Annet; Uziel, Yosef; Vastert, Sebastiaan J; Wulffraat, Nico M; Ozen, Seza; Brogan, Paul; Kamphuis, Sylvia; Beresford, Michael W
Antiphospholipid syndrome (APS) is rare in children, and evidence-based guidelines are sparse. Consequently, management is mostly based on observational studies and physician's experience, and treatment regimens differ widely. The Single Hub and Access point for paediatric Rheumatology in Europe (SHARE) initiative was launched to develop diagnostic and management regimens for children and young adults with rheumatic diseases. Here, we developed evidence-based recommendations for diagnosis and treatment of paediatric APS. Evidence-based recommendations were developed using the European League Against Rheumatism standard operating procedure. Following a detailed systematic review of the literature, a committee of paediatric rheumatologists and representation of paediatric haematology with expertise in paediatric APS developed recommendations. The literature review yielded 1473 articles, of which 15 were valid and relevant. In total, four recommendations for diagnosis and eight for treatment of paediatric APS (including paediatric Catastrophic Antiphospholipid Syndrome) were accepted. Additionally, two recommendations for children born to mothers with APS were accepted. It was agreed that new classification criteria for paediatric APS are necessary, and APS in association with childhood-onset systemic lupus erythematosus should be identified by performing antiphospholipid antibody screening. Treatment recommendations included prevention of thrombotic events, and treatment recommendations for venous and/or arterial thrombotic events. Notably, due to the paucity of studies on paediatric APS, level of evidence and strength of the recommendations is relatively low. The SHARE initiative provides international, evidence-based recommendations for diagnosis and treatment for paediatric APS, facilitating improvement and uniformity of care. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use
Zhang, Yu-xin; Cheng, Zhi-feng; Xu, Zheng-ping; Bai, Jing
In order to solve the problems such as complex operation, consumption for the carrier gas and long test period in traditional power transformer fault diagnosis approach based on dissolved gas analysis (DGA), this paper proposes a new method which is detecting 5 types of characteristic gas content in transformer oil such as CH4, C2H2, C2H4, C2H6 and H2 based on photoacoustic Spectroscopy and C2H2/C2H4, CH4/H2, C2H4/C2H6 three-ratios data are calculated. The support vector machine model was constructed using cross validation method under five support vector machine functions and four kernel functions, heuristic algorithms were used in parameter optimization for penalty factor c and g, which to establish the best SVM model for the highest fault diagnosis accuracy and the fast computing speed. Particles swarm optimization and genetic algorithm two types of heuristic algorithms were comparative studied in this paper for accuracy and speed in optimization. The simulation result shows that SVM model composed of C-SVC, RBF kernel functions and genetic algorithm obtain 97. 5% accuracy in test sample set and 98. 333 3% accuracy in train sample set, and genetic algorithm was about two times faster than particles swarm optimization in computing speed. The methods described in this paper has many advantages such as simple operation, non-contact measurement, no consumption for the carrier gas, long test period, high stability and sensitivity, the result shows that the methods described in this paper can instead of the traditional transformer fault diagnosis by gas chromatography and meets the actual project needs in transformer fault diagnosis.
Liu, Han; Zhou, Jianzhong; Zheng, Yang; Jiang, Wei; Zhang, Yuncheng
As the rolling bearings being the key part of rotary machine, its healthy condition is quite important for safety production. Fault diagnosis of rolling bearing has been research focus for the sake of improving the economic efficiency and guaranteeing the operation security. However, the collected signals are mixed with ambient noise during the operation of rotary machine, which brings great challenge to the exact diagnosis results. Using signals collected from multiple sensors can avoid the loss of local information and extract more helpful characteristics. Recurrent Neural Networks (RNN) is a type of artificial neural network which can deal with multiple time sequence data. The capacity of RNN has been proved outstanding for catching time relevance about time sequence data. This paper proposed a novel method for bearing fault diagnosis with RNN in the form of an autoencoder. In this approach, multiple vibration value of the rolling bearings of the next period are predicted from the previous period by means of Gated Recurrent Unit (GRU)-based denoising autoencoder. These GRU-based non-linear predictive denoising autoencoders (GRU-NP-DAEs) are trained with strong generalization ability for each different fault pattern. Then for the given input data, the reconstruction errors between the next period data and the output data generated by different GRU-NP-DAEs are used to detect anomalous conditions and classify fault type. Classic rotating machinery datasets have been employed to testify the effectiveness of the proposed diagnosis method and its preponderance over some state-of-the-art methods. The experiment results indicate that the proposed method achieves satisfactory performance with strong robustness and high classification accuracy. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.
Full Text Available Objectives: External cause International Classification of Diseases (ICD codes are commonly used to ascertain adverse drug reactions (ADRs related to hospitalisation. We quantified ascertainment of ADR-related hospitalisation using external cause codes and additional ICD-based hospital diagnosis codes. Methods: We reviewed the scientific literature to identify different ICD-based criteria for ADR-related hospitalisations, developed algorithms to capture ADRs based on candidate hospital ICD-10 diagnoses and external cause codes (Y40–Y59, and incorporated previously published causality ratings estimating the probability that a specific diagnosis was ADR related. We applied the algorithms to the NSW Admitted Patient Data Collection records of 45 and Up Study participants (2011–2013. Results: Of 493 442 hospitalisations among 267 153 study participants during 2011–2013, 18.8% (n = 92 953 had hospital diagnosis codes that were potentially ADR related; 1.1% (n = 5305 had high/very high–probability ADR-related diagnosis codes (causality ratings: A1 and A2; and 2.0% (n = 10 039 had ADR-related external cause codes. Overall, 2.2% (n = 11 082 of cases were classified as including an ADR-based hospitalisation on either external cause codes or high/very high–probability ADR-related diagnosis codes. Hence, adding high/very high–probability ADR-related hospitalisation codes to standard external cause codes alone (Y40–Y59 increased the number of hospitalisations classified as having an ADR-related diagnosis by 10.4%. Only 6.7% of cases with high-probability ADR-related mental symptoms were captured by external cause codes. Conclusion: Selective use of high-probability ADR-related hospital diagnosis codes in addition to external cause codes yielded a modest increase in hospitalised ADR incidence, which is of potential clinical significance. Clinically validated combinations of diagnosis codes could potentially further enhance capture.
National Aeronautics and Space Administration — Complex engineering systems require efficient on-line fault diagnosis methodologies to improve safety and reduce maintenance costs. Traditionally, diagnosis...
Duyar, A.; Guo, T.-H.; Merrill, W.; Musgrave, J.
In a previous study, Guo, Merrill and Duyar, 1990, reported a conceptual development of a fault detection and diagnosis system for actuation faults of the space shuttle main engine. This study, which is a continuation of the previous work, implements the developed fault detection and diagnosis scheme for the real time actuation fault diagnosis of the space shuttle main engine. The scheme will be used as an integral part of an intelligent control system demonstration experiment at NASA Lewis. The diagnosis system utilizes a model based method with real time identification and hypothesis testing for actuation, sensor, and performance degradation faults.
Rasuli, P.; Rasouli, F.; Rasouli, T.
Knowledge-based system is a decision support system in which an expert's knowledge and reasoning can be applied to problems in bounded knowledge domains. These systems, using knowledge and inference techniques, mimic human reasoning to solve problems. Knowledge-based systems are said to be 'intelligent' because they possess massive stores of information and exhibit many attributes commonly associated with human experts performing difficult tasks and using specialized knowledge and sophisticated problem-solving strategies. Knowledge-based systems differ from conventional software such as database systems in that they are able to reason about data and draw conclusions employing heuristic rules. Heuristics embody human expertise in some knowledge domain and are sometimes characterized as the 'rules of thumb' that one acquires through practical experience and uses to solve everyday problems. Knowledge-based systems have been developed in a variety of fields, including medical disciplines. A decision support system has been assisting clinicians in areas such as infectious disease therapy for many years. For example, these systems can help radiologists formulate and evaluate diagnostic hypotheses by recalling associations between diseases and imaging findings. Although radiologic technology relies heavily on computers, it has been slow to develop a knowledge-based system to aid in diagnoses. These systems can be valuable interactive educational tools for medical students. In 1992, we developed a DOS-based Bonex, a menu-driven expert system for the differential diagnosis of bone tumours using PDC Prolog. It was a rule-based expert system that led the user through a menu of questions and generated a hard copy report and a list of diagnoses with an estimate of the likelihood of each. Bonex was presented at the 1992 Annual Meeting of the Radiological Society of North America (RSNA) in Chicago. We also developed an expert system for the differential diagnosis of brain lesions
Rasuli, P. [Univ. of Ottawa, Dept. of Radiology, Ottawa Hospital, Ottawa, Ontario (Canada); Rasouli, F. [Research, Development and Engineering Center, PMUSA, Richmond, VA (United States); Rasouli, T. [Johns Hopkins Univ., Dept. of Cognitive Science, Baltimore, Maryland (United States)
Knowledge-based system is a decision support system in which an expert's knowledge and reasoning can be applied to problems in bounded knowledge domains. These systems, using knowledge and inference techniques, mimic human reasoning to solve problems. Knowledge-based systems are said to be 'intelligent' because they possess massive stores of information and exhibit many attributes commonly associated with human experts performing difficult tasks and using specialized knowledge and sophisticated problem-solving strategies. Knowledge-based systems differ from conventional software such as database systems in that they are able to reason about data and draw conclusions employing heuristic rules. Heuristics embody human expertise in some knowledge domain and are sometimes characterized as the 'rules of thumb' that one acquires through practical experience and uses to solve everyday problems. Knowledge-based systems have been developed in a variety of fields, including medical disciplines. A decision support system has been assisting clinicians in areas such as infectious disease therapy for many years. For example, these systems can help radiologists formulate and evaluate diagnostic hypotheses by recalling associations between diseases and imaging findings. Although radiologic technology relies heavily on computers, it has been slow to develop a knowledge-based system to aid in diagnoses. These systems can be valuable interactive educational tools for medical students. In 1992, we developed a DOS-based Bonex, a menu-driven expert system for the differential diagnosis of bone tumours using PDC Prolog. It was a rule-based expert system that led the user through a menu of questions and generated a hard copy report and a list of diagnoses with an estimate of the likelihood of each. Bonex was presented at the 1992 Annual Meeting of the Radiological Society of North America (RSNA) in Chicago. We also developed an expert system for the differential
Full Text Available Failure of the train axle box bearing will cause great loss. Now, condition-based maintenance of train axle box bearing has been a research hotspot around the world. Vibration signals generated by train axle box bearing have nonlinear and nonstationary characteristics. The methods used in traditional bearing fault diagnosis do not work well with the train axle box. To solve this problem, an effective method of axle box bearing fault diagnosis based on multifeature parameters is presented in this paper. This method can be divided into three parts, namely, weak fault signal extraction, feature extraction, and fault recognition. In the first part, a db4 wavelet is employed for denoising the original signals from the vibration sensors. In the second part, five time-domain parameters, five IMF energy-torque features, and two amplitude-ratio features are extracted. The latter seven frequency domain features are calculated based on the empirical mode decomposition and envelope spectrum analysis. In the third part, a fault classifier based on BP neural network is designed for automatic fault pattern recognition. A series of tests are carried out to verify the proposed method, which show that the accuracy is above 90%.
Luo, Yuan; Tang, Xiaoying
Voxel-based analysis is widely used in quantitative analysis of structural brain magnetic resonance imaging (MRI) and automated disease detection, such as Alzheimer's disease (AD). However, noise at the voxel level may cause low sensitivity to AD-induced structural abnormalities. This can be addressed with the use of a whole brain structural segmentation approach which greatly reduces the dimension of features (the number of voxels). In this paper, we propose an automatic AD diagnosis system that combines such whole brain segmen- tations with advanced machine learning methods. We used a multi-atlas segmentation technique to parcellate T1-weighted images into 54 distinct brain regions and extract their structural volumes to serve as the features for principal-component-analysis-based dimension reduction and support-vector-machine-based classification. The relationship between the number of retained principal components (PCs) and the diagnosis accuracy was systematically evaluated, in a leave-one-out fashion, based on 28 AD subjects and 23 age-matched healthy subjects. Our approach yielded pretty good classification results with 96.08% overall accuracy being achieved using the three foremost PCs. In addition, our approach yielded 96.43% specificity, 100% sensitivity, and 0.9891 area under the receiver operating characteristic curve.
Khawaja, Taimoor Saleem
A high-belief low-overhead Prognostics and Health Management (PHM) system is desired for online real-time monitoring of complex non-linear systems operating in a complex (possibly non-Gaussian) noise environment. This thesis presents a Bayesian Least Squares Support Vector Machine (LS-SVM) based framework for fault diagnosis and failure prognosis in nonlinear non-Gaussian systems. The methodology assumes the availability of real-time process measurements, definition of a set of fault indicators and the existence of empirical knowledge (or historical data) to characterize both nominal and abnormal operating conditions. An efficient yet powerful Least Squares Support Vector Machine (LS-SVM) algorithm, set within a Bayesian Inference framework, not only allows for the development of real-time algorithms for diagnosis and prognosis but also provides a solid theoretical framework to address key concepts related to classification for diagnosis and regression modeling for prognosis. SVM machines are founded on the principle of Structural Risk Minimization (SRM) which tends to find a good trade-off between low empirical risk and small capacity. The key features in SVM are the use of non-linear kernels, the absence of local minima, the sparseness of the solution and the capacity control obtained by optimizing the margin. The Bayesian Inference framework linked with LS-SVMs allows a probabilistic interpretation of the results for diagnosis and prognosis. Additional levels of inference provide the much coveted features of adaptability and tunability of the modeling parameters. The two main modules considered in this research are fault diagnosis and failure prognosis. With the goal of designing an efficient and reliable fault diagnosis scheme, a novel Anomaly Detector is suggested based on the LS-SVM machines. The proposed scheme uses only baseline data to construct a 1-class LS-SVM machine which, when presented with online data is able to distinguish between normal behavior
Hui, Kar Hoou; Ooi, Ching Sheng; Lim, Meng Hee; Leong, Mohd Salman; Al-Obaidi, Salah Mahdi
A major issue of machinery fault diagnosis using vibration signals is that it is over-reliant on personnel knowledge and experience in interpreting the signal. Thus, machine learning has been adapted for machinery fault diagnosis. The quantity and quality of the input features, however, influence the fault classification performance. Feature selection plays a vital role in selecting the most representative feature subset for the machine learning algorithm. In contrast, the trade-off relationship between capability when selecting the best feature subset and computational effort is inevitable in the wrapper-based feature selection (WFS) method. This paper proposes an improved WFS technique before integration with a support vector machine (SVM) model classifier as a complete fault diagnosis system for a rolling element bearing case study. The bearing vibration dataset made available by the Case Western Reserve University Bearing Data Centre was executed using the proposed WFS and its performance has been analysed and discussed. The results reveal that the proposed WFS secures the best feature subset with a lower computational effort by eliminating the redundancy of re-evaluation. The proposed WFS has therefore been found to be capable and efficient to carry out feature selection tasks.
Joerin, Florent; Desthieux, Gilles; Beuze, Sandrine Billeau; Nembrini, Aurore
Urban planning involves compromise between the diverse and often contradictory issues supported by the different stakeholders. The literature generally agrees on the need to broaden the participation base to overcome this difficulty. However, participation should not be limited to problem solving, but should also take place in the problem setting phase. This paper proposes a participatory diagnosis process for structuring the problem setting phase. We describe an experiment in a participatory diagnosis conducted with the residents of a Geneva neighborhood. The experiment began by identifying the residents' concerns, which were then reformulated under broader issues. Some 20 spatial indicators were built using GIS tools, and were then applied in a second phase of resident consultations to assess the relative importance of each issue. The ensuing priority issues formed the core of the diagnosis. The approach emphasized comparison between the daily experiences of residents and so-called official information (i.e. census tract, traffic measurement, and so on). The residents were therefore involved in a learning process that allowed them to consolidate or modify their opinions. The process led to the emergence of a clearly defined collective awareness that supplanted individual aspirations.
Full Text Available To diagnose rotating machinery fault for imbalanced data, a method based on fast clustering algorithm (FCA and support vector machine (SVM was proposed. Combined with variational mode decomposition (VMD and principal component analysis (PCA, sensitive features of the rotating machinery fault were obtained and constituted the imbalanced fault sample set. Next, a fast clustering algorithm was adopted to reduce the number of the majority data from the imbalanced fault sample set. Consequently, the balanced fault sample set consisted of the clustered data and the minority data from the imbalanced fault sample set. After that, SVM was trained with the balanced fault sample set and tested with the imbalanced fault sample set so the fault diagnosis model of the rotating machinery could be obtained. Finally, the gearbox fault data set and the rolling bearing fault data set were adopted to test the fault diagnosis model. The experimental results showed that the fault diagnosis model could effectively diagnose the rotating machinery fault for imbalanced data.
Full Text Available The main challenge of fault diagnosis lies in finding good fault features. A deep learning network has the ability to automatically learn good characteristics from input data in an unsupervised fashion, and its unique layer-wise pretraining and fine-tuning using the backpropagation strategy can solve the difficulties of training deep multilayer networks. Stacked sparse autoencoders or other deep architectures have shown excellent performance in speech recognition, face recognition, text classification, image recognition, and other application domains. Thus far, however, there have been very few research studies on deep learning in fault diagnosis. In this paper, a new rolling bearing fault diagnosis method that is based on short-time Fourier transform and stacked sparse autoencoder is first proposed; this method analyzes sound signals. After spectrograms are obtained by short-time Fourier transform, stacked sparse autoencoder is employed to automatically extract the fault features, and softmax regression is adopted as the method for classifying the fault modes. The proposed method, when applied to sound signals that are obtained from a rolling bearing test rig, is compared with empirical mode decomposition, Teager energy operator, and stacked sparse autoencoder when using vibration signals to verify the performance and effectiveness of the proposed method.
Jin, Xin; Sun, Pei; Tong, Gangsheng; Zhu, Xinyuan
As a novel kind of polymer with covalently linked core-shell structure, star polymers behave in nanostructure in aqueous medium at all concentration range, as unimolecular micelles at high dilution condition and multi-micelle aggregates in other situations. The unique morphologies endow star polymers with excellent stability and functions, making them a promising platform for bio-application. A variety of functions including imaging and therapeutics can be achieved through rational structure design of star polymers, and the existence of plentiful end-groups on shell offers the opportunity for further modification. In the last decades, star polymers have become an attracting platform on fabrication of novel nano-systems for bio-imaging and diagnosis. Focusing on the specific topology and physicochemical properties of star polymers, we have reviewed recent development of star polymer-based unimolecular micelles and their bio-application in imaging and diagnosis. The main content of this review summarizes the synthesis of integrated architecture of star polymers and their self-assembly behavior in aqueous medium, focusing especially on the recent advances on their bio-imaging application and diagnosis use. Finally, we conclude with remarks and give some outlooks for further exploration in this field. Copyright © 2018 Elsevier Ltd. All rights reserved.
Full Text Available Background: In India, only one woman is diagnosed with tuberculosis (TB for every 2.4 men. Previous studies have indicated gender disparities in care-seeking behavior and TB diagnosis; however, little is known about the specific barriers women face. Objectives: This study aimed to characterize socio-cultural and knowledge-based barriers that affected TB diagnosis for women in Bhopal, India. Materials and Methods: In-depth interviews were conducted with 13 affected women and 6 health-care workers. The Bhopal Diagnostic Microscopy Laboratory Register (n = 121 and the Bhopal district report (n = 261 were examined for diagnostic and care-seeking trends. Results: Women, especially younger women, faced socio-cultural barriers and stigma, causing many to hide their symptoms. Older women had little awareness about TB. Women often sought treatment from private practitioners, resulting in delayed diagnosis. Conclusions: Understanding these diagnostic and help-seeking behaviors barriers for women is critical for development of a gender-sensitive TB control program.
Kashyap, Rajpal S; Shekhawat, Seema D; Nayak, Amit R; Purohit, Hemant J; Taori, Girdhar M; Daginawala, Hatim F
The laboratory diagnosis of pulmonary tuberculosis (TB) and tuberculous meningitis (TBM) is particularly challenging. The aim of the present work is to develop an immunoassay for the diagnosis of TB infection, using synthetic peptides of antigen (Ag) 85 complex of M. tuberculosis (Mtb) H37Rv. Four peptides (7-10 amino acids long) corresponding to group-specific epitopes of Ag 85 complex of Mtb were synthesized. All peptides were evaluated by enzyme-linked immunosorbent assay (ELISA) for immunoreactivity with sera and CSF samples of TB and TBM patients respectively. The diagnostic value of the four peptides was evaluated in both the samples. It was observed that Ag 85 peptide 1, 3 and 4 had the highest positive rates in the pulmonary patients; however, Ag 85 peptide 1 and 2 had shown good positivity in the TBM subjects. The synthetic peptide based ELISA using Ag 85 complex peptides is a sensitive, specific, rapid and cost effective immunoassay for early diagnosis of pulmonary and extrapulmonary TB. In addition, these synthetic peptides are comparatively easy to produce in a reproducible manner compared with the whole antigen. Copyright © 2012 Elsevier B.V. All rights reserved.
Full Text Available Case-based reasoning (CBR is a process used for computer processing that tries to mimic the behavior of a human expert in making decisions regarding a subject and learn from the experience of past cases. CBR has demonstrated to be appropriate for working with unstructured domains data or difficult knowledge acquisition situations, such as medical diagnosis, where it is possible to identify diseases such as: cancer diagnosis, epilepsy prediction and appendicitis diagnosis. Some of the trends that may be developed for CBR in the health science are oriented to reduce the number of features in highly dimensional data. An important contribution may be the estimation of probabilities of belonging to each class for new cases. In this paper, in order to adequately represent the database and to avoid the inconveniences caused by the high dimensionality, noise and redundancy, a number of algorithms are used in the preprocessing stage for performing both variable selection and dimension reduction procedures. Also, a comparison of the performance of some representative multi-class classifiers is carried out to identify the most effective one to include within a CBR scheme. Particularly, four classification techniques and two reduction techniques are employed to make a comparative study of multiclass classifiers on CBR
Full Text Available The battery is a key component and the major fault source in electric vehicles (EVs. Ensuring power battery safety is of great significance to make the diagnosis more effective and predict the occurrence of faults, for the power battery is one of the core technologies of EVs. This paper proposes a voltage fault diagnosis detection mechanism using entropy theory which is demonstrated in an EV with a multiple-cell battery system during an actual operation situation. The preliminary analysis, after collecting and preprocessing the typical data periods from Operation Service and Management Center for Electric Vehicle (OSMC-EV in Beijing, shows that overvoltage fault for Li-ion batteries cell can be observed from the voltage curves. To further locate abnormal cells and predict faults, an entropy weight method is established to calculate the objective weight, which reduces the subjectivity and improves the reliability. The result clearly identifies the abnormity of cell voltage. The proposed diagnostic model can be used for EV real-time diagnosis without laboratory testing methods. It is more effective than traditional methods based on contrastive analysis.
Tiulpin, Aleksei; Thevenot, Jérôme; Rahtu, Esa; Lehenkari, Petri; Saarakkala, Simo
Knee osteoarthritis (OA) is the most common musculoskeletal disorder. OA diagnosis is currently conducted by assessing symptoms and evaluating plain radiographs, but this process suffers from subjectivity. In this study, we present a new transparent computer-aided diagnosis method based on the Deep Siamese Convolutional Neural Network to automatically score knee OA severity according to the Kellgren-Lawrence grading scale. We trained our method using the data solely from the Multicenter Osteoarthritis Study and validated it on randomly selected 3,000 subjects (5,960 knees) from Osteoarthritis Initiative dataset. Our method yielded a quadratic Kappa coefficient of 0.83 and average multiclass accuracy of 66.71% compared to the annotations given by a committee of clinical experts. Here, we also report a radiological OA diagnosis area under the ROC curve of 0.93. Besides this, we present attention maps highlighting the radiological features affecting the network decision. Such information makes the decision process transparent for the practitioner, which builds better trust toward automatic methods. We believe that our model is useful for clinical decision making and for OA research; therefore, we openly release our training codes and the data set created in this study.
Stoitsis, John; Valavanis, Ioannis; Mougiakakou, Stavroula G.; Golemati, Spyretta; Nikita, Alexandra; Nikita, Konstantina S.
Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atheromatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84% were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis
Stoitsis, John [National Technical University of Athens, School of Electrical and Computer Engineering, Athens 157 71 (Greece)]. E-mail: email@example.com; Valavanis, Ioannis [National Technical University of Athens, School of Electrical and Computer Engineering, Athens 157 71 (Greece); Mougiakakou, Stavroula G. [National Technical University of Athens, School of Electrical and Computer Engineering, Athens 157 71 (Greece); Golemati, Spyretta [National Technical University of Athens, School of Electrical and Computer Engineering, Athens 157 71 (Greece); Nikita, Alexandra [University of Athens, Medical School 152 28 Athens (Greece); Nikita, Konstantina S. [National Technical University of Athens, School of Electrical and Computer Engineering, Athens 157 71 (Greece)
Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atheromatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84% were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.
Kavuluru, Ramakanth; Han, Sifei; Harris, Daniel
Diagnosis codes are extracted from medical records for billing and reimbursement and for secondary uses such as quality control and cohort identification. In the US, these codes come from the standard terminology ICD-9-CM derived from the international classification of diseases (ICD). ICD-9 codes are generally extracted by trained human coders by reading all artifacts available in a patient’s medical record following specific coding guidelines. To assist coders in this manual process, this paper proposes an unsupervised ensemble approach to automatically extract ICD-9 diagnosis codes from textual narratives included in electronic medical records (EMRs). Earlier attempts on automatic extraction focused on individual documents such as radiology reports and discharge summaries. Here we use a more realistic dataset and extract ICD-9 codes from EMRs of 1000 inpatient visits at the University of Kentucky Medical Center. Using named entity recognition (NER), graph-based concept-mapping of medical concepts, and extractive text summarization techniques, we achieve an example based average recall of 0.42 with average precision 0.47; compared with a baseline of using only NER, we notice a 12% improvement in recall with the graph-based approach and a 7% improvement in precision using the extractive text summarization approach. Although diagnosis codes are complex concepts often expressed in text with significant long range non-local dependencies, our present work shows the potential of unsupervised methods in extracting a portion of codes. As such, our findings are especially relevant for code extraction tasks where obtaining large amounts of training data is difficult. PMID:28748227
Nanni, Loris; Lumini, Alessandra; Zaffonato, Nicolò
Alzheimer's disease (AD) is the most common cause of neurodegenerative dementia in the elderly population. Scientific research is very active in the challenge of designing automated approaches to achieve an early and certain diagnosis. Recently an international competition among AD predictors has been organized: "A Machine learning neuroimaging challenge for automated diagnosis of Mild Cognitive Impairment" (MLNeCh). This competition is based on pre-processed sets of T1-weighted Magnetic Resonance Images (MRI) to be classified in four categories: stable AD, individuals with MCI who converted to AD, individuals with MCI who did not convert to AD and healthy controls. In this work, we propose a method to perform early diagnosis of AD, which is evaluated on MLNeCh dataset. Since the automatic classification of AD is based on the use of feature vectors of high dimensionality, different techniques of feature selection/reduction are compared in order to avoid the curse-of-dimensionality problem, then the classification method is obtained as the combination of Support Vector Machines trained using different clusters of data extracted from the whole training set. The multi-classifier approach proposed in this work outperforms all the stand-alone method tested in our experiments. The final ensemble is based on a set of classifiers, each trained on a different cluster of the training data. The proposed ensemble has the great advantage of performing well using a very reduced version of the data (the reduction factor is more than 90%). The MATLAB code for the ensemble of classifiers will be publicly available 1 to other researchers for future comparisons. Copyright © 2017 Elsevier B.V. All rights reserved.
Kavuluru, Ramakanth; Han, Sifei; Harris, Daniel
Diagnosis codes are extracted from medical records for billing and reimbursement and for secondary uses such as quality control and cohort identification. In the US, these codes come from the standard terminology ICD-9-CM derived from the international classification of diseases (ICD). ICD-9 codes are generally extracted by trained human coders by reading all artifacts available in a patient's medical record following specific coding guidelines. To assist coders in this manual process, this paper proposes an unsupervised ensemble approach to automatically extract ICD-9 diagnosis codes from textual narratives included in electronic medical records (EMRs). Earlier attempts on automatic extraction focused on individual documents such as radiology reports and discharge summaries. Here we use a more realistic dataset and extract ICD-9 codes from EMRs of 1000 inpatient visits at the University of Kentucky Medical Center. Using named entity recognition (NER), graph-based concept-mapping of medical concepts, and extractive text summarization techniques, we achieve an example based average recall of 0.42 with average precision 0.47; compared with a baseline of using only NER, we notice a 12% improvement in recall with the graph-based approach and a 7% improvement in precision using the extractive text summarization approach. Although diagnosis codes are complex concepts often expressed in text with significant long range non-local dependencies, our present work shows the potential of unsupervised methods in extracting a portion of codes. As such, our findings are especially relevant for code extraction tasks where obtaining large amounts of training data is difficult.
Hartman, L C; Natiella, J R; Meenaghan, M A
Routine histochemistry, special stains, and energy dispersive x-ray microanalysis (EDX) were used to analyze 15 cases of presumptive amalgam tattoo. Histologically, the fine, spherical, golden brown granules of amalgam were remarkably similar to hemosiderin and melanin granules, and routine and special stains were not always reliable in differentiating one from the others. EDX provided a simple, fast, inexpensive, and reliable method for identifying the precise elemental composition of the pigmented material.
Colotelo, Alison HA; Smokorowski, Karen; Haxton, Tim; Cooke, Steven J.
Current methods of fish epithelial injury detection are limited to gross macroscopic examination that has a subjective bias as well as an inability to reliably quantify the degree of injury. Fluorescein, a presumptive test for blood, has been shown to have the capability to detect and quantify fish epithelial injury. However, there are several other presumptive tests for blood (Bluestar*, phenolphthalein, and HemastixH) that may have benefits over the use of fluorescein, particularly for field research on wild fish. This study investigated the capabilities of these four tests to detect and quantify a variety of injuries commonly encountered by fish (abrasion, cuts, fin frays, and punctures) using the freshwater bluegill Lepomis macrochirus as a model. Fluorescein was consistently found to be the most reliable (i.e., detected the highest proportion of true positive results and rarely detected false positive reactions) of the four presumptive tests for blood compared. Further testing was conducted to examine the reliability of fluorescein. By 24 h after an injury was inflicted, the injury was no longer detectable by fluorescein, and when fluorescein was applied to an injured fish, the fluorescein was no longer detectable 3 h after application. In a comparison of two common anaesthetics used in fisheries research, there was no significant difference in the proportion of injury detected when 3- aminobenzoic acid ethyl ester methanesulfate (tricaine) was used compared with a clove oil and ethanol (1:9) solution. In summary, fluorescein was the most reliable presumptive test for blood examined in this study for the detection and quantification of recent (hours) fish epithelial injury.
Koh, Jaehan; Chaudhary, Vipin; Dhillon, Gurmeet
In recent years the demand for an automated method for diagnosis of disc abnormalities has grown as more patients suffer from lumbar disorders and radiologists have to treat more patients reliably in a limited amount of time. In this paper, we propose and compare several classifiers that diagnose disc herniation, one of the common problems of the lumbar spine, based on lumbar MR images. Experimental results on a limited data set of 68 clinical cases with 340 lumbar discs show that our classifiers can diagnose disc herniation with 97% accuracy.
Zhang, Huisheng; Liu, Xinyu; Li, Qiaoliang; Rao, Jie; Zhang, Xiaofei; Wang, Xiaoxuan; Lin, Shumei; Yin, Li; Chen, Siping; Wang, Tianfu
Based on LCD Module and Visual C++ development environment, this paper proposes a new method which can quickly develop the human-machine interface .We define a LCD module programming interface by designing Serial Communication Class(SCS). On this basis,we achieve the transplantation on an Embedded ARM Platform to fulfil the requirements of Medical Diagnostic Instruments (MDI). Experimental results show that this method has advantages of short development cycle and high level transplantation which has broad application prospects in the field of Medical Diagnosis Instrument.
Cerrada, Mariela; Zurita, Grover; Cabrera, Diego; Sánchez, René-Vinicio; Artés, Mariano; Li, Chuan
There are growing demands for condition-based monitoring of gearboxes, and therefore new methods to improve the reliability, effectiveness, accuracy of the gear fault detection ought to be evaluated. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance of the diagnostic models. On the other hand, random forest classifiers are suitable models in industrial environments where large data-samples are not usually available for training such diagnostic models. The main aim of this research is to build up a robust system for the multi-class fault diagnosis in spur gears, by selecting the best set of condition parameters on time, frequency and time-frequency domains, which are extracted from vibration signals. The diagnostic system is performed by using genetic algorithms and a classifier based on random forest, in a supervised environment. The original set of condition parameters is reduced around 66% regarding the initial size by using genetic algorithms, and still get an acceptable classification precision over 97%. The approach is tested on real vibration signals by considering several fault classes, one of them being an incipient fault, under different running conditions of load and velocity.
Roychoudhury, I.; Hafiychuk, V.; Goebel, K.
A water recycling system (WRS) deployed at NASA Ames Research Center's Sustainability Base (an energy efficient office building that integrates some novel technologies developed for space applications) will serve as a testbed for long duration testing of next generation spacecraft water recycling systems for future human spaceflight missions. This system cleans graywater (waste water collected from sinks and showers) and recycles it into clean water. Like all engineered systems, the WRS is prone to standard degradation due to regular use, as well as other faults. Diagnostic and prognostic applications will be deployed on the WRS to ensure its safe, efficient, and correct operation. The diagnostic and prognostic results can be used to enable condition-based maintenance to avoid unplanned outages, and perhaps extend the useful life of the WRS. Diagnosis involves detecting when a fault occurs, isolating the root cause of the fault, and identifying the extent of damage. Prognosis involves predicting when the system will reach its end of life irrespective of whether an abnormal condition is present or not. In this paper, first, we develop a physics model of both nominal and faulty system behavior of the WRS. Then, we apply an integrated model-based diagnosis and prognosis framework to the simulation model of the WRS for several different fault scenarios to detect, isolate, and identify faults, and predict the end of life in each fault scenario, and present the experimental results.
He, Zhenzhu; Elbaz, Abdelrahman; Gao, Bingbing; Zhang, Junning; Su, Enben; Gu, Zhongze
Rapid early disease prevention or precise diagnosis is almost impossible in low-resource settings. Natural ordered structures in nature have great potential for the development of ultrasensitive biosensors. Here, motivated by the unique structures and extraordinary functionalities of ordered structures in nature, a biosensor based on butterfly wings is presented. In this study, a flexible Morpho menelaus (M. menelaus) based wearable sensor is integrated with a microfluidic system and electronic networks to facilitate the diagnosis of neurodegenerative disease (ND). In the microfluidic section, the structural characteristics of the M. menelaus wings up layer are combined with SiO 2 nanoparticles to form a heterostructure. The fluorescent enhancement property of the heterostructure is used to increase the fluorescent intensity for multiplex detection of two proteins: IgG and AD7c-NTP. For the electronic section, conductive ink is blade-coated on the under layer of wings for measuring resistance change rate to obtain the frequency of static tremors of ND patients. The disposable M. menelaus based flexible microfluidic and electronic sensor enables biochemical-physiological hybrid monitoring of ND. The sensor is also amenable to a variety of applications, such as comprehensive personal healthcare and human-machine interaction. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Full Text Available Objectives. Fibromyalgia syndrome (FMS, characterized by subjective complaints without physical or biomarker abnormality, courts controversy. Recommendations in recent guidelines addressing classification and diagnosis were examined for consistencies or differences. Methods. Systematic searches from January 2008 to February 2013 of the US-American National Guideline Clearing House, the Scottish Intercollegiate Guidelines Network, Guidelines International Network, and Medline for evidence-based guidelines for the management of FMS were conducted. Results. Three evidence-based interdisciplinary guidelines, independently developed in Canada, Germany, and Israel, recommended that FMS can be clinically diagnosed by a typical cluster of symptoms following a defined evaluation including history, physical examination, and selected laboratory tests, to exclude another somatic disease. Specialist referral is only recommended when some other physical or mental illness is reasonably suspected. The diagnosis can be based on the (modified preliminary American College of Rheumatology (ACR 2010 diagnostic criteria. Discussion. Guidelines from three continents showed remarkable consistency regarding the clinical concept of FMS, acknowledging that FMS is neither a distinct rheumatic nor mental disorder, but rather a cluster of symptoms, not explained by another somatic disease. While FMS remains an integral part of rheumatology, it is not an exclusive rheumatic condition and spans a broad range of medical disciplines.
Roychoudhury, Indranil; Hafiychuk, Vasyl; Goebel, Kai Frank
A water recycling system (WRS) deployed at NASA Ames Research Center s Sustainability Base (an energy efficient office building that integrates some novel technologies developed for space applications) will serve as a testbed for long duration testing of next generation spacecraft water recycling systems for future human spaceflight missions. This system cleans graywater (waste water collected from sinks and showers) and recycles it into clean water. Like all engineered systems, the WRS is prone to standard degradation due to regular use, as well as other faults. Diagnostic and prognostic applications will be deployed on the WRS to ensure its safe, efficient, and correct operation. The diagnostic and prognostic results can be used to enable condition-based maintenance to avoid unplanned outages, and perhaps extend the useful life of the WRS. Diagnosis involves detecting when a fault occurs, isolating the root cause of the fault, and identifying the extent of damage. Prognosis involves predicting when the system will reach its end of life irrespective of whether an abnormal condition is present or not. In this paper, first, we develop a physics model of both nominal and faulty system behavior of the WRS. Then, we apply an integrated model-based diagnosis and prognosis framework to the simulation model of the WRS for several different fault scenarios to detect, isolate, and identify faults, and predict the end of life in each fault scenario, and present the experimental results.
... situations in which there may be a risk that the divestiture will not result in the complete termination of a... the kinds of indebtedness that, standing alone, support a presumption that the creditor is able to...
Mugasa, Claire M.; Katiti, Diana; Boobo, Alex; Lubega, George W.; Schallig, Henk D. F. H.; Matovu, Enock
Diagnosis of human African trypanosomiasis (HAT) using molecular tests should ideally achieve high sensitivity without compromising specificity. This study compared 2 simplified tests, nucleic acid sequence-based amplification (NASBA) combined with oligochromatography (OC) and loop-mediated
National Aeronautics and Space Administration — Model-based diagnosis typically uses analytical redundancy to compare predictions from a model against observations from the system being diagnosed. However this...
Bhavani, Selvaraj Rani; Senthilkumar, Jagatheesan; Chilambuchelvan, Arul Gnanaprakasam; Manjula, Dhanabalachandran; Krishnamoorthy, Ramasamy; Kannan, Arputharaj
The Internet has greatly enhanced health care, helping patients stay up-to-date on medical issues and general knowledge. Many cancer patients use the Internet for cancer diagnosis and related information. Recently, cloud computing has emerged as a new way of delivering health services but currently, there is no generic and fully automated cloud-based self-management intervention for breast cancer patients, as practical guidelines are lacking. We investigated the prevalence and predictors of cloud use for medical diagnosis among women with breast cancer to gain insight into meaningful usage parameters to evaluate the use of generic, fully automated cloud-based self-intervention, by assessing how breast cancer survivors use a generic self-management model. The goal of this study was implemented and evaluated with a new prototype called "CIMIDx", based on representative association rules that support the diagnosis of medical images (mammograms). The proposed Cloud-Based System Support Intelligent Medical Image Diagnosis (CIMIDx) prototype includes two modules. The first is the design and development of the CIMIDx training and test cloud services. Deployed in the cloud, the prototype can be used for diagnosis and screening mammography by assessing the cancers detected, tumor sizes, histology, and stage of classification accuracy. To analyze the prototype's classification accuracy, we conducted an experiment with data provided by clients. Second, by monitoring cloud server requests, the CIMIDx usage statistics were recorded for the cloud-based self-intervention groups. We conducted an evaluation of the CIMIDx cloud service usage, in which browsing functionalities were evaluated from the end-user's perspective. We performed several experiments to validate the CIMIDx prototype for breast health issues. The first set of experiments evaluated the diagnostic performance of the CIMIDx framework. We collected medical information from 150 breast cancer survivors from hospitals
A community-based study was conducted to estimate the frequency of and evaluate the clinical features related to delayed diagnosis of aneurysmal subarachnoid hemorrhage (SAH). Between 1980 and 1998, 358 patients with aneurysmal SAH underwent treatment in Izumo, Japan. The diagnosis of SAH was delayed in 76 patients (21%) and was early in 282 (79%). Among the 76 patients whose diagnosis was delayed, the condition was misdiagnosed by clinicians in 46 cases (Subgroup A), and in the remaining 30 the patients were unaware that SAH had occurred and failed to seek prompt treatment (Subgroup B). The proportion of Subgroup A patients decreased significantly from 18% (30 of 170 patients) between 1980 and 1989 to 9% (16 of 188 patients) between 1990 and 1998 (p = 0.0098), whereas the proportion of Subgroup B patients during the same periods was 8% (13 of 170 patients) and 9% (17 of 188 patients), respectively (p = 0.6341). With regard to Subgroup A, the misdiagnosis rate in private clinics decreased from 14% (23 of 170 patients) between 1980 and 1989 to 5% (10 of 188 patients) between 1990 and 1998 (p = 0.0073), whereas the misdiagnosis rate in hospitals during the same periods was 4% (7 of 170 patients) and 3% (6 of 188 patients), respectively (p = 0.6399). Multivariate analysis revealed that World Federation of Neurosurgical Societies Grade I or II was the main risk factor for delayed diagnosis of SAH (OR 3.97 [95% CI 1.69-10.37]), and that the timing of SAH onset, that is, between 12:00 a.m. and 6:00 a.m., was an important reason for the condition in Subgroup B patients (OR 9.29 [95% CI 2.66-33.93]). Rebleeding before admission occurred in 26% of the patients in whom diagnosis was delayed, and in 3% of those who were diagnosed early (p better educated about SAH and the importance of getting prompt medical attention, even in the middle of the night.
Xiong, Xiyue; Sheng, Xiaoqi; Liu, Dan; Zeng, Ting; Peng, Ying; Wang, Yichao
), which showed that phenylacetic acid may be used as a reliable discriminator for the diagnosis of PKU. The low false positive rate (1-specificity, 0.064) can be eliminated or at least greatly reduced by simultaneously referring to other markers, especially phenylpyruvic acid, a unique marker in PKU. Additionally, this standard was obtained with high sensitivity and specificity in a less invasive manner for diagnosing PKU compared with the Phe/Tyr ratio. Therefore, we conclude that urinary metabolomic information based on the improved oximation-silylation method together with GC/MS may be reliable for the diagnosis and differential diagnosis of PKU.
Kyabayinze, Daniel J; Achan, Jane; Nakanjako, Damalie; Mpeka, Betty; Mawejje, Henry; Mugizi, Rukaaka; Kalyango, Joan N; D'Alessandro, Umberto; Talisuna, Ambrose; Jean-Pierre, Van geertruyden
Malaria case management is a key strategy for malaria control. Effective coverage of parasite-based malaria diagnosis (PMD) remains limited in malaria endemic countries. This study assessed the health system's capacity to absorb PMD at primary health care facilities in Uganda. In a cross sectional survey, using multi-stage cluster sampling, lower level health facilities (LLHF) in 11 districts in Uganda were assessed for 1) tools, 2) skills, 3) staff and infrastructure, and 4) structures, systems and roles necessary for the implementing of PMD. Tools for PMD (microscopy and/or RDTs) were available at 30 (24%) of the 125 LLHF. All LLHF had patient registers and 15% had functional in-patient facilities. Three months' long stock-out periods were reported for oral and parenteral quinine at 39% and 47% of LLHF respectively. Out of 131 health workers interviewed, 86 (66%) were nursing assistants; 56 (43%) had received on-job training on malaria case management and 47 (36%) had adequate knowledge in malaria case management. Overall, only 18% (131/730) Ministry of Health approved staff positions were filled by qualified personnel and 12% were recruited or transferred within six months preceding the survey. Of 186 patients that received referrals from LLHF, 130(70%) had received pre-referral anti-malarial drugs, none received pre-referral rectal artesunate and 35% had been referred due to poor response to antimalarial drugs. Primary health care facilities had inadequate human and infrastructural capacity to effectively implement universal parasite-based malaria diagnosis. The priority capacity building needs identified were: 1) recruitment and retention of qualified staff, 2) comprehensive training of health workers in fever management, 3) malaria diagnosis quality control systems and 4) strengthening of supply chain, stock management and referral systems.
Taheri-Garavand, Amin; Ahmadi, Hojjat; Omid, Mahmoud; Mohtasebi, Seyed Saeid; Mollazade, Kaveh; Russell Smith, Alan John; Carlomagno, Giovanni Maria
This research presents a new intelligent fault diagnosis and condition monitoring system for classification of different conditions of cooling radiator using infrared thermal images. The system was adopted to classify six types of cooling radiator faults; radiator tubes blockage, radiator fins blockage, loose connection between fins and tubes, radiator door failure, coolant leakage, and normal conditions. The proposed system consists of several distinct procedures including thermal image acquisition, image pre-processing, image processing, two-dimensional discrete wavelet transform (2D-DWT), feature extraction, feature selection using a genetic algorithm (GA), and finally classification by artificial neural networks (ANNs). The 2D-DWT is implemented to decompose the thermal images. Subsequently, statistical texture features are extracted from the original images and are decomposed into thermal images. The significant selected features are used to enhance the performance of the designed ANN classifier for the 6 types of cooling radiator conditions (output layer) in the next stage. For the tested system, the input layer consisted of 16 neurons based on the feature selection operation. The best performance of ANN was obtained with a 16-6-6 topology. The classification results demonstrated that this system can be employed satisfactorily as an intelligent condition monitoring and fault diagnosis for a class of cooling radiator. - Highlights: • Intelligent fault diagnosis of cooling radiator using thermal image processing. • Thermal image processing in a multiscale representation structure by 2D-DWT. • Selection features based on a hybrid system that uses both GA and ANN. • Application of ANN as classifier. • Classification accuracy of fault detection up to 93.83%
Full Text Available Since the 1990s, diagnosis-related group (DRG-based payment systems were gradually introduced in many countries. The main design characteristics of a DRG-based payment system are an exhaustive patient case classification system (ie, the system of diagnosis-related groupings and the payment formula, which is based on the base rate multiplied by a relative cost weight specific for each DRG. Cases within the same DRG code group are expected to undergo similar clinical evolution. Consecutively, they should incur the costs of diagnostics and treatment within a predefined scale. Such predictability was proven in a number of cost-of-illness studies conducted on major prosperity diseases alongside clinical trials on efficiency. This was the case with risky pregnancies, chronic obstructive pulmonary disease, diabetes, depression, alcohol addiction, hepatitis, and cancer. This article presents experience of introduced DRG-based payments in countries of western and eastern Europe, Scandinavia, United States, Canada, and Australia. This article presents the results of few selected reviews and systematic reviews of the following evidence: published reports on health system reforms by World Health Organization, World Bank, Organization for Economic Co-operation and Development, Canadian Institute for Health Information, Canadian Health Services Research Foundation, and Centre for Health Economics University of York. Diverse payment systems have different strengths and weaknesses in relation to the various objectives. The advantages of the DRG payment system are reflected in the increased efficiency and transparency and reduced average length of stay. The disadvantage of DRG is creating financial incentives toward earlier hospital discharges. Occasionally, such polices are not in full accordance with the clinical benefit priorities.
Liu, Chang-Feng; Shi, Xin-Ping; Chen, Yun; Jin, Ye; Zhang, Bing
The survival rate of septic patients mainly depends on a rapid and reliable diagnosis. A rapid, broad range, specific and sensitive quantitative diagnostic test is the urgent need. Thus, we developed a TaqMan-Based Multiplex real-time PCR assays to identify bloodstream pathogens within a few hours. Primers and TaqMan probes were designed to be complementary to conserved regions in the 16S rDNA gene of different kinds of bacteria. To evaluate accurately, sensitively, and specifically, the known bacteria samples (Standard strains, whole blood samples) are determined by TaqMan-Based Multiplex real-time PCR. In addition, 30 blood samples taken from patients with clinical symptoms of sepsis were tested by TaqMan-Based Multiplex real-time PCR and blood culture. The mean frequency of positive for Multiplex real-time PCR was 96% at a concentration of 100 CFU/mL, and it was 100% at a concentration greater than 1000 CFU/mL. All the known blood samples and Standard strains were detected positively by TaqMan-Based Multiplex PCR, no PCR products were detected when DNAs from other bacterium were used in the multiplex assay. Among the 30 patients with clinical symptoms of sepsis, 18 patients were confirmed positive by Multiplex real-time PCR and seven patients were confirmed positive by blood culture. TaqMan-Based Multiplex real-time PCR assay with highly sensitivity, specificity and broad detection range, is a rapid and accurate method in the detection of bacterial pathogens of sepsis and should have a promising usage in the diagnosis of sepsis. © 2017 Wiley Periodicals, Inc.
Lei, Yaguo; Zuo, Ming J
A Hilbert–Huang transform (HHT) is a time–frequency technique and has been widely applied to analyzing vibration signals in the field of fault diagnosis of rotating machinery. It analyzes the vibration signals using intrinsic mode functions (IMFs) extracted using empirical mode decomposition (EMD). However, EMD sometimes cannot reveal the signal characteristics accurately because of the problem of mode mixing. Ensemble empirical mode decomposition (EEMD) was developed recently to alleviate this problem. The IMFs generated by EEMD have different sensitivity to faults. Some IMFs are sensitive and closely related to the faults but others are irrelevant. To enhance the accuracy of the HHT in fault diagnosis of rotating machinery, an improved HHT based on EEMD and sensitive IMFs is proposed in this paper. Simulated signals demonstrate the effectiveness of the improved HHT in diagnosing the faults of rotating machinery. Finally, the improved HHT is applied to diagnosing an early rub-impact fault of a heavy oil catalytic cracking machine set, and the application results prove that the improved HHT is superior to the HHT based on all IMFs of EMD
Adami, Guy R; Tang, Jessica L; Markiewicz, Michael R
RNA-based diagnosis and prognosis of squamous cell carcinoma has been slow to come to the clinic. Improvements in RNA measurement, statistical evaluation, and sample preservation, along with increased sample numbers, have not made these methods reproducible enough to be used clinically. We propose that, in the case of squamous cell carcinoma of the oral cavity, a chief source of variability is sample dissection, which leads to variable amounts of stroma mixed in with tumor epithelium. This heterogeneity of the samples, which requires great care to avoid, makes it difficult to see changes in RNA levels specific to tumor cells. An evaluation of the data suggests that, paradoxically, brush biopsy samples of oral lesions may provide a more reproducible method than surgical acquisition of samples for miRNA measurement. The evidence also indicates that body fluid samples can show similar changes in miRNAs with oral squamous cell carcinoma (OSCC) as those seen in tumor brush biopsy samples - suggesting much of the miRNA in these samples is coming from the same source: tumor epithelium. We conclude that brush biopsy or body fluid samples may be superior to surgical samples in allowing miRNA-based diagnosis and prognosis of OSCC in that they feature a rapid method to obtain homogeneous tumor cells and/or RNA. Copyright © 2017 Elsevier Ltd. All rights reserved.
Full Text Available AIM: To figure out the contributed factors of the hospitalization expenses of senile cataract patients (HECP and build up an area-specified senile cataract diagnosis related group (DRG of Shanghai thereby formulating the reference range of HECP and providing scientific basis for the fair use and supervision of the health care insurance fund. METHODS: The data was collected from the first page of the medical records of 22 097 hospitalized patients from tertiary hospitals in Shanghai from 2010 to 2012 whose major diagnosis were senile cataract. Firstly, we analyzed the influence factors of HECP using univariate and multivariate analysis. DRG grouping was conducted according to the exhaustive Chi-squared automatic interaction detector (E-CHAID model, using HECP as target variable. Finally we evaluated the grouping results using non-parametric test such as Kruskal-Wallis H test, RIV, CV, etc. RESULTS: The 6 DRGs were established as well as criterion of HECP, using age, sex, type of surgery and whether complications/comorbidities occurred as the key variables of classification node of senile cataract cases. CONCLUSION: The grouping of senile cataract cases based on E-CHAID algorithm is reasonable. And the criterion of HECP based on DRG can provide a feasible way of management in the fair use and supervision of medical insurance fund.
Zheng, Jinde; Pan, Haiyang; Yang, Shubao; Cheng, Junsheng
Multiscale permutation entropy (MPE) is a recently proposed nonlinear dynamic method for measuring the randomness and detecting the nonlinear dynamic change of time series and can be used effectively to extract the nonlinear dynamic fault feature from vibration signals of rolling bearing. To solve the drawback of coarse graining process in MPE, an improved MPE method called generalized composite multiscale permutation entropy (GCMPE) was proposed in this paper. Also the influence of parameters on GCMPE and its comparison with the MPE are studied by analyzing simulation data. GCMPE was applied to the fault feature extraction from vibration signal of rolling bearing and then based on the GCMPE, Laplacian score for feature selection and the Particle swarm optimization based support vector machine, a new fault diagnosis method for rolling bearing was put forward in this paper. Finally, the proposed method was applied to analyze the experimental data of rolling bearing. The analysis results show that the proposed method can effectively realize the fault diagnosis of rolling bearing and has a higher fault recognition rate than the existing methods.
Bhateja, Vikrant; Moin, Aisha; Srivastava, Anuja; Bao, Le Nguyen; Lay-Ekuakille, Aimé; Le, Dac-Nhuong
Computer based diagnosis of Alzheimer’s disease can be performed by dint of the analysis of the functional and structural changes in the brain. Multispectral image fusion deliberates upon fusion of the complementary information while discarding the surplus information to achieve a solitary image which encloses both spatial and spectral details. This paper presents a Non-Sub-sampled Contourlet Transform (NSCT) based multispectral image fusion model for computer-aided diagnosis of Alzheimer’s disease. The proposed fusion methodology involves color transformation of the input multispectral image. The multispectral image in YIQ color space is decomposed using NSCT followed by dimensionality reduction using modified Principal Component Analysis algorithm on the low frequency coefficients. Further, the high frequency coefficients are enhanced using non-linear enhancement function. Two different fusion rules are then applied to the low-pass and high-pass sub-bands: Phase congruency is applied to low frequency coefficients and a combination of directive contrast and normalized Shannon entropy is applied to high frequency coefficients. The superiority of the fusion response is depicted by the comparisons made with the other state-of-the-art fusion approaches (in terms of various fusion metrics).
Schleier, P.; Berndt, A.; Zinner, K.; Zenk, W.; Dietel, W.; Pfister, W.
The aminolevulinic acid (5-ALA) -based fluorescence diagnosis has been found to be promising for an early detection and demarcation of superficial oral squamous cell carcinomas (OSCC). This method has previously demonstrated high sensitivity, however this clinical trial showed a specificity of approximately 62 %. This specificity was mainly restricted by tumor detection in the oral cavity in the presence of bacteria. After topical ALA application in the mouth of patients with previously diagnosed OSSC, red fluorescent areas were observed which did not correlate to confirm histological findings. Swabs and plaque samples were taken from 44 patients and cultivated microbiologically. Fluorescence was investigated (OMA-system) from 32 different bacteria strains found naturally in the oral cavity. After ALA incubation, 30 of 32 strains were found to synthesize fluorescent porphyrins, mainly Protoporphyrin IX. Also multiple fluorescent spectra were obtained having peak wavelengths of 636 nm and around 618 nm - 620 nm indicating synthesis of different porphyrins, such as the lipophylic Protoporphyrin IX (PpIX) and hydrophylic porphyrins (water soluble porphyrins, wsp). Of the 32 fluorescent bacterial strains, 18 produced wsp, often in combination with PpIX, and 5 produced solely wsp. These results clarify that ALA-based fluorescence diagnosis without consideration or suppression of bacteria fluorescence may lead to false-positive findings. It is necessary to suppress bacteria fluorescence with suitable antiseptics before starting the procedure. In this study, when specific antiseptic pre-treatment was performed bacterial associated fluorescence was significantly reduced.
Azami, Hamed; Escudero, Javier
Breast cancer is one of the most common types of cancer in women all over the world. Early diagnosis of this kind of cancer can significantly increase the chances of long-term survival. Since diagnosis of breast cancer is a complex problem, neural network (NN) approaches have been used as a promising solution. Considering the low speed of the back-propagation (BP) algorithm to train a feed-forward NN, we consider a number of improved NN trainings for the Wisconsin breast cancer dataset: BP with momentum, BP with adaptive learning rate, BP with adaptive learning rate and momentum, Polak-Ribikre conjugate gradient algorithm (CGA), Fletcher-Reeves CGA, Powell-Beale CGA, scaled CGA, resilient BP (RBP), one-step secant and quasi-Newton methods. An NN ensemble, which is a learning paradigm to combine a number of NN outputs, is used to improve the accuracy of the classification task. Results demonstrate that NN ensemble-based classification methods have better performance than NN-based algorithms. The highest overall average accuracy is 97.68% obtained by NN ensemble trained by RBP for 50%-50% training-test evaluation method.
Teimoori, Salma; Arimatsu, Yuji; Laha, Thewarach; Kaewkes, Sasithorn; Sereerak, Piya; Sripa, Manop; Tangkawattana, Sirikachorn; Brindley, Paul J; Sripa, Banchob
Diagnosis of Opisthorchis viverrini infection by conventional stool examination is increasingly difficult due to the low intensity of the infection after several rounds of control programmes in endemic regions as well as coinfections with intestinal flukes. Therefore sensitive and specific diagnostic test is needed. In this study, a coproantigen sandwich ELISA using recombinant O. viverrini cathepsin F (rOv-CF) was developed. This sandwich ELISA employing chicken IgY raised against rOv-CF in combination with rabbit IgG antibody to the somatic O. viverrini antigens showed a lower detection limit (LLD) of 70ng native O. viverrini somatic antigens by spiking the parasite antigens into control feces. When applied to the diagnosis, the IgY-based sandwich ELISA exhibited sensitivity and specificity of 93.3% and 76.7%, respectively, in an investigation of 90 human cases positive or negative for opisthorchiasis. The positive predictive value (PPV) and negative predictive value (NPV) for this coproantigen detection were 66.7% and 95.2%, respectively. This IgY-based sandwich ELISA using parasite cathepsin F detection shows a promising immunodiagnostic alternative for human opisthorchiasis in endemic regions. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Bhateja, Vikrant; Moin, Aisha; Srivastava, Anuja; Bao, Le Nguyen; Lay-Ekuakille, Aimé; Le, Dac-Nhuong
Computer based diagnosis of Alzheimer's disease can be performed by dint of the analysis of the functional and structural changes in the brain. Multispectral image fusion deliberates upon fusion of the complementary information while discarding the surplus information to achieve a solitary image which encloses both spatial and spectral details. This paper presents a Non-Sub-sampled Contourlet Transform (NSCT) based multispectral image fusion model for computer-aided diagnosis of Alzheimer's disease. The proposed fusion methodology involves color transformation of the input multispectral image. The multispectral image in YIQ color space is decomposed using NSCT followed by dimensionality reduction using modified Principal Component Analysis algorithm on the low frequency coefficients. Further, the high frequency coefficients are enhanced using non-linear enhancement function. Two different fusion rules are then applied to the low-pass and high-pass sub-bands: Phase congruency is applied to low frequency coefficients and a combination of directive contrast and normalized Shannon entropy is applied to high frequency coefficients. The superiority of the fusion response is depicted by the comparisons made with the other state-of-the-art fusion approaches (in terms of various fusion metrics).
Bhateja, Vikrant, E-mail: firstname.lastname@example.org, E-mail: email@example.com; Moin, Aisha; Srivastava, Anuja [Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC), Lucknow, Uttar Pradesh 226028 (India); Bao, Le Nguyen [Duytan University, Danang 550000 (Viet Nam); Lay-Ekuakille, Aimé [Department of Innovation Engineering, University of Salento, Lecce 73100 (Italy); Le, Dac-Nhuong, E-mail: firstname.lastname@example.org, E-mail: email@example.com [Duytan University, Danang 550000 (Viet Nam); Haiphong University, Haiphong 180000 (Viet Nam)
Computer based diagnosis of Alzheimer’s disease can be performed by dint of the analysis of the functional and structural changes in the brain. Multispectral image fusion deliberates upon fusion of the complementary information while discarding the surplus information to achieve a solitary image which encloses both spatial and spectral details. This paper presents a Non-Sub-sampled Contourlet Transform (NSCT) based multispectral image fusion model for computer-aided diagnosis of Alzheimer’s disease. The proposed fusion methodology involves color transformation of the input multispectral image. The multispectral image in YIQ color space is decomposed using NSCT followed by dimensionality reduction using modified Principal Component Analysis algorithm on the low frequency coefficients. Further, the high frequency coefficients are enhanced using non-linear enhancement function. Two different fusion rules are then applied to the low-pass and high-pass sub-bands: Phase congruency is applied to low frequency coefficients and a combination of directive contrast and normalized Shannon entropy is applied to high frequency coefficients. The superiority of the fusion response is depicted by the comparisons made with the other state-of-the-art fusion approaches (in terms of various fusion metrics).
We, Ji Sun; Young, Lee; Park, In Yang; Shin, Jong Chul; Im, Soo Ah
Our aim was to compare the value of fetal magnetic resonance imaging (MRI) with detailed ultrasound in the prenatal diagnosis of congenital abnormalities. This retrospective study reviewed the medical records of pregnant women and their neonates who, after ultrasound, were suspected to have congenital abnormalities. They then underwent a detailed ultrasound examination and a fetal MRI in our institutions. Fetal MRI was performed in 81 cases. Each prenatal presumptive diagnosis, based on detailed ultrasound examination and fetal MRI, was compared with the postnatal confirmed diagnosis. In 58 cases, the data collected were confirmed by the postnatal diagnosis. Supplemental information from fetal MRI was useful in 17 of the 22 cases involving the central nervous system (CNS), two of two cases involving the thorax, nine of nine cases involving the genitourinary system, two of eight cases involving the gastrointestinal system, and ten of ten cases involving complex malformations. Fetal MRI did not provide significantly useful information or facilitate a more accurate diagnosis except for CNS abnormalities. Fetal MRI was not superior to an ultrasound examination in the prenatal detection of congenital abnormalities. A detailed ultrasound examination performed by experienced obstetricians had satisfactory accuracy in the diagnosis of fetal abnormalities compared with fetal MRI. Fetal MRI might be useful in appropriate cases in Korea. Greater effort is required to increase the ultrasound knowledge and skill of competent obstetricians.
Full Text Available Locating the assignable causes by use of the abnormal patterns of control chart is a widely used technology for manufacturing quality control. If there are uncertainties about the occurrence degree of abnormal patterns, the diagnosis process is impossible to be carried out. Considering four common abnormal control chart patterns, this paper proposed a characteristic numbers based recognition method point by point to quantify the occurrence degree of abnormal patterns under uncertain conditions and a fuzzy inference system based on fuzzy logic to calculate the contribution degree of assignable causes with fuzzy abnormal patterns. Application case results show that the proposed approach can give a ranked causes list under fuzzy control chart abnormal patterns and support the abnormity eliminating.
Hou, Shi-Wang; Feng, Shunxiao; Wang, Hui
Locating the assignable causes by use of the abnormal patterns of control chart is a widely used technology for manufacturing quality control. If there are uncertainties about the occurrence degree of abnormal patterns, the diagnosis process is impossible to be carried out. Considering four common abnormal control chart patterns, this paper proposed a characteristic numbers based recognition method point by point to quantify the occurrence degree of abnormal patterns under uncertain conditions and a fuzzy inference system based on fuzzy logic to calculate the contribution degree of assignable causes with fuzzy abnormal patterns. Application case results show that the proposed approach can give a ranked causes list under fuzzy control chart abnormal patterns and support the abnormity eliminating.
Full Text Available This article presents a novel connectivity analysis method that is suitable for multi-node networks such as EEG, MEG or EcOG electrode recordings. Its diagnostic power and ability to interpret brain states in schizophrenia is demonstrated on a set of 50 subjects that constituted of 25 healthy and 25 diagnosed with schizophrenia and treated with medication. The method can also be used for the automatic detection of schizophrenia; it exhibits higher sensitivity than state-of-the-art methods with no false positives. The detection is based on an analysis from a minute long pattern-recognition computer task. Moreover, this connectivity analysis leads naturally to an optimal choice of electrodes and hence to highly statistically significant results that are based on data from only 3-5 electrodes. The method is general and can be used for the diagnosis of other psychiatric conditions, provided an appropriate computer task is devised.
Full Text Available Cognitive deficits in auditory or visual processing or in verbal short-term-memory are amongst others risk factors for the development of dyslexia (reading and spelling disability. By early identification and intervention (optimally before school entry, detrimental effects of these cognitive deficits on reading and spelling might be prevented. The goal of the CASPAR-project is to develop and evaluate web-based tools for diagnosis and therapy of cognitive prerequisites for reading and spelling, which are appropriate for kindergarten children. In the first approach CASPAR addresses auditory processing disorders. This article describes a computerized and web-based approach for screening and testing phoneme discrimination and for promoting phoneme discrimination abilities through interactive games in kindergarteners.
Full Text Available The semiconductor nanocrystal quantum dots (QDs have excellent photo-physical properties, and the QDs-based probes have achieved encouraging developments in cellular and in vivo molecular imaging. More and more researches showed that QDs-based technology may become a promising approach in cancer research. In this review, we focus on recent application of QDs in cancer diagnosis and treatment, including early detection of primary tumor such as ovarian cancer, breast cancer, prostate cancer and pancreatic cancer, as well as regional lymph nodes and distant metastases. With the development of QDs synthesis and modification, the effect of QDs on tumor metastasis investigation will become more and more important in the future.
Kashyap, Rajpal S; Bhullar, Shradha S; Chandak, Nitin H; Taori, Girdhar M
Synthetic peptide-based diagnosis of Chikungunya can be an efficient and more accessible approach in immunodiagnostics. Here, we describe the identification of Chikungunya-specific 40 kD protein for development of synthetic peptide-based enzyme-linked immunosorbent assay for the detection of Chikungunya virus-specific antibodies in the patient's sample. The total sodium dodecyl sulfate-polyacrylamide gel electrophoresis protein profile of the patient's sample can be done to identify specific protein bands. The identified proteins can be subjected to liquid chromatography-tandem mass spectrometry (LC-MS/MS) for characterization. After characterization, immunogenic peptides can be designed using softwares and subsequently synthesized chemically. The peptides can be used to develop more specific, sensitive, and simpler diagnostic assay.
Full Text Available Heart disease is one of the most common diseases in the world. The objective of this study is to aid the diagnosis of heart disease using a hybrid classification system based on the ReliefF and Rough Set (RFRS method. The proposed system contains two subsystems: the RFRS feature selection system and a classification system with an ensemble classifier. The first system includes three stages: (i data discretization, (ii feature extraction using the ReliefF algorithm, and (iii feature reduction using the heuristic Rough Set reduction algorithm that we developed. In the second system, an ensemble classifier is proposed based on the C4.5 classifier. The Statlog (Heart dataset, obtained from the UCI database, was used for experiments. A maximum classification accuracy of 92.59% was achieved according to a jackknife cross-validation scheme. The results demonstrate that the performance of the proposed system is superior to the performances of previously reported classification techniques.
Dvey-Aharon, Zack; Fogelson, Noa; Peled, Abraham; Intrator, Nathan
This article presents a novel connectivity analysis method that is suitable for multi-node networks such as EEG, MEG or EcOG electrode recordings. Its diagnostic power and ability to interpret brain states in schizophrenia is demonstrated on a set of 50 subjects that constituted of 25 healthy and 25 diagnosed with schizophrenia and treated with medication. The method can also be used for the automatic detection of schizophrenia; it exhibits higher sensitivity than state-of-the-art methods with no false positives. The detection is based on an analysis from a minute long pattern-recognition computer task. Moreover, this connectivity analysis leads naturally to an optimal choice of electrodes and hence to highly statistically significant results that are based on data from only 3-5 electrodes. The method is general and can be used for the diagnosis of other psychiatric conditions, provided an appropriate computer task is devised.
Maryam, Syeda; McCrackin, Laura; Crowley, Mark; Rathi, Yogesh; Michailovich, Oleg
The world's aging population has given rise to an increasing awareness towards neurodegenerative disorders, including Alzheimers Disease (AD). Treatment options for AD are currently limited, but it is believed that future success depends on our ability to detect the onset of the disease in its early stages. The most frequently used tools for this include neuropsychological assessments, along with genetic, proteomic, and image-based diagnosis. Recently, the applicability of Diffusion Magnetic Resonance Imaging (dMRI) analysis for early diagnosis of AD has also been reported. The sensitivity of dMRI to the microstructural organization of cerebral tissue makes it particularly well-suited to detecting changes which are known to occur in the early stages of AD. Existing dMRI approaches can be divided into two broad categories: region-based and tract-based. In this work, we propose a new approach, which extends region-based approaches to the simultaneous characterization of multiple brain regions. Given a predefined set of features derived from dMRI data, we compute the probabilistic distances between different brain regions and treat the resulting connectivity pattern as an undirected, fully-connected graph. The characteristics of this graph are then used as markers to discriminate between AD subjects and normal controls (NC). Although in this preliminary work we omit subjects in the prodromal stage of AD, mild cognitive impairment (MCI), our method demonstrates perfect separability between AD and NC subject groups with substantial margin, and thus holds promise for fine-grained stratification of NC, MCI and AD populations.
Lizardo-Daudt Helena Maria
Full Text Available Background and aims: The diagnosis of human fetal age is usually estimated based on the measurement of crown-rump length or crown-heel length and the weight of the fetus. However, this estimate is not totally accurate and sometimes is necessary to combine other data to determine the fetal age. An analysis of the normal embryological development of the kidney may assist in this determination. The histology of this process, although well described, lacks photographic documentation. We intend to fill this gap by providing histologists and pathologists, especially inexperienced ones, with information about the staging of the renal development through microphotography. The objective of the present study was to achieve greater accuracy for the diagnosis of human fetal age through the proposed classification and the photographic documentation presented. Material and methods: Normal embryological development of the human kidney was studied by light microscopy. The fetal period from 6 to 40 weeks of gestation was observed according the stage of maturity of glomeruli and tubules; localization of glomeruli, occurrence of nephrogenic tissue and cortico-medullary differentiation. At least 5 different exams were observed from each week of development. Two hundred four exams were analyzed in the whole study. The histological characteristics were quantified and the process was documented by microphotography. Results and final considerations: The fetal development of the kidney was divided into 8 stages, which was documented through microphotography. Nephron structural formation occurred until the 34th week of prenatal development. From the 35th week on, tubules and glomeruli continued to mature without the formation of new nephrons. The proposed classification intends to improve the accuracy of the fetal age diagnosis.
Mobed, Parham [Texas Tech Univ., Lubbock, TX (United States); Pednekar, Pratik [West Virginia Univ., Morgantown, WV (United States); Bhattacharyya, Debangsu [West Virginia Univ., Morgantown, WV (United States); Turton, Richard [West Virginia Univ., Morgantown, WV (United States); Rengaswamy, Raghunathan [Texas Tech Univ., Lubbock, TX (United States)
Design and operation of energy producing, near “zero-emission” coal plants has become a national imperative. This report on model-based sensor placement describes a transformative two-tier approach to identify the optimum placement, number, and type of sensors for condition monitoring and fault diagnosis in fossil energy system operations. The algorithms are tested on a high fidelity model of the integrated gasification combined cycle (IGCC) plant. For a condition monitoring network, whether equipment should be considered at a unit level or a systems level depends upon the criticality of the process equipment, its likeliness to fail, and the level of resolution desired for any specific failure. Because of the presence of a high fidelity model at the unit level, a sensor network can be designed to monitor the spatial profile of the states and estimate fault severity levels. In an IGCC plant, besides the gasifier, the sour water gas shift (WGS) reactor plays an important role. In view of this, condition monitoring of the sour WGS reactor is considered at the unit level, while a detailed plant-wide model of gasification island, including sour WGS reactor and the Selexol process, is considered for fault diagnosis at the system-level. Finally, the developed algorithms unify the two levels and identifies an optimal sensor network that maximizes the effectiveness of the overall system-level fault diagnosis and component-level condition monitoring. This work could have a major impact on the design and operation of future fossil energy plants, particularly at the grassroots level where the sensor network is yet to be identified. In addition, the same algorithms developed in this report can be further enhanced to be used in retrofits, where the objectives could be upgrade (addition of more sensors) and relocation of existing sensors.
Krill, Michael K; Rosas, Samuel; Kwon, KiHyun; Dakkak, Andrew; Nwachukwu, Benedict U; McCormick, Frank
The clinical examination of the shoulder joint is an undervalued diagnostic tool for evaluating acromioclavicular (AC) joint pathology. Applying evidence-based clinical tests enables providers to make an accurate diagnosis and minimize costly imaging procedures and potential delays in care. The purpose of this study was to create a decision tree analysis enabling simple and accurate diagnosis of AC joint pathology. A systematic review of the Medline, Ovid and Cochrane Review databases was performed to identify level one and two diagnostic studies evaluating clinical tests for AC joint pathology. Individual test characteristics were combined in series and in parallel to improve sensitivities and specificities. A secondary analysis utilized subjective pre-test probabilities to create a clinical decision tree algorithm with post-test probabilities. The optimal special test combination to screen and confirm AC joint pathology combined Paxinos sign and O'Brien's Test, with a specificity of 95.8% when performed in series; whereas, Paxinos sign and Hawkins-Kennedy Test demonstrated a sensitivity of 93.7% when performed in parallel. Paxinos sign and O'Brien's Test demonstrated the greatest positive likelihood ratio (2.71); whereas, Paxinos sign and Hawkins-Kennedy Test reported the lowest negative likelihood ratio (0.35). No combination of special tests performed in series or in parallel creates more than a small impact on post-test probabilities to screen or confirm AC joint pathology. Paxinos sign and O'Brien's Test is the only special test combination that has a small and sometimes important impact when used both in series and in parallel. Physical examination testing is not beneficial for diagnosis of AC joint pathology when pretest probability is unequivocal. In these instances, it is of benefit to proceed with procedural tests to evaluate AC joint pathology. Ultrasound-guided corticosteroid injections are diagnostic and therapeutic. An ultrasound-guided AC joint
Full Text Available Background: So far, non-invasive diagnostic approaches such as ultrasound, magnetic resonance imaging, or blood tests do not have sufficient diagnostic power for endometriosis disease. Lack of a non-invasive diagnostic test contributes to the long delay between onset of symptoms and diagnosis of endometriosis. Objective: The present study focuses on the identification of predictive biomarkers in serum by pattern recognition techniques and uses partial least square discriminant analysis, multi-layer feed forward artificial neural networks (ANNs and quadratic discriminant analysis (QDA modeling tools for the early diagnosis of endometriosis in a minimally invasive manner by 1H- NMR based metabolomics. Materials and Methods: This prospective cohort study was done in Pasteur Institute, Iran in June 2013. Serum samples of 31 infertile women with endometriosis (stage II and III who confirmed by diagnostic laparoscopy and 15 normal women were collected and analyzed by nuclear magnetic resonance spectroscopy. The model was built by using partial least square discriminant analysis, QDA, and ANNs to determine classifier metabolites for early prediction risk of disease. Results: The levels of 2- methoxyestron, 2-methoxy estradiol, dehydroepiandrostion androstendione, aldosterone, and deoxy corticosterone were enhanced significantly in infertile group. While cholesterol and primary bile acids levels were decreased. QDA model showed significant difference between two study groups. Positive and negative predict value levels obtained about 71% and 78%, respectively. ANNs provided also criteria for detection of endometriosis. Conclusion: The QDA and ANNs modeling can be used as computational tools in noninvasive diagnose of endometriosis. However, the model designed by QDA methods is more efficient compared to ANNs in diagnosis of endometriosis patients.
Sun, Wenqing; Zheng, Bin; Qian, Wei
This study aimed to analyze the ability of extracting automatically generated features using deep structured algorithms in lung nodule CT image diagnosis, and compare its performance with traditional computer aided diagnosis (CADx) systems using hand-crafted features. All of the 1018 cases were acquired from Lung Image Database Consortium (LIDC) public lung cancer database. The nodules were segmented according to four radiologists' markings, and 13,668 samples were generated by rotating every slice of nodule images. Three multichannel ROI based deep structured algorithms were designed and implemented in this study: convolutional neural network (CNN), deep belief network (DBN), and stacked denoising autoencoder (SDAE). For the comparison purpose, we also implemented a CADx system using hand-crafted features including density features, texture features and morphological features. The performance of every scheme was evaluated by using a 10-fold cross-validation method and an assessment index of the area under the receiver operating characteristic curve (AUC). The observed highest area under the curve (AUC) was 0.899±0.018 achieved by CNN, which was significantly higher than traditional CADx with the AUC=0.848±0.026. The results from DBN was also slightly higher than CADx, while SDAE was slightly lower. By visualizing the automatic generated features, we found some meaningful detectors like curvy stroke detectors from deep structured schemes. The study results showed the deep structured algorithms with automatically generated features can achieve desirable performance in lung nodule diagnosis. With well-tuned parameters and large enough dataset, the deep learning algorithms can have better performance than current popular CADx. We believe the deep learning algorithms with similar data preprocessing procedure can be used in other medical image analysis areas as well. Copyright © 2017. Published by Elsevier Ltd.
Irritable bowel syndrome (IBS) is a chronic and debilitating functional gastrointestinal disorder that affects 9%-23% of the population across the world. The percentage of patients seeking health care related to IBS approaches 12% in primary care practices and is by far the largest subgroup seen in gastroenterology clinics. It has been well documented that these patients exhibit a poorer quality of life and utilize the health care system to a greater degree than patients without this diagnosis. The pathophysiology of IBS is not clear. Many theories have been put forward, but the exact cause of IBS is still uncertain. According to the updated ROME III criteria, IBS is a clinical diagnosis and presents as one of the three predominant subtypes: (1) IBS with constipation (IBS-C); (2) IBS with diarrhea (IBS-D); and (3) mixed IBS (IBS-M); former ROME definitions refer to IBS-M as alternating IBS (IBS-A). Across the IBS subtypes, the presentation of symptoms may vary among patients and change over time. Patients report the most distressing symptoms to be abdominal pain, straining, myalgias, urgency, bloating and feelings of serious illness. The complexity and diversity of IBS presentation makes treatment difficult. Although there are reviews and guidelines for treating IBS, they focus on the efficacy of medications for IBS symptoms using high-priority endpoints, leaving those of lower priority largely unreported. Therefore, the aim of this review is to provide a comprehensive evidence-based review of the diagnosis, pathogenesis and treatment to guide clinicians diagnosing and treating their patients. PMID:24944467
National Aeronautics and Space Administration — Diagnosis and prognosis are necessary tasks for system re- configuration and fault-adaptive control in complex systems. Diagnosis consists of detection, isolation...
Full Text Available Background Chordoid neoplasms refer to tumors appearing to have histological features of embryonic notochord, which is characterized by cords and lobules of neoplastic cells arranged within myxoid matrix. Because of radiological and histological similarities with myxoid matrix and overlapping immunohistochemical profile, chordoma, chordoid meningioma, chordoid glioma, and rare extraskeletal myxoid chondrosarcoma enter in the radiological and histological differential diagnosis at the site of skull base. However, there is always a great challenge for histopathologists to make an accurate diagnosis when encountering a chordoid neoplasm within or near the central nervous system. The aim of this study is to investigate and summarize the radiological, histological features and immunohistochemical profiles of chordoid neoplasms in skull base, and to find a judicious panel of immunostains to unquestionably help in diagnostically challenging cases. Methods A total of 23 cases of chordoid neoplasms in skull base, including 10 chordomas, 5 chordoid meningiomas, 3 chordoid gliomas and 5 extraskeletal myxoid chondrosarcomas, were collected from the First Affiliated Hospital, Sun Yat-sen University and Guangdong Tongjiang Hospital. MRI examination was performed on the patients before surgical treatment. Microscopical examination and immunohistochemical staining study using vimentin (Vim, pan-cytokeratin (PCK, epithelial membrane antigen (EMA, S?100 protein (S-100, glial fibrillary acidic protein (GFAP, D2-40, Galectin-3, CD3, CD20, Ki-67 were performed on the samples of cases. The clinicopathological data of the patients was also analyzed retrospectively. Results Most of chordomas were localized in the clivus with heterogeneous hyperintensity on T2WI scanning. The breakage of clivus was observed in most cases. Histologically, the tumor cells of chordoma exhibited bland nuclear features and some contained abundant vacuolated cytoplasm (the so
Wong, Vincent Wai-Sun; Petta, Salvatore; Hiriart, Jean-Baptiste; Cammà, Calogero; Wong, Grace Lai-Hung; Marra, Fabio; Vergniol, Julien; Chan, Anthony Wing-Hung; Tuttolomondo, Antonino; Merrouche, Wassil; Chan, Henry Lik-Yuen; Le Bail, Brigitte; Arena, Umberto; Craxì, Antonio; de Lédinghen, Victor
Controlled attenuation parameter (CAP) can be performed together with liver stiffness measurement (LSM) by transient elastography (TE) and is often used to diagnose fatty liver. We aimed to define the validity criteria of CAP. CAP was measured by the M probe prior to liver biopsy in 754 consecutive patients with different liver diseases at three centers in Europe and Hong Kong (derivation cohort, n=340; validation cohort, n=414; 101 chronic hepatitis B, 154 chronic hepatitis C, 349 non-alcoholic fatty liver disease, 37 autoimmune hepatitis, 49 cholestatic liver disease, 64 others; 277 F3-4; age 52±14; body mass index 27.2±5.3kg/m 2 ). The primary outcome was the diagnosis of fatty liver, defined as steatosis involving ≥5% of hepatocytes. The area under the receiver-operating characteristics curve (AUROC) for CAP diagnosis of fatty liver was 0.85 (95% CI 0.82-0.88). The interquartile range (IQR) of CAP had a negative correlation with CAP (r=-0.32, pvalidity parameter. In the derivation cohort, the IQR of CAP was associated with the accuracy of CAP (AUROC 0.86, 0.89 and 0.76 in patients with IQR of CAP validation cohort was 0.90 and 0.77 in patients with IQR of CAP validity of CAP for the diagnosis of fatty liver is lower if the IQR of CAP is ≥40dB/m. Lay summary: Controlled attenuation parameter (CAP) is measured by transient elastography (TE) for the detection of fatty liver. In this large study, using liver biopsy as a reference, we show that the variability of CAP measurements based on its interquartile range can reflect the accuracy of fatty liver diagnosis. In contrast, other clinical factors such as adiposity and liver enzyme levels do not affect the performance of CAP. Copyright © 2017 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.
Chen, Ruoqing; Sjölander, Arvid; Valdimarsdóttir, Unnur; Varnum, Catherine; Almqvist, Catarina; Ye, Weimin; Fall, Katja; Czene, Kamila; Fang, Fang
Cancer diagnosis is known to induce severe psychological stress for the diagnosed patients; however, how it affects the next-of-kin is less well documented. This study aimed to assess the impact of parental cancer on the risk of childhood death. A population-based cohort study was conducted using the Swedish national registries, including 2,871,242 children followed during the period of 1991-2009. Parental cancer diagnosis was defined as a time-varying exposure. We used Cox proportional hazards regression to calculate the hazard ratio (HR) and its corresponding 95% confidence interval (CI) as an estimate of the association between parental cancer and childhood mortality. We adjusted for attained age, sex, gestational age, mode of delivery and birth weight of the child, maternal age at child's birth, as well as educational level and socio-economic classification of the parents in the analyses. Among 113,555 children with parental cancer, 127 deaths occurred during 561,198 person-years of follow-up. A parental cancer diagnosis was associated with an increased rate of death among children at the age of 1-18 (HR for all-cause death: 1.39; 95% CI: 1.16-1.66). For young children (aged 1-12), an increased rate was only noted for death due to cancer (HR: 2.06; 95% CI: 1.13-3.75) after parental cancer diagnosis. Among adolescents (aged 13-18), an increased rate was noted for all-cause death (HR: 1.52; 95% CI: 1.25-1.86), and for both non-cancer-related (HR: 1.43; 95% CI: 1.14-1.79) and cancer-related (HR: 2.07; 95% CI: 1.33-3.24) death in the exposed children. Children have an increased rate of death if they have a parent diagnosed with cancer as compared to children without such experience; this association appears to be slightly stronger among adolescents. Copyright © 2014 Elsevier Ltd. All rights reserved.
Full Text Available Power transformer is one of the most important equipment in power system. In order to predict the potential fault of power transformer and identify the fault types correctly, we proposed a transformer fault intelligent diagnosis model based on chemical reaction optimization (CRO algorithm and relevance vector machine(RVM. RVM is a powerful machine learning method, which can solve nonlinear, high-dimensional classification problems with a limited number of samples. CRO algorithm has well global optimization and simple calculation, so it is suitable to solve parameter optimization problems. In this paper, firstly, a multi-layer RVM classification model was built by binary tree recognition strategy. Secondly, CRO algorithm was adopted to optimize the kernel function parameters which could enhance the performance of RVM classifiers. Compared with IEC three-ratio method and the RVM model, the CRO-RVM model not only overcomes the coding defect problem of IEC three-ratio method, but also has higher classification accuracy than the RVM model. Finally, the new method was applied to analyze a transformer fault case, Its predicted result accord well with the real situation. The research provides a practical method for transformer fault intelligent diagnosis and prediction.
Full Text Available Gear and bearing play an important role as key components of rotating machinery power transmission systems in nuclear power plants. Their state conditions are very important for safety and normal operation of entire nuclear power plant. Vibration based condition monitoring is more complicated for the gear and bearing of planetary gearbox than those of fixed-axis gearbox. Many theoretical and engineering challenges in planetary gearbox fault diagnosis have not yet been resolved which are of great importance for nuclear power plants. A detailed vibration condition monitoring review of planetary gearbox used in nuclear power plants is conducted in this paper. A new fault diagnosis method of planetary gearbox gears is proposed. Bearing fault data, bearing simulation data, and gear fault data are used to test the new method. Signals preprocessed using dilation-erosion gradient filter and fast Fourier transform for fault information extraction. The length of structuring element (SE of dilation-erosion gradient filter is optimized by alpha stable distribution. Method experimental verification confirmed that parameter alpha is superior compared to kurtosis since it can reflect the form of entire signal and it cannot be influenced by noise similar to impulse.
Wang, Li-Hua; Zhao, Xiao-Ping; Wu, Jia-Xin; Xie, Yang-Yang; Zhang, Yong-Hong
With the rapid development of mechanical equipment, the mechanical health monitoring field has entered the era of big data. However, the method of manual feature extraction has the disadvantages of low efficiency and poor accuracy, when handling big data. In this study, the research object was the asynchronous motor in the drivetrain diagnostics simulator system. The vibration signals of different fault motors were collected. The raw signal was pretreated using short time Fourier transform (STFT) to obtain the corresponding time-frequency map. Then, the feature of the time-frequency map was adaptively extracted by using a convolutional neural network (CNN). The effects of the pretreatment method, and the hyper parameters of network diagnostic accuracy, were investigated experimentally. The experimental results showed that the influence of the preprocessing method is small, and that the batch-size is the main factor affecting accuracy and training efficiency. By investigating feature visualization, it was shown that, in the case of big data, the extracted CNN features can represent complex mapping relationships between signal and health status, and can also overcome the prior knowledge and engineering experience requirement for feature extraction, which is used by traditional diagnosis methods. This paper proposes a new method, based on STFT and CNN, which can complete motor fault diagnosis tasks more intelligently and accurately.
Jin, Shan; Cui, Wen; Jin, Zhigang; Wang, Ying
Wireless Sensor Networks (WSNs) have been utilized for node fault diagnosis in the fire detection field since the 1990s. However, the traditional methods have some problems, including complicated system structures, intensive computation needs, unsteady data detection and local minimum values. In this paper, a new diagnosis mechanism for WSN nodes is proposed, which is based on fuzzy theory and an Adaptive Fuzzy Discrete Hopfield Neural Network (AF-DHNN). First, the original status of each sensor over time is obtained with two features. One is the root mean square of the filtered signal (FRMS), the other is the normalized summation of the positive amplitudes of the difference spectrum between the measured signal and the healthy one (NSDS). Secondly, distributed fuzzy inference is introduced. The evident abnormal nodes' status is pre-alarmed to save time. Thirdly, according to the dimensions of the diagnostic data, an adaptive diagnostic status system is established with a Fuzzy C-Means Algorithm (FCMA) and Sorting and Classification Algorithm to reducing the complexity of the fault determination. Fourthly, a Discrete Hopfield Neural Network (DHNN) with iterations is improved with the optimization of the sensors' detected status information and standard diagnostic levels, with which the associative memory is achieved, and the search efficiency is improved. The experimental results show that the AF-DHNN method can diagnose abnormal WSN node faults promptly and effectively, which improves the WSN reliability.
Lee, S. C.
The objective is to develop a real time fault monitoring and diagnosis knowledge-based system (KBS) for space power systems which can save costly operational manpower and can achieve more reliable space power system operation. The proposed KBS was developed using the Autonomously Managed Power System (AMPS) test facility currently installed at NASA Marshall Space Flight Center (MSFC), but the basic approach taken for this project could be applicable for other space power systems. The proposed KBS is entitled Autonomously Managed Power-System Extendible Real-time Expert System (AMPERES). In Phase 1 the emphasis was put on the design of the overall KBS, the identification of the basic research required, the initial performance of the research, and the development of a prototype KBS. In Phase 2, emphasis is put on the completion of the research initiated in Phase 1, and the enhancement of the prototype KBS developed in Phase 1. This enhancement is intended to achieve a working real time KBS incorporated with the NASA space power system test facilities. Three major research areas were identified and progress was made in each area. These areas are real time data acquisition and its supporting data structure; sensor value validations; development of inference scheme for effective fault monitoring and diagnosis, and its supporting knowledge representation scheme.
Pagaduan, Jayson V; Ali, Mahesheema; Dowlin, Michael; Suo, Liye; Ward, Tabitha; Ruiz, Fadel; Devaraj, Sridevi
Recent sweat chloride guidelines published by the Cystic Fibrosis Foundation changed the intermediate sweat chloride concentration range from 40-59 mmol/L to 30-59 mmol/L for age > 6 months. We wanted to know how this new guideline would impact detection of cystic fibrosis among patients who previously had sweat tests done at Texas Children's Hospital. We revisited sweat chloride test results (n = 3012) in the last 5 years at Texas Children's Hospital based on the new guidelines on diagnosis of cystic fibrosis from the Cystic Fibrosis Foundation. We identified 125 patients that would be reclassified in the intermediate sweat chloride value with the new guidelines that were classified as "unlikely to have CF" in the previous guidelines. 8 (32%) patients with CFTR gene testing were positive for CFTR gene mutation(s). 4 (50%) of these patients were identified to have 2 CFTR mutations. One had variant combination that was reported to cause CF but all were diagnosed with CFTR-related metabolic syndrome. Our findings concur with the new CF diagnosis guidelines that changing the intermediate cut-off to 30-59 mmol/L sweat chloride concentration in combination with CFTR genetic analysis enhances the probability of identifying individuals that have risk of developing CF or have CF and enables for earlier therapeutic intervention.
Full Text Available As robots begin to perform jobs autonomously, with minimal or no human intervention, a new challenge arises: robots also need to autonomously detect errors and recover from faults. In this paper, we present a Support Vector Machine (SVM-based fault diagnosis system for a bio-inspired reconfigurable robot named Scorpio. The diagnosis system needs to detect and classify faults while Scorpio uses its crawling and rolling locomotion modes. Specifically, we classify between faulty and non-faulty conditions by analyzing onboard Inertial Measurement Unit (IMU sensor data. The data capture nine different locomotion gaits, which include rolling and crawling modes, at three different speeds. Statistical methods are applied to extract features and to reduce the dimensionality of original IMU sensor data features. These statistical features were given as inputs for training and testing. Additionally, the c-Support Vector Classification (c-SVC and nu-SVC models of SVM, and their fault classification accuracies, were compared. The results show that the proposed SVM approach can be used to autonomously diagnose locomotion gait faults while the reconfigurable robot is in operation.
Ramos-Pollán, Raúl; Guevara-López, Miguel Angel; Suárez-Ortega, Cesar; Díaz-Herrero, Guillermo; Franco-Valiente, Jose Miguel; Rubio-Del-Solar, Manuel; González-de-Posada, Naimy; Vaz, Mario Augusto Pires; Loureiro, Joana; Ramos, Isabel
This work explores the design of mammography-based machine learning classifiers (MLC) and proposes a new method to build MLC for breast cancer diagnosis. We massively evaluated MLC configurations to classify features vectors extracted from segmented regions (pathological lesion or normal tissue) on craniocaudal (CC) and/or mediolateral oblique (MLO) mammography image views, providing BI-RADS diagnosis. Previously, appropriate combinations of image processing and normalization techniques were applied to reduce image artifacts and increase mammograms details. The method can be used under different data acquisition circumstances and exploits computer clusters to select well performing MLC configurations. We evaluated 286 cases extracted from the repository owned by HSJ-FMUP, where specialized radiologists segmented regions on CC and/or MLO images (biopsies provided the golden standard). Around 20,000 MLC configurations were evaluated, obtaining classifiers achieving an area under the ROC curve of 0.996 when combining features vectors extracted from CC and MLO views of the same case.
Shehab, Ramsey; Mirabelli, Mark H
Patients with wrist pain commonly present with an acute injury or spontaneous onset of pain without a definite traumatic event. A fall onto an outstretched hand can lead to a scaphoid fracture, which is the most commonly fractured carpal bone. Conventional radiography alone can miss up to 30 percent of scaphoid fractures. Specialized views (e.g., posteroanterior in ulnar deviation, pronated oblique) and repeat radiography in 10 to 14 days can improve sensitivity for scaphoid fractures. If a suspected scaphoid fracture cannot be confirmed with plain radiography, a bone scan or magnetic resonance imaging can be used. Subacute or chronic wrist pain usually develops gradually with or without a prior traumatic event. In these cases, the differential diagnosis is wide and includes tendinopathy and nerve entrapment. Overuse of the muscles of the forearm and wrist may lead to tendinopathy. Radial pain involving mostly the first extensor compartment is commonly de Quervain tenosynovitis. The diagnosis is based on history and examination findings of a positive Finkelstein test and a negative grind test. Nerve entrapment at the wrist presents with pain and also with sensory and sometimes motor symptoms. In ulnar neuropathies of the wrist, the typical presentation is wrist discomfort with sensory changes in the fourth and fifth digits. Activities that involve repetitive or prolonged wrist extension, such as cycling, karate, and baseball (specifically catchers), may increase the risk of ulnar neuropathy. Electrodiagnostic tests identify the area of nerve entrapment and the extent of the pathology. Copyright © 2013 American Academy of Family Physicians.
Sun, Ruobin; Yang, Zhibo; Chen, Xuefeng; Tian, Shaohua; Xie, Yong
Over the last decade, sparse representation has become a powerful paradigm in mechanical fault diagnosis due to its excellent capability and the high flexibility for complex signal description. The structured sparsity time-frequency analysis (SSTFA) is a novel signal processing method, which utilizes mixed-norm priors on time-frequency coefficients to obtain a fine match for the structure of signals. In order to extract the transient feature from gear vibration signals, a gear fault diagnosis method based on SSTFA is proposed in this work. The steady modulation components and impulsive components of the defective gear vibration signals can be extracted simultaneously by choosing different time-frequency neighborhood and generalized thresholding operators. Besides, the time-frequency distribution with high resolution is obtained by piling different components in the same diagram. The diagnostic conclusion can be made according to the envelope spectrum of the impulsive components or by the periodicity of impulses. The effectiveness of the method is verified by numerical simulations, and the vibration signals registered from a gearbox fault simulator and a wind turbine. To validate the efficiency of the presented methodology, comparisons are made among some state-of-the-art vibration separation methods and the traditional time-frequency analysis methods. The comparisons show that the proposed method possesses advantages in separating feature signals under strong noise and accounting for the inner time-frequency structure of the gear vibration signals.
Liu, Hang; Xie, Ting; Ran, Jian; Gao, Shan
It is essential for a data center to maintain server security and stability. Long-time overload operation or high room temperature may cause service disruption even a server crash, which would result in great economic loss for business. Currently, the methods to avoid server outages are monitoring and forecasting. Thermal camera can provide fine texture information for monitoring and intelligent thermal management in large data center. This paper presents an efficient method for server thermal fault monitoring and diagnosis based on infrared image. Initially thermal distribution of server is standardized and the interest regions of the image are segmented manually. Then the texture feature, Hu moments feature as well as modified entropy feature are extracted from the segmented regions. These characteristics are applied to analyze and classify thermal faults, and then make efficient energy-saving thermal management decisions such as job migration. For the larger feature space, the principal component analysis is employed to reduce the feature dimensions, and guarantee high processing speed without losing the fault feature information. Finally, different feature vectors are taken as input for SVM training, and do the thermal fault diagnosis after getting the optimized SVM classifier. This method supports suggestions for optimizing data center management, it can improve air conditioning efficiency and reduce the energy consumption of the data center. The experimental results show that the maximum detection accuracy is 81.5%.
Oliveira, Osvaldo N; Iost, Rodrigo M; Siqueira, José R; Crespilho, Frank N; Caseli, Luciano
Clinical diagnosis has always been dependent on the efficient immobilization of biomolecules in solid matrices with preserved activity, but significant developments have taken place in recent years with the increasing control of molecular architecture in organized films. Of particular importance is the synergy achieved with distinct materials such as nanoparticles, antibodies, enzymes, and other nanostructures, forming structures organized on the nanoscale. In this review, emphasis will be placed on nanomaterials for biosensing based on molecular recognition, where the recognition element may be an enzyme, DNA, RNA, catalytic antibody, aptamer, and labeled biomolecule. All of these elements may be assembled in nanostructured films, whose layer-by-layer nature is essential for combining different properties in the same device. Sensing can be done with a number of optical, electrical, and electrochemical methods, which may also rely on nanostructures for enhanced performance, as is the case of reporting nanoparticles in bioelectronics devices. The successful design of such devices requires investigation of interface properties of functionalized surfaces, for which a variety of experimental and theoretical methods have been used. Because diagnosis involves the acquisition of large amounts of data, statistical and computational methods are now in widespread use, and one may envisage an integrated expert system where information from different sources may be mined to generate the diagnostics.
Jackson, Randy; Oda, Robert P; Bhandari, Raj K; Mahon, Sari B; Brenner, Matthew; Rockwood, Gary A; Logue, Brian A
Although commonly known as a highly toxic chemical, cyanide is also an essential reagent for many industrial processes in areas such as mining, electroplating, and synthetic fiber production. The "heavy" use of cyanide in these industries, along with its necessary transportation, increases the possibility of human exposure. Because the onset of cyanide toxicity is fast, a rapid, sensitive, and accurate method for the diagnosis of cyanide exposure is necessary. Therefore, a field sensor for the diagnosis of cyanide exposure was developed based on the reaction of naphthalene dialdehyde, taurine, and cyanide, yielding a fluorescent β-isoindole. An integrated cyanide capture "apparatus", consisting of sample and cyanide capture chambers, allowed rapid separation of cyanide from blood samples. Rabbit whole blood was added to the sample chamber, acidified, and the HCN gas evolved was actively transferred through a stainless steel channel to the capture chamber containing a basic solution of naphthalene dialdehyde (NDA) and taurine. The overall analysis time (including the addition of the sample) was cyanide exposure. Most importantly, the sensor was 100% accurate in diagnosing cyanide poisoning for acutely exposed rabbits.
Sammouda, Mohamed; Sammouda, Rachid; Niki, Noboru; Mukai, Kiyoshi
Liver cancer is one of the leading cancerous diseases that can disappoint a physician before reaching the final diagnosis. Thus far, all cancer diagnoses should and usually do have tissue diagnose. A physician gets a little piece of tissue from the abnormal area and a pathologist determines if it is cancer or not. Therefore, the biopsy is the definitive test for liver cancer. In this paper, we present an unsupervised approach using Hopfield Neural Network (HNN) to segment color images of liver tissues prepared by standard staining method. The segmentation problem is formulated as the minimization of an energy function synonymous to that of HNN for optimization. We modify the HNN to reach a status close to the global minimum in a prespecified time of convergence. Furthermore, the nuclei and their corresponding cytoplasm regions are automatically extracted based on the features of color image histogram. The nuclei and cytoplasm regions are then used to formulate the diagnostic rules. In the analysis, we show a tables of the ratio of (nuclei/cytoplasm) image areas inside different subwindow sizes of the image. Each liver color image is represented in the RGB, HSV and HLS color spaces to investigate the effect of color system choice on the results. The automation of the extraction process in the liver pathological image can be easily implemented in the clinic in order to provide more accurate quantitative information that can help for a better liver cancer diagnosis.
Wang, Xiaoxian; Guo, Jie; Lu, Siliang; Shen, Changqing; He, Qingbo
Diagnosis of motor bearing faults under variable speed is a problem. In this study, a new computer-vision-based order tracking method is proposed to address this problem. First, a video recorded by a high-speed camera is analyzed with the speeded-up robust feature extraction and matching algorithm to obtain the instantaneous rotating speed (IRS) of the motor. Subsequently, an audio signal recorded by a microphone is equi-angle resampled for order tracking in accordance with the IRS curve, through which the frequency-domain signal is transferred to an angular-domain one. The envelope order spectrum is then calculated to determine the fault characteristic order, and finally the bearing fault pattern is determined. The effectiveness and robustness of the proposed method are verified with two brushless direct-current motor test rigs, in which two defective bearings and a healthy bearing are tested separately. This study provides a new noninvasive measurement approach that simultaneously avoids the installation of a tachometer and overcomes the disadvantages of tacholess order tracking methods for motor bearing fault diagnosis under variable speed.
The aim of this study is to present efficacy of storage phosphor-based digital mammography (CR-mammography) in diagnosis of breast cancer. Ninety-seven cases with breast cancer including 44 cases less than 2 cm in macroscopic size (t1 cases) were evaluated using storage phosphor-based digital mammography (2000 x 2510 pixels by 10 bits). Abnormal findings on CR-mammography were detected in 86 cases (88.7%) of 97 women with breast cancer. Sensitivity of CR-mammography was 88.7%. It was superior to that of film-screen mammography. On t1 breast cancer cases, sensitivity on CR-mammography was 88.6%. False negative rate in t1 breast cancer cases was reduced by image processing using CR-mammography. To evaluate microcalcifications, CR-mammograms and film-screen mammograms were investigated in 22 cases of breast cancer proven pathologically the existence of microcalcifications and 11 paraffin tissue blocks of breast cancer. CR-mammography was superior to film-screen mammography in recognizing of microcalcifications. As regards the detectability for the number and the shape of microcalcifications, CR-mammography was equivalent to film-screen mammography. Receiver operating characteristic (ROC) analysis by eight observers was performed for CR-mammography and film-screen mammography with 54 breast cancer patients and 54 normal cases. The detectability of abnormal findings of breast cancer on CR-mammography (ROC area=0.91) was better than that on film-screen mammography (ROC area=0.88) (p<0.05). Efficacy of storage phosphor-based digital mammography in diagnosis of breast cancer was discussed and demonstrated in this study. (author)
Liu, Guodong; Lin, Ying-Ying; Wang, Jun; Wu, Hong; Wai, Chien M.; Lin, Yuehe
We describe a disposable electrochemical immunosensor diagnosis device that is based on the immunochromatographic strip technique and an electrochemical immunoassay based on quantum dot (QD, CdS@ZnS) labels. The device takes advantage of the speed and low-cost of the conventional immunochromatographic strip test and the high-sensitivity of the nanoparticle-based electrochemical immunoassay. A sandwich immunoreaction was performed on the immunochromatographic strip, and the captured QD labels in the test zone were determined by highly sensitive stripping voltammetric measurement of the dissolved metallic component (cadmium) with a disposable-screen-printed electrode, which is embedded underneath the membrane on the test zone. The new device coupled with a portable electrochemical analyzer shows great promise for in-field and point-of-care quantitative testing of disease-related protein biomarkers. The parameters (e.g., voltammetric measurement of QD labels, antibody immobilization, the loading amount of QD-antibody, and the immunoreaction time) that govern the sensitivity and reproducibility of the device were optimized with IgG model analyte. The voltammetric response of the optimized device is highly linear over the range of 0.1 to 10 ng mL-1 IgG, and the limit of detection is estimated to be 30 pg mL-1 in association with a 7-min immunoreaction time. The detection limit was improved to 10 pg mL-1 using a 20-min immunoreaction time. The new disposable electrochemical diagnosis device thus provides a more user-friendly, rapid, clinically accurate, less expensive, and quantitative tool for protein detection.
Full Text Available Over the last decade, electroencephalography (EEG has emerged as a reliable tool for the diagnosis of cortical disorders such as Alzheimer's disease (AD. EEG signals, however, are susceptible to several artifacts, such as ocular, muscular, movement, and environmental. To overcome this limitation, existing diagnostic systems commonly depend on experienced clinicians to manually select artifact-free epochs from the collected multi-channel EEG data. Manual selection, however, is a tedious and time-consuming process, rendering the diagnostic system ``semi-automated. Notwithstanding, a number of EEG artifact removal algorithms have been proposed in the literature. The (disadvantages of using such algorithms in automated AD diagnostic systems, however, have not been documented; this paper aims to fill this gap. Here, we investigate the effects of three state-of-the-art automated artifact removal (AAR algorithms (both alone and in combination with each other on AD diagnostic systems based on four different classes of EEG features, namely, spectral, amplitude modulation rate of change, coherence, and phase. The three AAR algorithms tested are statistical artifact rejection (SAR, blind source separation based on second order blind identification and canonical correlation analysis (BSS-SOBI-CCA, and wavelet enhanced independent component analysis (wICA. Experimental results based on 20-channel resting-awake EEG data collected from 59 participants (20 patients with mild AD, 15 with moderate-to-severe AD, and 24 age-matched healthy controls showed the wICA algorithm alone outperforming other enhancement algorithm combinations across three tasks: diagnosis (control vs. mild vs. moderate, early detection (control vs. mild, and disease progression (mild vs. moderate, thus opening the doors for fully-automated systems that can assist clinicians with early detection of AD, as well as disease severity progression assessment.
Full Text Available The family Arenaviridae, genus Arenavirus, consists of two phylogenetically independent groups: Old World (OW and New World (NW complexes. The Lassa and Lujo viruses in the OW complex and the Guanarito, Junin, Machupo, Sabia, and Chapare viruses in the NW complex cause viral hemorrhagic fever (VHF in humans, leading to serious public health concerns. These viruses are also considered potential bioterrorism agents. Therefore, it is of great importance to detect these pathogens rapidly and specifically in order to minimize the risk and scale of arenavirus outbreaks. However, these arenaviruses are classified as BSL-4 pathogens, thus making it difficult to develop diagnostic techniques for these virus infections in institutes without BSL-4 facilities. To overcome these difficulties, antibody detection systems in the form of an enzyme-linked immunosorbent assay (ELISA and an indirect immunofluorescence assay were developed using recombinant nucleoproteins (rNPs derived from these viruses. Furthermore, several antigen-detection assays were developed. For example, novel monoclonal antibodies (mAbs to the rNPs of Lassa and Junin viruses were generated. Sandwich antigen-capture (Ag-capture ELISAs using these mAbs as capture antibodies were developed and confirmed to be sensitive and specific for detecting the respective arenavirus NPs. These rNP-based assays were proposed to be useful not only for an etiological diagnosis of VHFs, but also for seroepidemiological studies on VHFs. We recently developed arenavirus neutralization assays using vesicular stomatitis virus (VSV-based pseudotypes bearing arenavirus recombinant glycoproteins. The goal of this article is to review the recent advances in developing laboratory diagnostic assays based on recombinant viral proteins for the diagnosis of VHFs and epidemiological studies on the VHFs caused by arenaviruses.
Full Text Available This paper focuses on the fault diagnosis for NC machine tools and puts forward a fault diagnosis method based on kernel principal component analysis (KPCA and k-nearest neighbor (kNN. A data-dependent KPCA based on covariance matrix of sample data is designed to overcome the subjectivity in parameter selection of kernel function and is used to transform original high-dimensional data into low-dimensional manifold feature space with the intrinsic dimensionality. The kNN method is modified to adapt the fault diagnosis of tools that can determine thresholds of multifault classes and is applied to detect potential faults. An experimental analysis in NC milling machine tools is developed; the testing result shows that the proposed method is outperforming compared to the other two methods in tool fault diagnosis.
Wang, Yalin; Yuan, Lei; Shi, Jie; Greve, Alexander; Ye, Jieping; Toga, Arthur W; Reiss, Allan L; Thompson, Paul M
Many methods have been proposed for computer-assisted diagnostic classification. Full tensor information and machine learning with 3D maps derived from brain images may help detect subtle differences or classify subjects into different groups. Here we develop a new approach to apply tensor-based morphometry to parametric surface models for diagnostic classification. We use this approach to identify cortical surface features for use in diagnostic classifiers. First, with holomorphic 1-forms, we compute an efficient and accurate conformal mapping from a multiply connected mesh to the so-called slit domain. Next, the surface parameterization approach provides a natural way to register anatomical surfaces across subjects using a constrained harmonic map. To analyze anatomical differences, we then analyze the full Riemannian surface metric tensors, which retain multivariate information on local surface geometry. As the number of voxels in a 3D image is large, sparse learning is a promising method to select a subset of imaging features and to improve classification accuracy. Focusing on vertices with greatest effect sizes, we train a diagnostic classifier using the surface features selected by an L1-norm based sparse learning method. Stability selection is applied to validate the selected feature sets. We tested the algorithm on MRI-derived cortical surfaces from 42 subjects with genetically confirmed Williams syndrome and 40 age-matched controls, multivariate statistics on the local tensors gave greater effect sizes for detecting group differences relative to other TBM-based statistics including analysis of the Jacobian determinant and the largest eigenvalue of the surface metric. Our method also gave reasonable classification results relative to the Jacobian determinant, the pair of eigenvalues of the Jacobian matrix and volume features. This analysis pipeline may boost the power of morphometry studies, and may assist with image-based classification. Copyright © 2013
Thompson, Kara; McDougall, Rosalind
As assisted reproductive technologies (ART) become increasingly popular, debate has intensified over the ethical justification for restricting access to ART based on various medical and non-medical factors. In 2010, the Australian state of Victoria enacted world-first legislation that denies access to ART for all patients with certain criminal or child protection histories. Patients and their partners are identified via a compulsory police and child protection check prior to commencing ART and, if found to have a previous relevant conviction or child protection order, are given a "presumption against treatment." This article reviews the legislation and identifies arguments that may be used to justify restricting access to ART for various reasons. The arguments reviewed include limitations of reproductive rights, inheriting undesirable genetic traits, distributive justice, and the welfare of the future child. We show that none of these arguments justifies restricting access to ART in the context of past criminal history. We show that a "presumption against treatment" is an unjustified infringement on reproductive freedom and that it creates various inconsistencies in current social, medical, and legal policy. We argue that a state-enforced policy of restricting access to ART based on the non-medical factor of past criminal history is an example of unjust discrimination and cannot be ethically justified, with one important exception: in cases where ART treatment may be considered futile on the basis that the parents are not expected to raise the resulting child.
Molinié, F; Leux, C; Delafosse, P; Ayrault-Piault, S; Arveux, P; Woronoff, A S; Guizard, A V; Velten, M; Ganry, O; Bara, S; Daubisse-Marliac, L; Tretarre, B
Waiting times are key indicators of a health's system performance, but are not routinely available in France. We studied waiting times for diagnosis and treatment according to patients' characteristics, tumours' characteristics and medical management options in a sample of 1494 breast cancers recorded in population-based registries. The median waiting time from the first imaging detection to the treatment initiation was 34 days. Older age, co-morbidity, smaller size of tumour, detection by organised screening, biopsy, increasing number of specimens removed, multidisciplinary consulting meetings and surgery as initial treatment were related to increased waiting times in multivariate models. Many of these factors were related to good practices guidelines. However, the strong influence of organised screening programme and the disparity of waiting times according to geographical areas were of concern. Better scheduling of diagnostic tests and treatment propositions should improve waiting times in the management of breast cancer in France. Copyright © 2013 Elsevier Ltd. All rights reserved.
Full Text Available The ability to capture and analyze fetal cells from maternal circulation or other sources during pregnancy has been a goal of prenatal diagnostics for over thirty years. The vision of replacing invasive prenatal diagnostic procedures with the prospect of having the entire fetal genome in hand non-invasively for chromosomal and molecular studies for both clinical and research use has brought many investigators and innovations into the effort. While the object of this desire, however, has remained elusive, the aspiration for this approach to non-invasive prenatal diagnosis remains and the inquiry has continued. With the advent of screening by cell-free DNA analysis, the standards for fetal cell based prenatal diagnostics have been sharpened. Relevant aspects of the history and the current status of investigations to meet the goal of having an accessible and reliable strategy for capturing and analyzing fetal cells during pregnancy are reviewed.
Tan, S. Veronica; Matthews, Emma; Barber, Melissa; Burge, James A; Rajakulendran, Sanjeev; Fialho, Doreen; Sud, Richa; Haworth, Andrea; Koltzenburg, Martin; Hanna, Michael G
Objective To improve the accuracy of genotype prediction and guide genetic testing in patients with muscle channelopathies we applied and refined specialised electrophysiological exercise test parameters. Methods We studied 56 genetically confirmed patients and 65 controls using needle electromyography, the long exercise test, and short exercise tests at room temperature, after cooling, and rewarming. Results Concordant amplitude-and-area decrements were more reliable than amplitude-only measurements when interpreting patterns of change during the short exercise tests. Concordant amplitude-and-area pattern I and pattern II decrements of >20% were 100% specific for PMC and MC respectively. When decrements at room temperature and after cooling were 20% allow more reliable interpretation of the short exercise tests and aid accurate DNA-based diagnosis. In patients with negative exercise tests, specific clinical features are helpful in differentiating sodium from chloride channel myotonia. A modified algorithm is suggested.. PMID:21387378
Wolf, Johannes; Petroff, David; Richter, Thomas; Auth, Marcus K H; Uhlig, Holm H; Laass, Martin W; Lauenstein, Peter; Krahl, Andreas; Händel, Norman; de Laffolie, Jan; Hauer, Almuthe C; Kehler, Thomas; Flemming, Gunter; Schmidt, Frank; Rodrigues, Astor; Hasenclever, Dirk; Mothes, Thomas
A diagnosis of celiac disease is made based on clinical, genetic, serologic, and duodenal morphology features. Recent pediatric guidelines, based largely on retrospective data, propose omitting biopsy analysis for patients with concentrations of IgA against tissue transglutaminase (IgA-TTG) >10-fold the upper limit of normal (ULN) and if further criteria are met. A retrospective study concluded that measurements of IgA-TTG and total IgA, or IgA-TTG and IgG against deamidated gliadin (IgG-DGL) could identify patients with and without celiac disease. Patients were assigned to categories of no celiac disease, celiac disease, or biopsy required, based entirely on antibody assays. We aimed to validate the positive and negative predictive values (PPV and NPV) of these diagnostic procedures. We performed a prospective study of 898 children undergoing duodenal biopsy analysis to confirm or rule out celiac disease at 13 centers in Europe. We compared findings from serologic analysis with findings from biopsy analyses, follow-up data, and diagnoses made by the pediatric gastroenterologists (celiac disease, no celiac disease, or no final diagnosis). Assays to measure IgA-TTG, IgG-DGL, and endomysium antibodies were performed by blinded researchers, and tissue sections were analyzed by local and blinded reference pathologists. We validated 2 procedures for diagnosis: total-IgA and IgA-TTG (the TTG-IgA procedure), as well as IgG-DGL with IgA-TTG (TTG-DGL procedure). Patients were assigned to categories of no celiac disease if all assays found antibody concentrations celiac disease if at least 1 assay measured antibody concentrations >10-fold the ULN. All other cases were considered to require biopsy analysis. ULN values were calculated using the cutoff levels suggested by the test kit manufacturers. HLA typing was performed for 449 participants. We used models that considered how specificity values change with prevalence to extrapolate the PPV and NPV to populations with lower
Brock, I; Weldingh, K; Leyten, EM
Specific T-cell epitopes for immunoassay-based diagnosis of Mycobacterium tuberculosis infection.Brock I, Weldingh K, Leyten EM, Arend SM, Ravn P, Andersen P. Department of Infectious Disease Immunology, Statens Serum Institute, Artillerivej 5, DK-2300 Copenhagen S, Denmark. The currently used...... method for immunological detection of tuberculosis infection, the tuberculin skin test, has low specificity. Antigens specific for Mycobacterium tuberculosis to replace purified protein derivative are therefore urgently needed. We have performed a rigorous assessment of the diagnostic potential of four...... selected and combined the specific peptide stretches from the four proteins not recognized by M. bovis BCG-vaccinated individuals. These peptide stretches were tested with peripheral blood mononuclear cells obtained from patients with microscopy- or culture-confirmed tuberculosis and from healthy M. bovis...
Zhang, Shengli; Tang, Jiong
Gearbox is one of the most vulnerable subsystems in wind turbines. Its healthy status significantly affects the efficiency and function of the entire system. Vibration based fault diagnosis methods are prevalently applied nowadays. However, vibration signals are always contaminated by noise that comes from data acquisition errors, structure geometric errors, operation errors, etc. As a result, it is difficult to identify potential gear failures directly from vibration signals, especially for the early stage faults. This paper utilizes synchronous averaging technique in time-frequency domain to remove the non-synchronous noise and enhance the fault related time-frequency features. The enhanced time-frequency information is further employed in gear fault classification and identification through feature extraction algorithms including Kernel Principal Component Analysis (KPCA), Multilinear Principal Component Analysis (MPCA), and Locally Linear Embedding (LLE). Results show that the LLE approach is the most effective to classify and identify different gear faults.
Full Text Available Rotating machinery vibration signals are nonstationary and nonlinear under complicated operating conditions. It is meaningful to extract optimal features from raw signal and provide accurate fault diagnosis results. In order to resolve the nonlinear problem, an enhancement deep feature extraction method based on Gaussian radial basis kernel function and autoencoder (AE is proposed. Firstly, kernel function is employed to enhance the feature learning capability, and a new AE is designed termed kernel AE (KAE. Subsequently, a deep neural network is constructed with one KAE and multiple AEs to extract inherent features layer by layer. Finally, softmax is adopted as the classifier to accurately identify different bearing faults, and error backpropagation algorithm is used to fine-tune the model parameters. Aircraft engine intershaft bearing vibration data are used to verify the method. The results confirm that the proposed method has a better feature extraction capability, requires fewer iterations, and has a higher accuracy than standard methods using a stacked AE.
Brock, I; Weldingh, K; Leyten, EM
Specific T-cell epitopes for immunoassay-based diagnosis of Mycobacterium tuberculosis infection.Brock I, Weldingh K, Leyten EM, Arend SM, Ravn P, Andersen P. Department of Infectious Disease Immunology, Statens Serum Institute, Artillerivej 5, DK-2300 Copenhagen S, Denmark. The currently used...... method for immunological detection of tuberculosis infection, the tuberculin skin test, has low specificity. Antigens specific for Mycobacterium tuberculosis to replace purified protein derivative are therefore urgently needed. We have performed a rigorous assessment of the diagnostic potential of four...... recently identified antigens (Rv2653, Rv2654, Rv3873, and Rv3878) from genomic regions that are lacking from the Mycobacterium bovis bacillus Calmette-Guerin (BCG) vaccine strains as well as from the most common nontuberculous mycobacteria. The fine specificity of potential epitopes in these molecules...
Rong, R. W.; Ming, T. F.
In order to solve the problem of slow computation speed, matching pursuit algorithm is applied to rolling bearing fault diagnosis, and the improvement are conducted from two aspects that are the construction of dictionary and the way to search for atoms. To be specific, Gabor function which can reflect time-frequency localization characteristic well is used to construct the dictionary, and the genetic algorithm to improve the searching speed. A time-frequency analysis method based on genetic algorithm matching pursuit (GAMP) algorithm is proposed. The way to set property parameters for the improvement of the decomposition results is studied. Simulation and experimental results illustrate that the weak fault feature of rolling bearing can be extracted effectively by this proposed method, at the same time, the computation speed increases obviously.
Full Text Available Bearing failure is one of the dominant causes of failure and breakdowns in rotating machinery, leading to huge economic loss. Aiming at the nonstationary and nonlinear characteristics of bearing vibration signals as well as the complexity of condition-indicating information distribution in the signals, a novel rolling element bearing fault diagnosis method based on multifractal theory and gray relation theory was proposed in the paper. Firstly, a generalized multifractal dimension algorithm was developed to extract the characteristic vectors of fault features from the bearing vibration signals, which can offer more meaningful and distinguishing information reflecting different bearing health status in comparison with conventional single fractal dimension. After feature extraction by multifractal dimensions, an adaptive gray relation algorithm was applied to implement an automated bearing fault pattern recognition. The experimental results show that the proposed method can identify various bearing fault types as well as severities effectively and accurately.
Full Text Available Fluidized bed combustion (FBC boilers have received increasing attention in recent decades. The erosion issue on the water wall is one of the most common and serious faults for FBC boilers. Unlike direct measurement of tube thickness used by ultrasonic methods, the wastage of water wall is reconsidered equally as the variation of the overall heat transfer coefficient in the furnace. In this paper, a model-based approach is presented to estimate internal states and heat transfer coefficient dually from the noisy measurable outputs. The estimated parameter is compared with the normal value. Then the modified Bayesian algorithm is adopted for fault detection and diagnosis (FDD. The simulation results demonstrate that the approach is feasible and effective.
Full Text Available The traditional methods of diagnosing dam service status are always suitable for single measuring point. These methods also reflect the local status of dams without merging multisource data effectively, which is not suitable for diagnosing overall service. This study proposes a new method involving multiple points to diagnose dam service status based on joint distribution function. The function, including monitoring data of multiple points, can be established with t-copula function. Therefore, the possibility, which is an important fusing value in different measuring combinations, can be calculated, and the corresponding diagnosing criterion is established with typical small probability theory. Engineering case study indicates that the fusion diagnosis method can be conducted in real time and the abnormal point can be detected, thereby providing a new early warning method for engineering safety.
Wei, Liyang; Yang, Yongyi; Nishikawa, Roberts M
In this paper we propose a microcalcification classification scheme, assisted by content-based mammogram retrieval, for breast cancer diagnosis. We recently developed a machine learning approach for mammogram retrieval where the similarity measure between two lesion mammograms was modeled after expert observers. In this work we investigate how to use retrieved similar cases as references to improve the performance of a numerical classifier. Our rationale is that by adaptively incorporating local proximity information into a classifier, it can help to improve its classification accuracy, thereby leading to an improved "second opinion" to radiologists. Our experimental results on a mammogram database demonstrate that the proposed retrieval-driven approach with an adaptive support vector machine (SVM) could improve the classification performance from 0.78 to 0.82 in terms of the area under the ROC curve.
Totu, Eugenia Eftimie; Isildak, Ibrahim; Nechifor, Aurelia Cristina; Cristache, Corina Marilena; Enachescu, Marius
A series of sodium selective membranes with magnetic nano-inclusions using p-tertbutyl calixarene as ionophore and polymeric matrix (polyvinyl chloride) have been developed, and the corresponding sodium selective sensors were obtained for the first time. A linear range was registered between 3.1 × 10 -5 and 10 -1 moldm -3 and near Nernstian electrochemical answer: 55.73mV/decade has been recoreded for PVC (polyvinyl chloride) - based sodium selective sensor, with a response time of 45s. Due to their small dimensions, sensors could be used for measuring ions from the gingival crevicular fluid directly into the peri-odontal pocket, avoiding the difficulties of collecting an appropriate amount of fluid for analysis. Alterations in the inorganic ions level could be evidenced with this new device, assisting the early diagnosis and prevention of periodontal disease. Copyright © 2017 Elsevier B.V. All rights reserved.
Li, Jingchao; Cao, Yunpeng; Ying, Yulong; Li, Shuying
Bearing failure is one of the dominant causes of failure and breakdowns in rotating machinery, leading to huge economic loss. Aiming at the nonstationary and nonlinear characteristics of bearing vibration signals as well as the complexity of condition-indicating information distribution in the signals, a novel rolling element bearing fault diagnosis method based on multifractal theory and gray relation theory was proposed in the paper. Firstly, a generalized multifractal dimension algorithm was developed to extract the characteristic vectors of fault features from the bearing vibration signals, which can offer more meaningful and distinguishing information reflecting different bearing health status in comparison with conventional single fractal dimension. After feature extraction by multifractal dimensions, an adaptive gray relation algorithm was applied to implement an automated bearing fault pattern recognition. The experimental results show that the proposed method can identify various bearing fault types as well as severities effectively and accurately.
Stein, J M; Hartmann, F; Cordes, H-J; Dignass, A U
Anaemia is the most frequent extraenteric complication of inflammatory bowel disease (IBD, Crohn's disease and ulcerative colitis). A disabling complication of IBD, anaemia worsens the patient's general condition and quality of life, and increases hospitalization rates. The main types of anemia in IBD are iron deficiency anemia and anemia of chronic disease. The combination of the serum transferrin receptor with ferritin concentrations and inflammatory markers allows a reliable assessment of the iron status. Iron deficiency is usually treated with oral iron supplements. However, it is less effective in IBD and may lead to an increased inflammatory activity through the generation of reactive oxygen species. A systematic review of anemia in IBD, its pathogenetic features, epidemiology, diagnosis and therapy based on the evidence from recent studies will be the focus of this article.
Borges, R M; Mattedi, A; Munaro, C J; Franci Gonçalves, R
A modular diagnosis system (MDS), based on the framework of fuzzy logic, is proposed for upflow anaerobic sludge blanket (UASB) reactors treating sewage. In module 1, turbidity and rainfall information are used to estimate the influent organic content. In module 2, a dynamic fuzzy model is used to estimate the current biogas production from on-line measured variables, such as daily average temperature and the previous biogas flow rate, as well as the organic load. Finally, in module 3, all the information above and the residual value between the measured and estimated biogas production are used to provide diagnostic information about the operation status of the plant. The MDS was validated through its application to two pilot UASB reactors and the results showed that the tool can provide useful diagnoses to avoid plant failures.
Sorrentino, Marco; Acconcia, Matteo; Panagrosso, Davide; Trifirò, Alena
A methodology is proposed for on-line monitoring of cooling load supplied by Telecommunication (TLC) cooling systems. Sensible cooling load is estimated via a proportional integral controller-based input estimator, whereas a lumped parameters model was developed aiming at estimating air handling units (AHUs) latent heat load removal. The joint deployment of above estimators enables accurate prediction of total cooling load, as well as of related AHUs and free-coolers energy performance. The procedure was then proven effective when extended to cooling systems having a centralized chiller, through model-based estimation of a key performance metric, such as the energy efficiency ratio. The results and experimental validation presented throughout the paper confirm the suitability of the proposed procedure as a reliable and effective energy monitoring and diagnostic tool for TLC applications. Moreover, the proposed modeling approach, beyond its direct contribution towards smart use and conservation of energy, can be fruitfully deployed as a virtual sensor of removed heat load into a variety of residential and industrial applications. - Highlights: • Accurate cooling load prediction in telecommunication rooms. • Development of an input-estimator for sensible cooling load simulation. • Model-based estimation of latent cooling load. • Model-based prediction of centralized chiller energy performance in central offices. • Diagnosis-oriented application of proposed cooling load estimator.
Garbe, Claus; Peris, Ketty; Hauschild, Axel; Saiag, Philippe; Middleton, Mark; Bastholt, Lars; Grob, Jean-Jacques; Malvehy, Josep; Newton-Bishop, Julia; Stratigos, Alexander J; Pehamberger, Hubert; Eggermont, Alexander M
Cutaneous melanoma (CM) is potentially the most dangerous form of skin tumour and causes 90% of skin cancer mortality. A unique collaboration of multi-disciplinary experts from the European Dermatology Forum, the European Association of Dermato-Oncology and the European Organisation of Research and Treatment of Cancer was formed to make recommendations on CM diagnosis and treatment, based on systematic literature reviews and the experts' experience. Diagnosis is made clinically using dermoscopy and staging is based upon the AJCC system. CMs are excised with 1-2 cm safety margins. Sentinel lymph node dissection is routinely offered as a staging procedure in patients with tumours >1 mm in thickness, although there is as yet no clear survival benefit for this approach. Interferon-α treatment may be offered to patients with stage II and III melanoma as an adjuvant therapy, as this treatment increases at least the disease-free survival and less clear the overall survival (OS) time. The treatment is however associated with significant toxicity. In distant metastasis, all options of surgical therapy have to be considered thoroughly. In the absence of surgical options, systemic treatment is indicated. For first-line treatment particularly in BRAF wild-type patients, immunotherapy with PD-1 antibodies alone or in combination with CTLA-4 antibodies should be considered. BRAF inhibitors like dabrafenib and vemurafenib in combination with the MEK inhibitors trametinib and cobimetinib for BRAF mutated patients should be offered as first or second line treatment. Therapeutic decisions in stage IV patients should be primarily made by an interdisciplinary oncology team ('Tumour Board'). Copyright © 2016 Elsevier Ltd. All rights reserved.
Schneider, Joachim; Bitterlich, Norman; Velcovsky, Hans-Georg; Morr, Harald; Katz, Norbert; Eigenbrodt, Erich
The aim of this study was to improve the diagnostic efficiency of tumor markers in the diagnosis of lung cancer, by the mathematical evaluation of a tumor marker profile employing fuzzy logic modelling. A panel of four tumor markers, i.e., carcinoembryonic antigen (CEA), cytokeratin 19 antibody (CYFRA 21-1), neuron-specific enolase (NSE), squamous cell carcinoma-related antigen (SCC) and, additionally, C-reactive protein (CRP), was measured in 175 newly diagnosed lung cancer patients with different histological types and stages. Results were compared with those in 120 control subjects, including 27 with chronic obstructive pulmonary diseases (COPD), 65 with pneumoconiosis, and 11 persons with acute inflammatory lung diseases. A classificator was developed using a fuzzy-logic rule-based system. Application of the fuzzy-logic rule-based system to the tumor marker values of CYFRA 21-1, NSE, and CRP yielded an increase in sensitivity of approximately 20%, i.e., 92%, compared with that of the best single marker, CYFRA 21-1(sensitivity, 72%). The corresponding specificity was 95%. The fuzzy classificator significantly improved the sensitivity of the tumor marker panel in stages I and IIIa for non-small-cell lung cancer, as well as in "limited disease" status for small-cell lung cancer. Also, the diagnosis of other stages of lung cancer was enhanced. Fuzzy-logic analysis was proven to be more powerful than the measurement of single markers alone or combinations using multiple logistic regression analysis of all markers. Therefore, fuzzy logic offers a promising diagnostic tool to improve tumor marker efficiency.
Full Text Available Acute ankle injuries are among the most common injuries in emergency departments. However, a standardized examination and an evidence-based treatment are missing. Therefore, aim of this study was to systematically search the current literature, classify the evidence and develop an algorithm for diagnosis and treatment of acute ankle injuries. We systematically searched PubMed and the Cochrane Database for randomized controlled trials, meta-analysis, systematic reviews, or if applicable observational studies and classified them according to their level of evidence. According to the currently available literature, the following recommendations are given. The Ottawa Ankle/Foot Rule should be applied in order to rule out fractures, Physical examination is sufficient for diagnosing injuries to the lateral ligament complex. Classification into stable and unstable injuries is applicable and of clinical importance. The squeeze-, crossed leg- and external rotation test are indicative for injuries of the syndesmosis. Magnetic resonance imaging is recommended to verify such injuries. Stable ankle sprains have a good prognosis, while for unstable ankle sprains conservative treatment is at least as effective as operative treatment without carrying possible complications. Early functional treatment leads to the fastest recovery and the least rate of re-injury. Supervised rehabilitation reduces residual symptoms and re-injuries. Taken these recommendations into account, we here present an applicable and evidence-based step by step decision pathway for the diagnosis and treatment of acute ankle injuries, which can be implemented in any emergency department or doctor’s practice. It provides quality assurance for the patient and confidence for the attending physician.
Polzer, Hans; Kanz, Karl Georg; Prall, Wolf Christian; Haasters, Florian; Ockert, Ben; Mutschler, Wolf; Grote, Stefan
Acute ankle injuries are among the most common injuries in emergency departments. However, there are still no standardized examination procedures or evidence-based treatment. Therefore, the aim of this study was to systematically search the current literature, classify the evidence, and develop an algorithm for the diagnosis and treatment of acute ankle injuries. We systematically searched PubMed and the Cochrane Database for randomized controlled trials, meta-analyses, systematic reviews or, if applicable, observational studies and classified them according to their level of evidence. According to the currently available literature, the following recommendations have been formulated: i) the Ottawa Ankle/Foot Rule should be applied in order to rule out fractures; ii) physical examination is sufficient for diagnosing injuries to the lateral ligament complex; iii) classification into stable and unstable injuries is applicable and of clinical importance; iv) the squeeze-, crossed leg- and external rotation test are indicative for injuries of the syndesmosis; v) magnetic resonance imaging is recommended to verify injuries of the syndesmosis; vi) stable ankle sprains have a good prognosis while for unstable ankle sprains, conservative treatment is at least as effective as operative treatment without the related possible complications; vii) early functional treatment leads to the fastest recovery and the least rate of reinjury; viii) supervised rehabilitation reduces residual symptoms and re-injuries. Taken these recommendations into account, we present an applicable and evidence-based, step by step, decision pathway for the diagnosis and treatment of acute ankle injuries, which can be implemented in any emergency department or doctor's practice. It provides quality assurance for the patient and promotes confidence in the attending physician. PMID:22577506
Modi, Jayesh; Sharma, Pranshu; Earl, Alex; Simpson, Mark; Mitchell, J Ross; Goyal, Mayank
To assess the feasibility of iPhone-based teleradiology as a potential solution for the diagnosis of acute cervico-dorsal spine trauma. We have developed a solution that allows visualization of images on the iPhone. Our system allows rapid, remote, secure, visualization of medical images without storing patient data on the iPhone. This retrospective study is comprised of cervico-dorsal computed tomogram (CT) scan examination of 75 consecutive patients having clinically suspected cervico-dorsal spine fracture. Two radiologists reviewed CT scan images on the iPhone. Computed tomogram spine scans were analyzed for vertebral body fracture and posterior elements fractures, any associated subluxation-dislocation and cord lesion. The total time taken from the launch of viewing application on the iPhone until interpretation was recorded. The results were compared with that of a diagnostic workstation monitor. Inter-rater agreement was assessed. The sensitivity and accuracy of detecting vertebral body fractures was 80% and 97% by both readers using the iPhone system with a perfect inter-rater agreement (kappa:1). The sensitivity and accuracy of detecting posterior elements fracture was 75% and 98% for Reader 1 and 50% and 97% for Reader 2 using the iPhone. There was good inter-rater agreement (kappa: 0.66) between both readers. No statistically significant difference was noted between time on the workstation and the iPhone system. iPhone-based teleradiology system is accurate in the diagnosis of acute cervicodorsal spinal trauma. It allows rapid, remote, secure, visualization of medical images without storing patient data on the iPhone.
Liu, Jen-Jane; Wu, Katherine; Adams, Winifred; Hsiao, Shelly T.; Mach, Kathleen E.; Beck, Andrew H.; Jensen, Kristin C.; Liao, Joseph C.
Probe-based confocal laser endomicroscopy (pCLE) is an emerging technology for in vivo optical imaging of the urinary tract. Particularly for bladder cancer, real time optical biopsy of suspected lesions will likely lead to improved management of bladder cancer. With pCLE, micron scale resolution is achieved with sterilizable imaging probes (1.4 or 2.6 mm diameter), which are compatible with standard cystoscopes and resectoscopes. Based on our initial experience to date (n = 66 patients), we have demonstrated the safety profile of intravesical fluorescein administration and established objective diagnostic criteria to differentiate between normal, benign, and neoplastic urothelium. Confocal images of normal bladder showed organized layers of umbrella cells, intermediate cells, and lamina propria. Low grade bladder cancer is characterized by densely packed monomorphic cells with central fibrovascular cores, whereas high grade cancer consists of highly disorganized microarchitecture and pleomorphic cells with indistinct cell borders. Currently, we are conducting a diagnostic accuracy study of pCLE for bladder cancer diagnosis. Patients scheduled to undergo transurethral resection of bladder tumor are recruited. Patients undergo first white light cystocopy (WLC), followed by pCLE, and finally histologic confirmation of the resected tissues. The diagnostic accuracy is determined both in real time by the operative surgeon and offline after additional image processing. Using histology as the standard, the sensitivity, specificity, positive and negative predictive value of WLC and WLC + pCLE are calculated. With additional validation, pCLE may prove to be a valuable adjunct to WLC for real time diagnosis of bladder cancer.
Jin, Kai; Zhou, Mei; Wang, Shaoze; Lou, Lixia; Xu, Yufeng; Ye, Juan; Qian, Dahong
Retinal imaging is an important and effective tool for detecting retinal diseases. However, degraded images caused by the aberrations of the eye can disguise lesions, so that a diseased eye can be mistakenly diagnosed as normal. In this work, we propose a new image enhancement method to improve the quality of degraded images. A new method is used to enhance degraded-quality fundus images. In this method, the image is converted from the input RGB colour space to LAB colour space and then each normalized component is enhanced using contrast-limited adaptive histogram equalization. Human visual system (HVS)-based fundus image quality assessment, combined with diagnosis by experts, is used to evaluate the enhancement. The study included 191 degraded-quality fundus photographs of 143 subjects with optic media opacity. Objective quality assessment of image enhancement (range: 0-1) indicated that our method improved colour retinal image quality from an average of 0.0773 (variance 0.0801) to an average of 0.3973 (variance 0.0756). Following enhancement, area under curves (AUC) were 0.996 for the glaucoma classifier, 0.989 for the diabetic retinopathy (DR) classifier, 0.975 for the age-related macular degeneration (AMD) classifier and 0.979 for the other retinal diseases classifier. The relatively simple method for enhancing degraded-quality fundus images achieves superior image enhancement, as demonstrated in a qualitative HVS-based image quality assessment. This retinal image enhancement may, therefore, be employed to assist ophthalmologists in more efficient screening of retinal diseases and the development of computer-aided diagnosis. © 2017 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.
Paulev, P E; Zubieta-Calleja, G R
This report describes the historical development in the clinical application of chemical variables for the interpretation of acid-base disturbances. The pH concept was already introduced in 1909. Following World War II, disagreements concerning the definition of acids and bases occurred, and since then two strategies have been competing. Danish scientists in 1923 defined an acid as a substance able to give off a proton at a given pH, and a base as a substance that could bind a proton, whereas the North American Singer-Hasting school in 1948 defined acids as strong non-buffer anions and bases as non-buffer cations. As a consequence of this last definition, electrolyte disturbances were mixed up with real acid-base disorders and the variable, strong ion difference (SID), was introduced as a measure of non-respiratory acid-base disturbances. However, the SID concept is only an empirical approximation. In contrast, the Astrup/Siggaard-Andersen school of scientists, using computer strategies and the Acid-base Chart, has made diagnosis of acid-base disorders possible at a glance on the Chart, when the data are considered in context with the clinical development. Siggaard-Andersen introduced Base Excess (BE) or Standard Base Excess (SBE) in the extracellular fluid volume (ECF), extended to include the red cell volume (eECF), as a measure of metabolic acid-base disturbances and recently replaced it by the term Concentration of Titratable Hydrogen Ion (ctH). These two concepts (SBE and ctH) represent the same concentration difference, but with opposite signs. Three charts modified from the Siggaard-Andersen Acid-Base Chart are presented for use at low, medium and high altitudes of 2500 m, 3500 m, and 4000 m, respectively. In this context, the authors suggest the use of Titratable Hydrogen Ion concentration Difference (THID) in the extended extracellular fluid volume, finding it efficient and better than any other determination of the metabolic component in acid-base
El-Baz, A. [University of Louisville, Bioengineering Department, Louisville, KY (United States); Casanova, M.; Mott, M.; Switala, A. [University of Louisville, Department of Psychiatry and Behavioral Science, Louisville, KY (United States); Gimel' farb, G. [University of Auckland, Computer Science Department, Auckland (New Zealand)
A computer-aided diagnosis (CAD) system for early diagnosis of dyslexia was developed and tested. Dyslexia can severely impair the learning abilities of children so improved diagnostic methods are needed. Neuropathological studies show abnormal anatomy of the cerebral white matter (CWM) in dyslexic brains. We sought to develop an MRI-based macroscopic neuropathological correlate to the minicolumnopathy of dyslexia that relates to cortical connectivity: the gyral window. The brains of dyslexic patients often exhibit decreased gyrifications, so the thickness of gyral CWM for dyslexic subjects is greater than for normal subjects. We developed an MRI-based method for assessment of gyral CWM thickness with automated recognition of abnormal (e.g., dyslexic) brains. In vivo data was collected from 16 right-handed dyslexic men aged 18-40 years, and a group of 14 controls matched for gender, age, educational level, socioeconomic background, handedness and general intelligence. All the subjects were physically healthy and free of history of neurological diseases and head injury. Images were acquired with the same 1.5T MRI scanner (GE, Milwaukee, WI, USA) with voxel resolution 0.9375 x 0.9375 x 1.5 mm using a T1-weighted imaging sequence protocol. The ''ground truth'' diagnosis to evaluate the classification accuracy for each patient was given by the clinicians. The accuracy of diagnosis/classification of both the training and test subjects was evaluated using the Chi-square test at the three confidence levels - 85, 90 and 95% - in order to examine significant differences in the Levy distances. As expected, the 85% confidence level yielded the best results, the system correctly classified 16 out of 16 dyslexic subjects (a 100% accuracy) and 14 out of 14 control subjects (a 100% accuracy). At the 90% confidence level, 16 out of 16 dyslexic subjects were still classified correctly; however, only 13 out of 14 control subjects were correct, bringing the
El-Baz, A.; Casanova, M.; Mott, M.; Switala, A.; Gimel'farb, G.
A computer-aided diagnosis (CAD) system for early diagnosis of dyslexia was developed and tested. Dyslexia can severely impair the learning abilities of children so improved diagnostic methods are needed. Neuropathological studies show abnormal anatomy of the cerebral white matter (CWM) in dyslexic brains. We sought to develop an MRI-based macroscopic neuropathological correlate to the minicolumnopathy of dyslexia that relates to cortical connectivity: the gyral window. The brains of dyslexic patients often exhibit decreased gyrifications, so the thickness of gyral CWM for dyslexic subjects is greater than for normal subjects. We developed an MRI-based method for assessment of gyral CWM thickness with automated recognition of abnormal (e.g., dyslexic) brains. In vivo data was collected from 16 right-handed dyslexic men aged 18-40 years, and a group of 14 controls matched for gender, age, educational level, socioeconomic background, handedness and general intelligence. All the subjects were physically healthy and free of history of neurological diseases and head injury. Images were acquired with the same 1.5T MRI scanner (GE, Milwaukee, WI, USA) with voxel resolution 0.9375 x 0.9375 x 1.5 mm using a T1-weighted imaging sequence protocol. The ''ground truth'' diagnosis to evaluate the classification accuracy for each patient was given by the clinicians. The accuracy of diagnosis/classification of both the training and test subjects was evaluated using the Chi-square test at the three confidence levels - 85, 90 and 95% - in order to examine significant differences in the Levy distances. As expected, the 85% confidence level yielded the best results, the system correctly classified 16 out of 16 dyslexic subjects (a 100% accuracy) and 14 out of 14 control subjects (a 100% accuracy). At the 90% confidence level, 16 out of 16 dyslexic subjects were still classified correctly; however, only 13 out of 14 control subjects were correct, bringing the accuracy rate for the
Bai, Lin; Ren, Yulan; Guo, Taipin; Chen, Lin; Zhou, Yumei; Feng, Shuwei; Li, Ji; Liang, Fanrong
To perform a bibliometrics analysis on patent literature regarding diagnosis and treatment devices of acupuncture in China, aiming to provide references for the development of diagnosis and treatment devices of acupuncture. Based on SooPAT, a patent database, the patent literature regarding diagnosis and treatment devices of acupuncture in China was collected. With bibliometrics methods, the annual distribution of type, quantity, classification and content of diagnosis and treatment devices of acupuncture were analyzed. The number of acupuncture diagnosis and treatment devices reached its peak in 2012 and 2013 in China. The A61N in patent and utility model patent were the most, which were mainly related to electrotherapy, magnetic therapy, radioactive therapy and ultrasound therapy, etc. The main content was acupuncture treatment devices and meridian treatment devices. The 24-01 in design patent was the most, involving fixation devices used by doctors, hospitals and laboratories, etc. Currently the majority of diagnosis and treatment devices of acupuncture is therapeutic apparatus, while the acupuncture diagnosis devices are needed.
Dysmenorrhea occurs in the majority of adolescent girls and is the leading cause of recurrent short-term school absence in this group. In the vast majority of cases, a presumptive diagnosis of primary dysmenorrhea can be made based on a typical history of low anterior pelvic pain coinciding with the onset of menses and lasting 1-3 days with a negative physical examination. Risk factors for primary dysmenorrhea include nulliparity, heavy menstrual flow, and smoking. Poor mental health and social supports are other associations. Empiric therapy for primary dysmenorrhea can be initiated without diagnostic testing. Effective therapies include NSAIDs, oral contraceptives, and pharmacologic suppression of menstrual cycles. In atypical, severe, or refractory cases, imaging and/or laparoscopy should be performed to investigate secondary causes of dysmenorrhea. The most common cause of secondary dysmenorrhea is endometriosis, the treatment of which may include medical and surgical approaches. Pharmacologic treatment of young women with pain related to endometriosis is similar to treatment of primary dysmenorrhea but may infrequently include gonadotropin-releasing hormone agonists in severe refractory cases.
Sandra Soares Melo
Full Text Available Objective: This study sought to determine the presence of diagnosis suggestive of binge eating disorder in individuals with type 2 diabetes mellitus, and to evaluate the influence of such disorder on the metabolic control. Methods: sixty-three patients with type 2 diabetes mellitus and registered at the Diabetes and Hypertension Program of a Health Unit in the town of Balneário Camboriú, Santa Catarina, Brazil, were evaluated. The diagnosis of binge eating disorder was made by analysis of the Questionnaire on Eating and Weight Patterms – Revised. For the evaluation of metabolic control, 10 ml of blood was collected, and the serum glucose, glycated hemoglobin, tryglicerides, cholestrol and fractions were determined. Weight and height were determined for evaluation of national nutritional state, according to the body mass index. Rresults: Among the evaluated individuals, 29% presented a diagnosis suggestive of binge eating disorder, with higher prevalence among females. The individuals with diagnosis suggestive of binge eating disorder presented a higher average body mass index value than the group without diagnosis. The serum concentrations of glycated hemoglobin (p = 0.02 and triglicerides (p = 0.03 were statistically higher in the group with diagnosis suggestive of binge eating disorder. Cconclusions: Based on the results of this study, it is possible to conclude that the presence of binge eating disorder in individuals with type 2 diabetes mellitus favors an increase in body weight and has a negative influence on metabolic control, contributing to the early emergence of complications related to the disease.
Mourato, F A; Mattos, S S; Lima Filho, J L; Mourato, M F; Nadruz JÚnior, W
High blood pressure (BP) is usually underdiagnosed in children and adolescents, particularly due to its complex diagnosis process. This study describes novel height-based equations for the detection of BP disorders (BP>90th percentile) and compares the accuracy of this approach with previously described screening methods to identify BP disorders. Height-based equations were built using the 90th percentile values for systolic and diastolic BP and respective height values from the current guideline of high BP management in children. This guideline was also used as the gold standard method for identification of BP disorders. The equations were tested in Brazilian (n=2,936) and American (n=6,541) populations of children with 8-13 years-old. the obtained equations were 70+0.3*height (in cm) for systolic BP and 35+0.25*height (in cm) for diastolic BP. The new equations were tested in Height-based equations presented sensitivity and negative predictive value of near 100% and specificity >91%, and showed higher specificity and positive predictive value when compared to other screening tools. Importantly, height-based equations had greater agreement (kappa coefficient=0.75-0.81) with the gold standard method than the other methods (kappa coefficient=0.53-0.73). Further analysis showed that alternative height-based equations designed to identify hypertension (BP≥95th percentile) also showed superior performance (kappa coefficient=0.89-0.92) compared to other screening methods (kappa coefficient=0.43-0.85). These findings suggest that the use of height-based equations may be a simple and feasible approach to improve the detection of high BP in the pediatric population.
Chen, Jinglong; Li, Zipeng; Pan, Jun; Chen, Gaige; Zi, Yanyang; Yuan, Jing; Chen, Binqiang; He, Zhengjia
As a significant role in industrial equipment, rotating machinery fault diagnosis (RMFD) always draws lots of attention for guaranteeing product quality and improving economic benefit. But non-stationary vibration signal with a large amount of noise on abnormal condition of weak fault or compound fault in many cases would lead to this task challenging. As one of the most powerful non-stationary signal processing techniques, wavelet transform (WT) has been extensively studied and widely applied in RMFD. Numerous publications about the study and applications of WT for RMFD have been presented to academic journals, technical reports and conference proceedings. Many previous publications admit that WT can be realized by means of inner product principle of signal and wavelet base. This paper verifies the essence on inner product operation of WT by simulation and field experiments. Then the development process of WT based on inner product is concluded and the applications of major developments in RMFD are also summarized. Finally, super wavelet transform as an important prospect of WT based on inner product are presented and discussed. It is expected that this paper can offer an in-depth and comprehensive references for researchers and help them with finding out further research topics.
Xu, Songhua; Tourassi, Georgia
In this paper, we introduce a new local learning based approach and apply it for the well-studied problem of breast cancer diagnosis using BIRADS-based mammographic features. To learn from our clinical dataset the latent relationship between these features and the breast biopsy result, our method first dynamically partitions the whole sample population into multiple sub-population groups through stochastically searching the sample population clustering space. Each encountered clustering scheme in our online searching process is then used to create a certain sample population partition plan. For every resultant sub-population group identified according to a partition plan, our method then trains a dedicated local learner to capture the underlying data relationship. In our study, we adopt the linear logistic regression model as our local learning method's base learner. Such a choice is made both due to the well-understood linear nature of the problem, which is compellingly revealed by a rich body of prior studies, and the computational efficiency of linear logistic regression--the latter feature allows our local learning method to more effectively perform its search in the sample population clustering space. Using a database of 850 biopsy-proven cases, we compared the performance of our method with a large collection of publicly available state-of-the-art machine learning methods and successfully demonstrated its performance advantage with statistical significance.
Ding, Steven X
Guaranteeing a high system performance over a wide operating range is an important issue surrounding the design of automatic control systems with successively increasing complexity. As a key technology in the search for a solution, advanced fault detection and identification (FDI) is receiving considerable attention. This book introduces basic model-based FDI schemes, advanced analysis and design algorithms, and mathematical and control-theoretic tools. This second edition of Model-Based Fault Diagnosis Techniques contains: · new material on fault isolation and identification, and fault detection in feedback control loops; · extended and revised treatment of systematic threshold determination for systems with both deterministic unknown inputs and stochastic noises; addition of the continuously-stirred tank heater as a representative process-industrial benchmark; and · enhanced discussion of residual evaluation in stochastic processes. Model-based Fault Diagno...
Inbarani, H Hannah; Azar, Ahmad Taher; Jothi, G
Medical datasets are often classified by a large number of disease measurements and a relatively small number of patient records. All these measurements (features) are not important or irrelevant/noisy. These features may be especially harmful in the case of relatively small training sets, where this irrelevancy and redundancy is harder to evaluate. On the other hand, this extreme number of features carries the problem of memory usage in order to represent the dataset. Feature Selection (FS) is a solution that involves finding a subset of prominent features to improve predictive accuracy and to remove the redundant features. Thus, the learning model receives a concise structure without forfeiting the predictive accuracy built by using only the selected prominent features. Therefore, nowadays, FS is an essential part of knowledge discovery. In this study, new supervised feature selection methods based on hybridization of Particle Swarm Optimization (PSO), PSO based Relative Reduct (PSO-RR) and PSO based Quick Reduct (PSO-QR) are presented for the diseases diagnosis. The experimental result on several standard medical datasets proves the efficiency of the proposed technique as well as enhancements over the existing feature selection techniques. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.
Full Text Available This study investigates a novel method for roller bearing fault diagnosis based on local characteristic-scale decomposition (LCD energy entropy, together with a support vector machine designed using an Artificial Chemical Reaction Optimisation Algorithm, referred to as an ACROA-SVM. First, the original acceleration vibration signals are decomposed into intrinsic scale components (ISCs. Second, the concept of LCD energy entropy is introduced. Third, the energy features extracted from a number of ISCs that contain the most dominant fault information serve as input vectors for the support vector machine classifier. Finally, the ACROA-SVM classifier is proposed to recognize the faulty roller bearing pattern. The analysis of roller bearing signals with inner-race and outer-race faults shows that the diagnostic approach based on the ACROA-SVM and using LCD to extract the energy levels of the various frequency bands as features can identify roller bearing fault patterns accurately and effectively. The proposed method is superior to approaches based on Empirical Mode Decomposition method and requires less time.
Metzler, Jürgen; Kroschel, Kristian; Willersinn, Dieter
Monitoring of the heart rhythm is the cornerstone of the diagnosis of cardiac arrhythmias. It is done by means of electrocardiography which relies on electrodes attached to the skin of the patient. We present a new system approach based on the so-called vibrocardiogram that allows an automatic non-contact registration of the heart rhythm. Because of the contactless principle, the technique offers potential application advantages in medical fields like emergency medicine (burn patient) or premature baby care where adhesive electrodes are not easily applicable. A laser-based, mobile, contactless vibrometer for on-site diagnostics that works with the principle of laser Doppler vibrometry allows the acquisition of vital functions in form of a vibrocardiogram. Preliminary clinical studies at the Klinikum Karlsruhe have shown that the region around the carotid artery and the chest region are appropriate therefore. However, the challenge is to find a suitable measurement point in these parts of the body that differs from person to person due to e. g. physiological properties of the skin. Therefore, we propose a new Microsoft Kinect-based approach. When a suitable measurement area on the appropriate parts of the body are detected by processing the Kinect data, the vibrometer is automatically aligned on an initial location within this area. Then, vibrocardiograms on different locations within this area are successively acquired until a sufficient measuring quality is achieved. This optimal location is found by exploiting the autocorrelation function.
E. Schwartz (Emanuel); R. Izmailov (Rauf); M. Spain (Michael); A. Barnes (Anthony); J.P. Mapes (James); P.C. Guest (Paul); H. Rahmoune (Hassan); S. Pietsch (Sandra); F.M. Leweke (Marcus); M. Rothermundt (Matthias); J. Steiner (Johann); D. Koethe (Dagmar); L. Kranaster (Laura); P. Ohrmann (Patricia); T. Suslow (Thomas); Y. Levin (Yishai); B. Bogerts (Bernhard); N.J.M. van Beveren (Nico); G. McAllister (George); N. Weber (Natalya); D. Niebuhr (David); D. Cowan (David); R.H. Yolken (Robert); S. Bahn (Sabine)
textabstractAbstract: We describe the validation of a serum-based test developed by Rules-Based Medicine which can be used to help confirm the diagnosis of schizophrenia. In preliminary studies using multiplex immunoassay profiling technology, we identified a disease signature comprised of 51
Efforts to overcome the problems of descriptive and categorical approaches have not yielded results. In the present article, psychiatric diagnosis using Bayesian networks is proposed. Instead of a yes/no decision, Bayesian networks give the probability of diagnostic category inclusion, thereby yielding both a graded, i.e., dimensional diagnosis, and a value of the certainty of the diagnosis. With the use of Bayesian networks in the diagnosis of mental disorders, information about etiology, associated features, treatment outcome, and laboratory results may be used in addition to clinical signs and symptoms, with each of these factors contributing proportionally to their own specificity and sensitivity. Furthermore, a diagnosis (albeit one with a lower probability) can be made even with incomplete, uncertain, or partially erroneous information, and patients whose symptoms are below the diagnostic threshold can be evaluated. Lastly, there is no need of NOS or "unspecified" categories, and comorbid disorders become different dimensions of the diagnostic evaluation. Bayesian diagnoses allow the preservation of current categories and assessment methods, and may be used concurrently with criteria-based diagnoses. Users need not put in extra effort except to collect more comprehensive information. Unlike the Research Domain Criteria (RDoC) project, the Bayesian approach neither increases the diagnostic validity of existing categories nor explains the pathophysiological mechanisms of mental disorders. It, however, can be readily integrated to present classification systems. Therefore, the Bayesian approach may be an intermediate phase between criteria-based diagnosis and the RDoC ideal.
Sonje, M. Deepak; Kundu, P.; Chowdhury, A.
Fault diagnosis and detection is the important area in health monitoring of electrical machines. This paper proposes the recently developed machine learning classifier for multi class fault diagnosis in induction machine. The classification is based on random forest (RF) algorithm. Initially, stator currents are acquired from the induction machine under various conditions. After preprocessing the currents, fourteen statistical time features are estimated for each phase of the current. These parameters are considered as inputs to the classifier. The main scope of the paper is to evaluate effectiveness of RF classifier for individual and mixed fault diagnosis in induction machine. The stator, rotor and mixed faults (stator and rotor faults) are classified using the proposed classifier. The obtained performance measures are compared with the multilayer perceptron neural network (MLPNN) classifier. The results show the much better performance measures and more accurate than MLPNN classifier. For demonstration of planned fault diagnosis algorithm, experimentally obtained results are considered to build the classifier more practical.
Bocsi, József; Nieschke, Kathleen; Mittag, Anja; Reichert, Thomas; Laffers, Wiebke; Marecka, Monika; Pierzchalski, Arkadiusz; Piltz, Joachim; Esche, Hans-Jürgen; Wolf, Günther; Dähnert, Ingo; Baumgartner, Adolf; Tarnok, Attila
Myocardial infarction (MI) is an acute life-threatening disease with a high incidence worldwide. Aim of this study was to test lectin-carbohydrate binding-induced red blood cell (RBC) agglutination as an innovative tool for fast, precise and cost effective diagnosis of MI. Five lectins (Ricinus communis agglutinin (RCA), Phaseolus vulgaris erythroagglutinin (PHA), Datura stramonium agglutinin (DSA), Artocarpus agglutinin (ArA), Triticum agglutinin (TA)) were tested for ability to differentiate between agglutination characteristics in patients with MI (n = 101) or angina pectoris without MI (AP) (n = 34) and healthy volunteers (HV) as control (n =68) . RBC agglutination was analyzed by light absorbance of a stirred RBC suspension in the green to red light spectrum in an agglutimeter (amtec, Leipzig, Germany) for 15 min after lectin addition. Mean cell count in aggregates was estimated from light absorbance by a mathematical model. Each lectin induced RBC agglutination. RCA led to the strongest RBC agglutination (~500 RBCs/aggregate), while the others induced substantially slower agglutination and lead to smaller aggregate sizes (5-150 RBCs/aggregate). For all analyzed lectins the lectin-induced RBC agglutination of MI or AP patients was generally higher than for HV. However, only PHA induced agglutination that clearly distinguished MI from HV. Variance analysis showed that aggregate size after 15 min. agglutination induced by PHA was significantly higher in the MI group (143 RBCs/ aggregate) than in the HV (29 RBC-s/aggregate, p = 0.000). We hypothesize that pathological changes during MI induce modification of the carbohydrate composition on the RBC membrane and thus modify RBC agglutination. Occurrence of carbohydrate-lectin binding sites on RBC membranes provides evidence about MI. Due to significant difference in the rate of agglutination between MI > HV the differentiation between these groups is possible based on PHA-induced RBC-agglutination. This novel assay
Moreno-Iribas, Conchi; Sayon-Orea, Carmen; Delfrade, Josu; Ardanaz, Eva; Gorricho, Javier; Burgui, Rosana; Nuin, Marian; Guevara, Marcela
The increasing burden of type 2 diabetes mellitus makes the continuous surveillance of its prevalence and incidence advisable. Electronic health records (EHRs) have great potential for research and surveillance purposes; however the quality of their data must first be evaluated for fitness for use. The aim of this study was to assess the validity of type 2 diabetes diagnosis in a primary care EHR database covering more than half a million inhabitants, 97% of the population in Navarra, Spain. In the Navarra EPIC-InterAct study, the validity of the T90 code from the International Classification of Primary Care, Second Edition was studied in a primary care EHR database to identify incident cases of type 2 diabetes, using a multi-source approach as the gold standard. The sensitivity, specificity, positive predictive value, negative predictive value and the kappa index were calculated. Additionally, type 2 diabetes prevalence from the EHR database was compared with estimations from a health survey. The sensitivity, specificity, positive predictive value and negative predictive value of incident type 2 diabetes recorded in the EHRs were 98.2, 99.3, 92.2 and 99.8%, respectively, and the kappa index was 0.946. Overall prevalence of type 2 diabetes diagnosed in the EHRs among adults (35-84 years of age) was 7.2% (95% confidence interval [CI] 7.2-7.3) in men and 5.9% (95% CI 5.8-5.9) in women, which was similar to the prevalence estimated from the health survey: 8.5% (95% CI 7.1-9.8) and 5.5% (95% CI 4.4-6.6) in men and women, respectively. The high sensitivity and specificity of type 2 diabetes diagnosis found in the primary care EHRs make this database a good source for population-based surveillance of incident and prevalent type 2 diabetes, as well as for monitoring quality of care and health outcomes in diabetic patients.
Su, Jie; Liu, Shuai; Song, Jinming
The diagnosis of acute myeloid leukemia (AML) is purely dependent on counting the percentages of blasts (>20%) in the peripheral blood or bone marrow. Manual microscopic examination of peripheral blood or bone marrow aspirate smears is time consuming and less accurate. The first and very important step in blast recognition is the segmentation of the cells from the background for further cell feature extraction and cell classification. In this paper, we aimed to utilize computer technologies in image analysis and artificial intelligence to develop an automatic program for blast recognition and counting in the aspirate smears. We proposed a method to analyze the aspirate smear images, which first performs segmentation of the cells by k-means cluster, then builds cell image representing model by HMRF (Hidden-Markov Random Field), estimates model parameters through probability of EM (expectation maximization), carries out convergence iteration until optimal value, and finally achieves second stage refined segmentation. Furthermore, the segmentation results are compared with several other methods using six classes of cells respectively. The proposed method was applied to six groups of cells from 61 bone marrow aspirate images, and compared with other algorithms for its performance on the analysis of the whole images, the segmentation of nucleus, and the efficiency of calculation. It showed improved segmentation results in both the cropped images and the whole images, which provide the base for down-stream cell feature extraction and identification. Segmentation of the aspirate smear images using the proposed method helps the analyst in differentiating six groups of cells and in the determination of blasts counting, which will be of great significance for the diagnosis of acute myeloid leukemia. Copyright © 2017 Elsevier B.V. All rights reserved.
Sadr, Nadi; Jayawardhana, Madhuka; de Chazal, Philip
A measure of the respiratory effort during a sleep study is an important contributor to the diagnosis of sleep apnoea. A common way of measuring respiratory effort is with bands with stretch sensors placed around the chest and/or abdomen. An alternative, and more convenient method from the patient's perspective, is via the ECG derived respiration (EDR) signal which provides an estimate of the respiratory effort at each heartbeat. In this study we performed a side-by-side comparison of the discrimination information for identifying epochs of sleep apnoea contained in the chest respiratory effort signal and three methods of calculating the EDR signal. Using simultaneously recorded chest band and ECG signals extracted from overnight polysomnogram (PSG) data from 8 subjects (4 controls, 4 apnoeas. MIT PhysioNet Apnea-ECG database), we extracted identical features from the two sensors and used the features to train a linear discriminant classifier to classify one-minute epochs as being apneic or normal. Ground truth labelling of each epoch was achieved with an expert using the full PSG as a reference. Our cross validation results revealed that the full respiratory effort signal resulted in an accuracy of 87% in correctly identifying the epoch label. When the respiratory signal was resampled at each heartbeat (as occurs with the EDR signal) the accuracy was 86%, suggesting that the sampling process inherent to the EDR signal does not materially affect its discrimination ability. The best EDR method was based on the calculating the QRS area for every heart and achieved an accuracy of 81%. Our results suggest that, while there is some information loss in the EDR estimation process, the EDR signal is a convenient and useful signal for sleep apnoea diagnosis.
Xu, Ying; Hackett, Maree L; Glozier, Nick; Nikpour, Armin; Bleasel, Andrew; Somerville, Ernest; Lawson, John; Jan, Stephen; Hyde, Lorne; Todd, Lisa; Martiniuk, Alexandra; Ireland, Carol; Anderson, Craig S
The objective of the study was to determine the frequency and predictors of psychological distress after a diagnosis of epilepsy. The Sydney Epilepsy Incidence Study to Measure Illness Consequences (SEISMIC) was a prospective, multicenter, community-based study of people of all ages with newly diagnosed epilepsy in Sydney, Australia. Analyses involved multivariate logistic regression and multinomial logit regression to identify predictors of psychological distress, assessed using the Hospital Anxiety and Depression Scale (HADS) and the Strengths and Difficulties Questionnaire (SDQ), as part of structured interviews. Psychological distress occurred in 33% (95% confidence interval [CI] 26 to 40%) and 24% (95% CI 18 to 31%) of 180 adults at baseline and 12months, respectively, and 23% (95% CI 14 to 33%) of 77 children at both time points. Thirty adults and 7 children had distress at baseline who recovered at 12months, while 15 adults and 7 children had new onset of distress during this period. History of psychiatric or behavioral disorder (for adults, odds ratio [OR] 6.82, 95% CI 3.08 to 15.10; for children, OR 28.85, 95% CI 2.88 to 288.60) and higher psychosocial disability (adults, OR 1.17, 95% CI 1.07 to 1.27) or lower family functioning (children, OR 1.80, 95% CI 1.08 to 3.02) were associated with psychological distress (C statistics 0.80 and 0.78). Psychological distress is common and fluctuates in frequency after a diagnosis of epilepsy. Those with premorbid psychological, psychosocial, and family problems are at high risk of this adverse outcome. Copyright © 2017 Elsevier Inc. All rights reserved.
Potipitak, Tiparat; Ngrenngarmlert, Warunee; Promptmas, Chamras; Chomean, Sirinart; Ittarat, Wanida
Malaria infection with Plasmodium falciparum is an important basic health problem in the tropical and sub-tropical countries. The standard diagnostic method is blood film examination to visualize parasite morphology. However, in cases of low parasitemia or mixed infection with very low cryptic species, microscopy is not sensitive enough. Therefore, molecular techniques have been widely employed. A label-free DNA biosensor based on quartz crystal microbalance (QCM) to diagnose and genotype P. falciparum was developed. Avidin-biotin interaction was used to coat the specific biotinylated probe on the gold surface of QCM. The gene encoding merozoite surface protein 2 (msp2) was amplified and the PCR products were then cut with restriction enzyme (MwoI). Enzymatic cutting made the PCR products suitable for QCM development. Hybridization between probe and enzymatic cutting DNA fragments resulted in frequency changes of the QCM. The newly developed QCM was tested for its diagnosis ability using both malaria laboratory strains and clinical isolates. The biosensor was sensitive at the sub-nanogram level, specific for only P. falciparum detection, no cross-reaction with P. vivax, and stable at room temperature for up to 6 months. Selection of msp2 as a target gene and a geno-typing marker made the QCM potentially useful for falciparum diagnosis simultaneously with genotyping. Potency was tested by genotyping two allelic families of P. falciparum, FC27 and IC1, using malaria laboratory strains, K1 and 3D7, respectively. The dual function QCM was successfully developed with high sensitivity and specificity, and was cost-effective, stable and field adaptable.
El-Sappagh, Shaker; Elmogy, Mohammed; Riad, A M
Case-based reasoning (CBR) is a problem-solving paradigm that uses past knowledge to interpret or solve new problems. It is suitable for experience-based and theory-less problems. Building a semantically intelligent CBR that mimic the expert thinking can solve many problems especially medical ones. Knowledge-intensive CBR using formal ontologies is an evolvement of this paradigm. Ontologies can be used for case representation and storage, and it can be used as a background knowledge. Using standard medical ontologies, such as SNOMED CT, enhances the interoperability and integration with the health care systems. Moreover, utilizing vague or imprecise knowledge further improves the CBR semantic effectiveness. This paper proposes a fuzzy ontology-based CBR framework. It proposes a fuzzy case-base OWL2 ontology, and a fuzzy semantic retrieval algorithm that handles many feature types. This framework is implemented and tested on the diabetes diagnosis problem. The fuzzy ontology is populated with 60 real diabetic cases. The effectiveness of the proposed approach is illustrated with a set of experiments and case studies. The resulting system can answer complex medical queries related to semantic understanding of medical concepts and handling of vague terms. The resulting fuzzy case-base ontology has 63 concepts, 54 (fuzzy) object properties, 138 (fuzzy) datatype properties, 105 fuzzy datatypes, and 2640 instances. The system achieves an accuracy of 97.67%. We compare our framework with existing CBR systems and a set of five machine-learning classifiers; our system outperforms all of these systems. Building an integrated CBR system can improve its performance. Representing CBR knowledge using the fuzzy ontology and building a case retrieval algorithm that treats different features differently improves the accuracy of the resulting systems. Copyright © 2015 Elsevier B.V. All rights reserved.
Krashenyi, Ihor Eduardovych; Popov, Anton Oleksandrovych; Ramirez, Haver; Gorriz, Huan Manuel
This work was dedicated to clustering methods application in fuzzy inference system for Alzheimer’s disease diagnosis using PET-images. Three methods (Subtractive Clustering, C-means and Fuzzy Grid Partition) of clustering were discussed and their performance in Alzheimer’s disease diagnosis were measured. Recommendation of the future use of Subtractive Clustering algorithm in the computeraided diagnosis system for Alzheimer’s disease are given. The performance of this algorithm is AUC=0,8791...
De la Torre Vega, H. Octavio; Garcia Tevillo, Arturo; Campuzano Martinez, Roberto [Instituto de Investigaciones Electricas, Temixco, Morelos (Mexico); Lopez Azamar, Ernesto [Comision Federal de Electricidad (Mexico)
The development of a system for the diagnosis of electrical generators that apply techniques of artificial intelligence, is presented, as it is the reasoning based on cases, to support the work of the diagnosis engineer. This system is part of a system called CADIS, dedicated to the diagnosis of electrical generators out of line and reason of previous articles. In this occasion the characteristics of the reasoning module based on experiences (SirBE) are emphasized, indicating how to make a diagnosis using similar cases and how to edit the system base of experience, using the interactive editor of cases. It is included, in addition, a summarized example which represents a case for SirBE and how the system helps to make a diagnosis. [Spanish] Se presenta el desarrollo de un sistema de diagnostico de generadores electricos que aplica tecnicas de inteligencia artificial, como es el razonamiento basado en casos, para apoyar la labor del ingeniero de diagnostico. Este sistema es parte de un sistema denominado CADIS, dedicado al diagnostico de generadores electricos fuera de linea y motivo de articulos anteriores. En esta ocasion se resaltan las caracteristicas del modulo de razonamiento basado en experiencias (SirBE), indicando como realizar un diagnostico utilizando casos similares y como editar la base de experiencia del sistema utilizando el editor interactivo de casos. Se incluye, ademas, un ejemplo resumido de lo que representa un caso para SiRBE y como el sistema ayuda a realizar un diagnostico.
NOGUEIRA Maurício L.
Full Text Available Herpetic infections are common complications in AIDS patients. The clinical features could be uncommon and antiviral chemotherapy is imperative. A rapid diagnosis could prevent incorrect approaches and treatment. The polymerase chain reaction is a rapid, specific and sensible method for DNA amplification and diagnosis of infectious diseases, especially viral diseases. This approach has some advantages compared with conventional diagnostic procedures. Recently we have reported a new PCR protocol to rapid diagnosis of herpetic infections with suppression of the DNA extraction step. In this paper we present a case of herpetic whitlow with rapid diagnosis by HSV-1 specific polymerase chain reaction using the referred protocol.
Wang, Xianfang; Du, Haoze; Tan, Jinglu
Fault diagnosis is becoming an important issue in biochemical process, and a novel online fault detection and diagnosis approach is designed by combining fuzzy c-means (FCM) and support vector machine (SVM). The samples are preprocessed via FCM algorithm to enhance the ability of classification firstly. Then, those samples are input to the SVM classifier to realize the biochemical process fault diagnosis. In this study, a glutamic acid fermentation process is chosen as an example to diagnose the fault by this method, the result shows that the diagnosis time is largely shortened, and the accuracy is extremely improved by comparing to a single SVM method.
Park, Yoon-Joo; Chun, Se-Hak; Kim, Byung-Chun
The paper studies the new learning technique called cost-sensitive case-based reasoning (CSCBR) incorporating unequal misclassification cost into CBR model. Conventional CBR is now considered as a suitable technique for diagnosis, prognosis and prescription in medicine. However it lacks the ability to reflect asymmetric misclassification and often assumes that the cost of a positive diagnosis (an illness) as a negative one (no illness) is the same with that of the opposite situation. Thus, the objective of this research is to overcome the limitation of conventional CBR and encourage applying CBR to many real world medical cases associated with costs of asymmetric misclassification errors. The main idea involves adjusting the optimal cut-off classification point for classifying the absence or presence of diseases and the cut-off distance point for selecting optimal neighbors within search spaces based on similarity distribution. These steps are dynamically adapted to new target cases using a genetic algorithm. We apply this proposed method to five real medical datasets and compare the results with two other cost-sensitive learning methods-C5.0 and CART. Our finding shows that the total misclassification cost of CSCBR is lower than other cost-sensitive methods in many cases. Even though the genetic algorithm has limitations in terms of unstable results and over-fitting training data, CSCBR results with GA are better overall than those of other methods. Also the paired t-test results indicate that the total misclassification cost of CSCBR is significantly less than C5.0 and CART for several datasets. We have proposed a new CBR method called cost-sensitive case-based reasoning (CSCBR) that can incorporate unequal misclassification costs into CBR and optimize the number of neighbors dynamically using a genetic algorithm. It is meaningful not only for introducing the concept of cost-sensitive learning to CBR, but also for encouraging the use of CBR in the medical area
Fernandez Montenegro, Juan Manuel; Argyriou, Vasileios
Alzheimer's screening tests are commonly used by doctors to diagnose the patient's condition and stage as early as possible. Most of these tests are based on pen-paper interaction and do not embrace the advantages provided by new technologies. This paper proposes novel Alzheimer's screening tests based on virtual environments and game principles using new immersive technologies combined with advanced Human Computer Interaction (HCI) systems. These new tests are focused on the immersion of the patient in a virtual room, in order to mislead and deceive the patient's mind. In addition, we propose two novel variations of Turing Test proposed by Alan Turing as a method to detect dementia. As a result, four tests are introduced demonstrating the wide range of screening mechanisms that could be designed using virtual environments and game concepts. The proposed tests are focused on the evaluation of memory loss related to common objects, recent conversations and events; the diagnosis of problems in expressing and understanding language; the ability to recognize abnormalities; and to differentiate between virtual worlds and reality, or humans and machines. The proposed screening tests were evaluated and tested using both patients and healthy adults in a comparative study with state-of-the-art Alzheimer's screening tests. The results show the capacity of the new tests to distinguish healthy people from Alzheimer's patients. Copyright © 2017. Published by Elsevier Inc.
Full Text Available For planetary gear has the characteristics of small volume, light weight and large transmission ratio, it is widely used in high speed and high power mechanical system. Poor working conditions result in frequent failures of planetary gear. A method is proposed for diagnosing faults in planetary gear based on permutation entropy of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN Adaptive Neuro-fuzzy Inference System (ANFIS in this paper. The original signal is decomposed into 6 intrinsic mode functions (IMF and residual components by CEEMDAN. Since the IMF contains the main characteristic information of planetary gear faults, time complexity of IMFs are reflected by permutation entropies to quantify the fault features. The permutation entropies of each IMF component are defined as the input of ANFIS, and its parameters and membership functions are adaptively adjusted according to training samples. Finally, the fuzzy inference rules are determined, and the optimal ANFIS is obtained. The overall recognition rate of the test sample used for ANFIS is 90%, and the recognition rate of gear with one missing tooth is relatively high. The recognition rates of different fault gears based on the method can also achieve better results. Therefore, the proposed method can be applied to planetary gear fault diagnosis effectively.
Wang, Songyuan; Zhang, Guopeng; Liao, Qimei; Zhang, Junying; Jiao, Chun; Lu, Hongbing
Image-based computer-aided detection and diagnosis (CAD) has been a very active research topic aiming to assist physicians to detect lesions and distinguish them from benign to malignant. However, the datasets fed into a classifier usually suffer from small number of samples, as well as significantly less samples available in one class (have a disease) than the other, resulting in the classifier's suboptimal performance. How to identifying the most characterizing features of the observed data for lesion detection is critical to improve the sensitivity and minimize false positives of a CAD system. In this study, we propose a novel feature selection method mR-FAST that combines the minimal-redundancymaximal relevance (mRMR) framework with a selection metric FAST (feature assessment by sliding thresholds) based on the area under a ROC curve (AUC) generated on optimal simple linear discriminants. With three feature datasets extracted from CAD systems for colon polyps and bladder cancer, we show that the space of candidate features selected by mR-FAST is more characterizing for lesion detection with higher AUC, enabling to find a compact subset of superior features at low cost.
Septem Riza, Lala; Pradini, Mila; Fitrajaya Rahman, Eka; Rasim
Sleep disorder is an anomaly that could cause problems for someone’ sleeping pattern. Nowadays, it becomes an issue since people are getting busy with their own business and have no time to visit the doctors. Therefore, this research aims to develop a system used for diagnosis of sleep disorder using Fuzzy Rule-Based Classification System (FRBCS). FRBCS is a method based on the fuzzy set concepts. It consists of two steps: (i) constructing a model/knowledge involving rulebase and database, and (ii) prediction over new data. In this case, the knowledge is obtained from experts whereas in the prediction stage, we perform fuzzification, inference, and classification. Then, a platform implementing the method is built with a combination between PHP and the R programming language using the “Shiny” package. To validate the system that has been made, some experiments have been done using data from a psychiatric hospital in West Java, Indonesia. Accuracy of the result and computation time are 84.85% and 0.0133 seconds, respectively.
Yang, Ni; Zhang, Shuqing; Zhang, Liguo; Zhang, Kexin; Sun, Lingyun
Small current grounding system is the system that the neutral point doesn't ground or grounds across the arc suppressing coils, which has been applied commonly in distribution system of many countries. As the grounding fault occurs, current is the one caused by capacity of circuit to ground only and it is rather small. The status of fault is complexity, e.g., the electromagnet interferes together with the amplified impact of zero-order loops to high-order singularity waves and various temporary variables. All these result in the lower ratio of the fault element signal to noise caused by zero-order current. In this paper, the position of signal singularity and the magnitude of the singularity degree are analyzed based on the variable focus character of wavelet, and the time fault occurs is then determined. The series db wavelet with close sustain is adopted, and the line selection is according to the zero-order voltage of the generatrix and the current of various outlet line. It is proved by the experiment that the fault circuit diagnosis method based on wavelet analysis to the character of temporary status of single-phase grounding fault plays an important role to a finer line selection.
Full Text Available Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms.
Full Text Available This paper first explains how the relations between Japanese Alzheimer’s disease (AD patients and their mitochondrial SNP frequencies at individual mtDNA positions examined using the radial basis function (RBF network and a method based on RBF network predictions and that Japanese AD patients are associated with the haplogroups G2a and N9b1. It then describes a method for the initial diagnosis of Alzheimer’s disease that is based on the mtSNP haplogroups of the AD patients. The method examines the relations between someone’s mtDNA mutations and the mtSNPs of AD patients. As the mtSNP haplogroups thus obtained indicate which nucleotides of mtDNA loci are changed in the Alzheimer’s patients, a person’s probability of becoming an AD patient can be predicted by comparing those mtDNA mutations with that person’s mtDNA mutations. The proposed method can also be used to diagnose diseases such as Parkinson’s disease and type 2 diabetes and to identify people likely to become centenarians.