Diagnosis and Classification of 17 Diseases from 1404 Subjects via Pattern Analysis of Exhaled Molecules

PubMed Central

2016-01-01

We report on an artificially intelligent nanoarray based on molecularly modified gold nanoparticles and a random network of single-walled carbon nanotubes for noninvasive diagnosis and classification of a number of diseases from exhaled breath. The performance of this artificially intelligent nanoarray was clinically assessed on breath samples collected from 1404 subjects having one of 17 different disease conditions included in the study or having no evidence of any disease (healthy controls). Blind experiments showed that 86% accuracy could be achieved with the artificially intelligent nanoarray, allowing both detection and discrimination between the different disease conditions examined. Analysis of the artificially intelligent nanoarray also showed that each disease has its own unique breathprint, and that the presence of one disease would not screen out others. Cluster analysis showed a reasonable classification power of diseases from the same categories. The effect of confounding clinical and environmental factors on the performance of the nanoarray did not significantly alter the obtained results. The diagnosis and classification power of the nanoarray was also validated by an independent analytical technique, i.e., gas chromatography linked with mass spectrometry. This analysis found that 13 exhaled chemical species, called volatile organic compounds, are associated with certain diseases, and the composition of this assembly of volatile organic compounds differs from one disease to another. Overall, these findings could contribute to one of the most important criteria for successful health intervention in the modern era, viz. easy-to-use, inexpensive (affordable), and miniaturized tools that could also be used for personalized screening, diagnosis, and follow-up of a number of diseases, which can clearly be extended by further development. PMID:28000444

The utility of the diagnosis of pedophilia: a comparison of various classification procedures.

PubMed

Kingston, Drew A; Firestone, Philip; Moulden, Heather M; Bradford, John M

2007-06-01

This study examined the utility of the diagnosis of pedophilia in a sample of extra-familial child molesters assessed at a university teaching hospital between 1982 and 1992. Pedophilia was defined in one of four ways: (1) DSM diagnosis made by a psychiatrist; (2) deviant phallometric profile; (3) DSM diagnosis and a deviant phallometric profile; and, (4) high scores based on the Screening Scale for Pedophilic Interest (Seto & Lalumière, 2001). Demographic data, psychological tests, and offence history were obtained and group differences were analyzed along with the ability of certain variables to contribute uniquely to the classification of pedophilia. Results indicated that few significant differences existed on psychological measures between pedophilic and nonpedophilic extra-familial child molesters regardless of the classification system employed. Finally, results indicated that the procedures used to define pedophilia were not significantly related to one another. Results are discussed in terms of the utility of the diagnosis of pedophilia.

Comparison of clinical causes of death with autopsy diagnosis using discrepency classification.

PubMed

Ullah, Khalil; Alamgir, Wasim

2006-12-01

To determine the usefulness of autopsy findings in the quality improvement of patients care. An observational study. Departments of Pathology and Medicine, Combined Military Hospital (CMH) Kharian, a tertiary care hospital, from January 2001 to December 2003. The clinical and necropsy findings of all the cases, who died in hospital and had undergone autopsy examination at CMH, Kharian, from January 2001 to December 2003, were retrieved from record of clinical case sheet data and autopsy record of the hospital. The two were analyzed and compared according to the discrepancy classification. The exclusion and inclusion criteria, the international classification of disease (ICD) to code deaths, the global burden of disease (GBD) system to classify and group diseases, and the Goldman discrepancy classification to compare clinical and autopsy diagnosis and classify the discrepancies, were used as described. The death rate varied from 0.94% to 1.29% and autopsy rate from 4.69% to 10.10% annually between January 2001 and December 2003. The number of cases classified according to GBD system was 3 (5%) in Group 1, 26 (43.33 %) in Group 2 and 31 (51.66 %) in Group 3. The discrepancy classes included 9 (15 %) class I major discrepancies and 3 (5 %) class II major discrepancies. Non-discrepant diagnosis was seen in 37 cases (61.66 %) and 11 cases (18.32 %) were non-classifiable. This study showed the usefulness of autopsy findings in the quality improvement of the diagnosis and management of the disease by showing only a minority of cases with discrepant diagnosis of the cause of death.

Texture Feature Extraction and Classification for Iris Diagnosis

NASA Astrophysics Data System (ADS)

Ma, Lin; Li, Naimin

Appling computer aided techniques in iris image processing, and combining occidental iridology with the traditional Chinese medicine is a challenging research area in digital image processing and artificial intelligence. This paper proposes an iridology model that consists the iris image pre-processing, texture feature analysis and disease classification. To the pre-processing, a 2-step iris localization approach is proposed; a 2-D Gabor filter based texture analysis and a texture fractal dimension estimation method are proposed for pathological feature extraction; and at last support vector machines are constructed to recognize 2 typical diseases such as the alimentary canal disease and the nerve system disease. Experimental results show that the proposed iridology diagnosis model is quite effective and promising for medical diagnosis and health surveillance for both hospital and public use.

Evaluation of different classification methods for the diagnosis of schizophrenia based on functional near-infrared spectroscopy.

PubMed

Li, Zhaohua; Wang, Yuduo; Quan, Wenxiang; Wu, Tongning; Lv, Bin

2015-02-15

Based on near-infrared spectroscopy (NIRS), recent converging evidence has been observed that patients with schizophrenia exhibit abnormal functional activities in the prefrontal cortex during a verbal fluency task (VFT). Therefore, some studies have attempted to employ NIRS measurements to differentiate schizophrenia patients from healthy controls with different classification methods. However, no systematic evaluation was conducted to compare their respective classification performances on the same study population. In this study, we evaluated the classification performance of four classification methods (including linear discriminant analysis, k-nearest neighbors, Gaussian process classifier, and support vector machines) on an NIRS-aided schizophrenia diagnosis. We recruited a large sample of 120 schizophrenia patients and 120 healthy controls and measured the hemoglobin response in the prefrontal cortex during the VFT using a multichannel NIRS system. Features for classification were extracted from three types of NIRS data in each channel. We subsequently performed a principal component analysis (PCA) for feature selection prior to comparison of the different classification methods. We achieved a maximum accuracy of 85.83% and an overall mean accuracy of 83.37% using a PCA-based feature selection on oxygenated hemoglobin signals and support vector machine classifier. This is the first comprehensive evaluation of different classification methods for the diagnosis of schizophrenia based on different types of NIRS signals. Our results suggested that, using the appropriate classification method, NIRS has the potential capacity to be an effective objective biomarker for the diagnosis of schizophrenia. Copyright © 2014 Elsevier B.V. All rights reserved.

New Classification of Focal Cortical Dysplasia: Application to Practical Diagnosis

PubMed Central

Bae, Yoon-Sung; Kang, Hoon-Chul; Kim, Heung Dong; Kim, Se Hoon

2012-01-01

Background and Purpose: Malformation of cortical development (MCD) is a well-known cause of drug-resistant epilepsy and focal cortical dysplasia (FCD) is the most common neuropathological finding in surgical specimens from drug-resistant epilepsy patients. Palmini’s classification proposed in 2004 is now widely used to categorize FCD. Recently, however, Blumcke et al. recommended a new system for classifying FCD in 2011. Methods: We applied the new classification system in practical diagnosis of a sample of 117 patients who underwent neurosurgical operations due to drug-resistant epilepsy at Severance Hospital in Seoul, Korea. Results: Among 117 cases, a total of 16 cases were shifted to other FCD subtypes under the new classification system. Five cases were reclassified to type IIIa and five cases were categorized as dual pathology. The other six cases were changed within the type I category. Conclusions: The most remarkable changes in the new classification system are the advent of dual pathology and FCD type III. Thus, it will be very important for pathologists and clinicians to discriminate between these new categories. More large-scale research needs to be conducted to elucidate the clinical influence of the alterations within the classification of type I disease. Although the new FCD classification system has several advantages compared to the former, the correlation with clinical characteristics is not yet clear. PMID:24649461

Computer-aided diagnosis system: a Bayesian hybrid classification method.

PubMed

Calle-Alonso, F; Pérez, C J; Arias-Nicolás, J P; Martín, J

2013-10-01

A novel method to classify multi-class biomedical objects is presented. The method is based on a hybrid approach which combines pairwise comparison, Bayesian regression and the k-nearest neighbor technique. It can be applied in a fully automatic way or in a relevance feedback framework. In the latter case, the information obtained from both an expert and the automatic classification is iteratively used to improve the results until a certain accuracy level is achieved, then, the learning process is finished and new classifications can be automatically performed. The method has been applied in two biomedical contexts by following the same cross-validation schemes as in the original studies. The first one refers to cancer diagnosis, leading to an accuracy of 77.35% versus 66.37%, originally obtained. The second one considers the diagnosis of pathologies of the vertebral column. The original method achieves accuracies ranging from 76.5% to 96.7%, and from 82.3% to 97.1% in two different cross-validation schemes. Even with no supervision, the proposed method reaches 96.71% and 97.32% in these two cases. By using a supervised framework the achieved accuracy is 97.74%. Furthermore, all abnormal cases were correctly classified. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

[Vasculitic neuropathy: novel classification, diagnosis and treatment].

PubMed

Kanda, Takashi

2014-01-01

The international standard of nomenclature and classification in vasculitis, CHCC 1994,was revised as CHCC 2012. In the first part of this review article I briefly summarized the CHCC 2012 and pointed out the changes in this revision, especially on the disorders related to vasculitic neuropathy. Notable changes include the introduction of new terms such as granulomatosis with polyangiitis and eosinophilic granulomatosis with polyangiitis. In the second part, I mentioned the tips for the diagnosis and treatment of vasculitic neuropathy. Because most of the vasculitic neuropathy patients require rigorous, long-standing immunosuppressive therapy, the accurate diagnosis based on the pathological detection of vasculitic changes is mandatory. In this regard, the value of sural nerve biopsy is still not ignorable. In the treatment of vascultic neuropathy, there are no controlled treatment trials and clinical practice is guided by experience from case series and indirectly by analogy with systemic vasculitis. Although combined therapy using prednisolone and cyclophosphamide is usually recommended by experts, tailor-made treatment regimen based on the conditions of each patient would produce the best outcome in vasculitic neuropathy.

Factors influencing accuracy of cortical thickness in the diagnosis of Alzheimer's disease.

PubMed

Belathur Suresh, Mahanand; Fischl, Bruce; Salat, David H

2018-04-01

There is great value to use of structural neuroimaging in the assessment of Alzheimer's disease (AD). However, to date, predictive value of structural imaging tend to range between 80% and 90% in accuracy and it is unclear why this is the case given that structural imaging should parallel the pathologic processes of AD. There is a possibility that clinical misdiagnosis relative to the gold standard pathologic diagnosis and/or additional brain pathologies are confounding factors contributing to reduced structural imaging classification accuracy. We examined potential factors contributing to misclassification of individuals with clinically diagnosed AD purely from cortical thickness measures. Correctly classified and incorrectly classified groups were compared across a range of demographic, biological, and neuropsychological data including cerebrospinal fluid biomarkers, amyloid imaging, white matter hyperintensity (WMH) volume, cognitive, and genetic factors. Individual subject analyses suggested that at least a portion of the control individuals misclassified as AD from structural imaging additionally harbor substantial AD biomarker pathology and risk, yet are relatively resistant to cognitive symptoms, likely due to "cognitive reserve," and therefore clinically unimpaired. In contrast, certain clinical control individuals misclassified as AD from cortical thickness had increased WMH volume relative to other controls in the sample, suggesting that vascular conditions may contribute to classification accuracy from cortical thickness measures. These results provide examples of factors that contribute to the accuracy of structural imaging in predicting a clinical diagnosis of AD, and provide important information about considerations for future work aimed at optimizing structural based diagnostic classifiers for AD. © 2017 Wiley Periodicals, Inc.

Classification of MR brain images by combination of multi-CNNs for AD diagnosis

NASA Astrophysics Data System (ADS)

Cheng, Danni; Liu, Manhua; Fu, Jianliang; Wang, Yaping

2017-07-01

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder with progressive impairment of memory and cognitive functions. Its early diagnosis is crucial for development of future treatment. Magnetic resonance images (MRI) play important role to help understand the brain anatomical changes related to AD. Conventional methods extract the hand-crafted features such as gray matter volumes and cortical thickness and train a classifier to distinguish AD from other groups. Different from these methods, this paper proposes to construct multiple deep 3D convolutional neural networks (3D-CNNs) to learn the various features from local brain images which are combined to make the final classification for AD diagnosis. First, a number of local image patches are extracted from the whole brain image and a 3D-CNN is built upon each local patch to transform the local image into more compact high-level features. Then, the upper convolution and fully connected layers are fine-tuned to combine the multiple 3D-CNNs for image classification. The proposed method can automatically learn the generic features from imaging data for classification. Our method is evaluated using T1-weighted structural MR brain images on 428 subjects including 199 AD patients and 229 normal controls (NC) from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results show that the proposed method achieves an accuracy of 87.15% and an AUC (area under the ROC curve) of 92.26% for AD classification, demonstrating the promising classification performances.

Diagnosis and classification of Idiopathic Inflammatory Myopathies

PubMed Central

Lundberg, Ingrid E.; Miller, Frederick W.; Tjärnlund, Anna; Bottai, Matteo

2016-01-01

The idiopathic inflammatory myopathies (IIMs) are a heterogeneous group of diseases, collectively named myositis, sharing symptoms of muscle weakness and muscle fatigue and inflammation in muscle tissue. Other organs are frequently involved supporting that these are systemic inflammatory diseases. The IIMs can be sub-grouped into dermatomyositis, polymyositis and inclusion body myositis. The myositis-specific autoantibodies (MSAs) identify other and often more distinct clinical phenotypes, such as the anti-synthetase syndrome with antisynthetase autoantibodies and frequent interstitial lung disease (ILD) and anti-SRP and anti-HMGCR autoantibodies that identify necrotizing myopathy. The MSAs are important both to support myositis diagnosis and to identify subgroups with different patterns of extramuscular organ involvement such as ILD. Another cornerstone in the diagnostic procedure is muscle biopsy to identify inflammation and to exclude non-inflammatory myopathies. Treatment effect and prognosis varies by subgroup. To develop new and better therapies, validated classification criteria that identify distinct subgroups of myositis are critical.. The lack of such criteria was the main rationale for the development of new classification criteria for inflammatory myopathies, which are summarized in this review, along with an historical background on previous diagnostic and classification criteria. As these are rare diseases with a prevalence of 10 in 100 000 individuals an international collaboration was essential, as was the interdisciplinary effort including adult and paediatric experts in rheumatology, neurology, dermatology and epidemiology. The new criteria have been developed based on data from more than 1 500 patients from 47 centers world-wide and are based on clinically easily available variables. PMID:27320359

Automated database-guided expert-supervised orientation for immunophenotypic diagnosis and classification of acute leukemia

PubMed Central

Lhermitte, L; Mejstrikova, E; van der Sluijs-Gelling, A J; Grigore, G E; Sedek, L; Bras, A E; Gaipa, G; Sobral da Costa, E; Novakova, M; Sonneveld, E; Buracchi, C; de Sá Bacelar, T; te Marvelde, J G; Trinquand, A; Asnafi, V; Szczepanski, T; Matarraz, S; Lopez, A; Vidriales, B; Bulsa, J; Hrusak, O; Kalina, T; Lecrevisse, Q; Martin Ayuso, M; Brüggemann, M; Verde, J; Fernandez, P; Burgos, L; Paiva, B; Pedreira, C E; van Dongen, J J M; Orfao, A; van der Velden, V H J

2018-01-01

Precise classification of acute leukemia (AL) is crucial for adequate treatment. EuroFlow has previously designed an AL orientation tube (ALOT) to guide towards the relevant classification panel (T-cell acute lymphoblastic leukemia (T-ALL), B-cell precursor (BCP)-ALL and/or acute myeloid leukemia (AML)) and final diagnosis. Now we built a reference database with 656 typical AL samples (145 T-ALL, 377 BCP-ALL, 134 AML), processed and analyzed via standardized protocols. Using principal component analysis (PCA)-based plots and automated classification algorithms for direct comparison of single-cells from individual patients against the database, another 783 cases were subsequently evaluated. Depending on the database-guided results, patients were categorized as: (i) typical T, B or Myeloid without or; (ii) with a transitional component to another lineage; (iii) atypical; or (iv) mixed-lineage. Using this automated algorithm, in 781/783 cases (99.7%) the right panel was selected, and data comparable to the final WHO-diagnosis was already provided in >93% of cases (85% T-ALL, 97% BCP-ALL, 95% AML and 87% mixed-phenotype AL patients), even without data on the full-characterization panels. Our results show that database-guided analysis facilitates standardized interpretation of ALOT results and allows accurate selection of the relevant classification panels, hence providing a solid basis for designing future WHO AL classifications. PMID:29089646

Classification of Microcalcification of the Diagnosis of Breast Cancer using Artificial Neural Networks.

DTIC Science & Technology

1995-09-01

employed to classify benign and malignant microcalcifications in the radiographs of pathological specimen. Digital images were acquired by digitizing...associated with benign and malignant processes. The classification of microcalcifications for the diagnosis of breast cancer was achieved at a high level in

Inter-examiner classification reliability of Mechanical Diagnosis and Therapy for extremity problems - Systematic review.

PubMed

Takasaki, Hiroshi; Okuyama, Kousuke; Rosedale, Richard

2017-02-01

Mechanical Diagnosis and Therapy (MDT) is used in the treatment of extremity problems. Classifying clinical problems is one method of providing effective treatment to a target population. Classification reliability is a key factor to determine the precise clinical problem and to direct an appropriate intervention. To explore inter-examiner reliability of the MDT classification for extremity problems in three reliability designs: 1) vignette reliability using surveys with patient vignettes, 2) concurrent reliability, where multiple assessors decide a classification by observing someone's assessment, 3) successive reliability, where multiple assessors independently assess the same patient at different times. Systematic review with data synthesis in a quantitative format. Agreement of MDT subgroups was examined using the Kappa value, with the operational definition of acceptable reliability set at ≥ 0.6. The level of evidence was determined considering the methodological quality of the studies. Six studies were included and all studies met the criteria for high quality. Kappa values for the vignette reliability design (five studies) were ≥ 0.7. There was data from two cohorts in one study for the concurrent reliability design and the Kappa values ranged from 0.45 to 1.0. Kappa values for the successive reliability design (data from three cohorts in one study) were < 0.6. The current review found strong evidence of acceptable inter-examiner reliability of MDT classification for extremity problems in the vignette reliability design, limited evidence of acceptable reliability in the concurrent reliability design and unacceptable reliability in the successive reliability design. Copyright © 2017 Elsevier Ltd. All rights reserved.

Computer-aided classification of optical images for diagnosis of osteoarthritis in the finger joints.

PubMed

Zhang, Jiang; Wang, James Z; Yuan, Zhen; Sobel, Eric S; Jiang, Huabei

2011-01-01

This study presents a computer-aided classification method to distinguish osteoarthritis finger joints from healthy ones based on the functional images captured by x-ray guided diffuse optical tomography. Three imaging features, joint space width, optical absorption, and scattering coefficients, are employed to train a Least Squares Support Vector Machine (LS-SVM) classifier for osteoarthritis classification. The 10-fold validation results show that all osteoarthritis joints are clearly identified and all healthy joints are ruled out by the LS-SVM classifier. The best sensitivity, specificity, and overall accuracy of the classification by experienced technicians based on manual calculation of optical properties and visual examination of optical images are only 85%, 93%, and 90%, respectively. Therefore, our LS-SVM based computer-aided classification is a considerably improved method for osteoarthritis diagnosis.

The Comparative Experimental Study of Multilabel Classification for Diagnosis Assistant Based on Chinese Obstetric EMRs

PubMed Central

Zhang, Kunli; Zhao, Yueshu; Zan, Hongying; Zhuang, Lei

2018-01-01

Obstetric electronic medical records (EMRs) contain massive amounts of medical data and health information. The information extraction and diagnosis assistants of obstetric EMRs are of great significance in improving the fertility level of the population. The admitting diagnosis in the first course record of the EMR is reasoned from various sources, such as chief complaints, auxiliary examinations, and physical examinations. This paper treats the diagnosis assistant as a multilabel classification task based on the analyses of obstetric EMRs. The latent Dirichlet allocation (LDA) topic and the word vector are used as features and the four multilabel classification methods, BP-MLL (backpropagation multilabel learning), RAkEL (RAndom k labELsets), MLkNN (multilabel k-nearest neighbor), and CC (chain classifier), are utilized to build the diagnosis assistant models. Experimental results conducted on real cases show that the BP-MLL achieves the best performance with an average precision up to 0.7413 ± 0.0100 when the number of label sets and the word dimensions are 71 and 100, respectively. The result of the diagnosis assistant can be introduced as a supplementary learning method for medical students. Additionally, the method can be used not only for obstetric EMRs but also for other medical records. PMID:29666671

A Hybrid Classification System for Heart Disease Diagnosis Based on the RFRS Method.

PubMed

Liu, Xiao; Wang, Xiaoli; Su, Qiang; Zhang, Mo; Zhu, Yanhong; Wang, Qiugen; Wang, Qian

2017-01-01

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.

Diagnosis and classification of pancreatic and duodenal injuries in emergency radiology.

PubMed

Linsenmaier, Ulrich; Wirth, Stefan; Reiser, Maximilian; Körner, Markus

2008-10-01

Pancreatic and duodenal injuries after blunt abdominal trauma are rare; however, delays in diagnosis and treatment can significantly increase morbidity and mortality. Multidetector computed tomography (CT) has a major role in early diagnosis of pancreatic and duodenal injuries. Detecting the often subtle signs of injury with whole-body CT can be difficult because this technique usually does not include a dedicated protocol for scanning the pancreas. Specific injury patterns in the pancreas and duodenum often have variable expression at early posttraumatic multidetector CT: They may be hardly visible, or there may be considerable exudate, hematomas, organ ruptures, or active bleeding. An accurate multidetector CT technique allows optimized detection of subtle abnormalities. In duodenal injuries, differentiation between a contusion of the duodenal wall or mural hematoma and a duodenal perforation is vital. In pancreatic injuries, determination of involvement of the pancreatic duct is essential. The latter conditions require immediate surgical intervention. Use of organ injury scales and a surgical classification adapted for multidetector CT enables classification of organ injuries for trauma scoring, treatment planning, and outcome control. In addition, multidetector CT reliably demonstrates potential complications of duodenal and pancreatic injuries, such as posttraumatic pancreatitis, pseudocysts, fistulas, exudates, and abscesses. (c) RSNA, 2008.

42 CFR 412.517 - Revision of LTC-DRG group classifications and weighting factors.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 42 Public Health 2 2011-10-01 2011-10-01 false Revision of LTC-DRG group classifications and... classifications and weighting factors. (a) CMS adjusts the classifications and weighting factors annually to... the LTC-DRG classifications and recalibration of the weighting factors described in paragraph (a) of...

42 CFR 412.517 - Revision of LTC-DRG group classifications and weighting factors.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 42 Public Health 2 2010-10-01 2010-10-01 false Revision of LTC-DRG group classifications and... classifications and weighting factors. (a) CMS adjusts the classifications and weighting factors annually to... the LTC-DRG classifications and recalibration of the weighting factors described in paragraph (a) of...

Diagnosis and classification of pernicious anemia.

PubMed

Bizzaro, Nicola; Antico, Antonio

2014-01-01

Pernicious anemia (PA) is a complex disorder consisting of hematological, gastric and immunological alterations. Diagnosis of PA relies on histologically proven atrophic body gastritis, peripheral blood examination showing megaloblastic anemia with hypersegmented neutrophils, cobalamin deficiency and antibodies to intrinsic factor and to gastric parietal cells. Anti-parietal cell antibodies are found in 90% of patients with PA, but have low specificity and are seen in atrophic gastritis without megaloblastic anemia as well as in various autoimmune disorders. Anti-intrinsic factor antibodies are less sensitive, being found in only 60% of patients with PA, but are considered highly specific for PA. The incidence of PA increases with age and is rare in persons younger than 30 years of age. The highest prevalence is seen in Northern Europeans, especially those in the United Kingdom and Scandinavia, although PA has been reported in virtually every ethnic group. Because of the complexity of the diagnosis, PA prevalence is probably underestimated and no reliable data are available on the risk of gastric cancer as the end-stage evolution of atrophic gastritis in these patients. Copyright © 2014 Elsevier B.V. All rights reserved.

Acoustic diagnosis of pulmonary hypertension: automated speech- recognition-inspired classification algorithm outperforms physicians

NASA Astrophysics Data System (ADS)

Kaddoura, Tarek; Vadlamudi, Karunakar; Kumar, Shine; Bobhate, Prashant; Guo, Long; Jain, Shreepal; Elgendi, Mohamed; Coe, James Y.; Kim, Daniel; Taylor, Dylan; Tymchak, Wayne; Schuurmans, Dale; Zemp, Roger J.; Adatia, Ian

2016-09-01

We hypothesized that an automated speech- recognition-inspired classification algorithm could differentiate between the heart sounds in subjects with and without pulmonary hypertension (PH) and outperform physicians. Heart sounds, electrocardiograms, and mean pulmonary artery pressures (mPAp) were recorded simultaneously. Heart sound recordings were digitized to train and test speech-recognition-inspired classification algorithms. We used mel-frequency cepstral coefficients to extract features from the heart sounds. Gaussian-mixture models classified the features as PH (mPAp ≥ 25 mmHg) or normal (mPAp < 25 mmHg). Physicians blinded to patient data listened to the same heart sound recordings and attempted a diagnosis. We studied 164 subjects: 86 with mPAp ≥ 25 mmHg (mPAp 41 ± 12 mmHg) and 78 with mPAp < 25 mmHg (mPAp 17 ± 5 mmHg) (p  < 0.005). The correct diagnostic rate of the automated speech-recognition-inspired algorithm was 74% compared to 56% by physicians (p = 0.005). The false positive rate for the algorithm was 34% versus 50% (p = 0.04) for clinicians. The false negative rate for the algorithm was 23% and 68% (p = 0.0002) for physicians. We developed an automated speech-recognition-inspired classification algorithm for the acoustic diagnosis of PH that outperforms physicians that could be used to screen for PH and encourage earlier specialist referral.

Factors associated with mode of colorectal cancer detection and time to diagnosis: a population level study.

PubMed

Sikdar, Khokan C; Dickinson, James; Winget, Marcy

2017-01-05

Although it is well-known that early detection of colorectal cancer (CRC) is important for optimal patient survival, the relationship of patient and health system factors with delayed diagnosis are unclear. The purpose of this study was to identify the demographic, clinical and healthcare factors related to mode of CRC detection and length of the diagnostic interval. All residents of Alberta, Canada diagnosed with first-ever incident CRC in years 2004-2010 were identified from the Alberta Cancer Registry. Population-based administrative health datasets, including hospital discharge abstract, ambulatory care classification system and physician billing data, were used to identify healthcare services related to CRC diagnosis. The time to diagnosis was defined as the time from the first CRC-related healthcare visit to the date of CRC diagnosis. Mode of CRC detection was classified into three groups: urgent, screen-detected and symptomatic. Quantile regression was performed to assess factors associated with time to diagnosis. 9626 patients were included in the study; 25% of patients presented as urgent, 32% were screen-detected and 43% were symptomatic. The median time to diagnosis for urgent, screen-detected and symptomatic patients were 6 days (interquartile range (IQR) 2-14 days), 74 days (IQR 36-183 days), 84 days (IQR 39-223 days), respectively. Time to diagnosis was greater than 6 months for 27% of non-urgent patients. Healthcare factors had the largest impact on time to diagnosis: 3 or more visits to a GP increased the median by 140 days whereas 2 or more visits to a GI-specialist increased it by 108 days compared to 0-1 visits to a GP or GI-specialist, respectively. A large proportion of CRC patients required urgent work-up or had to wait more than 6 months for diagnosis. Actions are needed to reduce the frequency of urgent presentation as well as improve the timeliness of diagnosis. Findings suggest a need to improve coordination of care across multiple

The Effects of Q-Matrix Design on Classification Accuracy in the Log-Linear Cognitive Diagnosis Model.

PubMed

Madison, Matthew J; Bradshaw, Laine P

2015-06-01

Diagnostic classification models are psychometric models that aim to classify examinees according to their mastery or non-mastery of specified latent characteristics. These models are well-suited for providing diagnostic feedback on educational assessments because of their practical efficiency and increased reliability when compared with other multidimensional measurement models. A priori specifications of which latent characteristics or attributes are measured by each item are a core element of the diagnostic assessment design. This item-attribute alignment, expressed in a Q-matrix, precedes and supports any inference resulting from the application of the diagnostic classification model. This study investigates the effects of Q-matrix design on classification accuracy for the log-linear cognitive diagnosis model. Results indicate that classification accuracy, reliability, and convergence rates improve when the Q-matrix contains isolated information from each measured attribute.

Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders.

PubMed

Subasi, Abdulhamit

2013-06-01

Support vector machine (SVM) is an extensively used machine learning method with many biomedical signal classification applications. In this study, a novel PSO-SVM model has been proposed that hybridized the particle swarm optimization (PSO) and SVM to improve the EMG signal classification accuracy. This optimization mechanism involves kernel parameter setting in the SVM training procedure, which significantly influences the classification accuracy. The experiments were conducted on the basis of EMG signal to classify into normal, neurogenic or myopathic. In the proposed method the EMG signals were decomposed into the frequency sub-bands using discrete wavelet transform (DWT) and a set of statistical features were extracted from these sub-bands to represent the distribution of wavelet coefficients. The obtained results obviously validate the superiority of the SVM method compared to conventional machine learning methods, and suggest that further significant enhancements in terms of classification accuracy can be achieved by the proposed PSO-SVM classification system. The PSO-SVM yielded an overall accuracy of 97.41% on 1200 EMG signals selected from 27 subject records against 96.75%, 95.17% and 94.08% for the SVM, the k-NN and the RBF classifiers, respectively. PSO-SVM is developed as an efficient tool so that various SVMs can be used conveniently as the core of PSO-SVM for diagnosis of neuromuscular disorders. Copyright © 2013 Elsevier Ltd. All rights reserved.

[Chronic pain disorder with somatic and psychological factors (F45.41) : Validation criteria on operationalization of the ICD-10-GM diagnosis].

PubMed

Arnold, B; Lutz, J; Nilges, P; Pfingsten, M; Rief, Winfried; Böger, A; Brinkschmidt, T; Casser, H-R; Irnich, D; Kaiser, U; Klimczyk, K; Sabatowski, R; Schiltenwolf, M; Söllner, W

2017-12-01

In 2009 the diagnosis chronic pain disorder with somatic and psychological factors (F45.41) was integrated into the German version of the International Classification of Diseases, version 10 (ICD-10-GM). In 2010 Paul Nilges and Winfried Rief published operationalization criteria for this diagnosis. In the present publication the ad hoc commission on multimodal interdisciplinary pain therapy of the German Pain Society now presents a formula for a clear validation of these operationalization criteria of the ICD code F45.41.

Appendectomy and diagnosis-related groups (DRGs): patient classification and hospital reimbursement in 11 European countries.

PubMed

Quentin, Wilm; Scheller-Kreinsen, David; Geissler, Alexander; Busse, Reinhard

2012-02-01

As part of the EuroDRG project, researchers from 11 countries (i.e., Austria, England, Estonia, Finland, France, Germany, Ireland, Netherlands, Poland, Sweden, and Spain) compared how their diagnosis-related groups (DRG) systems deal with appendectomy patients. The study aims to assist surgeons and national authorities to optimize their DRG systems. National or regional databases were used to identify hospital cases with a diagnosis of appendicitis treated with a procedure of appendectomy. DRG classification algorithms and indicators of resource consumption were compared for those DRGs that together comprised at least 97% of cases. Six standardized case vignettes were defined, and quasi prices according to national DRG-based hospital payment systems were ascertained. European DRG systems vary widely: they classify appendectomy patients according to different sets of variables (between two and six classification variables) into diverging numbers of DRGs (between two and 11 DRGs). The most complex DRG is valued 5.1 times more resource intensive than an index case in France but only 1.1 times more resource intensive than an index case in Finland. Comparisons of quasi prices for the case vignettes show that hypothetical payments for the most complex case vignette amount to only 1,005 in Poland but to 12,304 in France. Large variations in the classification of appendectomy patients raise concerns whether all systems rely on the most appropriate classification variables. Surgeons and national DRG authorities should consider how other countries' DRG systems classify appendectomy patients in order to optimize their DRG system and to ensure fair and appropriate reimbursement.

Classification of hand eczema.

PubMed

Agner, T; Aalto-Korte, K; Andersen, K E; Foti, C; Gimenéz-Arnau, A; Goncalo, M; Goossens, A; Le Coz, C; Diepgen, T L

2015-12-01

Classification of hand eczema (HE) is mandatory in epidemiological and clinical studies, and also important in clinical work. The aim was to test a recently proposed classification system of HE in clinical practice in a prospective multicentre study. Patients were recruited from nine different tertiary referral centres. All patients underwent examination by specialists in dermatology and were checked using relevant allergy testing. Patients were classified into one of the six diagnostic subgroups of HE: allergic contact dermatitis, irritant contact dermatitis, atopic HE, protein contact dermatitis/contact urticaria, hyperkeratotic endogenous eczema and vesicular endogenous eczema, respectively. An additional diagnosis was given if symptoms indicated that factors additional to the main diagnosis were of importance for the disease. Four hundred and twenty-seven patients were included, 379 (89%) of the patients could be classified directly into one of the six diagnostic subgroups, with irritant and allergic contact dermatitis comprising 249 patients (58%). For 32 (7%) more than one of the six diagnostic subgroups had been formulated as a main diagnosis, and 16 (4%) could not be classified. 38% had one additional diagnosis and 26% had two or more additional diagnoses. Eczema on feet was found in 30% of the patients, statistically significantly more frequently associated with hyperkeratotic and vesicular endogenous eczema. We find that the classification system investigated in the present study was useful, being able to give an appropriate main diagnosis for 89% of HE patients, and for another 7% when using two main diagnoses. The fact that more than half of the patients had one or more additional diagnoses illustrates that HE is a multifactorial disease. © 2015 European Academy of Dermatology and Venereology.

[Definition of the Diagnosis Osteomyelitis-Osteomyelitis Diagnosis Score (ODS)].

PubMed

Schmidt, H G K; Tiemann, A H; Braunschweig, R; Diefenbeck, M; Bühler, M; Abitzsch, D; Haustedt, N; Walter, G; Schoop, R; Heppert, V; Hofmann, G O; Glombitza, M; Grimme, C; Gerlach, U-J; Flesch, I

2011-08-01

The disease "osteomyelitis" is characterised by different symptoms and parameters. Decisive roles in the development of the disease are played by the causative bacteria, the route of infection and the individual defense mechanisms of the host. The diagnosis is based on different symptoms and findings from the clinical history, clinical symptoms, laboratory results, diagnostic imaging, microbiological and histopathological analyses. While different osteomyelitis classifications have been published, there is to the best of our knowledge no score that gives information how sure the diagnosis "osteomyelitis" is in general. For any scientific study of a disease a valid definition is essential. We have developed a special osteomyelitis diagnosis score for the reliable classification of clinical, laboratory and technical findings. The score is based on five diagnostic procedures: 1) clinical history and risk factors, 2) clinical examination and laboratory results, 3) diagnostic imaging (ultrasound, radiology, CT, MRI, nuclear medicine and hybrid methods), 4) microbiology, and 5) histopathology. Each diagnostic procedure is related to many individual findings, which are weighted by a score system, in order to achieve a relevant value for each assessment. If the sum of the five diagnostic criteria is 18 or more points, the diagnosis of osteomyelitis can be viewed as "safe" (diagnosis class A). Between 8-17 points the diagnosis is "probable" (diagnosis class B). Less than 8 points means that the diagnosis is "possible, but unlikely" (class C diagnosis). Since each parameter can score six points at a maximum, a reliable diagnosis can only be achieved if at least 3 parameters are scored with 6 points. The osteomyelitis diagnosis score should help to avoid the false description of a clinical presentation as "osteomyelitis". A safe diagnosis is essential for the aetiology, treatment and outcome studies of osteomyelitis. © Georg Thieme Verlag KG Stuttgart · New York.

How Factor Analysis Can Be Used in Classification.

ERIC Educational Resources Information Center

Harman, Harry H.

This is a methodological study that suggests a taxometric technique for objective classification of yeasts. It makes use of the minres method of factor analysis and groups strains of yeast according to their factor profiles. The similarities are judged in the higher-dimensional space determined by the factor analysis, but otherwise rely on the…

Intelligence system based classification approach for medical disease diagnosis

NASA Astrophysics Data System (ADS)

Sagir, Abdu Masanawa; Sathasivam, Saratha

2017-08-01

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.

Prognostic factors of non-functioning pancreatic neuroendocrine tumor revisited: The value of WHO 2010 classification.

PubMed

Bu, Jiyoung; Youn, Sangmin; Kwon, Wooil; Jang, Kee Taek; Han, Sanghyup; Han, Sunjong; You, Younghun; Heo, Jin Seok; Choi, Seong Ho; Choi, Dong Wook

2018-02-01

Various factors have been reported as prognostic factors of non-functional pancreatic neuroendocrine tumors (NF-pNETs). There remains some controversy as to the factors which might actually serve to successfully prognosticate future manifestation and diagnosis of NF-pNETs. As well, consensus regarding management strategy has never been achieved. The aim of this study is to further investigate potential prognostic factors using a large single-center cohort to help determine the management strategy of NF-pNETs. During the time period 1995 through 2013, 166 patients with NF-pNETs who underwent surgery in Samsung Medical Center were entered in a prospective database, and those factors thought to represent predictors of prognosis were tested in uni- and multivariate models. The median follow-up time was 46.5 months; there was a maximum follow-up period of 217 months. The five-year overall survival and disease-free survival rates were 88.5% and 77.0%, respectively. The 2010 WHO classification was found to be the only prognostic factor which affects overall survival and disease-free survival in multivariate analysis. Also, pathologic tumor size and preoperative image tumor size correlated strongly with the WHO grades ( p <0.001, and p <0.001). Our study demonstrates that 2010 WHO classification represents a valuable prognostic factor of NF-pNETs and tumor size on preoperative image correlated with WHO grade. In view of the foregoing, the preoperative image size is thought to represent a reasonable reference with regard to determination and development of treatment strategy of NF-pNETs.

Mastectomy or breast conserving surgery? Factors affecting type of surgical treatment for breast cancer--a classification tree approach.

PubMed

Martin, Michael A; Meyricke, Ramona; O'Neill, Terry; Roberts, Steven

2006-04-20

A critical choice facing breast cancer patients is which surgical treatment--mastectomy or breast conserving surgery (BCS)--is most appropriate. Several studies have investigated factors that impact the type of surgery chosen, identifying features such as place of residence, age at diagnosis, tumor size, socio-economic and racial/ethnic elements as relevant. Such assessment of "propensity" is important in understanding issues such as a reported under-utilisation of BCS among women for whom such treatment was not contraindicated. Using Western Australian (WA) data, we further examine the factors associated with the type of surgical treatment for breast cancer using a classification tree approach. This approach deals naturally with complicated interactions between factors, and so allows flexible and interpretable models for treatment choice to be built that add to the current understanding of this complex decision process. Data was extracted from the WA Cancer Registry on women diagnosed with breast cancer in WA from 1990 to 2000. Subjects' treatment preferences were predicted from covariates using both classification trees and logistic regression. Tumor size was the primary determinant of patient choice, subjects with tumors smaller than 20 mm in diameter preferring BCS. For subjects with tumors greater than 20 mm in diameter factors such as patient age, nodal status, and tumor histology become relevant as predictors of patient choice. Classification trees perform as well as logistic regression for predicting patient choice, but are much easier to interpret for clinical use. The selected tree can inform clinicians' advice to patients.

Factors that determin color appearance and color classification.

PubMed

Janelidze, D

2011-11-01

The purpose of this work was to consider the objective and subjective factors involved in color perception and on their basis offer a color classification that would allow for determining which of these factors are significant for each particular class of colors. In the first part of the article it is considered that physical correlates of subjective sensation of color have mainly a dual nature and sometimes correlate with spectral-power content of light coming from a given area of visual scene to retina, and sometimes with surface reflectance of the given area. Other objective and subjective factors which participate in the formation of color appearance are also considered. According to the characteristics of the visual stimulus, viewing conditions and functional state of visual system, composition of objective and subjective factors participating in the formation of color appearance, as well as the share of each factor in this process are changeable. In the second part of the article one of the possible version of color classification according to which it is possible to distinguish nine different classes of colors is proposed. Among differences between these classes, the most noticeable is that in the case of all classes of color except constant colors, the physical parameter that determines the color category of a given area is the spectral-power distribution of the light coming from this area to the retina. However, in the case of constant colors, the physical parameter that determines the color category of a given area is its reflectance. In the case of considered different classes of colors, composition of objective and subjective factors participating in the formation of color appearance is different. The proposed classification allows determining which of these factors are significant in the case of each specific class of color.

Cross-mapping the ICNP with NANDA, HHCC, Omaha System and NIC for unified nursing language system development. International Classification for Nursing Practice. International Council of Nurses. North American Nursing Diagnosis Association. Home Health Care Classification. Nursing Interventions Classification.

PubMed

Hyun, S; Park, H A

2002-06-01

Nursing language plays an important role in describing and defining nursing phenomena and nursing actions. There are numerous vocabularies describing nursing diagnoses, interventions and outcomes in nursing. However, the lack of a standardized unified nursing language is considered a problem for further development of the discipline of nursing. In an effort to unify the nursing languages, the International Council of Nurses (ICN) has proposed the International Classification for Nursing Practice (ICNP) as a unified nursing language system. The purpose of this study was to evaluate the inclusiveness and expressiveness of the ICNP terms by cross-mapping them with the existing nursing terminologies, specifically the North American Nursing Diagnosis Association (NANDA) taxonomy I, the Omaha System, the Home Health Care Classification (HHCC) and the Nursing Interventions Classification (NIC). Nine hundred and seventy-four terms from these four classifications were cross-mapped with the ICNP terms. This was performed in accordance with the Guidelines for Composing a Nursing Diagnosis and Guidelines for Composing a Nursing Intervention, which were suggested by the ICNP development team. An expert group verified the results. The ICNP Phenomena Classification described 87.5% of the NANDA diagnoses, 89.7% of the HHCC diagnoses and 72.7% of the Omaha System problem classification scheme. The ICNP Action Classification described 79.4% of the NIC interventions, 80.6% of the HHCC interventions and 71.4% of the Omaha System intervention scheme. The results of this study suggest that the ICNP has a sound starting structure for a unified nursing language system and can be used to describe most of the existing terminologies. Recommendations for the addition of terms to the ICNP are provided.

[Nursing diagnosis "impaired walking" in elderly patients: integrative literature review].

PubMed

Marques-Vieira, Cristina Maria Alves; de Sousa, Luís Manuel Mota; de Matos Machado Carias, João Filipe; Caldeira, Sílvia Maria Alves

2015-03-01

The impaired walking nursing diagnosis has been included in NANDA International classification taxonomy in 1998, and this review aims to identify the defining characteristics and related factors in elderly patients in recent literature. Integrative literature review based on the following guiding question: Are there more defining characteristics and factors related to the nursing diagnosis impaired walking than those included in NANDA International classification taxonomy in elderly patients? Search conducted in 2007-2013 on international and Portuguese databases. Sample composed of 15 papers. Among the 6 defining characteristics classified at NANDA International, 3 were identified in the search results, but 13 were not included in the classification. Regarding the 14 related factors that are classified, 9 were identified in the sample and 12 were not included in the NANDA International taxonomy. This review allowed the identification of new elements not included in NANDA International Taxonomy and may contribute to the development of taxonomy and nursing knowledge.

Diagnosis of mild chronic pancreatitis (Cambridge classification): comparative study using secretin injection-magnetic resonance cholangiopancreatography and endoscopic retrograde pancreatography.

PubMed

Sai, Jin-Kan; Suyama, Masafumi; Kubokawa, Yoshihiro; Watanabe, Sumio

2008-02-28

To investigate the usefulness of secretin injection-MRCP for the diagnosis of mild chronic pancreatitis. Sixteen patients having mild chronic pancreatitis according to the Cambridge classification and 12 control subjects with no abnormal findings on the pancreatogram were examined for the diagnostic accuracy of secretin injection-MRCP regarding abnormal branch pancreatic ducts associated with mild chronic pancreatitis (Cambridge Classification), using endoscopic retrograde cholangiopancreatography (ERCP) for comparison. The sensitivity and specificity for abnormal branch pancreatic ducts determined by two reviewers were respectively 55%-63% and 75%-83% in the head, 57%-64% and 82%-83% in the body, and 44%-44% and 72%-76% in the tail of the pancreas. The sensitivity and specificity for mild chronic pancreatitis were 56%-63% and 92%-92%, respectively. Interobserver agreement (kappa statistics) concerning the diagnosis of an abnormal branch pancreatic duct and of mild chronic pancreatitis was good to excellent. Secretin injection-MRCP might be useful for the diagnosis of mild chronic pancreatitis.

Australian diagnosis related groups: Drivers of complexity adjustment.

PubMed

Jackson, Terri; Dimitropoulos, Vera; Madden, Richard; Gillett, Steve

2015-11-01

In undertaking a major revision to the Australian Refined Diagnosis Related Group (ARDRG) classification, we set out to contrast Australia's approach to using data on additional (not principal) diagnoses with major international approaches in splitting base or Adjacent Diagnosis Related Groups (ADRGs). Comparative policy analysis/narrative review of peer-reviewed and grey literature on international approaches to use of additional (secondary) diagnoses in the development of Australian and international DRG systems. European and US approaches to characterise complexity of inpatient care are well-documented, providing useful points of comparison with Australia's. Australia, with good data sources, has continued to refine its national DRG classification using increasingly sophisticated approaches. Hospital funders in Australia and in other systems are often under pressure from provider groups to expand classifications to reflect clinical complexity. DRG development in most healthcare systems reviewed here reflects four critical factors: these socio-political factors, the quality and depth of the coded data available to characterise the mix of cases in a healthcare system, the size of the underlying population, and the intended scope and use of the classification. Australia's relatively small national population has constrained the size of its DRG classifications, and development has been concentrated on inpatient care in public hospitals. Development of casemix classifications in health care is driven by both technical and socio-political factors. Use of additional diagnoses to adjust for patient complexity and cost needs to respond to these in each casemix application. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Differential Diagnosis of Erythmato-Squamous Diseases Using Classification and Regression Tree.

PubMed

Maghooli, Keivan; Langarizadeh, Mostafa; Shahmoradi, Leila; Habibi-Koolaee, Mahdi; Jebraeily, Mohamad; Bouraghi, Hamid

2016-10-01

Differential diagnosis of Erythmato-Squamous Diseases (ESD) is a major challenge in the field of dermatology. The ESD diseases are placed into six different classes. Data mining is the process for detection of hidden patterns. In the case of ESD, data mining help us to predict the diseases. Different algorithms were developed for this purpose. we aimed to use the Classification and Regression Tree (CART) to predict differential diagnosis of ESD. we used the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology. For this purpose, the dermatology data set from machine learning repository, UCI was obtained. The Clementine 12.0 software from IBM Company was used for modelling. In order to evaluation of the model we calculate the accuracy, sensitivity and specificity of the model. The proposed model had an accuracy of 94.84% (. 24.42) in order to correct prediction of the ESD disease. Results indicated that using of this classifier could be useful. But, it would be strongly recommended that the combination of machine learning methods could be more useful in terms of prediction of ESD.

Refinement of diagnosis and disease classification in psychiatry.

PubMed

Lecrubier, Yves

2008-03-01

to be related to risk factors especially at certain periods when associated with environmental events. One of the major aims for a classification of patients is to identify groups to whom a best possible therapeutic strategy can be proposed. Drugs may improve fear extinction while the genetic and/or acquired avoidance may be called phobia. The basic mechanism and or the corresponding phenotype should appear in the classification. Progresses in early identification of disturbances by taking into account all the information available (prodromal symptoms, cognitive, biological, imaging, genetic, family information) are crucial for the future therapeutic strategy: prevention.

Mixed-phenotype acute leukemia: state-of-the-art of the diagnosis, classification and treatment.

PubMed

Cernan, Martin; Szotkowski, Tomas; Pikalova, Zuzana

2017-09-01

Mixed-phenotype acute leukemia (MPAL) is a heterogeneous group of hematopoietic malignancies in which blasts show markers of multiple developmental lineages and cannot be clearly classified as acute myeloid or lymphoblastic leukemias. Historically, various names and classifications were used for this rare entity accounting for 2-5% of all acute leukemias depending on the diagnostic criterias used. The currently valid classification of myeloid neoplasms and acute leukemia published by the World Health Organization (WHO) in 2016 refers to this group of diseases as MPAL. Because adverse cytogenetic abnormalities are frequently present, MPAL is generally considered a disease with a poor prognosis. Knowledge of its treatment is limited to retrospective analyses of small patient cohorts. So far, no treatment recommendations verified by prospective studies have been published. The reported data suggest that induction therapy for acute lymphoblastic leukemia followed by allogeneic hematopoietic cell transplantation is more effective than induction therapy for acute myeloid leukemia or consolidation chemotherapy. The establishment of cooperative groups and international registries based on the recent WHO criterias are required to ensure further progress in understanding and treatment of MPAL. This review summarizes current knowledge on the diagnosis, classification, prognosis and treatment of MPAL patients.

Differential Diagnosis of Erythmato-Squamous Diseases Using Classification and Regression Tree

PubMed Central

Maghooli, Keivan; Langarizadeh, Mostafa; Shahmoradi, Leila; Habibi-koolaee, Mahdi; Jebraeily, Mohamad; Bouraghi, Hamid

2016-01-01

Introduction: Differential diagnosis of Erythmato-Squamous Diseases (ESD) is a major challenge in the field of dermatology. The ESD diseases are placed into six different classes. Data mining is the process for detection of hidden patterns. In the case of ESD, data mining help us to predict the diseases. Different algorithms were developed for this purpose. Objective: we aimed to use the Classification and Regression Tree (CART) to predict differential diagnosis of ESD. Methods: we used the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology. For this purpose, the dermatology data set from machine learning repository, UCI was obtained. The Clementine 12.0 software from IBM Company was used for modelling. In order to evaluation of the model we calculate the accuracy, sensitivity and specificity of the model. Results: The proposed model had an accuracy of 94.84% ( Standard Deviation: 24.42) in order to correct prediction of the ESD disease. Conclusions: Results indicated that using of this classifier could be useful. But, it would be strongly recommended that the combination of machine learning methods could be more useful in terms of prediction of ESD. PMID:28077889

[Evaluation of traditional pathological classification at molecular classification era for gastric cancer].

PubMed

Yu, Yingyan

2014-01-01

Histopathological classification is in a pivotal position in both basic research and clinical diagnosis and treatment of gastric cancer. Currently, there are different classification systems in basic science and clinical application. In medical literatures, different classifications are used including Lauren and WHO systems, which have confused many researchers. Lauren classification has been proposed for half a century, but is still used worldwide. It shows many advantages of simple, easy handling with prognostic significance. The WHO classification scheme is better than Lauren classification in that it is continuously being revised according to the progress of gastric cancer, and is always used in the clinical and pathological diagnosis of common scenarios. Along with the progression of genomics, transcriptomics, proteomics, metabolomics researches, molecular classification of gastric cancer becomes the current hot topics. The traditional therapeutic approach based on phenotypic characteristics of gastric cancer will most likely be replaced with a gene variation mode. The gene-targeted therapy against the same molecular variation seems more reasonable than traditional chemical treatment based on the same morphological change.

Congenital neutropenia in the era of genomics: classification, diagnosis, and natural history.

PubMed

Donadieu, Jean; Beaupain, Blandine; Fenneteau, Odile; Bellanné-Chantelot, Christine

2017-11-01

This review focuses on the classification, diagnosis and natural history of congenital neutropenia (CN). CN encompasses a number of genetic disorders with chronic neutropenia and, for some, affecting other organ systems, such as the pancreas, central nervous system, heart, bone and skin. To date, 24 distinct genes have been associated with CN. The number of genes involved makes gene screening difficult. This can be solved by next-generation sequencing (NGS) of targeted gene panels. One of the major complications of CN is spontaneous leukaemia, which is preceded by clonal somatic evolution, and can be screened by a targeted NGS panel focused on somatic events. © 2017 John Wiley & Sons Ltd.

EUS-guided biopsy for the diagnosis and classification of lymphoma.

PubMed

Ribeiro, Afonso; Pereira, Denise; Escalón, Maricer P; Goodman, Mark; Byrne, Gerald E

2010-04-01

EUS-guided FNA and Tru-cut biopsy (TCB) is highly accurate in the diagnosis of lymphoma. Subclassification, however, may be difficult in low-grade non-Hodgkin lymphoma and Hodgkin lymphoma. To determine the yield of EUS-guided biopsy to classify lymphoma based on the World Health Organization classification of tumors of hematopoietic lymphoid tissues. Retrospective study. Tertiary referral center. A total of 24 patients referred for EUS-guided biopsy who had a final diagnosis of lymphoma or "highly suspicious for lymphoma." EUS-guided FNA and TCB combined with flow cytometry (FC) analysis. MAIN OUTCOMES MEASUREMENT: Lymphoma subclassification accuracy of EUS guided biopsy. Twenty-four patients were included in this study. Twenty-three patients underwent EUS-FNA, and 1 patient had only TCB. Twenty-two underwent EUS-TCB combined with FNA. EUS correctly diagnosed lymphoma in 19 out of 24 patients (79%), and subclassification was determined in 16 patients (66.6%). Flow cytometry correctly identified B-cell monoclonality in 95% (18 out of 19). In 1 patient diagnosed as having marginal-zone lymphoma by EUS-FNA/FC only, the diagnosis was changed to hairy cell leukemia after a bone marrow biopsy was obtained. EUS had a lower yield in nonlarge B-cell lymphoma (only 9 out of 15 cases [60%]) compared with large B-cell lymphoma (78%; P = .3 [Fisher exact test]). Retrospective, small number of patients. EUS-guided biopsy has a lower yield to correctly classify Hodgkin lymphoma and low-grade lymphoma compared with high-grade diffuse large B-cell lymphoma. Copyright 2010 American Society for Gastrointestinal Endoscopy. Published by Mosby, Inc. All rights reserved.

Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson’s Disease

NASA Astrophysics Data System (ADS)

Adeli, Ehsan; Wu, Guorong; Saghafi, Behrouz; An, Le; Shi, Feng; Shen, Dinggang

2017-01-01

Feature selection methods usually select the most compact and relevant set of features based on their contribution to a linear regression model. Thus, these features might not be the best for a non-linear classifier. This is especially crucial for the tasks, in which the performance is heavily dependent on the feature selection techniques, like the diagnosis of neurodegenerative diseases. Parkinson’s disease (PD) is one of the most common neurodegenerative disorders, which progresses slowly while affects the quality of life dramatically. In this paper, we use the data acquired from multi-modal neuroimaging data to diagnose PD by investigating the brain regions, known to be affected at the early stages. We propose a joint kernel-based feature selection and classification framework. Unlike conventional feature selection techniques that select features based on their performance in the original input feature space, we select features that best benefit the classification scheme in the kernel space. We further propose kernel functions, specifically designed for our non-negative feature types. We use MRI and SPECT data of 538 subjects from the PPMI database, and obtain a diagnosis accuracy of 97.5%, which outperforms all baseline and state-of-the-art methods.

Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson’s Disease

PubMed Central

Adeli, Ehsan; Wu, Guorong; Saghafi, Behrouz; An, Le; Shi, Feng; Shen, Dinggang

2017-01-01

Feature selection methods usually select the most compact and relevant set of features based on their contribution to a linear regression model. Thus, these features might not be the best for a non-linear classifier. This is especially crucial for the tasks, in which the performance is heavily dependent on the feature selection techniques, like the diagnosis of neurodegenerative diseases. Parkinson’s disease (PD) is one of the most common neurodegenerative disorders, which progresses slowly while affects the quality of life dramatically. In this paper, we use the data acquired from multi-modal neuroimaging data to diagnose PD by investigating the brain regions, known to be affected at the early stages. We propose a joint kernel-based feature selection and classification framework. Unlike conventional feature selection techniques that select features based on their performance in the original input feature space, we select features that best benefit the classification scheme in the kernel space. We further propose kernel functions, specifically designed for our non-negative feature types. We use MRI and SPECT data of 538 subjects from the PPMI database, and obtain a diagnosis accuracy of 97.5%, which outperforms all baseline and state-of-the-art methods. PMID:28120883

Clinical application of a microfluidic chip for immunocapture and quantification of circulating exosomes to assist breast cancer diagnosis and molecular classification.

PubMed

Fang, Shimeng; Tian, Hongzhu; Li, Xiancheng; Jin, Dong; Li, Xiaojie; Kong, Jing; Yang, Chun; Yang, Xuesong; Lu, Yao; Luo, Yong; Lin, Bingcheng; Niu, Weidong; Liu, Tingjiao

2017-01-01

Increasing attention has been attracted by exosomes in blood-based diagnosis because cancer cells release more exosomes in serum than normal cells and these exosomes overexpress a certain number of cancer-related biomarkers. However, capture and biomarker analysis of exosomes for clinical application are technically challenging. In this study, we developed a microfluidic chip for immunocapture and quantification of circulating exosomes from small sample volume and applied this device in clinical study. Circulating EpCAM-positive exosomes were measured in 6 cases breast cancer patients and 3 healthy controls to assist diagnosis. A significant increase in the EpCAM-positive exosome level in these patients was detected, compared to healthy controls. Furthermore, we quantified circulating HER2-positive exosomes in 19 cases of breast cancer patients for molecular classification. We demonstrated that the exosomal HER2 expression levels were almost consistent with that in tumor tissues assessed by immunohistochemical staining. The microfluidic chip might provide a new platform to assist breast cancer diagnosis and molecular classification.

[Differences between Chicago and traditional classifications in the diagnosis of esophageal motor disorders with high-resolution manometry and topography of esophageal pressure].

PubMed

Abreu-Y Abreu, A T; González Sánchez, C B; Villanueva Sáenz, E; Valdovinos Díaz, M A

2010-01-01

With the introduction of high resolution manometry (HRM) and esophageal topography a novel classification (Chicago Classification) has been proposed for the diagnosis of esophageal motor disorders (EMD). Clinical differences with the traditional classification are currently under evaluation. To investigate differences between the Chicago (CC) and traditional (TC) classifications in the diagnosis of EMD. Consecutive patients with indication for esophageal manometry were studied. HRM was performed with a 36 sensors solid-state catheter and Manoview software (V2.0).Conventional manometric tracings were analyzed by an investigator blinded to the results of HRM. Diagnosis by CC and CT were compared. Two hundred patients were studied, 106 (53%) of them women (53%) with a mean patient age of 43.4 (range 16 - 84) years. Preoperative evaluation for GERD 152 (76%) was the most frequent indication. Achalasia (8), scleroderma (2) and peristaltic dysfunction (60 vs. 59) were similarly diagnosed by CC and CT. Spastic disorders were more frequently identified by CC: nutcracker esophagus (NC) in 3, spastic NC in3 and segmental NC in 11 patients versus TC: NC 5. Three patients had spasm with CC and 1 with TC. Non specific motor disorder was diagnosed by TC and 2 patients had functional obstruction with CC. Hypotensive lower esophageal sphincter was identified in 63 patients with CC vs.57 with TC. Spastic disorders and functional obstruction were the EMD better identified by HRM and CC.

A classification system for characterization of physical and non-physical work factors.

PubMed

Genaidy, A; Karwowski, W; Succop, P; Kwon, Y G; Alhemoud, A; Goyal, D

2000-01-01

A comprehensive evaluation of work-related performance factors is a prerequisite to developing integrated and long-term solutions to workplace performance improvement. This paper describes a work-factor classification system that categorizes the entire domain of workplace factors impacting performance. A questionnaire-based instrument was developed to implement this classification system in industry. Fifty jobs were evaluated in 4 different service and manufacturing companies using the proposed questionnaire-based instrument. The reliability coefficients obtained from the analyzed jobs were considered good (0.589 to 0.862). In general, the physical work factors resulted in higher reliability coefficients (0.847 to 0.862) than non-physical work factors (0.589 to 0.768).

Diagnosis-related groups for stroke in Europe: patient classification and hospital reimbursement in 11 countries.

PubMed

Peltola, Mikko; Quentin, Wilm

2013-01-01

Diagnosis-related groups (DRGs) are increasingly being used for various purposes in many countries. However, there are no studies comparing different DRG systems in the care of stroke. As part of the EuroDRG project, researchers from 11 countries (i.e. Austria, England, Estonia, Finland, France, Germany, Ireland, the Netherlands, Poland, Sweden and Spain) compared how their DRG systems deal with stroke patients. The study aims to assist clinicians and national authorities to optimize their DRG systems. National or regional databases were used to identify hospital cases with a diagnosis of stroke. DRG classification algorithms and indicators of resource consumption were compared for those DRGs that individually represent at least 1% of stroke cases. In addition, standardized case vignettes were defined, and quasi prices according to national DRG-based hospital payment systems were ascertained. European DRG systems vary widely: they classify stroke patients according to different sets of variables (between 1 and 7 classification variables) into diverging numbers of DRGs (between 1 and 10 DRGs). In 6 of the countries more than half of the patients are concentrated within a single DRG. The countries' systems also vary with respect to the evaluation of different kinds of stroke patients. The most complex DRG is considered 3.8 times more resource intensive than an index case in Finland. By contrast, in England, the DRG system does not account for complex cases. Comparisons of quasi prices for the case vignettes show that hypothetical payments for the index case amount to only EUR 907 in Poland but to EUR 7,881 in Ireland. Large variations in the classification of stroke patients raise concerns whether all systems rely on the most appropriate classification variables and whether the DRGs adequately reflect differences in the complexity of treating different groups of patients. Learning from other DRG systems may help in improving the national systems. Clinicians and

An Expert System for Diagnosis of Sleep Disorder Using Fuzzy Rule-Based Classification Systems

NASA Astrophysics Data System (ADS)

Septem Riza, Lala; Pradini, Mila; Fitrajaya Rahman, Eka; Rasim

2017-03-01

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.

Experimental Investigation for Fault Diagnosis Based on a Hybrid Approach Using Wavelet Packet and Support Vector Classification

PubMed Central

Li, Pengfei; Jiang, Yongying; Xiang, Jiawei

2014-01-01

To deal with the difficulty to obtain a large number of fault samples under the practical condition for mechanical fault diagnosis, a hybrid method that combined wavelet packet decomposition and support vector classification (SVC) is proposed. The wavelet packet is employed to decompose the vibration signal to obtain the energy ratio in each frequency band. Taking energy ratios as feature vectors, the pattern recognition results are obtained by the SVC. The rolling bearing and gear fault diagnostic results of the typical experimental platform show that the present approach is robust to noise and has higher classification accuracy and, thus, provides a better way to diagnose mechanical faults under the condition of small fault samples. PMID:24688361

Acute myocardial infarction and diagnosis-related groups: patient classification and hospital reimbursement in 11 European countries.

PubMed

Quentin, Wilm; Rätto, Hanna; Peltola, Mikko; Busse, Reinhard; Häkkinen, Unto

2013-07-01

As part of the diagnosis related groups in Europe (EuroDRG) project, researchers from 11 countries (i.e. Austria, England, Estonia, Finland, France, Germany, Ireland, Netherlands, Poland, Spain, and Sweden) compared how their DRG systems deal with patients admitted to hospital for acute myocardial infarction (AMI). The study aims to assist cardiologists and national authorities to optimize their DRG systems. National or regional databases were used to identify hospital cases with a primary diagnosis of AMI. Diagnosis-related group classification algorithms and indicators of resource consumption were compared for those DRGs that individually contained at least 1% of cases. Six standardized case vignettes were defined, and quasi prices according to national DRG-based hospital payment systems were ascertained. European DRG systems vary widely: they classify AMI patients according to different sets of variables into diverging numbers of DRGs (between 4 DRGs in Estonia and 16 DRGs in France). The most complex DRG is valued 11 times more resource intensive than an index case in Estonia but only 1.38 times more resource intensive than an index case in England. Comparisons of quasi prices for the case vignettes show that hypothetical payments for the index case amount to only €420 in Poland but to €7930 in Ireland. Large variation exists in the classification of AMI patients across Europe. Cardiologists and national DRG authorities should consider how other countries' DRG systems classify AMI patients in order to identify potential scope for improvement and to ensure fair and appropriate reimbursement.

Acute myocardial infarction and diagnosis-related groups: patient classification and hospital reimbursement in 11 European countries

PubMed Central

Quentin, Wilm; Rätto, Hanna; Peltola, Mikko; Busse, Reinhard; Häkkinen, Unto

2013-01-01

Aims As part of the diagnosis related groups in Europe (EuroDRG) project, researchers from 11 countries (i.e. Austria, England, Estonia, Finland, France, Germany, Ireland, Netherlands, Poland, Spain, and Sweden) compared how their DRG systems deal with patients admitted to hospital for acute myocardial infarction (AMI). The study aims to assist cardiologists and national authorities to optimize their DRG systems. Methods and results National or regional databases were used to identify hospital cases with a primary diagnosis of AMI. Diagnosis-related group classification algorithms and indicators of resource consumption were compared for those DRGs that individually contained at least 1% of cases. Six standardized case vignettes were defined, and quasi prices according to national DRG-based hospital payment systems were ascertained. European DRG systems vary widely: they classify AMI patients according to different sets of variables into diverging numbers of DRGs (between 4 DRGs in Estonia and 16 DRGs in France). The most complex DRG is valued 11 times more resource intensive than an index case in Estonia but only 1.38 times more resource intensive than an index case in England. Comparisons of quasi prices for the case vignettes show that hypothetical payments for the index case amount to only €420 in Poland but to €7930 in Ireland. Conclusions Large variation exists in the classification of AMI patients across Europe. Cardiologists and national DRG authorities should consider how other countries' DRG systems classify AMI patients in order to identify potential scope for improvement and to ensure fair and appropriate reimbursement. PMID:23364755

Diagnosis of periodontal diseases using different classification algorithms: a preliminary study.

PubMed

Ozden, F O; Özgönenel, O; Özden, B; Aydogdu, A

2015-01-01

The purpose of the proposed study was to develop an identification unit for classifying periodontal diseases using support vector machine (SVM), decision tree (DT), and artificial neural networks (ANNs). A total of 150 patients was divided into two groups such as training (100) and testing (50). The codes created for risk factors, periodontal data, and radiographically bone loss were formed as a matrix structure and regarded as inputs for the classification unit. A total of six periodontal conditions was the outputs of the classification unit. The accuracy of the suggested methods was compared according to their resolution and working time. DT and SVM were best to classify the periodontal diseases with a high accuracy according to the clinical research based on 150 patients. The performances of SVM and DT were found 98% with total computational time of 19.91 and 7.00 s, respectively. ANN had the worst correlation between input and output variable, and its performance was calculated as 46%. SVM and DT appeared to be sufficiently complex to reflect all the factors associated with the periodontal status, simple enough to be understandable and practical as a decision-making aid for prediction of periodontal disease.

Non-negative Matrix Factorization and Co-clustering: A Promising Tool for Multi-tasks Bearing Fault Diagnosis

NASA Astrophysics Data System (ADS)

Shen, Fei; Chen, Chao; Yan, Ruqiang

2017-05-01

Classical bearing fault diagnosis methods, being designed according to one specific task, always pay attention to the effectiveness of extracted features and the final diagnostic performance. However, most of these approaches suffer from inefficiency when multiple tasks exist, especially in a real-time diagnostic scenario. A fault diagnosis method based on Non-negative Matrix Factorization (NMF) and Co-clustering strategy is proposed to overcome this limitation. Firstly, some high-dimensional matrixes are constructed using the Short-Time Fourier Transform (STFT) features, where the dimension of each matrix equals to the number of target tasks. Then, the NMF algorithm is carried out to obtain different components in each dimension direction through optimized matching, such as Euclidean distance and divergence distance. Finally, a Co-clustering technique based on information entropy is utilized to realize classification of each component. To verity the effectiveness of the proposed approach, a series of bearing data sets were analysed in this research. The tests indicated that although the diagnostic performance of single task is comparable to traditional clustering methods such as K-mean algorithm and Guassian Mixture Model, the accuracy and computational efficiency in multi-tasks fault diagnosis are improved.

ILAE Classification of the Epilepsies Position Paper of the ILAE Commission for Classification and Terminology

PubMed Central

Scheffer, Ingrid E; Berkovic, Samuel; Capovilla, Giuseppe; Connolly, Mary B; French, Jacqueline; Guilhoto, Laura; Hirsch, Edouard; Jain, Satish; Mathern, Gary W.; Moshé, Solomon L; Nordli, Douglas R; Perucca, Emilio; Tomson, Torbjörn; Wiebe, Samuel; Zhang, Yue-Hua; Zuberi, Sameer M

2017-01-01

Summary The ILAE Classification of the Epilepsies has been updated to reflect our gain in understanding of the epilepsies and their underlying mechanisms following the major scientific advances which have taken place since the last ratified classification in 1989. As a critical tool for the practising clinician, epilepsy classification must be relevant and dynamic to changes in thinking, yet robust and translatable to all areas of the globe. Its primary purpose is for diagnosis of patients, but it is also critical for epilepsy research, development of antiepileptic therapies and communication around the world. The new classification originates from a draft document submitted for public comments in 2013 which was revised to incorporate extensive feedback from the international epilepsy community over several rounds of consultation. It presents three levels, starting with seizure type where it assumes that the patient is having epileptic seizures as defined by the new 2017 ILAE Seizure Classification. After diagnosis of the seizure type, the next step is diagnosis of epilepsy type, including focal epilepsy, generalized epilepsy, combined generalized and focal epilepsy, and also an unknown epilepsy group. The third level is that of epilepsy syndrome where a specific syndromic diagnosis can be made. The new classification incorporates etiology along each stage, emphasizing the need to consider etiology at each step of diagnosis as it often carries significant treatment implications. Etiology is broken into six subgroups, selected because of their potential therapeutic consequences. New terminology is introduced such as developmental and epileptic encephalopathy. The term benign is replaced by the terms self-limited and pharmacoresponsive, to be used where appropriate. It is hoped that this new framework will assist in improving epilepsy care and research in the 21st century. PMID:28276062

Diagnosis and classification of hematologic malignancies on the basis of genetics

PubMed Central

2017-01-01

Genomic analysis has greatly influenced the diagnosis and clinical management of patients affected by diverse forms of hematologic malignancies. Here, we review how genetic alterations define subclasses of patients with acute leukemias, myelodysplastic syndromes (MDS), myeloproliferative neoplasms (MPNs), non-Hodgkin lymphomas, and classical Hodgkin lymphoma. These include new subtypes of acute myeloid leukemia defined by mutations in RUNX1 or BCR-ABL1 translocations as well as a constellation of somatic structural DNA alterations in acute lymphoblastic leukemia. Among patients with MDS, detection of mutations in SF3B1 define a subgroup of patients with the ring sideroblast form of MDS and a favorable prognosis. For patients with MPNs, detection of the BCR-ABL1 fusion delineates chronic myeloid leukemia from classic BCR-ABL1− MPNs, which are largely defined by mutations in JAK2, CALR, or MPL. In the B-cell lymphomas, detection of characteristic rearrangements involving MYC in Burkitt lymphoma, BCL2 in follicular lymphoma, and MYC/BCL2/BCL6 in high-grade B-cell lymphomas are essential for diagnosis. In T-cell lymphomas, anaplastic large-cell lymphoma is defined by mutually exclusive rearrangements of ALK, DUSP22/IRF4, and TP63. Genetic alterations affecting TP53 and the mutational status of the immunoglobulin heavy-chain variable region are important in clinical management of chronic lymphocytic leukemia. Additionally, detection of BRAFV600E mutations is helpful in the diagnosis of classical hairy cell leukemia and a number of histiocytic neoplasms. Numerous additional examples provided here demonstrate how clinical evaluation of genomic alterations have refined classification of myeloid neoplasms and major forms of lymphomas arising from B, T, or natural killer cells. PMID:28600336

Examining applying high performance genetic data feature selection and classification algorithms for colon cancer diagnosis.

PubMed

Al-Rajab, Murad; Lu, Joan; Xu, Qiang

2017-07-01

This paper examines the accuracy and efficiency (time complexity) of high performance genetic data feature selection and classification algorithms for colon cancer diagnosis. The need for this research derives from the urgent and increasing need for accurate and efficient algorithms. Colon cancer is a leading cause of death worldwide, hence it is vitally important for the cancer tissues to be expertly identified and classified in a rapid and timely manner, to assure both a fast detection of the disease and to expedite the drug discovery process. In this research, a three-phase approach was proposed and implemented: Phases One and Two examined the feature selection algorithms and classification algorithms employed separately, and Phase Three examined the performance of the combination of these. It was found from Phase One that the Particle Swarm Optimization (PSO) algorithm performed best with the colon dataset as a feature selection (29 genes selected) and from Phase Two that the Support Vector Machine (SVM) algorithm outperformed other classifications, with an accuracy of almost 86%. It was also found from Phase Three that the combined use of PSO and SVM surpassed other algorithms in accuracy and performance, and was faster in terms of time analysis (94%). It is concluded that applying feature selection algorithms prior to classification algorithms results in better accuracy than when the latter are applied alone. This conclusion is important and significant to industry and society. Copyright © 2017 Elsevier B.V. All rights reserved.

Classification of hyperlipidaemias and hyperlipoproteinaemias*

PubMed Central

1970-01-01

Many studies of atherosclerosis have indicated hyperlipidaemia as a predisposing factor to vascular disease. The relationship holds even for mild degrees of hyperlipidaemia, a fact that underlines the importance of this category of disorders. Both primary and secondary hyperlipidaemias represent such a variety of abnormalities that an internationally acceptable provisional classification is highly desirable in order to facilitate communication between scientists with different backgrounds. The present memorandum presents such a classification; it briefly describes the criteria for diagnosis of the main types of hyperlipidaemia as well as the methods of their determination. Because lipoproteins offer more information than analysis of plasma lipids (most of the plasma lipids being bound to various proteins), the classification is based on lipoprotein analyses by electrophoresis and ultracentrifugation. Simpler methods, however, such as the observation of plasma and measurements of cholesterol and triglycerides, are used to the fullest possible extent in determining the lipoprotein patterns. PMID:4930042

Midline cystic malformations of the brain: imaging diagnosis and classification based on embryologic analysis.

PubMed

Utsunomiya, Hidetsuna; Yamashita, Shinichi; Takano, Koichi; Ueda, Yukiyo; Fujii, Akira

2006-07-01

This article describes a classification and imaging diagnosis of intracranial midline cystic malformations based on neuroembryologic analysis. Midline cystic malformations are classified into two categories from an embryologic point of view. In one category, the cyst represents expansion of the roof plate of the brain vesicle, and in the other the cyst consists of extraaxial structures such as an arachnoid membrane or migrating ependymal cells. Infratentorial cysts, such as the Dandy-Walker cyst or Blake's pouch cyst, and supratentorial cysts, such as a communicating interhemispheric cyst with callosal agenesis or a dorsal cyst with holoprosencephaly, are included in the first category. Infratentorial arachnoid cavities, such as the arachnoid cyst, arachnoid pouch, and mega cisterna magna, are in the second category. Noncommunicating interhemispheric cysts, such as interhemispheric arachnoid cyst or ependymal cyst, with callosal agenesis are also in the second category. A careful review of embryologic development is essential for understanding these midline cysts and for making a more accurate radiologic diagnosis.

Tumor necrosis factor receptor 1 (TNFRI) for ventilator-associated pneumonia diagnosis by cytokine multiplex analysis.

PubMed

Martin-Loeches, Ignacio; Bos, Lieuwe D; Povoa, Pedro; Ramirez, Paula; Schultz, Marcus J; Torres, Antoni; Artigas, Antonio

2015-12-01

The diagnosis of ventilator-associated pneumonia (VAP) is challenging. An important aspect to improve outcome is early recognition of VAP and the initiation of the appropriate empirical treatment. We hypothesized that biological markers in plasma can rule out VAP at the moment of clinical suspicion and could rule in VAP before the diagnosis can be made clinically. In this prospective study, patients with VAP (n = 24, microbiology confirmed) were compared to controls (n = 19) with a similar duration of mechanical ventilation. Blood samples from the day of VAP diagnosis and 1 and 3 days before were analyzed with a multiplex array for markers of inflammation, coagulation, and apoptosis. The best biomarker combination was selected and the diagnostic accuracy was given by the area under the receiver operating characteristic curve (ROC-AUC). TNF-receptor 1 (TNFRI) and granulocyte colony-stimulating factor (GCSF) were selected as optimal biomarkers at the day of VAP diagnosis, which resulted in a ROC-AUC of 0.96, with excellent sensitivity. Three days before the diagnosis TNFRI and plasminogen activator inhibitor-1 (PAI-1) levels in plasma predicted VAP with a ROC-AUC of 0.79. The slope of IL-10 and PAI-1 resulted in a ROC-AUC of 0.77. These biomarkers improved the classification of the clinical pulmonary infection score when combined. Concentration of TNFRI and PAI-1 and the slope of PAI-1 and IL-10 may be used to predict the development of VAP as early as 3 days before the diagnosis made clinically. TNFRI and GCSF may be used to exclude VAP at the moment of clinical suspicion. Especially TNFRI seems to be a promising marker for the prediction and diagnosis of VAP.

Automated image processing method for the diagnosis and classification of malaria on thin blood smears.

PubMed

Ross, Nicholas E; Pritchard, Charles J; Rubin, David M; Dusé, Adriano G

2006-05-01

Malaria is a serious global health problem, and rapid, accurate diagnosis is required to control the disease. An image processing algorithm to automate the diagnosis of malaria on thin blood smears is developed. The image classification system is designed to positively identify malaria parasites present in thin blood smears, and differentiate the species of malaria. Images are acquired using a charge-coupled device camera connected to a light microscope. Morphological and novel threshold selection techniques are used to identify erythrocytes (red blood cells) and possible parasites present on microscopic slides. Image features based on colour, texture and the geometry of the cells and parasites are generated, as well as features that make use of a priori knowledge of the classification problem and mimic features used by human technicians. A two-stage tree classifier using backpropogation feedforward neural networks distinguishes between true and false positives, and then diagnoses the species (Plasmodium falciparum, P. vivax, P. ovale or P. malariae) of the infection. Malaria samples obtained from the Department of Clinical Microbiology and Infectious Diseases at the University of the Witwatersrand Medical School are used for training and testing of the system. Infected erythrocytes are positively identified with a sensitivity of 85% and a positive predictive value (PPV) of 81%, which makes the method highly sensitive at diagnosing a complete sample provided many views are analysed. Species were correctly determined for 11 out of 15 samples.

Maternal and Neonatal Birth Factors Affecting the Age of ASD Diagnosis.

PubMed

Darcy-Mahoney, Ashley; Minter, Bonnie; Higgins, Melinda; Guo, Ying; Zauche, Lauren Head; Hirst, Jessica

2016-12-01

Early diagnosis of autism spectrum disorders (ASD) enables early intervention that improves long term functioning of children with ASD but is often delayed until age of school entry. Few studies have identified factors that affect timely diagnosis. This study addressed how maternal education, race, age, marital status as well as neonatal birth factors affect the age at which a child is diagnosed with ASD. This study involved a retrospective analysis of 664 records of children treated at one of the largest autism treatment centers in the United States from March 1, 2009 to December 30, 2010. Logistic regression and Cox proportional hazards regression were used to identify maternal and neonatal factors associated with age of diagnosis. Infant gender, maternal race, marital status, and maternal age were identified as significant factors for predicting the age of ASD diagnosis. In the Cox proportional hazards regression model, only maternal race and marital status were included. Median survival age till diagnosis of children born to married mothers was 53.4 months compared to 57.8 months and 63.7 months of children born to single and divorced or widowed mothers respectively. Median survival age till diagnosis for children of African American mothers was 53.8 months compared to 57.2 months for children of Caucasian mothers. No statistically significant difference of timing of ASD diagnosis was found for children of varying gestational age. Children born to older or married mothers and mothers of minority races were more likely to have an earlier ASD diagnosis. No statistically significant differences in timing of ASD diagnosis were found for children born at varying gestational ages. Identification of these factors has the potential to inform public health outreach aimed at promoting timely ASD diagnosis. This work could enhance clinical practice for timelier diagnoses of ASD by supporting parents and clinicians around the world in identifying risk factors beyond gender

Automated classification of bone marrow cells in microscopic images for diagnosis of leukemia: a comparison of two classification schemes with respect to the segmentation quality

NASA Astrophysics Data System (ADS)

Krappe, Sebastian; Benz, Michaela; Wittenberg, Thomas; Haferlach, Torsten; Münzenmayer, Christian

2015-03-01

The morphological analysis of bone marrow smears is fundamental for the diagnosis of leukemia. Currently, the counting and classification of the different types of bone marrow cells is done manually with the use of bright field microscope. This is a time consuming, partly subjective and tedious process. Furthermore, repeated examinations of a slide yield intra- and inter-observer variances. For this reason an automation of morphological bone marrow analysis is pursued. This analysis comprises several steps: image acquisition and smear detection, cell localization and segmentation, feature extraction and cell classification. The automated classification of bone marrow cells is depending on the automated cell segmentation and the choice of adequate features extracted from different parts of the cell. In this work we focus on the evaluation of support vector machines (SVMs) and random forests (RFs) for the differentiation of bone marrow cells in 16 different classes, including immature and abnormal cell classes. Data sets of different segmentation quality are used to test the two approaches. Automated solutions for the morphological analysis for bone marrow smears could use such a classifier to pre-classify bone marrow cells and thereby shortening the examination duration.

The "Not Guilty Verdict": Psychological Reactions to a Diagnosis of Asperger Syndrome in Adulthood

ERIC Educational Resources Information Center

Punshon, C.; Skirrow, P.; Murphy, G.

2009-01-01

Asperger syndrome is a relatively new diagnostic classification. A number of factors make receiving a diagnosis of Asperger syndrome in adulthood a unique experience. This study used a phenomenological approach to examine the experiences of 10 adults receiving such a diagnosis. Results suggested that six major themes were associated with receiving…

Automatic classification of tissue malignancy for breast carcinoma diagnosis.

PubMed

Fondón, Irene; Sarmiento, Auxiliadora; García, Ana Isabel; Silvestre, María; Eloy, Catarina; Polónia, António; Aguiar, Paulo

2018-05-01

Breast cancer is the second leading cause of cancer death among women. Its early diagnosis is extremely important to prevent avoidable deaths. However, malignancy assessment of tissue biopsies is complex and dependent on observer subjectivity. Moreover, hematoxylin and eosin (H&E)-stained histological images exhibit a highly variable appearance, even within the same malignancy level. In this paper, we propose a computer-aided diagnosis (CAD) tool for automated malignancy assessment of breast tissue samples based on the processing of histological images. We provide four malignancy levels as the output of the system: normal, benign, in situ and invasive. The method is based on the calculation of three sets of features related to nuclei, colour regions and textures considering local characteristics and global image properties. By taking advantage of well-established image processing techniques, we build a feature vector for each image that serves as an input to an SVM (Support Vector Machine) classifier with a quadratic kernel. The method has been rigorously evaluated, first with a 5-fold cross-validation within an initial set of 120 images, second with an external set of 30 different images and third with images with artefacts included. Accuracy levels range from 75.8% when the 5-fold cross-validation was performed to 75% with the external set of new images and 61.11% when the extremely difficult images were added to the classification experiment. The experimental results indicate that the proposed method is capable of distinguishing between four malignancy levels with high accuracy. Our results are close to those obtained with recent deep learning-based methods. Moreover, it performs better than other state-of-the-art methods based on feature extraction, and it can help improve the CAD of breast cancer. Copyright © 2018 Elsevier Ltd. All rights reserved.

[Fibromyalgia syndrome. Definition, classification, clinical diagnosis and prognosis].

PubMed

Eich, W; Häuser, W; Arnold, B; Jäckel, W; Offenbächer, M; Petzke, F; Schiltenwolf, M; Settan, M; Sommer, C; Tölle, T; Uçeyler, N; Henningsen, P

2012-06-01

The scheduled update to the German S3 guidelines on fibromyalgia syndrome (FMS) by the Association of the Scientific Medical Societies ("Arbeitsgemeinschaft der Wissenschaftlichen Medizinischen Fachgesellschaften", AWMF; registration number 041/004) was planned starting in March 2011. The development of the guidelines was coordinated by the German Interdisciplinary Association for Pain Therapy ("Deutsche Interdisziplinären Vereinigung für Schmerztherapie", DIVS), 9 scientific medical societies and 2 patient self-help organizations. Eight working groups with a total of 50 members were evenly balanced in terms of gender, medical field, potential conflicts of interest and hierarchical position in the medical and scientific fields. Literature searches were performed using the Medline, PsycInfo, Scopus and Cochrane Library databases (until December 2010). The grading of the strength of the evidence followed the scheme of the Oxford Centre for Evidence-Based Medicine. The formulation and grading of recommendations was accomplished using a multi-step, formal consensus process. The guidelines were reviewed by the boards of the participating scientific medical societies. The clinical diagnosis of FMS can be established by the American College of Rheumatology (ACR) 1990 classification criteria (with tender point examination), by the modified preliminary diagnostic ACR 2010 criteria or by the diagnostic criteria of the German interdisciplinary guideline (AWMF) on FMS. The English full-text version of this article is available at SpringerLink (under "Supplemental").

A Fast SVM-Based Tongue's Colour Classification Aided by k-Means Clustering Identifiers and Colour Attributes as Computer-Assisted Tool for Tongue Diagnosis.

PubMed

Kamarudin, Nur Diyana; Ooi, Chia Yee; Kawanabe, Tadaaki; Odaguchi, Hiroshi; Kobayashi, Fuminori

2017-01-01

In tongue diagnosis, colour information of tongue body has kept valuable information regarding the state of disease and its correlation with the internal organs. Qualitatively, practitioners may have difficulty in their judgement due to the instable lighting condition and naked eye's ability to capture the exact colour distribution on the tongue especially the tongue with multicolour substance. To overcome this ambiguity, this paper presents a two-stage tongue's multicolour classification based on a support vector machine (SVM) whose support vectors are reduced by our proposed k -means clustering identifiers and red colour range for precise tongue colour diagnosis. In the first stage, k -means clustering is used to cluster a tongue image into four clusters of image background (black), deep red region, red/light red region, and transitional region. In the second-stage classification, red/light red tongue images are further classified into red tongue or light red tongue based on the red colour range derived in our work. Overall, true rate classification accuracy of the proposed two-stage classification to diagnose red, light red, and deep red tongue colours is 94%. The number of support vectors in SVM is improved by 41.2%, and the execution time for one image is recorded as 48 seconds.

A Fast SVM-Based Tongue's Colour Classification Aided by k-Means Clustering Identifiers and Colour Attributes as Computer-Assisted Tool for Tongue Diagnosis

PubMed Central

Ooi, Chia Yee; Kawanabe, Tadaaki; Odaguchi, Hiroshi; Kobayashi, Fuminori

2017-01-01

In tongue diagnosis, colour information of tongue body has kept valuable information regarding the state of disease and its correlation with the internal organs. Qualitatively, practitioners may have difficulty in their judgement due to the instable lighting condition and naked eye's ability to capture the exact colour distribution on the tongue especially the tongue with multicolour substance. To overcome this ambiguity, this paper presents a two-stage tongue's multicolour classification based on a support vector machine (SVM) whose support vectors are reduced by our proposed k-means clustering identifiers and red colour range for precise tongue colour diagnosis. In the first stage, k-means clustering is used to cluster a tongue image into four clusters of image background (black), deep red region, red/light red region, and transitional region. In the second-stage classification, red/light red tongue images are further classified into red tongue or light red tongue based on the red colour range derived in our work. Overall, true rate classification accuracy of the proposed two-stage classification to diagnose red, light red, and deep red tongue colours is 94%. The number of support vectors in SVM is improved by 41.2%, and the execution time for one image is recorded as 48 seconds. PMID:29065640

Comparative analysis of classification based algorithms for diabetes diagnosis using iris images.

PubMed

Samant, Piyush; Agarwal, Ravinder

2018-01-01

Photo-diagnosis is always an intriguing area for the researchers, with the advancement of image processing and computer machine vision techniques it have become more reliable and popular in recent years. The objective of this paper is to study the change in the features of iris, particularly irregularities in the pigmentation of certain areas of the iris with respect to diabetic health of an individual. Apart from the point that iris recognition concentrates on the overall structure of the iris, diagnostic techniques emphasises the local variations in the particular area of iris. Pre-image processing techniques have been applied to extract iris and thereafter, region of interest from the extracted iris have been cropped out. In order to observe the changes in the tissue pigmentation of region of interest, statistical, texture textural and wavelet features have been extracted. At the end, a comparison of accuracies of five different classifiers has been presented to classify two subject groups of diabetic and non-diabetic. Best classification accuracy has been calculated as 89.66% by the random forest classifier. Results have been shown the effectiveness and diagnostic significance of the proposed methodology. Presented piece of work offers a novel systemic perspective of non-invasive and automatic diabetic diagnosis.

Cause of and factors associated with stillbirth: a systematic review of classification systems.

PubMed

Aminu, Mamuda; Bar-Zeev, Sarah; van den Broek, Nynke

2017-05-01

An estimated 2.6 million stillbirths occur worldwide each year. A standardized classification system setting out possible cause of death and contributing factors is useful to help obtain comparative data across different settings. We undertook a systematic review of stillbirth classification systems to highlight their strengths and weaknesses for practitioners and policymakers. We conducted a systematic search and review of the literature to identify the classification systems used to aggregate information for stillbirth and perinatal deaths. Narrative synthesis was used to compare the range and depth of information required to apply the systems, and the different categories provided for cause of and factors contributing to stillbirth. A total of 118 documents were screened; 31 classification systems were included, of which six were designed specifically for stillbirth, 14 for perinatal death, three systems included neonatal deaths and two included infant deaths. Most (27/31) were developed in and first tested using data obtained from high-income settings. All systems required information from clinical records. One-third of the classification systems (11/31) included information obtained from histology or autopsy. The percentage where cause of death remained unknown ranged from 0.39% using the Nordic-Baltic classification to 46.4% using the Keeling system. Over time, classification systems have become more complex. The success of application is dependent on the availability of detailed clinical information and laboratory investigations. Systems that adopt a layered approach allow for classification of cause of death to a broad as well as to a more detailed level. © 2017 The Authors. Acta Obstetricia et Gynecologica Scandinavica published by John Wiley & Sons Ltd on behalf of Nordic Federation of Societies of Obstetrics and Gynecology (NFOG).

Evaluation of host and viral factors associated with severe dengue based on the 2009 WHO classification.

PubMed

Pozo-Aguilar, Jorge O; Monroy-Martínez, Verónica; Díaz, Daniel; Barrios-Palacios, Jacqueline; Ramos, Celso; Ulloa-García, Armando; García-Pillado, Janet; Ruiz-Ordaz, Blanca H

2014-12-11

Dengue fever (DF) is the most prevalent arthropod-borne viral disease affecting humans. The World Health Organization (WHO) proposed a revised classification in 2009 to enable the more effective identification of cases of severe dengue (SD). This was designed primarily as a clinical tool, but it also enables cases of SD to be differentiated into three specific subcategories (severe vascular leakage, severe bleeding, and severe organ dysfunction). However, no study has addressed whether this classification has advantage in estimating factors associated with the progression of disease severity or dengue pathogenesis. We evaluate in a dengue outbreak associated risk factors that could contribute to the development of SD according to the 2009 WHO classification. A prospective cross-sectional study was performed during an epidemic of dengue in 2009 in Chiapas, Mexico. Data were analyzed for host and viral factors associated with dengue cases, using the 1997 and 2009 WHO classifications. The cost-benefit ratio (CBR) was also estimated. The sensitivity in the 1997 WHO classification for determining SD was 75%, and the specificity was 97.7%. For the 2009 scheme, these were 100% and 81.1%, respectively. The 2009 classification showed a higher benefit (537%) with a lower cost (10.2%) than the 1997 WHO scheme. A secondary antibody response was strongly associated with SD. Early viral load was higher in cases of SD than in those with DF. Logistic regression analysis identified predictive SD factors (secondary infection, disease phase, viral load) within the 2009 classification. However, within the 1997 scheme it was not possible to differentiate risk factors between DF and dengue hemorrhagic fever or dengue shock syndrome. The critical clinical stage for determining SD progression was the transition from fever to defervescence in which plasma leakage can occur. The clinical phenotype of SD is influenced by the host (secondary response) and viral factors (viral load). The 2009

Pattern Classification of Endocervical Adenocarcinoma: Reproducibility and Review of Criteria

PubMed Central

Rutgers, Joanne K.L.; Roma, Andres; Park, Kay; Zaino, Richard J.; Johnson, Abbey; Alvarado, Isabel; Daya, Dean; Rasty, Golnar; Longacre, Teri; Ronnett, Brigitte; Silva, Elvio

2017-01-01

Previously, our international team proposed a 3-tiered pattern classification (Pattern Classification) system for endocervical adenocarcinoma of the usual type that correlates with nodal disease and recurrence. Pattern Classification- A have well demarcated glands lacking destructive stromal invasion or lymphovascular invasion (lymphovascular invasion), Pattern Classification- B show localized, limited destructive invasion arising from A-type glands, and Pattern Classification- C have diffuse destructive stromal invasion, significant (filling a 4× field) confluence, or solid architecture. 24 Pattern Classification-A, 22 Pattern Classification-B, 38 Pattern Classification-C from the tumor set used in the original description were chosen using the reference diagnosis (reference diagnosis) originally established. 1 H&E slide per case was reviewed by 7 gynecologic pathologists, 4 from the original study. Kappa statistics were prepared, and cases with discrepancies reviewed. We found a majority agreement with reference diagnosis in 81% of cases, with complete or near complete (6 of 7) agreement in 50%. Overall concordance was 74%. Overall Kappa (agreement among pathologists) was .488 (moderate agreement). Pattern Classification- B has lowest kappa, and agreement is not improved by combining B+C. 6 of 7 reviewers had substantial agreement by weighted kappas (>.6), with one reviewer accounting for the majority of cases under or overcalled by 2 tiers. Confluence filling a 4× field, labyrinthine glands, or solid architecture accounted for undercalling other reference diagnosis-C cases. Missing a few individually infiltrative cells was the most common cause of undercalling reference diagnosis- B. Small foci of inflamed, loose or desmoplastic stroma lacking infiltrative tumor cells in reference diagnosis-A appeared to account for those cases up-graded to Pattern Classification-B. In summary, an overall concordance of 74% indicates that the criteria can be reproducibly

Vaginismus: a review of the literature on the classification/diagnosis, etiology and treatment.

PubMed

Lahaie, Marie-André; Boyer, Stéphanie C; Amsel, Rhonda; Khalifé, Samir; Binik, Yitzchak M

2010-09-01

Vaginismus is currently defined as an involuntary vaginal muscle spasm interfering with sexual intercourse that is relatively easy to diagnose and treat. As a result, there has been a lack of research interest with very few well-controlled diagnostic, etiological or treatment outcome studies. Interestingly, the few empirical studies that have been conducted on vaginismus do not support the view that it is easily diagnosed or treated and have shed little light on potential etiology. A review of the literature on the classification/diagnosis, etiology and treatment of vaginismus will be presented with a focus on the latest empirical findings. This article suggests that vaginismus cannot be easily differentiated from dyspareunia and should be treated from a multidisciplinary point of view.

A method for classification of transient events in EEG recordings: application to epilepsy diagnosis.

PubMed

Tzallas, A T; Karvelis, P S; Katsis, C D; Fotiadis, D I; Giannopoulos, S; Konitsiotis, S

2006-01-01

The aim of the paper is to analyze transient events in inter-ictal EEG recordings, and classify epileptic activity into focal or generalized epilepsy using an automated method. A two-stage approach is proposed. In the first stage the observed transient events of a single channel are classified into four categories: epileptic spike (ES), muscle activity (EMG), eye blinking activity (EOG), and sharp alpha activity (SAA). The process is based on an artificial neural network. Different artificial neural network architectures have been tried and the network having the lowest error has been selected using the hold out approach. In the second stage a knowledge-based system is used to produce diagnosis for focal or generalized epileptic activity. The classification of transient events reported high overall accuracy (84.48%), while the knowledge-based system for epilepsy diagnosis correctly classified nine out of ten cases. The proposed method is advantageous since it effectively detects and classifies the undesirable activity into appropriate categories and produces a final outcome related to the existence of epilepsy.

Automatic diagnosis of tuberculosis disease based on Plasmonic ELISA and color-based image classification.

PubMed

AbuHassan, Kamal J; Bakhori, Noremylia M; Kusnin, Norzila; Azmi, Umi Z M; Tania, Marzia H; Evans, Benjamin A; Yusof, Nor A; Hossain, M A

2017-07-01

Tuberculosis (TB) remains one of the most devastating infectious diseases and its treatment efficiency is majorly influenced by the stage at which infection with the TB bacterium is diagnosed. The available methods for TB diagnosis are either time consuming, costly or not efficient. This study employs a signal generation mechanism for biosensing, known as Plasmonic ELISA, and computational intelligence to facilitate automatic diagnosis of TB. Plasmonic ELISA enables the detection of a few molecules of analyte by the incorporation of smart nanomaterials for better sensitivity of the developed detection system. The computational system uses k-means clustering and thresholding for image segmentation. This paper presents the results of the classification performance of the Plasmonic ELISA imaging data by using various types of classifiers. The five-fold cross-validation results show high accuracy rate (>97%) in classifying TB images using the entire data set. Future work will focus on developing an intelligent mobile-enabled expert system to diagnose TB in real-time. The intelligent system will be clinically validated and tested in collaboration with healthcare providers in Malaysia.

Cholecystectomy and Diagnosis-Related Groups (DRGs): patient classification and hospital reimbursement in 11 European countries.

PubMed

Paat-Ahi, Gerli; Aaviksoo, Ain; Swiderek, Maria

2014-12-01

As part of the EuroDRG project, researchers from eleven countries (i.e. Austria, England, Estonia, Finland, France, Germany, Ireland, Netherlands, Poland, Sweden, and Spain) compared how their Diagnosis-Related Groups (DRG) systems deal with cholecystectomy patients. The study aims to assist surgeons and national authorities to optimize their DRG systems. National or regional databases were used to identify hospital cases with a procedure of cholecystectomy. DRG classification algorithms and indicators of resource consumption were compared for those DRGs that individually contained at least 1% of cases. Six standardised case vignettes were defined, and quasi prices according to national DRG-based hospital payment systems were ascertained and compared to an index case. European DRG systems vary widely: they classify cholecystectomy patients according to different sets of variables into diverging numbers of DRGs (between two DRGs in Austria and Poland to nine DRGs in England). The most complex DRG is valued at four times more resource intensive than the index case in Ireland but only 1.3 times more resource intensive than the index case in Austria. Large variations in the classification of cholecystectomy patients raise concerns whether all systems rely on the most appropriate classification variables. Surgeons, hospital managers and national DRG authorities should consider how other countries' DRG systems classify cholecystectomy patients in order to optimize their DRG systems and to ensure fair and appropriate reimbursement.

Perspectives on Next Steps in Classification of Orofacial Pain – Part 2: Role of psychosocial factors

PubMed Central

Durham, Justin; Raphael, Karen G.; Benoliel, Rafael; Ceusters, Werner; Michelotti, Ambra; Ohrbach, Richard

2015-01-01

This paper was initiated by a symposium, in which the present authors contributed, organised by the International RDC/TMD Consortium Network in March 2013. The purpose of the paper is to review the status of biobehavioural research – both quantitative and qualitative – related to orofacial pain with respect to the etiology, pathophysiology, diagnosis and management of orofacial pain conditions, and how this information can optimally be used for developing a structured orofacial pain classification system for research. In particular, we address: representation of psychosocial entities in classification systems, use of qualitative research to identify and understand the full scope of psychosocial entities and their interaction, and the usage of classification system for guiding treatment. We then provide recommendations for addressing these problems, including how ontological principles can inform this process. PMID:26257252

Age of diagnosis in Rett syndrome: patterns of recognition among diagnosticians and risk factors for late diagnosis

PubMed Central

Tarquinio, Daniel C.; Hou, Wei; Neul, Jeffrey L.; Lane, Jane B.; Barnes, Katherine V.; O’Leary, Heather M.; Bruck, Natalie M.; Kaufmann, Walter E.; Motil, Kathleen J.; Glaze, Daniel G.; Skinner, Steven A.; Annese, Fran; Baggett, Lauren; Barrish, Judy O.; Geerts, Suzanne P.; Percy, Alan K.

2015-01-01

Purpose Diagnosis of Rett syndrome (RTT) is often delayed. We sought to determine type of physician who typically makes the diagnosis of RTT and to identify risk factors for delayed diagnosis. Methods One-thousand eighty-five participants from the multicenter longitudinal RTT natural history study with classic and atypical RTT were recruited from 2006 to 2014. Age of diagnosis, diagnostician, diagnostic criteria, clinical and developmental data were collected. Results Among 919 classic and 166 atypical RTT participants, median diagnosis age was 2.7 years (interquartile range 2.0–4.1) in classic and 3.8 years (interquartile range 2.3–6.9) in atypical RTT. Pediatricians made the diagnosis of classic RTT rarely (5.2%); however, proportion diagnosed by pediatricians increased since 2006. Since the first diagnostic criteria, the age of diagnosis decreased among subspecialists but not pediatricians. Odds of a pediatrician making the diagnosis of classic RTT were higher if a child stopped responding to parental interaction, and lower if they possessed gastro-esophageal reflux, specific stereotypies, lost babbling or the ability to follow commands. Delayed acquisition of basic gross motor skills or finger feeding were associated with younger diagnosis; delayed acquisition of higher level fine motor skills, later onset of supportive features, and normal head circumference were associated with late diagnosis. 33% with microcephaly before 2.5 years were diagnosed after the median age of 2.7 years. Conclusions Age of RTT diagnosis has improved among subspecialists, and pediatricians have made the diagnosis of classic RTT more frequently since 2006. Strategies for educating diagnosticians should incorporate specific risk factors for delayed diagnosis. PMID:25801175

Developing a contributing factor classification scheme for Rasmussen's AcciMap: Reliability and validity evaluation.

PubMed

Goode, N; Salmon, P M; Taylor, N Z; Lenné, M G; Finch, C F

2017-10-01

One factor potentially limiting the uptake of Rasmussen's (1997) Accimap method by practitioners is the lack of a contributing factor classification scheme to guide accident analyses. This article evaluates the intra- and inter-rater reliability and criterion-referenced validity of a classification scheme developed to support the use of Accimap by led outdoor activity (LOA) practitioners. The classification scheme has two levels: the system level describes the actors, artefacts and activity context in terms of 14 codes; the descriptor level breaks the system level codes down into 107 specific contributing factors. The study involved 11 LOA practitioners using the scheme on two separate occasions to code a pre-determined list of contributing factors identified from four incident reports. Criterion-referenced validity was assessed by comparing the codes selected by LOA practitioners to those selected by the method creators. Mean intra-rater reliability scores at the system (M = 83.6%) and descriptor (M = 74%) levels were acceptable. Mean inter-rater reliability scores were not consistently acceptable for both coding attempts at the system level (M T1  = 68.8%; M T2  = 73.9%), and were poor at the descriptor level (M T1  = 58.5%; M T2  = 64.1%). Mean criterion referenced validity scores at the system level were acceptable (M T1  = 73.9%; M T2  = 75.3%). However, they were not consistently acceptable at the descriptor level (M T1  = 67.6%; M T2  = 70.8%). Overall, the results indicate that the classification scheme does not currently satisfy reliability and validity requirements, and that further work is required. The implications for the design and development of contributing factors classification schemes are discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Risk factors and classification of stillbirth in a Middle Eastern population: a retrospective study.

PubMed

Kunjachen Maducolil, Mariam; Abid, Hafsa; Lobo, Rachael Marian; Chughtai, Ambreen Qayyum; Afzal, Arjumand Muhammad; Saleh, Huda Abdullah Hussain; Lindow, Stephen W

2017-12-21

To estimate the incidence of stillbirth, explore the associated maternal and fetal factors and to evaluate the most appropriate classification of stillbirth for a multiethnic population. This is a retrospective population-based study of stillbirth in a large tertiary unit. Data of each stillbirth with a gestational age >/=24 weeks in the year 2015 were collected from electronic medical records and analyzed. The stillbirth rate for our multiethnic population is 7.81 per 1000 births. Maternal medical factors comprised 52.4% in which the rates of hypertensive disorders, diabetes and other medical disorders were 22.5%, 20.8% and 8.3%, respectively. The most common fetal factor was intrauterine growth restriction (IUGR) (22.5%) followed by congenital anomalies (21.6%). All cases were categorized using the Wigglesworth, Aberdeen, Tulip, ReCoDe and International Classification of Diseases-perinatal mortality (ICD-PM) classifications and the rates of unclassified stillbirths were 59.2%, 46.6%, 16.6%, 11.6% and 7.5%, respectively. An autopsy was performed in 9.1% of cases reflecting local religious and cultural sensitivities. This study highlighted the modifiable risk factors among the Middle Eastern population. The most appropriate classification was the ICD-PM. The low rates of autopsy prevented a detailed evaluation of stillbirths, therefore it is suggested that a minimally invasive autopsy [postmortem magnetic resonance imaging (MRI)] may improve the quality of care.

A practicable approach for periodontal classification

PubMed Central

Mittal, Vishnu; Bhullar, Raman Preet K.; Bansal, Rachita; Singh, Karanprakash; Bhalodi, Anand; Khinda, Paramjit K.

2013-01-01

The Diagnosis and classification of periodontal diseases has remained a dilemma since long. Two distinct concepts have been used to define diseases: Essentialism and Nominalism. Essentialistic concept implies the real existence of disease whereas; nominalistic concept states that the names of diseases are the convenient way of stating concisely the endpoint of a diagnostic process. It generally advances from assessment of symptoms and signs toward knowledge of causation and gives a feasible option to name the disease for which etiology is either unknown or it is too complex to access in routine clinical practice. Various classifications have been proposed by the American Academy of Periodontology (AAP) in 1986, 1989 and 1999. The AAP 1999 classification is among the most widely used classification. But this classification also has demerits which provide impediment for its use in day to day practice. Hence a classification and diagnostic system is required which can help the clinician to access the patient's need and provide a suitable treatment which is in harmony with the diagnosis for that particular case. Here is an attempt to propose a practicable classification and diagnostic system of periodontal diseases for better treatment outcome. PMID:24379855

Should Age at Diagnosis Be Included as an Additional Variable in the Risk of Recurrence Classification System in Patients with Differentiated Thyroid Cancer.

PubMed

Pitoia, Fabián; Jerkovich, Fernando; Smulever, Anabella; Brenta, Gabriela; Bueno, Fernanda; Cross, Graciela

2017-07-01

To evaluate the influence of age at diagnosis on the frequency of structural incomplete response (SIR) according to the modified risk of recurrence (RR) staging system from the American Thyroid Association guidelines. We performed a retrospective analysis of 268 patients with differentiated thyroid cancer (DTC) followed up for at least 3 years after initial treatment (total thyroidectomy and remnant ablation). The median follow-up in the whole cohort was 74.3 months (range: 36.1-317.9) and the median age at diagnosis was 45.9 years (range: 18-87). The association between age at diagnosis and the initial and final response to treatment was assessed with analysis of variance (ANOVA). Patients were also divided into several groups considering age younger and older than 40, 50, and 60 years. Age at diagnosis was not associated with either an initial or final statistically significant different SIR to treatment ( p = 0.14 and p = 0.58, respectively). Additionally, we did not find any statistically significant differences when the percentages of SIR considering the classification of RR were compared between different groups of patients by using several age cutoffs. When patients are correctly risk stratified, it seems that age at diagnosis is not involved in the frequency of having a SIR at the initial evaluation or at the final follow-up, so it should not be included as an additional variable to be considered in the RR classifications.

Should Age at Diagnosis Be Included as an Additional Variable in the Risk of Recurrence Classification System in Patients with Differentiated Thyroid Cancer

PubMed Central

Pitoia, Fabián; Jerkovich, Fernando; Smulever, Anabella; Brenta, Gabriela; Bueno, Fernanda; Cross, Graciela

2017-01-01

Objective To evaluate the influence of age at diagnosis on the frequency of structural incomplete response (SIR) according to the modified risk of recurrence (RR) staging system from the American Thyroid Association guidelines. Patients and Methods We performed a retrospective analysis of 268 patients with differentiated thyroid cancer (DTC) followed up for at least 3 years after initial treatment (total thyroidectomy and remnant ablation). The median follow-up in the whole cohort was 74.3 months (range: 36.1-317.9) and the median age at diagnosis was 45.9 years (range: 18-87). The association between age at diagnosis and the initial and final response to treatment was assessed with analysis of variance (ANOVA). Patients were also divided into several groups considering age younger and older than 40, 50, and 60 years. Results Age at diagnosis was not associated with either an initial or final statistically significant different SIR to treatment (p = 0.14 and p = 0.58, respectively). Additionally, we did not find any statistically significant differences when the percentages of SIR considering the classification of RR were compared between different groups of patients by using several age cutoffs. Conclusions When patients are correctly risk stratified, it seems that age at diagnosis is not involved in the frequency of having a SIR at the initial evaluation or at the final follow-up, so it should not be included as an additional variable to be considered in the RR classifications. PMID:28785543

An approach to the diagnosis of flat intraepithelial lesions of the urinary bladder using the World Health Organization/ International Society of Urological Pathology consensus classification system.

PubMed

Amin, Mahul B; McKenney, Jesse K

2002-07-01

The classification of flat urothelial (transitional cell) lesions with atypia has historically varied in its application from institution to institution with no fewer than six major nomenclature systems proposed in the past 25 years. In 1998, the World Health Organization/ International Society of Urological Pathology (WHO/ISUP) published a consensus classification that included the following categories for flat urinary bladder lesions: reactive atypia, atypia of unknown significance, dysplasia (low-grade intraepithelial neoplasia), and carcinoma in situ (high-grade intraepithelial neoplasia). This classification expands the definition traditionally used for urothelial carcinoma in situ, basing its diagnosis primarily on the severity of cytologic changes. In proposing the classification system for flat lesions of the bladder with atypia, it was hoped that consistent use of uniform diagnostic terminology would ultimately aid in a better understanding of the biology of these lesions. In this review, the authors discuss the history of the concept of flat urothelial neoplasia, the rationale and histologic criteria for the WHO/ISUP diagnostic categories, an approach to the diagnosis of flat lesions, and problems and pitfalls associated with their recognition in routine surgical pathology specimens.

Climate Classification is an Important Factor in ­Assessing Hospital Performance Metrics

NASA Astrophysics Data System (ADS)

Boland, M. R.; Parhi, P.; Gentine, P.; Tatonetti, N. P.

2017-12-01

Context/Purpose: Climate is a known modulator of disease, but its impact on hospital performance metrics remains unstudied. Methods: We assess the relationship between Köppen-Geiger climate classification and hospital performance metrics, specifically 30-day mortality, as reported in Hospital Compare, and collected for the period July 2013 through June 2014 (7/1/2013 - 06/30/2014). A hospital-level multivariate linear regression analysis was performed while controlling for known socioeconomic factors to explore the relationship between all-cause mortality and climate. Hospital performance scores were obtained from 4,524 hospitals belonging to 15 distinct Köppen-Geiger climates and 2,373 unique counties. Results: Model results revealed that hospital performance metrics for mortality showed significant climate dependence (p<0.001) after adjusting for socioeconomic factors. Interpretation: Currently, hospitals are reimbursed by Governmental agencies using 30-day mortality rates along with 30-day readmission rates. These metrics allow Government agencies to rank hospitals according to their `performance' along these metrics. Various socioeconomic factors are taken into consideration when determining individual hospitals performance. However, no climate-based adjustment is made within the existing framework. Our results indicate that climate-based variability in 30-day mortality rates does exist even after socioeconomic confounder adjustment. Use of standardized high-level climate classification systems (such as Koppen-Geiger) would be useful to incorporate in future metrics. Conclusion: Climate is a significant factor in evaluating hospital 30-day mortality rates. These results demonstrate that climate classification is an important factor when comparing hospital performance across the United States.

Cholecystectomy and Diagnosis-Related Groups (DRGs): patient classification and hospital reimbursement in 11 European countries

PubMed Central

Paat-Ahi, Gerli; Aaviksoo, Ain; Świderek, Maria

2014-01-01

Background: As part of the EuroDRG project, researchers from eleven countries (i.e. Austria, England, Estonia, Finland, France, Germany, Ireland, Netherlands, Poland, Sweden, and Spain) compared how their Diagnosis-Related Groups (DRG) systems deal with cholecystectomy patients. The study aims to assist surgeons and national authorities to optimize their DRG systems. Methods: National or regional databases were used to identify hospital cases with a procedure of cholecystectomy. DRG classification algorithms and indicators of resource consumption were compared for those DRGs that individually contained at least 1% of cases. Six standardised case vignettes were defined, and quasi prices according to national DRG-based hospital payment systems were ascertained and compared to an index case. Results: European DRG systems vary widely: they classify cholecystectomy patients according to different sets of variables into diverging numbers of DRGs (between two DRGs in Austria and Poland to nine DRGs in England). The most complex DRG is valued at four times more resource intensive than the index case in Ireland but only 1.3 times more resource intensive than the index case in Austria. Conclusion: Large variations in the classification of cholecystectomy patients raise concerns whether all systems rely on the most appropriate classification variables. Surgeons, hospital managers and national DRG authorities should consider how other countries’ DRG systems classify cholecystectomy patients in order to optimize their DRG systems and to ensure fair and appropriate reimbursement. PMID:25489596

Inter-observer variance with the diagnosis of myelodysplastic syndromes (MDS) following the 2008 WHO classification.

PubMed

Font, P; Loscertales, J; Benavente, C; Bermejo, A; Callejas, M; Garcia-Alonso, L; Garcia-Marcilla, A; Gil, S; Lopez-Rubio, M; Martin, E; Muñoz, C; Ricard, P; Soto, C; Balsalobre, P; Villegas, A

2013-01-01

Morphology is the basis of the diagnosis of myelodysplastic syndromes (MDS). The WHO classification offers prognostic information and helps with the treatment decisions. However, morphological changes are subject to potential inter-observer variance. The aim of our study was to explore the reliability of the 2008 WHO classification of MDS, reviewing 100 samples previously diagnosed with MDS using the 2001 WHO criteria. Specimens were collected from 10 hospitals and were evaluated by 10 morphologists, working in five pairs. Each observer evaluated 20 samples, and each sample was analyzed independently by two morphologists. The second observer was blinded to the clinical and laboratory data, except for the peripheral blood (PB) counts. Nineteen cases were considered as unclassified MDS (MDS-U) by the 2001 WHO classification, but only three remained as MDS-U by the 2008 WHO proposal. Discordance was observed in 26 of the 95 samples considered suitable (27 %). Although there were a high number of observers taking part, the rate of discordance was quite similar among the five pairs. The inter-observer concordance was very good regarding refractory anemia with excess blasts type 1 (RAEB-1) (10 of 12 cases, 84 %), RAEB-2 (nine of 10 cases, 90 %), and also good regarding refractory cytopenia with multilineage dysplasia (37 of 50 cases, 74 %). However, the categories with unilineage dysplasia were not reproducible in most of the cases. The rate of concordance with refractory cytopenia with unilineage dysplasia was 40 % (two of five cases) and 25 % with RA with ring sideroblasts (two of eight). Our results show that the 2008 WHO classification gives a more accurate stratification of MDS but also illustrates the difficulty in diagnosing MDS with unilineage dysplasia.

Epidemiological and clinical relevance of insomnia diagnosis algorithms according to the DSM-IV and the International Classification of Sleep Disorders (ICSD).

PubMed

Ohayon, Maurice M; Reynolds, Charles F

2009-10-01

Although the epidemiology of insomnia in the general population has received considerable attention in the past 20 years, few studies have investigated the prevalence of insomnia using operational definitions such as those set forth in the ICSD and DSM-IV, specifying what proportion of respondents satisfied the criteria to reach a diagnosis of insomnia disorder. This is a cross-sectional study involving 25,579 individuals aged 15 years and over representative of the general population of France, the United Kingdom, Germany, Italy, Portugal, Spain and Finland. The participants were interviewed on sleep habits and disorders managed by the Sleep-EVAL expert system using DSM-IV and ICSD classifications. At the complaint level, too short sleep (20.2%), light sleep (16.6%), and global sleep dissatisfaction (8.2%) were reported by 37% of the subjects. At the symptom level (difficulty initiating or maintaining sleep and non-restorative sleep at least 3 nights per week), 34.5% of the sample reported at least one of them. At the criterion level, (symptoms+daytime consequences), 9.8% of the total sample reported having them. At the diagnostic level, 6.6% satisfied the DSM-IV requirement for positive and differential diagnosis. However, many respondents failed to meet diagnostic criteria for duration, frequency and severity in the two classifications, suggesting that multidimensional measures are needed. A significant proportion of the population with sleep complaints do not fit into DSM-IV and ICSD classifications. Further efforts are needed to identify diagnostic criteria and dimensional measures that will lead to insomnia diagnoses and thus provide a more reliable, valid and clinically relevant classification.

Computer-aided diagnosis of focal liver lesions by use of physicians' subjective classification of echogenic patterns in baseline and contrast-enhanced ultrasonography.

PubMed

Sugimoto, Katsutoshi; Shiraishi, Junji; Moriyasu, Fuminori; Doi, Kunio

2009-04-01

To develop a computer-aided diagnostic (CAD) scheme for classifying focal liver lesions (FLLs) by use of physicians' subjective classification of echogenic patterns of FLLs on baseline and contrast-enhanced ultrasonography (US). A total of 137 hepatic lesions in 137 patients were evaluated with B-mode and NC100100 (Sonazoid)-enhanced pulse-inversion US; lesions included 74 hepatocellular carcinomas (HCCs) (23: well-differentiated, 36: moderately differentiated, 15: poorly differentiated HCCs), 33 liver metastases, and 30 liver hemangiomas. Three physicians evaluated single images at B-mode and arterial phases with a cine mode. Physicians were asked to classify each lesion into one of eight B-mode and one of eight enhancement patterns, but did not make a diagnosis. To classify five types of FLLs, we employed a decision tree model with four decision nodes and four artificial neural networks (ANNs). The results of the physicians' pattern classifications were used successively for four different ANNs in making decisions at each of the decision nodes in the decision tree model. The classification accuracies for the 137 FLLs were 84.8% for metastasis, 93.3% for hemangioma, and 98.6% for all HCCs. In addition, the classification accuracies for histological differentiation types of HCCs were 65.2% for well-differentiated HCC, 41.7% for moderately differentiated HCC, and 80.0% for poorly differentiated HCC. This CAD scheme has the potential to improve the diagnostic accuracy of liver lesions. However, the accuracy in the histologic differential diagnosis of HCC based on baseline and contrast-enhanced US is still limited.

A Robust Deep Model for Improved Classification of AD/MCI Patients

PubMed Central

Li, Feng; Tran, Loc; Thung, Kim-Han; Ji, Shuiwang; Shen, Dinggang; Li, Jiang

2015-01-01

Accurate classification of Alzheimer’s Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), plays a critical role in possibly preventing progression of memory impairment and improving quality of life for AD patients. Among many research tasks, it is of particular interest to identify noninvasive imaging biomarkers for AD diagnosis. In this paper, we present a robust deep learning system to identify different progression stages of AD patients based on MRI and PET scans. We utilized the dropout technique to improve classical deep learning by preventing its weight co-adaptation, which is a typical cause of over-fitting in deep learning. In addition, we incorporated stability selection, an adaptive learning factor, and a multi-task learning strategy into the deep learning framework. We applied the proposed method to the ADNI data set and conducted experiments for AD and MCI conversion diagnosis. Experimental results showed that the dropout technique is very effective in AD diagnosis, improving the classification accuracies by 5.9% on average as compared to the classical deep learning methods. PMID:25955998

21 CFR 866.5775 - Rheumatoid factor immuno-logical test system.

Code of Federal Regulations, 2010 CFR

2010-04-01

.... Measurement of rheumatoid factor may aid in the diagnosis of rheumatoid arthritis. (b) Classification. Class... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Rheumatoid factor immuno-logical test system. 866....5775 Rheumatoid factor immuno-logical test system. (a) Identification. A rheumatoid factor...

21 CFR 866.5775 - Rheumatoid factor immuno-logical test system.

Code of Federal Regulations, 2011 CFR

2011-04-01

.... Measurement of rheumatoid factor may aid in the diagnosis of rheumatoid arthritis. (b) Classification. Class... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Rheumatoid factor immuno-logical test system. 866....5775 Rheumatoid factor immuno-logical test system. (a) Identification. A rheumatoid factor...

21 CFR 866.5775 - Rheumatoid factor immuno-logical test system.

Code of Federal Regulations, 2012 CFR

2012-04-01

.... Measurement of rheumatoid factor may aid in the diagnosis of rheumatoid arthritis. (b) Classification. Class... 21 Food and Drugs 8 2012-04-01 2012-04-01 false Rheumatoid factor immuno-logical test system. 866....5775 Rheumatoid factor immuno-logical test system. (a) Identification. A rheumatoid factor...

21 CFR 866.5775 - Rheumatoid factor immuno-logical test system.

Code of Federal Regulations, 2013 CFR

2013-04-01

.... Measurement of rheumatoid factor may aid in the diagnosis of rheumatoid arthritis. (b) Classification. Class... 21 Food and Drugs 8 2013-04-01 2013-04-01 false Rheumatoid factor immuno-logical test system. 866....5775 Rheumatoid factor immuno-logical test system. (a) Identification. A rheumatoid factor...

21 CFR 866.5775 - Rheumatoid factor immuno-logical test system.

Code of Federal Regulations, 2014 CFR

2014-04-01

.... Measurement of rheumatoid factor may aid in the diagnosis of rheumatoid arthritis. (b) Classification. Class... 21 Food and Drugs 8 2014-04-01 2014-04-01 false Rheumatoid factor immuno-logical test system. 866....5775 Rheumatoid factor immuno-logical test system. (a) Identification. A rheumatoid factor...

Poisoning by Herbs and Plants: Rapid Toxidromic Classification and Diagnosis.

PubMed

Diaz, James H

2016-03-01

The American Association of Poison Control Centers has continued to report approximately 50,000 telephone calls or 8% of incoming calls annually related to plant exposures, mostly in children. Although the frequency of plant ingestions in children is related to the presence of popular species in households, adolescents may experiment with hallucinogenic plants; and trekkers and foragers may misidentify poisonous plants as edible. Since plant exposures have continued at a constant rate, the objectives of this review were (1) to review the epidemiology of plant poisonings; and (2) to propose a rapid toxidromic classification system for highly toxic plant ingestions for field use by first responders in comparison to current classification systems. Internet search engines were queried to identify and select peer-reviewed articles on plant poisonings using the key words in order to classify plant poisonings into four specific toxidromes: cardiotoxic, neurotoxic, cytotoxic, and gastrointestinal-hepatotoxic. A simple toxidromic classification system of plant poisonings may permit rapid diagnoses of highly toxic versus less toxic and nontoxic plant ingestions both in households and outdoors; direct earlier management of potentially serious poisonings; and reduce costly inpatient evaluations for inconsequential plant ingestions. The current textbook classification schemes for plant poisonings were complex in comparison to the rapid classification system; and were based on chemical nomenclatures and pharmacological effects, and not on clearly presenting toxidromes. Validation of the rapid toxidromic classification system as compared to existing chemical classification systems for plant poisonings will require future adoption and implementation of the toxidromic system by its intended users. Copyright © 2016 Wilderness Medical Society. Published by Elsevier Inc. All rights reserved.

Semi-Supervised Projective Non-Negative Matrix Factorization for Cancer Classification.

PubMed

Zhang, Xiang; Guan, Naiyang; Jia, Zhilong; Qiu, Xiaogang; Luo, Zhigang

2015-01-01

Advances in DNA microarray technologies have made gene expression profiles a significant candidate in identifying different types of cancers. Traditional learning-based cancer identification methods utilize labeled samples to train a classifier, but they are inconvenient for practical application because labels are quite expensive in the clinical cancer research community. This paper proposes a semi-supervised projective non-negative matrix factorization method (Semi-PNMF) to learn an effective classifier from both labeled and unlabeled samples, thus boosting subsequent cancer classification performance. In particular, Semi-PNMF jointly learns a non-negative subspace from concatenated labeled and unlabeled samples and indicates classes by the positions of the maximum entries of their coefficients. Because Semi-PNMF incorporates statistical information from the large volume of unlabeled samples in the learned subspace, it can learn more representative subspaces and boost classification performance. We developed a multiplicative update rule (MUR) to optimize Semi-PNMF and proved its convergence. The experimental results of cancer classification for two multiclass cancer gene expression profile datasets show that Semi-PNMF outperforms the representative methods.

Classification of stillbirths is an ongoing dilemma.

PubMed

Nappi, Luigi; Trezza, Federica; Bufo, Pantaleo; Riezzo, Irene; Turillazzi, Emanuela; Borghi, Chiara; Bonaccorsi, Gloria; Scutiero, Gennaro; Fineschi, Vittorio; Greco, Pantaleo

2016-10-01

To compare different classification systems in a cohort of stillbirths undergoing a comprehensive workup; to establish whether a particular classification system is most suitable and useful in determining cause of death, purporting the lowest percentage of unexplained death. Cases of stillbirth at gestational age 22-41 weeks occurring at the Department of Gynecology and Obstetrics of Foggia University during a 4 year period were collected. The World Health Organization (WHO) diagnosis of stillbirth was used. All the data collection was based on the recommendations of an Italian diagnostic workup for stillbirth. Two expert obstetricians reviewed all cases and classified causes according to five classification systems. Relevant Condition at Death (ReCoDe) and Causes Of Death and Associated Conditions (CODAC) classification systems performed best in retaining information. The ReCoDe system provided the lowest rate of unexplained stillbirth (14%) compared to de Galan-Roosen (16%), CODAC (16%), Tulip (18%), Wigglesworth (62%). Classification of stillbirth is influenced by the multiplicity of possible causes and factors related to fetal death. Fetal autopsy, placental histology and cytogenetic analysis are strongly recommended to have a complete diagnostic evaluation. Commonly employed classification systems performed differently in our experience, the most satisfactory being the ReCoDe. Given the rate of "unexplained" cases, none can be considered optimal and further efforts are necessary to work out a clinically useful system.

Evaluation of the WHO criteria for the classification of patients with mastocytosis.

PubMed

Sánchez-Muñoz, Laura; Alvarez-Twose, Ivan; García-Montero, Andrés C; Teodosio, Cristina; Jara-Acevedo, María; Pedreira, Carlos E; Matito, Almudena; Morgado, Jose Mario T; Sánchez, Maria Luz; Mollejo, Manuela; Gonzalez-de-Olano, David; Orfao, Alberto; Escribano, Luis

2011-09-01

Diagnosis and classification of mastocytosis is currently based on the World Health Organization (WHO) criteria. Here, we evaluate the utility of the WHO criteria for the diagnosis and classification of a large series of mastocytosis patients (n=133), and propose a new algorithm that could be routinely applied for refined diagnosis and classification of the disease. Our results confirm the utility of the WHO criteria and provide evidence for the need of additional information for (1) a more precise diagnosis of mastocytosis, (2) specific identification of new forms of the disease, (3) the differential diagnosis between cutaneous mastocytosis vs systemic mastocytosis, and (4) improved distinction between indolent systemic mastocytosis and aggressive systemic mastocytosis. Based on our results, a new algorithm is proposed for a better diagnostic definition and prognostic classification of mastocytosis, as confirmed prospectively in an independent validation series of 117 mastocytosis patients.

Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study

PubMed Central

Qureshi, Muhammad Naveed Iqbal; Min, Beomjun; Jo, Hang Joon; Lee, Boreom

2016-01-01

The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical extreme learning machine (H-ELM) classifier. We compared the performance of this classifier with that of a support vector machine (SVM) and basic extreme learning machine (ELM) for cortical MRI data from attention deficit/hyperactivity disorder (ADHD) patients. We used 159 structural MRI images of children from the publicly available ADHD-200 MRI dataset. The data consisted of three types, namely, typically developing (TDC), ADHD-inattentive (ADHD-I), and ADHD-combined (ADHD-C). We carried out feature selection by using standard SVM-based recursive feature elimination (RFE-SVM) that enabled us to achieve good classification accuracy (60.78%). In this study, we found the RFE-SVM feature selection approach in combination with H-ELM to effectively enable the acquisition of high multiclass classification accuracy rates for structural neuroimaging data. In addition, we found that the most important features for classification were the surface area of the superior frontal lobe, and the cortical thickness, volume, and mean surface area of the whole cortex. PMID:27500640

Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study.

PubMed

Qureshi, Muhammad Naveed Iqbal; Min, Beomjun; Jo, Hang Joon; Lee, Boreom

2016-01-01

The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical extreme learning machine (H-ELM) classifier. We compared the performance of this classifier with that of a support vector machine (SVM) and basic extreme learning machine (ELM) for cortical MRI data from attention deficit/hyperactivity disorder (ADHD) patients. We used 159 structural MRI images of children from the publicly available ADHD-200 MRI dataset. The data consisted of three types, namely, typically developing (TDC), ADHD-inattentive (ADHD-I), and ADHD-combined (ADHD-C). We carried out feature selection by using standard SVM-based recursive feature elimination (RFE-SVM) that enabled us to achieve good classification accuracy (60.78%). In this study, we found the RFE-SVM feature selection approach in combination with H-ELM to effectively enable the acquisition of high multiclass classification accuracy rates for structural neuroimaging data. In addition, we found that the most important features for classification were the surface area of the superior frontal lobe, and the cortical thickness, volume, and mean surface area of the whole cortex.

Automated morphological analysis of bone marrow cells in microscopic images for diagnosis of leukemia: nucleus-plasma separation and cell classification using a hierarchical tree model of hematopoesis

NASA Astrophysics Data System (ADS)

Krappe, Sebastian; Wittenberg, Thomas; Haferlach, Torsten; Münzenmayer, Christian

2016-03-01

The morphological differentiation of bone marrow is fundamental for the diagnosis of leukemia. Currently, the counting and classification of the different types of bone marrow cells is done manually under the use of bright field microscopy. This is a time-consuming, subjective, tedious and error-prone process. Furthermore, repeated examinations of a slide may yield intra- and inter-observer variances. For that reason a computer assisted diagnosis system for bone marrow differentiation is pursued. In this work we focus (a) on a new method for the separation of nucleus and plasma parts and (b) on a knowledge-based hierarchical tree classifier for the differentiation of bone marrow cells in 16 different classes. Classification trees are easily interpretable and understandable and provide a classification together with an explanation. Using classification trees, expert knowledge (i.e. knowledge about similar classes and cell lines in the tree model of hematopoiesis) is integrated in the structure of the tree. The proposed segmentation method is evaluated with more than 10,000 manually segmented cells. For the evaluation of the proposed hierarchical classifier more than 140,000 automatically segmented bone marrow cells are used. Future automated solutions for the morphological analysis of bone marrow smears could potentially apply such an approach for the pre-classification of bone marrow cells and thereby shortening the examination time.

Contribution of non-negative matrix factorization to the classification of remote sensing images

NASA Astrophysics Data System (ADS)

Karoui, M. S.; Deville, Y.; Hosseini, S.; Ouamri, A.; Ducrot, D.

2008-10-01

Remote sensing has become an unavoidable tool for better managing our environment, generally by realizing maps of land cover using classification techniques. The classification process requires some pre-processing, especially for data size reduction. The most usual technique is Principal Component Analysis. Another approach consists in regarding each pixel of the multispectral image as a mixture of pure elements contained in the observed area. Using Blind Source Separation (BSS) methods, one can hope to unmix each pixel and to perform the recognition of the classes constituting the observed scene. Our contribution consists in using Non-negative Matrix Factorization (NMF) combined with sparse coding as a solution to BSS, in order to generate new images (which are at least partly separated images) using HRV SPOT images from Oran area, Algeria). These images are then used as inputs of a supervised classifier integrating textural information. The results of classifications of these "separated" images show a clear improvement (correct pixel classification rate improved by more than 20%) compared to classification of initial (i.e. non separated) images. These results show the contribution of NMF as an attractive pre-processing for classification of multispectral remote sensing imagery.

[The questionnaire survey on glaucoma diagnosis and treatment in China (2016)].

PubMed

Zhang, Q; Cao, K; Kang, M T; Sun, Y X; Gan, J H; Tian, J X; Ran, A R; Zhang, X; Su, Y D; Li, S N

2017-02-11

Objective: To investigate the present situation of diagnosis and treatment for primary angle-closure glaucoma (PACG) and primary open-angle glaucoma (POAG) and awareness of the relevant progress among Chinese ophthalmologists. Methods: This study was a cross-sectional, non-randomized sampling survey. Participants were ophthalmologists who attended the 11st Chinese Glaucoma Society Congress during November 11 to 12, 2016. They were invited to fill out a questionnaire. The questionnaire included participants' basic information and their knowledge about glaucoma diagnosis and treatment. The data collected through questionnaire were analyzed with SAS9.4. Results: A total of 450 questionnaires were distributed and 372 valid questionnaires were retrieved, with a response rate of 82. 7%(372/450). ISGEO classification system was adopted by 58.9% (219/372) of the participants as the diagnostic criteria for PACG. Of the respondents, 48.1% (179/372) of the participants believed that "anterior chamber angle closure mechanism-based PACG classification system" was more instructive for treatment, the percentage was higher than ISGEO classification system (42.2%, 157/372). Most (72.3%, 269/372) of the participants knew the 3-minute dark room prone test, but only 27.7%(103/372) of them applied it in clinical practice. A total of 83.4%(310/372) of the participants believed that low cerebrospinal fluid pressure is a risk factor for POAG. In all, 71.8% (267/372) of the participants reported that their institutes had applied compound trabeculectomy with adjustable suture, with 76.9%(286/372) of the participants agreeing that the adjustable suture reduced the rate of complications after trabeculectomy. Conclusions: Currently, both ISGEO classification system and anterior chamber angle closure mechanism-based PACG classification system were adopted in the diagnosis and treatment of glaucoma. Low cerebrospinal fluid pressure as new risk factors for POAG has been widely acknowledged and

Computer-Aided Diagnosis of Acute Lymphoblastic Leukaemia

PubMed Central

2018-01-01

Leukaemia is a form of blood cancer which affects the white blood cells and damages the bone marrow. Usually complete blood count (CBC) and bone marrow aspiration are used to diagnose the acute lymphoblastic leukaemia. It can be a fatal disease if not diagnosed at the earlier stage. In practice, manual microscopic evaluation of stained sample slide is used for diagnosis of leukaemia. But manual diagnostic methods are time-consuming, less accurate, and prone to errors due to various human factors like stress, fatigue, and so forth. Therefore, different automated systems have been proposed to wrestle the glitches in the manual diagnostic methods. In recent past, some computer-aided leukaemia diagnosis methods are presented. These automated systems are fast, reliable, and accurate as compared to manual diagnosis methods. This paper presents review of computer-aided diagnosis systems regarding their methodologies that include enhancement, segmentation, feature extraction, classification, and accuracy. PMID:29681996

Factors Affecting Age at ASD Diagnosis in UK: No Evidence That Diagnosis Age Has Decreased between 2004 and 2014

ERIC Educational Resources Information Center

Brett, Denise; Warnell, Frances; McConachie, Helen; Parr, Jeremy R.

2016-01-01

Clinical initiatives have aimed to reduce the age at ASD diagnosis in the UK. This study investigated whether the median age at diagnosis in childhood has reduced in recent years, and identified the factors associated with earlier diagnosis in the UK. Data on 2,134 children with ASD came from two large family databases. Results showed that the age…

Alzheimer Disease and Behavioral Variant Frontotemporal Dementia: Automatic Classification Based on Cortical Atrophy for Single-Subject Diagnosis.

PubMed

Möller, Christiane; Pijnenburg, Yolande A L; van der Flier, Wiesje M; Versteeg, Adriaan; Tijms, Betty; de Munck, Jan C; Hafkemeijer, Anne; Rombouts, Serge A R B; van der Grond, Jeroen; van Swieten, John; Dopper, Elise; Scheltens, Philip; Barkhof, Frederik; Vrenken, Hugo; Wink, Alle Meije

2016-06-01

Purpose To investigate the diagnostic accuracy of an image-based classifier to distinguish between Alzheimer disease (AD) and behavioral variant frontotemporal dementia (bvFTD) in individual patients by using gray matter (GM) density maps computed from standard T1-weighted structural images obtained with multiple imagers and with independent training and prediction data. Materials and Methods The local institutional review board approved the study. Eighty-four patients with AD, 51 patients with bvFTD, and 94 control subjects were divided into independent training (n = 115) and prediction (n = 114) sets with identical diagnosis and imager type distributions. Training of a support vector machine (SVM) classifier used diagnostic status and GM density maps and produced voxelwise discrimination maps. Discriminant function analysis was used to estimate suitability of the extracted weights for single-subject classification in the prediction set. Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) were calculated for image-based classifiers and neuropsychological z scores. Results Training accuracy of the SVM was 85% for patients with AD versus control subjects, 72% for patients with bvFTD versus control subjects, and 79% for patients with AD versus patients with bvFTD (P ≤ .029). Single-subject diagnosis in the prediction set when using the discrimination maps yielded accuracies of 88% for patients with AD versus control subjects, 85% for patients with bvFTD versus control subjects, and 82% for patients with AD versus patients with bvFTD, with a good to excellent AUC (range, 0.81-0.95; P ≤ .001). Machine learning-based categorization of AD versus bvFTD based on GM density maps outperforms classification based on neuropsychological test results. Conclusion The SVM can be used in single-subject discrimination and can help the clinician arrive at a diagnosis. The SVM can be used to distinguish disease-specific GM patterns in patients with AD

Segmentation, Splitting, and Classification of Overlapping Bacteria in Microscope Images for Automatic Bacterial Vaginosis Diagnosis.

PubMed

Song, Youyi; He, Liang; Zhou, Feng; Chen, Siping; Ni, Dong; Lei, Baiying; Wang, Tianfu

2017-07-01

Quantitative analysis of bacterial morphotypes in the microscope images plays a vital role in diagnosis of bacterial vaginosis (BV) based on the Nugent score criterion. However, there are two main challenges for this task: 1) It is quite difficult to identify the bacterial regions due to various appearance, faint boundaries, heterogeneous shapes, low contrast with the background, and small bacteria sizes with regards to the image. 2) There are numerous bacteria overlapping each other, which hinder us to conduct accurate analysis on individual bacterium. To overcome these challenges, we propose an automatic method in this paper to diagnose BV by quantitative analysis of bacterial morphotypes, which consists of a three-step approach, i.e., bacteria regions segmentation, overlapping bacteria splitting, and bacterial morphotypes classification. Specifically, we first segment the bacteria regions via saliency cut, which simultaneously evaluates the global contrast and spatial weighted coherence. And then Markov random field model is applied for high-quality unsupervised segmentation of small object. We then decompose overlapping bacteria clumps into markers, and associate a pixel with markers to identify evidence for eventual individual bacterium splitting. Next, we extract morphotype features from each bacterium to learn the descriptors and to characterize the types of bacteria using an Adaptive Boosting machine learning framework. Finally, BV diagnosis is implemented based on the Nugent score criterion. Experiments demonstrate that our proposed method achieves high accuracy and efficiency in computation for BV diagnosis.

Diagnosis and classification of celiac disease and gluten sensitivity.

PubMed

Tonutti, Elio; Bizzaro, Nicola

2014-01-01

Celiac disease is a complex disorder, the development of which is controlled by a combination of genetic (HLA alleles) and environmental (gluten ingestion) factors. New diagnostic guidelines developed by ESPGHAN emphasize the crucial role of serological tests in the diagnostic process of symptomatic subjects, and of the detection of HLA DQ2/DQ8 alleles in defining a diagnosis in asymptomatic subjects belonging to at-risk groups. The serological diagnosis of CD is based on the detection of class IgA anti-tissue transglutaminase (anti-tTG) and anti-endomysial antibodies. In patients with IgA deficiency, anti-tTG or anti-deamidated gliadin peptide antibody assays of the IgG class are used. When anti-tTG antibody levels are very high, antibody specificity is absolute and CD can be diagnosed without performing a duodenum biopsy. Non-celiac gluten sensitivity is a gluten reaction in which both allergic and autoimmune mechanisms have been ruled out. Diagnostic criteria include the presence of symptoms similar to those of celiac or allergic patients; negative allergological tests and absence of anti-tTG and EMA antibodies; normal duodenal histology; evidence of disappearance of the symptoms with a gluten-free diet; relapse of the symptoms when gluten is reintroduced. Copyright © 2014 Elsevier B.V. All rights reserved.

A reliable Raman-spectroscopy-based approach for diagnosis, classification and follow-up of B-cell acute lymphoblastic leukemia

NASA Astrophysics Data System (ADS)

Managò, Stefano; Valente, Carmen; Mirabelli, Peppino; Circolo, Diego; Basile, Filomena; Corda, Daniela; de Luca, Anna Chiara

2016-04-01

Acute lymphoblastic leukemia type B (B-ALL) is a neoplastic disorder that shows high mortality rates due to immature lymphocyte B-cell proliferation. B-ALL diagnosis requires identification and classification of the leukemia cells. Here, we demonstrate the use of Raman spectroscopy to discriminate normal lymphocytic B-cells from three different B-leukemia transformed cell lines (i.e., RS4;11, REH, MN60 cells) based on their biochemical features. In combination with immunofluorescence and Western blotting, we show that these Raman markers reflect the relative changes in the potential biological markers from cell surface antigens, cytoplasmic proteins, and DNA content and correlate with the lymphoblastic B-cell maturation/differentiation stages. Our study demonstrates the potential of this technique for classification of B-leukemia cells into the different differentiation/maturation stages, as well as for the identification of key biochemical changes under chemotherapeutic treatments. Finally, preliminary results from clinical samples indicate high consistency of, and potential applications for, this Raman spectroscopy approach.

Casemix classification systems.

PubMed

Fetter, R B

1999-01-01

The idea of using casemix classification to manage hospital services is not new, but has been limited by available technology. It was not until after the introduction of Medicare in the United States in 1965 that serious attempts were made to measure hospital production in order to contain spiralling costs. This resulted in a system of casemix classification known as diagnosis related groups (DRGs). This paper traces the development of DRGs and their evolution from the initial version to the All Patient Refined DRGs developed in 1991.

Concordance of clinical diagnosis of T classification among physicians for locally advanced unresectable thoracic esophageal cancer.

PubMed

Yokota, Tomoya; Yasuda, Takushi; Kato, Hiroyuki; Nozaki, Isao; Sato, Hiroshi; Miyata, Yoshinori; Kuroki, Yoshifumi; Kato, Ken; Hamamoto, Yasuo; Tsubosa, Yasuhiro; Ogawa, Hirofumi; Ito, Yoshinori; Kitagawa, Yuko

2018-02-01

We conducted a multicenter phase II trial assessing chemoselection with docetaxel plus 5-fluorouracil and cisplatin induction chemotherapy and subsequent conversion surgery for locally advanced, unresectable esophageal cancer. The aim of this study was to validate the concordance of clinical T diagnosis among physicians in the cases of this trial. Computed tomography scans and esophagoscopic images of 48 patients taken at baseline were centrally reviewed by 6 senior physicians with experience in esophageal oncology. Individual reviewers voted for definitive T4, relative T4, relative T3, or definitive T3. Discordant diagnoses between reviewers were resolved by the majority opinion. The reviewers were blinded to patient clinical outcome data and to the vote of the other reviewers. Ninety percent of cases were diagnosed as clinical T4 by investigators, while 33.3-75.0% (median 70.8%) of cases were judged to be T4 by 6 reviewers. Discordant diagnosis between investigators and reviewers occurred in 33% (16/48) of all cases (Cohen's kappa coefficient 0.0519), including 12 cases where curative resection was considered possible (48%, n = 25) and 4 cases where curative resection was considered impossible (17%, n = 23). Critical discordance (one reviewer voted for definitive T3 but the other voted for definitive T4, and vice versa) between reviewers occurred in 0-12.5% of cases (median 2.1%). There were inter-observer variations in clinical diagnosis of the T category of locally advanced, unresectable esophageal cancer. Accurate clinical diagnosis of T classification is required for determining the optimum treatment for each patient.

Opinion Paper: On the Diagnosis/Classification of Sexual Arousal Concerns in Women.

PubMed

Althof, Stanley E; Meston, Cindy M; Perelman, Michael A; Handy, Ariel B; Kilimnik, Chelsea D; Stanton, Amelia M

2017-11-01

, then one would expect to see evidence of low subjective arousal in women with low sexual desire. Optimized treatment efficacy requires differentiating mental and physical factors that contribute to female sexual dysfunction. Support for our conclusion is based on clinical qualitative evidence and quantitative evidence. However, the quantitative support is from only one laboratory at this time. These findings strongly support the view that female sexual arousal disorder includes a subjective arousal subtype and that subjective arousal and desire are related but not similar constructs. We advocate for the relevance of maintaining subjective arousal disorder in the diagnostic nomenclature and present several lines of evidence to support this contention. Althof SE, Meston CM, Perelman M, et al. Opinion Paper: On the Diagnosis/Classification of Sexual Arousal Concerns in Women. J Sex Med 2017;14:1365-1371. Copyright © 2017 International Society for Sexual Medicine. Published by Elsevier Inc. All rights reserved.

Combined target factor analysis and Bayesian soft-classification of interference-contaminated samples: forensic fire debris analysis.

PubMed

Williams, Mary R; Sigman, Michael E; Lewis, Jennifer; Pitan, Kelly McHugh

2012-10-10

A bayesian soft classification method combined with target factor analysis (TFA) is described and tested for the analysis of fire debris data. The method relies on analysis of the average mass spectrum across the chromatographic profile (i.e., the total ion spectrum, TIS) from multiple samples taken from a single fire scene. A library of TIS from reference ignitable liquids with assigned ASTM classification is used as the target factors in TFA. The class-conditional distributions of correlations between the target and predicted factors for each ASTM class are represented by kernel functions and analyzed by bayesian decision theory. The soft classification approach assists in assessing the probability that ignitable liquid residue from a specific ASTM E1618 class, is present in a set of samples from a single fire scene, even in the presence of unspecified background contributions from pyrolysis products. The method is demonstrated with sample data sets and then tested on laboratory-scale burn data and large-scale field test burns. The overall performance achieved in laboratory and field test of the method is approximately 80% correct classification of fire debris samples. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Value of the BI-RADS classification in MR-Mammography for diagnosis of benign and malignant breast tumors.

PubMed

Sohns, Christian; Scherrer, Martin; Staab, Wieland; Obenauer, Silvia

2011-12-01

To assess whether the BI-RADS classification in MR-Mammography (MRM) can distinguish between benign and malignant lesions. 207 MRM investigations were categorised according to BI-RADS. The results were compared to histology. All MRM studies were interpreted by two examiners. Statistical significance for the accuracy of MRM was calculated. A significant correlation between specific histology and MRM-tumour-morphology could not be reported. Mass (68%) was significant for malignancy. Significance raised with irregular shape (88%), spiculated margin (97%), rim enhancement (98%), fast initial increase (90%), post initial plateau (65%), and intermediate T2 result (82%). Highly significant for benignity was an oval mass (79%), slow initial increase (94%) and a hyperintense T2 result (77%), also an inconspicuous MRM result (77%) was often seen in benign histology. Symmetry (90%) and further post initial increase (90%) were significant, whereas a regional distribution (74%) was lowly significant for benignity. On basis of the BI-RADS classification an objective comparability and statement of diagnosis could be made highly significant. Due to the fact of false-negative and false-positive MRM-results, histology is necessary.

Urogenital tuberculosis: definition and classification.

PubMed

Kulchavenya, Ekaterina

2014-10-01

To improve the approach to the diagnosis and management of urogenital tuberculosis (UGTB), we need clear and unique classification. UGTB remains an important problem, especially in developing countries, but it is often an overlooked disease. As with any other infection, UGTB should be cured by antibacterial therapy, but because of late diagnosis it may often require surgery. Scientific literature dedicated to this problem was critically analyzed and juxtaposed with the author's own more than 30 years' experience in tuberculosis urology. The conception, terms and definition were consolidated into one system; classification stage by stage as well as complications are presented. Classification of any disease includes dispersion on forms and stages and exact definitions for each stage. Clinical features and symptoms significantly vary between different forms and stages of UGTB. A simple diagnostic algorithm was constructed. UGTB is multivariant disease and a standard unified approach to it is impossible. Clear definition as well as unique classification are necessary for real estimation of epidemiology and the optimization of therapy. The term 'UGTB' has insufficient information in order to estimate therapy, surgery and prognosis, or to evaluate the epidemiology.

Striving towards efficiency in the Greek hospitals by reviewing case mix classifications.

PubMed

Polyzos, Nicholas M

2002-09-01

In order to verify the efficiency level of Greek public hospitals, this paper evaluates the most recent indicators. Relevant data were collected from the two following databases: (a) hospitals' utilisation data generally and per clinical speciality [Ministry of Health, Athens, (Data based) 1995]; (b) Patients' and hospitals' characteristics per diagnosis [National Statistical Office, Athens, (Data based) 1993]. As explanatory variables, the study examines supply and demand factors following case mix classifications. Firstly, average length of stay (ALOS) and secondly, cost per case were regressed as dependent variables. The study highlights the extent of variability across hospitals for different groups of patients with the same condition. The results specify the most important factors that affect ALOS and cost pertaining to efficiency. Per speciality analysis shows occupancy, size-type of the hospital, beds and doctors per speciality, access and use of outpatient services, and surgical operations, etc. as the most significant factors. Per disease-diagnosis analysis shows age of over 65 years, gender, residence, marital status, surgical operation and insurance as the most important factors. General cost analysis in all National Health Systems (NHS) hospitals shows that economies of scale appear in: (a) district and/or specialised hospitals of 250-400 beds; (b) regional and/or teaching hospitals of over but near to 400 beds. Consequently, the author determines the 'Greek' Diagnostic Related Groups (DRGs), based on the cost per clinical speciality in the nine basic specialities and on the cost per diagnosis of the top 15 diagnoses. Further to the scientific results, such studies will enhance much necessary discussions on the organisation of service delivery and financing, by following case mix classification. Copyright 2002 Elsevier Science Ireland Ltd.

Classification of anemia for gastroenterologists

PubMed Central

Moreno Chulilla, Jose Antonio; Romero Colás, Maria Soledad; Gutiérrez Martín, Martín

2009-01-01

Most anemia is related to the digestive system by dietary deficiency, malabsorption, or chronic bleeding. We review the World Health Organization definition of anemia, its morphological classification (microcytic, macrocytic and normocytic) and pathogenic classification (regenerative and hypo regenerative), and integration of these classifications. Interpretation of laboratory tests is included, from the simplest (blood count, routine biochemistry) to the more specific (iron metabolism, vitamin B12, folic acid, reticulocytes, erythropoietin, bone marrow examination and Schilling test). In the text and various algorithms, we propose a hierarchical and logical way to reach a diagnosis as quickly as possible, by properly managing the medical interview, physical examination, appropriate laboratory tests, bone marrow examination, and other complementary tests. The prevalence is emphasized in all sections so that the gastroenterologist can direct the diagnosis to the most common diseases, although the tables also include rare diseases. Digestive diseases potentially causing anemia have been studied in preference, but other causes of anemia have been included in the text and tables. Primitive hematological diseases that cause anemia are only listed, but are not discussed in depth. The last section is dedicated to simplifying all items discussed above, using practical rules to guide diagnosis and medical care with the greatest economy of resources and time. PMID:19787825

Diagnosis and management of factor V Leiden.

PubMed

Campello, Elena; Spiezia, Luca; Simioni, Paolo

2016-12-01

The discovery of the factor V Leiden (FVL) missense mutation (Arg506Gln) causing factor V resistance to the anticoagulant action of activated protein C was a landmark that allowed a better understanding of the basis of inherited thrombotic risk. FVL mutation is currently the most common known hereditary defect predisposing to venous thrombosis. Areas covered: Novel data-driven FVL diagnosis and therapeutic approaches in the management of FVL carriers in various clinical settings. Brief conclusions on topics of direct clinical relevance including currently available indications for primary and secondary prophylaxis, the management of female, pediatric carriers and asymptomatic relatives. Latest evidence on the association between FVL and cancer, as well as the possible use of direct oral anticoagulant therapy. Expert commentary: Although FVL diagnosis nowadays is highly accurate, many doubts remain regarding the best management and therapeutic protocols. The main role of clinicians is to tailor therapeutic strategies to carriers and their relatives. High familial penetrance, distinctive aspects of the first thrombotic event (provoked/unprovoked, age, etc.) and laboratory biomarkers can guide the optimal management of secondary antithrombotic prophylaxis, primary prophylaxis in asymptomatic individuals, and whether to screen relatives.

Knee replacement and Diagnosis-Related Groups (DRGs): patient classification and hospital reimbursement in 11 European countries.

PubMed

Tan, Siok Swan; Chiarello, Pietro; Quentin, Wilm

2013-11-01

Researchers from 11 countries (Austria, England, Estonia, Finland, France, Germany, Ireland, Netherlands, Poland, Spain, and Sweden) compared how their Diagnosis-Related Group (DRG) systems deal with knee replacement cases. The study aims to assist knee surgeons and national authorities to optimize the grouping algorithm of their DRG systems. National or regional databases were used to identify hospital cases treated with a procedure of knee replacement. DRG classification algorithms and indicators of resource consumption were compared for those DRGs that together comprised at least 97 % of cases. Five standardized case scenarios were defined and quasi-prices according to national DRG-based hospital payment systems ascertained. Grouping algorithms for knee replacement vary widely across countries: they classify cases according to different variables (between one and five classification variables) into diverging numbers of DRGs (between one and five DRGs). Even the most expensive DRGs generally have a cost index below 2.00, implying that grouping algorithms do not adequately account for cases that are more than twice as costly as the index DRG. Quasi-prices for the most complex case vary between euro 4,920 in Estonia and euro 14,081 in Spain. Most European DRG systems were observed to insufficiently consider the most important determinants of resource consumption. Several countries' DRG system might be improved through the introduction of classification variables for revision of knee replacement or for the presence of complications or comorbidities. Ultimately, this would contribute to assuring adequate performance comparisons and fair hospital reimbursement on the basis of DRGs.

Comparison of staging diagnosis by two magnifying endoscopy classification for superficial oesophageal cancer.

PubMed

Ebi, Masahide; Shimura, Takaya; Murakami, Kenji; Yamada, Tomonori; Hirata, Yoshikazu; Tsukamoto, Hironobu; Mizoshita, Tsutomu; Tanida, Satoshi; Kataoka, Hiromi; Kamiya, Takeshi; Joh, Takashi

2012-11-01

Due to the possibility of lymph node metastasis, surgical resection is indicated for superficial oesophageal cancer with invasion to a depth greater than the muscularis mucosa. Although two magnifying endoscopy classifications are currently used to diagnose the depth of invasion, which classification is more suitable remains controversial. To compare and evaluate the clinical outcomes of two classifications for superficial oesophageal squamous cell carcinoma. This cross-sectional study consists of 44 superficial oesophageal squamous cell carcinoma lesions with magnification image-enhanced endoscopy images. Only magnifying endoscopic images were displayed to two experienced endoscopists who independently diagnosed the depth of invasion according to both classifications. The sensitivity of invasion greater than the muscularis mucosa tended to be higher in Inoue's classification than Arima's classification (78.3±6.2% vs. 50.0±3.0%; P=0.144), whereas the specificity was significantly lower in Inoue's classification than in Arima's classification (61.9±0.0% vs. 97.6±3.4%; P=0.043). For both classifications, rates of concordance were 90.9% and 84.4%, and κ statistics were 0.81 and 0.66, respectively. Our results suggest that Arima's classification is suitable for general screening before treatment to avoid unnecessary surgery. Inoue's classification is appropriate for assessing wide lesion. Copyright © 2012 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.

Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

PubMed Central

Zhao, Changbo; Li, Guo-Zheng; Wang, Chengjun; Niu, Jinling

2015-01-01

As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification. PMID:26246834

Antibodies to native DNA and serum complement (C3) levels. Application to diagnosis and classification of systemic lupus erythematosus.

PubMed

Weinstein, A; Bordwell, B; Stone, B; Tibbetts, C; Rothfield, N F

1983-02-01

The sensitivity and specificity of the presence of antibodies to native DNA and low serum C3 levels were investigated in a prospective study in 98 patients with systemic lupus erythematosus who were followed for a mean of 38.4 months. Hospitalized patients, patients with other connective tissue diseases, and subjects without any disease served as the control group. Seventy-two percent of the patients with systemic lupus erythematosus had a high DNA-binding value (more than 33 percent) initially, and an additional 20 percent had a high DNA-binding value later in the course of the illness. Similarly, C3 levels were low (less than 81 mg/100 ml) in 38 percent of the patients with systemic lupus erythematosus initially and in 66 percent of the patients at any time during the study. High DNA-binding and low C3 levels each showed extremely high predictive value (94 percent) for the diagnosis of systemic lupus erythematosus when applied in a patient population in which that diagnosis was considered. The presence of both abnormalities was 100 percent correct in predicting the diagnosis os systemic lupus erythematosus. Both tests should be included in future criteria for the diagnosis and classification of systemic lupus erythematosus.

Benefits and limitations of fine needle aspiration cytology in the diagnosis and classification of leprosy in primary and secondary healthcare settings.

PubMed

Ray, R; Mondal, R K; Pathak, S

2015-08-01

The goal of the World Health Organization (WHO) is to eliminate leprosy as a public health problem. This will only be possible when all patients are detected and cured using multidrug therapy, which requires accurate diagnosis prior to treatment. The objective of this study was to evaluate the possibility of the diagnosis of leprosy lesions by fine needle aspiration cytology according to a modification of the Ridley-Jopling scale, as it can be used in primary and secondary healthcare centres, especially in low-resource settings in which leprosy is prevalent. A prospective study comprising 54 cases with cardinal features of leprosy was performed. Among the 54 cases, 27 patients consented to a histopathological biopsy procedure. The slides were stained with Giemsa, modified Ziehl-Neelsen, Papanicolaou and haematoxylin and eosin methods. Among the 54 cases, 34 were reported as tuberculoid leprosy, five as mid-borderline (BB), three as borderline lepromatous (BL) and eight as lepromatous leprosy (LL); four were unsatisfactory. Histopathological study was performed in 27 cases, which showed cyto-histological correlation in 21 cases (78%). Agreement between histological and cytological diagnosis was achieved in 12 of the 15 tuberculoid cases, one of the three BB cases, one of the two BL cases and all seven LL cases. With the implementation of the WHO classification based on patch counting, there is the possibility of the over-treatment of paucibacillary cases and under-treatment of multibacillary cases. Cytology in terms of cellular type morphology and bacteriological study can complement the WHO classification. © 2014 John Wiley & Sons Ltd.

A Mapping from the Human Factors Analysis and Classification System (DOD-HFACS) to the Domains of Human Systems Integration (HSI)

DTIC Science & Technology

2009-11-01

Equation Chapter 1 Section 1 A MAPPING FROM THE HUMAN FACTORS ANALYSIS AND CLASSIFICATION SYSTEM (DOD...OMB control number. 1. REPORT DATE NOV 2009 2. REPORT TYPE 3. DATES COVERED 4. TITLE AND SUBTITLE A Mapping from the Human Factors Analysis ...7 The Human Factors Analysis and Classification System .................................................. 7 Mapping of DoD

Risk Factors in Adolescent Hypertension

PubMed Central

Ewald, D. Rose; Haldeman, Lauren A.

2016-01-01

Hypertension is a complex and multifaceted disease, with many contributing factors. While diet and nutrition are important influences, the confounding effects of overweight and obesity, metabolic and genetic factors, racial and ethnic predispositions, socioeconomic status, cultural influences, growth rate, and pubertal stage have even more influence and make diagnosis quite challenging. The prevalence of hypertension in adolescents far exceeds the numbers who have been diagnosed; studies have found that 75% or more go undiagnosed. This literature review summarizes the challenges of blood pressure classification in adolescents, discusses the impact of these confounding influences, and identifies actions that will improve diagnosis and treatment outcomes. PMID:27335997

PET Index of Bone Glucose Metabolism (PIBGM) Classification of PET/CT Data for Fever of Unknown Origin Diagnosis

PubMed Central

Yang, Jian; Liu, Xinxin; Ai, Danni; Fan, Jingfan; Zheng, Youjing; Li, Fang; Huo, Li; Wang, Yongtian

2015-01-01

Objectives Fever of unknown origin (FUO) remains a challenge in clinical practice. Fluorine-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) is helpful in diagnosing the etiology of FUO. This paper aims to develop a completely automatic classification method based on PET/CT data for the computer-assisted diagnosis of FUO. Methods We retrospectively analyzed the FDG PET/CT scan of 175 FUO patients, 79 males and 96 females. The final diagnosis of all FUO patients was achieved through pathology or clinical evaluation, including 108 normal patients and 67 FUO patients. CT anatomic information was used to acquire bone functional information from PET images. The skeletal system of FUO patients was classified by analyzing the standardized uptake value (SUV) and the PET index of bone glucose metabolism (PIBGM). The SUV distributions in the bone marrow and the bone cortex were also studied in detail. Results The SUV and PIBGM of the bone marrow only slightly differed between the FUO patients and normal people, whereas the SUV of whole bone structures and the PIBGM of the bone cortex significantly differed between the normal people and FUO patients. The method detected 43 patients from 67 FUO patients, with sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 64.18%, 95%, 93.48%, 72.73%, and 83.33%, respectively. Conclusion The experimental results demonstrate that the study can achieve automatic classification of FUO patients by the proposed novel biomarker of PIBGM, which has the potential to be utilized in clinical practice. PMID:26076139

A Qualitative Study of Factors Influencing Decision-Making after Prenatal Diagnosis of down Syndrome.

PubMed

Reed, Amy R; Berrier, Kathryn L

2017-08-01

Previous research has identified twenty-six factors that may affect pregnancy management decisions following prenatal diagnosis of DS; however, there is no consensus about the relative importance or effects of these factors. In order to better understand patient decision-making, we conducted expansive cognitive interviews with nine former patients who received a prenatal diagnosis of DS. Our results suggest that patients attached unique meanings to factors influencing decision-making regardless of the pregnancy outcome. Nineteen of the twenty-six factors previously studied and four novel factors (rationale for testing, information quality, pregnancy experience, and perception of parenting abilities and goals) were found to be important to decision-making. We argue that qualitative studies can help characterize the complexity of decision-making following prenatal diagnosis of DS.

Diagnostic classification scheme in Iranian breast cancer patients using a decision tree.

PubMed

Malehi, Amal Saki

2014-01-01

The objective of this study was to determine a diagnostic classification scheme using a decision tree based model. The study was conducted as a retrospective case-control study in Imam Khomeini hospital in Tehran during 2001 to 2009. Data, including demographic and clinical-pathological characteristics, were uniformly collected from 624 females, 312 of them were referred with positive diagnosis of breast cancer (cases) and 312 healthy women (controls). The decision tree was implemented to develop a diagnostic classification scheme using CART 6.0 Software. The AUC (area under curve), was measured as the overall performance of diagnostic classification of the decision tree. Five variables as main risk factors of breast cancer and six subgroups as high risk were identified. The results indicated that increasing age, low age at menarche, single and divorced statues, irregular menarche pattern and family history of breast cancer are the important diagnostic factors in Iranian breast cancer patients. The sensitivity and specificity of the analysis were 66% and 86.9% respectively. The high AUC (0.82) also showed an excellent classification and diagnostic performance of the model. Decision tree based model appears to be suitable for identifying risk factors and high or low risk subgroups. It can also assists clinicians in making a decision, since it can identify underlying prognostic relationships and understanding the model is very explicit.

[New International Classification of Chronic Pancreatitis (M-ANNHEIM multifactor classification system, 2007): principles, merits, and demerits].

PubMed

Tsimmerman, Ia S

2008-01-01

The new International Classification of Chronic Pancreatitis (designated as M-ANNHEIM) proposed by a group of German specialists in late 2007 is reviewed. All its sections are subjected to analysis (risk group categories, clinical stages and phases, variants of clinical course, diagnostic criteria for "established" and "suspected" pancreatitis, instrumental methods and functional tests used in the diagnosis, evaluation of the severity of the disease using a scoring system, stages of elimination of pain syndrome). The new classification is compared with the earlier classification proposed by the author. Its merits and demerits are discussed.

Welcoming the new WHO classification of pituitary tumors 2017: revolution in TTF-1-positive posterior pituitary tumors.

PubMed

Shibuya, Makoto

2018-04-01

The fourth edition of the World Health Organization classification of endocrine tumors (EN-WHO2017) was released in 2017. In this new edition, changes in the classification of non-neuroendocrine tumors are proposed particularly in tumors arising in the posterior pituitary. These tumors are a distinct group of low-grade neoplasms of the sellar region that express thyroid transcription factor-1, and include pituicytoma, granular cell tumor of the sellar region, spindle cell oncocytoma, and sellar ependymoma. This short review focuses on the classification of posterior pituitary tumors newly proposed in EN-WHO2017, and controversies in their pathological differential diagnosis are discussed based on recent cases.

[Research progress in molecular classification of gastric cancer].

PubMed

Zhou, Menglong; Li, Guichao; Zhang, Zhen

2016-09-25

Gastric cancer(GC) is a highly heterogeneous malignancy. The present widely used histopathological classifications have gradually failed to meet the needs of individualized diagnosis and treatment. Development of technologies such as microarray and next-generation sequencing (NGS) has allowed GC to be studied at the molecular level. Mechanisms about tumorigenesis and progression of GC can be elucidated in the aspects of gene mutations, chromosomal alterations, transcriptional and epigenetic changes, on the basis of which GC can be divided into several subtypes. The classifications of Tan's, Lei's, TCGA and ACRG are relatively comprehensive. Especially the TCGA and ACRG classifications have large sample size and abundant molecular profiling data, thus, the genomic characteristics of GC can be depicted more accurately. However, significant differences between both classifications still exist so that they cannot be substituted for each other. So far there is no widely accepted molecular classification of GC. Compared with TCGA classification, ACRG system may have more clinical significance in Chinese GC patients since the samples are mostly from Asian population and show better association with prognosis. The molecular classification of GC may provide the theoretical and experimental basis for early diagnosis, therapeutic efficacy prediction and treatment stratification while their clinical application is still limited. Future work should involve the application of molecular classifications in the clinical settings for improving the medical management of GC.

Using random forest for reliable classification and cost-sensitive learning for medical diagnosis.

PubMed

Yang, Fan; Wang, Hua-zhen; Mi, Hong; Lin, Cheng-de; Cai, Wei-wen

2009-01-30

Most machine-learning classifiers output label predictions for new instances without indicating how reliable the predictions are. The applicability of these classifiers is limited in critical domains where incorrect predictions have serious consequences, like medical diagnosis. Further, the default assumption of equal misclassification costs is most likely violated in medical diagnosis. In this paper, we present a modified random forest classifier which is incorporated into the conformal predictor scheme. A conformal predictor is a transductive learning scheme, using Kolmogorov complexity to test the randomness of a particular sample with respect to the training sets. Our method show well-calibrated property that the performance can be set prior to classification and the accurate rate is exactly equal to the predefined confidence level. Further, to address the cost sensitive problem, we extend our method to a label-conditional predictor which takes into account different costs for misclassifications in different class and allows different confidence level to be specified for each class. Intensive experiments on benchmark datasets and real world applications show the resultant classifier is well-calibrated and able to control the specific risk of different class. The method of using RF outlier measure to design a nonconformity measure benefits the resultant predictor. Further, a label-conditional classifier is developed and turn to be an alternative approach to the cost sensitive learning problem that relies on label-wise predefined confidence level. The target of minimizing the risk of misclassification is achieved by specifying the different confidence level for different class.

Using classification tree modelling to investigate drug prescription practices at health facilities in rural Tanzania.

PubMed

Kajungu, Dan K; Selemani, Majige; Masanja, Irene; Baraka, Amuri; Njozi, Mustafa; Khatib, Rashid; Dodoo, Alexander N; Binka, Fred; Macq, Jean; D'Alessandro, Umberto; Speybroeck, Niko

2012-09-05

Drug prescription practices depend on several factors related to the patient, health worker and health facilities. A better understanding of the factors influencing prescription patterns is essential to develop strategies to mitigate the negative consequences associated with poor practices in both the public and private sectors. A cross-sectional study was conducted in rural Tanzania among patients attending health facilities, and health workers. Patients, health workers and health facilities-related factors with the potential to influence drug prescription patterns were used to build a model of key predictors. Standard data mining methodology of classification tree analysis was used to define the importance of the different factors on prescription patterns. This analysis included 1,470 patients and 71 health workers practicing in 30 health facilities. Patients were mostly treated in dispensaries. Twenty two variables were used to construct two classification tree models: one for polypharmacy (prescription of ≥3 drugs) on a single clinic visit and one for co-prescription of artemether-lumefantrine (AL) with antibiotics. The most important predictor of polypharmacy was the diagnosis of several illnesses. Polypharmacy was also associated with little or no supervision of the health workers, administration of AL and private facilities. Co-prescription of AL with antibiotics was more frequent in children under five years of age and the other important predictors were transmission season, mode of diagnosis and the location of the health facility. Standard data mining methodology is an easy-to-implement analytical approach that can be useful for decision-making. Polypharmacy is mainly due to the diagnosis of multiple illnesses.

Factors Associated With Age of Diagnosis Among Children With Autism Spectrum Disorders

PubMed Central

Mandell, David S.; Novak, Maytali M.; Zubritsky, Cynthia D.

2010-01-01

Objective Early diagnosis of children with autism spectrum disorders (ASD) is critical but often delayed until school age. Few studies have identified factors that may delay diagnosis. This study attempted to identify these factors among a community sample of children with ASD. Methods Survey data were collected in Pennsylvania from 969 caregivers of children who had ASD and were younger than 21 years regarding their service experiences. Linear regression was used to identify clinical and demographic characteristics associated with age of diagnosis. Results The average age of diagnosis was 3.1 years for children with autistic disorder, 3.9 years for pervasive developmental disorder not otherwise specified, and 7.2 years for Asperger’s disorder. The average age of diagnosis increased 0.2 years for each year of age. Rural children received a diagnosis 0.4 years later than urban children. Near-poor children received a diagnosis 0.9 years later than those with incomes >100% above the poverty level. Children with severe language deficits received a diagnosis an average of 1.2 years earlier than other children. Hand flapping, toe walking, and sustained odd play were associated with a decrease in the age of diagnosis, whereas oversensitivity to pain and hearing impairment were associated with an increase. Children who had 4 or more primary care physicians before diagnosis received a diagnosis 0.5 years later than other children, whereas those whose pediatricians referred them to a specialist received a diagnosis 0.3 years sooner. Conclusion These findings suggest improvements over time in decreasing the age at which children with ASD, especially higher functioning children, receive a diagnosis. They also suggest a lack of resources in rural areas and for near-poor families and the importance of continuous pediatric care and specialty referrals. That only certain ASD-related behaviors, some of which are not required to satisfy diagnostic criteria, decreased the age of

A review of the automated detection and classification of acute leukaemia: Coherent taxonomy, datasets, validation and performance measurements, motivation, open challenges and recommendations.

PubMed

Alsalem, M A; Zaidan, A A; Zaidan, B B; Hashim, M; Madhloom, H T; Azeez, N D; Alsyisuf, S

2018-05-01

Acute leukaemia diagnosis is a field requiring automated solutions, tools and methods and the ability to facilitate early detection and even prediction. Many studies have focused on the automatic detection and classification of acute leukaemia and their subtypes to promote enable highly accurate diagnosis. This study aimed to review and analyse literature related to the detection and classification of acute leukaemia. The factors that were considered to improve understanding on the field's various contextual aspects in published studies and characteristics were motivation, open challenges that confronted researchers and recommendations presented to researchers to enhance this vital research area. We systematically searched all articles about the classification and detection of acute leukaemia, as well as their evaluation and benchmarking, in three main databases: ScienceDirect, Web of Science and IEEE Xplore from 2007 to 2017. These indices were considered to be sufficiently extensive to encompass our field of literature. Based on our inclusion and exclusion criteria, 89 articles were selected. Most studies (58/89) focused on the methods or algorithms of acute leukaemia classification, a number of papers (22/89) covered the developed systems for the detection or diagnosis of acute leukaemia and few papers (5/89) presented evaluation and comparative studies. The smallest portion (4/89) of articles comprised reviews and surveys. Acute leukaemia diagnosis, which is a field requiring automated solutions, tools and methods, entails the ability to facilitate early detection or even prediction. Many studies have been performed on the automatic detection and classification of acute leukaemia and their subtypes to promote accurate diagnosis. Research areas on medical-image classification vary, but they are all equally vital. We expect this systematic review to help emphasise current research opportunities and thus extend and create additional research fields. Copyright �

Importance of recurrence rating, morphology, hernial gap size, and risk factors in ventral and incisional hernia classification.

PubMed

Dietz, U A; Winkler, M S; Härtel, R W; Fleischhacker, A; Wiegering, A; Isbert, C; Jurowich, Ch; Heuschmann, P; Germer, C-T

2014-02-01

There is limited evidence on the natural course of ventral and incisional hernias and the results of hernia repair, what might partially be explained by the lack of an accepted classification system. The aim of the present study is to investigate the association of the criteria included in the Wuerzburg classification system of ventral and incisional hernias with postoperative complications and long-term recurrence. In a retrospective cohort study, the data on 330 consecutive patients who underwent surgery to repair ventral and incisional hernias were analyzed. The following four classification criteria were applied: (a) recurrence rating (ventral, incisional or incisional recurrent); (b) morphology (location); (c) size of the hernial gap; and (d) risk factors. The primary endpoint was the occurrence of a recurrence during follow-up. Secondary endpoints were incidence of postoperative complications. Independent association between classification criteria, type of surgical procedures and postoperative complications was calculated by multivariate logistic regression analysis and between classification criteria, type of surgical procedures and risk of long-term recurrence by Cox regression analysis. Follow-up lasted a mean 47.7 ± 23.53 months (median 45 months) or 3.9 ± 1.96 years. The criterion "recurrence rating" was found as predictive factor for postoperative complications in the multivariate analysis (OR 2.04; 95 % CI 1.09-3.84; incisional vs. ventral hernia). The criterion "morphology" had influence neither on the incidence of the critical event "recurrence during follow-up" nor on the incidence of postoperative complications. Hernial gap "width" predicted postoperative complications in the multivariate analysis (OR 1.98; 95 % CI 1.19-3.29; ≤5 vs. >5 cm). Length of the hernial gap was found to be an independent prognostic factor for the critical event "recurrence during follow-up" (HR 2.05; 95 % CI 1.25-3.37; ≤5 vs. >5 cm). The presence of 3 or more risk

The Use and Abuse of Diagnostic/Classification Criteria

PubMed Central

June, Rayford R.; Aggarwal, Rohit

2015-01-01

In rheumatic diseases, classification criteria have been developed to identify well-defined homogenous cohorts for clinical research. Although, they are commonly used in clinical practice, their use may not be appropriate for routine diagnostic clinical care. Classification criteria are being revised with improved methodology and further understanding of disease pathophysiology, but still may not encompass all unique clinical situations to be applied for diagnosis of heterogeneous, rare, evolving rheumatic diseases. Diagnostic criteria development is challenging primarily due to difficulty for universal application given significant differences in prevalence of rheumatic diseases based on geographical area and clinic settings. Despite these shortcomings, the clinician can still use classification criteria for understanding the disease as well as a guide for diagnosis with a few caveats. We present the limits of current classification criteria, describe their use and abuse in clinical practice, and how they should be used with caution when applied in clinics. PMID:26096094

The Nutraceutical Bioavailability Classification Scheme: Classifying Nutraceuticals According to Factors Limiting their Oral Bioavailability.

PubMed

McClements, David Julian; Li, Fang; Xiao, Hang

2015-01-01

The oral bioavailability of a health-promoting dietary component (nutraceutical) may be limited by various physicochemical and physiological phenomena: liberation from food matrices, solubility in gastrointestinal fluids, interaction with gastrointestinal components, chemical degradation or metabolism, and epithelium cell permeability. Nutraceutical bioavailability can therefore be improved by designing food matrices that control their bioaccessibility (B*), absorption (A*), and transformation (T*) within the gastrointestinal tract (GIT). This article reviews the major factors influencing the gastrointestinal fate of nutraceuticals, and then uses this information to develop a new scheme to classify the major factors limiting nutraceutical bioavailability: the nutraceutical bioavailability classification scheme (NuBACS). This new scheme is analogous to the biopharmaceutical classification scheme (BCS) used by the pharmaceutical industry to classify drug bioavailability, but it contains additional factors important for understanding nutraceutical bioavailability in foods. The article also highlights potential strategies for increasing the oral bioavailability of nutraceuticals based on their NuBACS designation (B*A*T*).

Charting the landscape of priority problems in psychiatry, part 1: classification and diagnosis.

PubMed

Stephan, Klaas E; Bach, Dominik R; Fletcher, Paul C; Flint, Jonathan; Frank, Michael J; Friston, Karl J; Heinz, Andreas; Huys, Quentin J M; Owen, Michael J; Binder, Elisabeth B; Dayan, Peter; Johnstone, Eve C; Meyer-Lindenberg, Andreas; Montague, P Read; Schnyder, Ulrich; Wang, Xiao-Jing; Breakspear, Michael

2016-01-01

Contemporary psychiatry faces major challenges. Its syndrome-based disease classification is not based on mechanisms and does not guide treatment, which largely depends on trial and error. The development of therapies is hindered by ignorance of potential beneficiary patient subgroups. Neuroscientific and genetics research have yet to affect disease definitions or contribute to clinical decision making. In this challenging setting, what should psychiatric research focus on? In two companion papers, we present a list of problems nominated by clinicians and researchers from different disciplines as candidates for future scientific investigation of mental disorders. These problems are loosely grouped into challenges concerning nosology and diagnosis (this Personal View) and problems related to pathogenesis and aetiology (in the companion Personal View). Motivated by successful examples in other disciplines, particularly the list of Hilbert's problems in mathematics, this subjective and eclectic list of priority problems is intended for psychiatric researchers, helping to re-focus existing research and providing perspectives for future psychiatric science. Copyright © 2016 Elsevier Ltd. All rights reserved.

Seven Steps to the Diagnosis of NSAIDs Hypersensitivity: How to Apply a New Classification in Real Practice?

PubMed Central

Makowska, Joanna S.

2015-01-01

Frequent use of non-steroidal anti-inflammatory drugs (NSAIDs) has been paralleled by increasing occurrence of adverse reactions, which vary from mild local skin rashes or gastric irritation to severe, generalized symptoms and even life-threatening anaphylaxis. NSAID-induced hypersensitivity reactions may involve both immunological and non-immunological mechanisms and should be differentiated from type A adverse reactions. Clinical diagnosis and effective management of a hypersensitive patient cannot be achieved without identifying the underlying mechanism. In this review, we discuss the current classification of NSAID-induced adverse reactions and propose a practical diagnostic algorithm that involves 7 steps leading to the determination of the type of NSAID-induced hypersensitivity and allows for proper patient management. PMID:25749768

Social and demographic factors that influence the diagnosis of autistic spectrum disorders.

PubMed

Russell, Ginny; Steer, Colin; Golding, Jean

2011-12-01

Recent studies in epidemiology have highlighted the existence of children with autistic difficulties who remain undiagnosed. Other studies have identified 'access barriers' to clinics which include factors mediated by parents as well as health and education services. The purpose of this study was to examine whether social and demographic factors play a role in receiving a diagnosis of autistic spectrum disorder (ASD) independently of symptom severity. Retrospective secondary analysis of a longitudinal UK cohort study, namely, the Avon Longitudinal Study of Parents and Children (ALSPAC). With the severity of autistic traits held constant, boys were more likely to receive an ASD diagnosis than girls. Younger mothers and mothers of first-born children were significantly less likely to have children diagnosed with ASD. Maternal depression before and around the time of their children's autistic difficulties was associated with lack of diagnosis. The study provides evidence that social as well as biological factors can influence whether children are brought to the clinic.

I-CAN: The Classification and Prediction of Support Needs

ERIC Educational Resources Information Center

Arnold, Samuel R. C.; Riches, Vivienne C.; Stancliffe, Roger J.

2014-01-01

Background: Since 1992, the diagnosis and classification of intellectual disability has been dependent upon three constructs: intelligence, adaptive behaviour and support needs (Luckasson "et al." 1992. Mental Retardation: Definition, Classification and Systems of Support. American Association on Intellectual and Developmental…

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

PubMed

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

2017-01-01

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

Chromogenic Factor VIII Assays for Improved Diagnosis of Hemophilia A.

PubMed

Rodgers, Susan; Duncan, Elizabeth

2017-01-01

Hemophilia A is an inherited bleeding disorder caused by a reduced level of factor VIII coagulant activity (FVIII:C) in blood. Bleeding episodes may occur spontaneously in the severe form of hemophilia or after trauma in the milder forms. It is important that patients are diagnosed correctly, which includes placing them into the correct severity category of the disorder so that appropriate treatment can be given. Diagnosis is made by determination of the amount of FVIII:C in the blood, usually using a one-stage factor VIII:C assay. However, approximately one third of patients with mild or moderate hemophilia will have much lower results by the chromogenic assay, with some of them having normal results by the one-stage assay. The chromogenic factor VIII assay is used in some specialized hemophilia reference centers and is recommended for the diagnosis of mild hemophilia A, as this assay is considered to better reflect the severity status of hemophilia patients than the one-stage assay.

Assessment and Classification of Attention Deficit Hyperactive Disorders.

ERIC Educational Resources Information Center

Schaughency, Elizabeth A.; Rothlind, Johannes

1991-01-01

Issues concerning evaluation, assessment, and classification of Attention-Deficit Hyperactive Disorders (ADHD) are discussed. The diagnosis of ADHD should be a best-estimate diagnosis, based on a behavioral assessment strategy with multimethod assessment. The selection and use of assessment techniques are discussed. (SLD)

Fused man-machine classification schemes to enhance diagnosis of breast microcalcifications

NASA Astrophysics Data System (ADS)

Andreadis, Ioannis; Sevastianos, Chatzistergos; George, Spyrou; Konstantina, Nikita

2017-11-01

Computer aided diagnosis (CAD x ) approaches are developed towards the effective discrimination between benign and malignant clusters of microcalcifications. Different sources of information are exploited, such as features extracted from the image analysis of the region of interest, features related to the location of the cluster inside the breast, age of the patient and descriptors provided by the radiologists while performing their diagnostic task. A series of different CAD x schemes are implemented, each of which uses a different category of features and adopts a variety of machine learning algorithms and alternative image processing techniques. A novel framework is introduced where these independent diagnostic components are properly combined according to features critical to a radiologist in an attempt to identify the most appropriate CAD x schemes for the case under consideration. An open access database (Digital Database of Screening Mammography (DDSM)) has been elaborated to construct a large dataset with cases of varying subtlety, in order to ensure the development of schemes with high generalization ability, as well as extensive evaluation of their performance. The obtained results indicate that the proposed framework succeeds in improving the diagnostic procedure, as the achieved overall classification performance outperforms all the independent single diagnostic components, as well as the radiologists that assessed the same cases, in terms of accuracy, sensitivity, specificity and area under the curve following receiver operating characteristic analysis.

Mastocytosis: current concepts in diagnosis and treatment.

PubMed

Escribano, L; Akin, C; Castells, M; Orfao, A; Metcalfe, D D

2002-12-01

Mastocytosis consists of a group of disorders characterized by a pathologic increase in mast cells in tissues including skin, bone marrow, liver, spleen, and lymph nodes. Mastocytosis is a rare disease. Because of this, general practitioners have limited exposure to its clinical manifestations, diagnosis, classification, and management. Diagnosis of mastocytosis is suspected on clinical grounds and is established by histopathologic examination of involved tissues such as skin and bone marrow. The most common clinical sign of mastocytosis is the presence of typical skin lesions of urticaria pigmentosa. Most patients experience symptoms related to mast cell mediator release, and prevention of the effects of these mediators on tissues constitutes the major therapeutic goal in the management of mastocytosis. Despite recent advances in knowledge about the pathophysiology, diagnosis, and classification of mastocytosis, a curative treatment for mastocytosis does not now exist. Management of patients within all categories of mastocytosis includes: (1) a careful counseling of patients (parents in pediatric cases) and care providers, (2) avoidance of factors triggering acute mediator release, (3) treatment of acute mast cell mediator release, (4) treatment of chronic mast cell mediator release, and if indicated (5) an attempt to treat organ infiltration by mast cells. The goal of this manuscript is to provide an overview of the mediators produced and released by mast cells, the diagnostic criteria for the different variants of mastocytosis, and the treatment options currently available.

Bi-Factor Multidimensional Item Response Theory Modeling for Subscores Estimation, Reliability, and Classification

ERIC Educational Resources Information Center

Md Desa, Zairul Nor Deana

2012-01-01

In recent years, there has been increasing interest in estimating and improving subscore reliability. In this study, the multidimensional item response theory (MIRT) and the bi-factor model were combined to estimate subscores, to obtain subscores reliability, and subscores classification. Both the compensatory and partially compensatory MIRT…

Factors influencing diagnosis delay of advanced breast cancer in Moroccan women.

PubMed

Maghous, A; Rais, F; Ahid, S; Benhmidou, N; Bellahamou, K; Loughlimi, H; Marnouche, E; Elmajjaoui, S; Elkacemi, H; Kebdani, T; Benjaafar, N

2016-06-07

Delay in the diagnosis of breast cancer in symptomatic women of 3 months or more is associated with advanced stage and low survival. We conducted this study to learn more about the extent and reasons behind diagnosis delay of advanced breast cancer in Moroccan women. A group of patients with advanced breast cancer were interviewed at the National Institute of Oncology in Rabat during the period from February to December 2014. Diagnosis delay was devised into patient delay and system delay. Patient delay was defined as time from first symptoms until first medical consultation. System delay was defined as time from first presentation to a health care provider until definite diagnosis or treatment. Prospective information and clinical data were collected on a form during an interview with each patient and from medical records. In all, 137 patients were interviewed. The mean age of women was 48.3 ± 10.4 years. The median of consultation time was 6[4,12] months and the median of diagnosis time was 1[1,3] months. Diagnosis delay was associated to a personal reason in 96 (70.1 %) patients and to a medical reason in 19 (13.9 %) patients. A number of factors predicted diagnosis delay: symptoms were not considered serious in 66 (55.9 %) patients; traditional therapy was applied in 15 (12.7 %) patients and fear of cancer diagnosis and/or treatment in 14 (11.9 %) patients. A use of traditional methods was significantly associated with rural residence and far away from basic health center (p = 0.000). Paradoxically, a family history of breast cancer was significantly higher in who report a fear of cancer diagnosis and/or treatment to diagnosis delay (p < 0.001). Also, a significantly higher risk of more than 6 months delay was found among rural women (P = 0.035) and women who live far away from specialized care center (P = 0.001). Diagnosis delay is very serious problem in Morocco. Diagnosis delay was associated with complex interactions between

Nursing diagnoses for the elderly using the International Classification for Nursing Practice and the activities of living model.

PubMed

de Medeiros, Ana Claudia Torres; da Nóbrega, Maria Miriam Lima; Rodrigues, Rosalina Aparecida Partezani; Fernandes, Maria das Graças Melo

2013-01-01

To develop nursing diagnosis statements for the elderly based on the Activities of Living Model and on the International Classification for Nursing Practice. Descriptive and exploratory study, put in practice in two stages: 1) collection of terms and concepts that are considered clinically and culturally relevant for nursing care delivered to the elderly, in order to develop a database of terms and 2) development of nursing diagnosis statements for the elderly in primary health care, based on the guidelines of the International Council of Nurses and on the database of terms for nursing practice involving the elderly. 414 terms were identified and submitted to the content validation process, with the participation of ten nursing experts, which resulted in 263 validated terms. These terms were submitted to cross mapping with the terms of the International Classification for Nursing Practice, resulting in the identification of 115 listed terms and 148 non-listed terms, which constituted the database of terms, from which 127 nursing diagnosis statements were prepared and classified into factors that affect the performance of the elderly's activities of living - 69 into biological factors, 19 into psychological, 31 into sociocultural, five into environmental, and three into political-economic factors. After clinical validation, these statements can serve as a guide for nursing consultations with elderly patients, without ignoring clinical experience, critical thinking and decision-making.

Candidiasis: predisposing factors, prevention, diagnosis and alternative treatment.

PubMed

Martins, Natália; Ferreira, Isabel C F R; Barros, Lillian; Silva, Sónia; Henriques, Mariana

2014-06-01

Candidiasis is the most common opportunistic yeast infection. Candida species and other microorganisms are involved in this complicated fungal infection, but Candida albicans continues to be the most prevalent. In the past two decades, it has been observed an abnormal overgrowth in the gastrointestinal, urinary and respiratory tracts, not only in immunocompromised patients, but also related to nosocomial infections and even in healthy individuals. There is a widely variety of causal factors that contribute to yeast infection which means that candidiasis is a good example of a multifactorial syndrome. Due to rapid increase in the incidence in these infections, this is the subject of numerous studies. Recently, the focus of attention is the treatment and, above all, the prevention of those complications. The diagnosis of candidiasis could become quite complicated. Prevention is the most effective "treatment," much more than eradication of the yeast with antifungal agents. There are several aspects to consider in the daily routine that can provide a strength protection. However, a therapeutic approach is necessary when the infection is established, and therefore, other alternatives should be explored. This review provides an overview on predisposition factors, prevention and diagnosis of candidiasis, highlighting alternative approaches for candidiasis treatment.

Indonesian railway accidents--utilizing Human Factors Analysis and Classification System in determining potential contributing factors.

PubMed

Iridiastadi, Hardianto; Ikatrinasari, Zulfa Fitri

2012-01-01

The prevalence of Indonesian railway accidents has not been declining, with hundreds of fatalities reported in the past decade. As an effort to help the National Transportation Safety Committee (NTSC), this study was conducted that aimed at understanding factors that might have contributed to the accidents. Human Factors Analysis and Classification System (HFACS) was utilized for this purpose. A total of nine accident reports (provided by the Indonesian NTSC) involving fatalities were studied using the technique. Results of this study indicated 72 factors that were closely related to the accidents. Of these, roughly 22% were considered as operator acts while about 39% were related to preconditions for operator acts. Supervisory represented 14% of the factors, and the remaining (about 25%) were associated with organizational factors. It was concluded that, while train drivers indeed played an important role in the accidents, interventions solely directed toward train drivers may not be adequate. A more comprehensive approach in minimizing the accidents should be conducted that addresses all the four aspects of HFACS.

Automatic grade classification of Barretts Esophagus through feature enhancement

NASA Astrophysics Data System (ADS)

Ghatwary, Noha; Ahmed, Amr; Ye, Xujiong; Jalab, Hamid

2017-03-01

Barretts Esophagus (BE) is a precancerous condition that affects the esophagus tube and has the risk of developing esophageal adenocarcinoma. BE is the process of developing metaplastic intestinal epithelium and replacing the normal cells in the esophageal area. The detection of BE is considered difficult due to its appearance and properties. The diagnosis is usually done through both endoscopy and biopsy. Recently, Computer Aided Diagnosis systems have been developed to support physicians opinion when facing difficulty in detection/classification in different types of diseases. In this paper, an automatic classification of Barretts Esophagus condition is introduced. The presented method enhances the internal features of a Confocal Laser Endomicroscopy (CLE) image by utilizing a proposed enhancement filter. This filter depends on fractional differentiation and integration that improve the features in the discrete wavelet transform of an image. Later on, various features are extracted from each enhanced image on different levels for the multi-classification process. Our approach is validated on a dataset that consists of a group of 32 patients with 262 images with different histology grades. The experimental results demonstrated the efficiency of the proposed technique. Our method helps clinicians for more accurate classification. This potentially helps to reduce the need for biopsies needed for diagnosis, facilitate the regular monitoring of treatment/development of the patients case and can help train doctors with the new endoscopy technology. The accurate automatic classification is particularly important for the Intestinal Metaplasia (IM) type, which could turn into deadly cancerous. Hence, this work contributes to automatic classification that facilitates early intervention/treatment and decreasing biopsy samples needed.

Integration of data mining classification techniques and ensemble learning to identify risk factors and diagnose ovarian cancer recurrence.

PubMed

Tseng, Chih-Jen; Lu, Chi-Jie; Chang, Chi-Chang; Chen, Gin-Den; Cheewakriangkrai, Chalong

2017-05-01

Ovarian cancer is the second leading cause of deaths among gynecologic cancers in the world. Approximately 90% of women with ovarian cancer reported having symptoms long before a diagnosis was made. Literature shows that recurrence should be predicted with regard to their personal risk factors and the clinical symptoms of this devastating cancer. In this study, ensemble learning and five data mining approaches, including support vector machine (SVM), C5.0, extreme learning machine (ELM), multivariate adaptive regression splines (MARS), and random forest (RF), were integrated to rank the importance of risk factors and diagnose the recurrence of ovarian cancer. The medical records and pathologic status were extracted from the Chung Shan Medical University Hospital Tumor Registry. Experimental results illustrated that the integrated C5.0 model is a superior approach in predicting the recurrence of ovarian cancer. Moreover, the classification accuracies of C5.0, ELM, MARS, RF, and SVM indeed increased after using the selected important risk factors as predictors. Our findings suggest that The International Federation of Gynecology and Obstetrics (FIGO), Pathologic M, Age, and Pathologic T were the four most critical risk factors for ovarian cancer recurrence. In summary, the above information can support the important influence of personality and clinical symptom representations on all phases of guide interventions, with the complexities of multiple symptoms associated with ovarian cancer in all phases of the recurrent trajectory. Copyright © 2017 Elsevier B.V. All rights reserved.

Subpopulations of Older Foster Youths With Differential Risk of Diagnosis for Alcohol Abuse or Dependence*

PubMed Central

Keller, Thomas E.; Blakeslee, Jennifer E.; Lemon, Stephenie C.; Courtney, Mark E.

2010-01-01

Objective: Distinctive combinations of factors are likely to be associated with serious alcohol problems among adolescents about to emancipate from the foster care system and face the difficult transition to independent adulthood. This study identifies particular subpopulations of older foster youths that differ markedly in the probability of a lifetime diagnosis for alcohol abuse or dependence. Method: Classification and regression tree (CART) analysis was applied to a large, representative sample (N = 732) of individuals, 17 years of age or older, placed in the child welfare system for more than 1 year. CART evaluated two exploratory sets of variables for optimal splits into groups distinguished from each other on the criterion of lifetime alcohol-use disorder diagnosis. Results: Each classification tree yielded four terminal groups with different rates of lifetime alcohol-use disorder diagnosis. Notable groups in the first tree included one characterized by high levels of both delinquency and violence exposure (53% diagnosed) and another that featured lower delinquency but an independent-living placement (21% diagnosed). Notable groups in the second tree included African American adolescents (only 8% diagnosed), White adolescents not close to caregivers (40% diagnosed), and White adolescents closer to caregivers but with a history of psychological abuse (36% diagnosed). Conclusions: Analyses incorporating variables that could be comorbid with or symptomatic of alcohol problems, such as delinquency, yielded classifications potentially useful for assessment and service planning. Analyses without such variables identified other factors, such as quality of caregiving relationships and maltreatment, associated with serious alcohol problems, suggesting opportunities for prevention or intervention. PMID:20946738

[Hand eczema. The clinical classification of the roles of exogenous and endogenous factors in each type].

PubMed

Tamiya, Y

1994-08-01

Hand eczema is one of the most common dermatological disorders. Although it is a general term referring to eczematous dermatitis of the hands, it actually covers a wide range of diseases. The classification of hand eczema is controversial even now, as definitions of individual diseases have not yet been established. It is well-known that exogenous factors, such as chemicals or water, are associated with the occurrence of hand eczema. In this study, we focused on endogenous factors, especially personal or family history of atopy as a causative factor in hand eczema. According to exogenous and endogenous factors, we classified hand eczema into three types: atopic dermatitis, contact dermatitis and dysidrosis. This classification is useful because it makes the definition of each disease clear. Skin-humidity and sebum measurement are simple and rapid methods of determining personal atopy, skin condition and the effect of treatment on hand eczema patients.

Towards a needle-free diagnosis of malaria: in vivo identification and classification of red and white blood cells containing haemozoin.

PubMed

Burnett, Jennifer L; Carns, Jennifer L; Richards-Kortum, Rebecca

2017-11-07

Optical detection of circulating haemozoin has been suggested as a needle free method to diagnose malaria using in vivo microscopy. Haemozoin is generated within infected red blood cells by the malaria parasite, serving as a highly specific, endogenous biomarker of malaria. However, phagocytosis of haemozoin by white blood cells which persist after the infection is resolved presents the potential for false positive diagnosis; therefore, the focus of this work is to identify a feature of the haemozoin signal to discriminate between infected red blood cells and haemozoin-containing white blood cells. Conventional brightfield microscopy of thin film blood smears was used to analyse haemozoin absorbance signal in vitro. Cell type and parasite maturity were morphologically determined using colocalized DAPI staining. The ability of features to discriminate between infected red blood cells and haemozoin-containing white blood cells was evaluated using images of smears from subjects infected with two species of Plasmodium, Plasmodium yoelii and Plasmodium falciparum. Discriminating features identified by blood smear microscopy were characterized in vivo in P. yoelii-infected mice. Two features of the haemozoin signal, haemozoin diameter and normalized intensity difference, were identified as potential parameters to differentiate infected red blood cells and haemozoin-containing white blood cells. Classification performance was evaluated using the area under the receiver operating characteristic curve, with area under the curve values of 0.89 for the diameter parameter and 0.85 for the intensity parameter when assessed in P. yoelii samples. Similar results were obtained from P. falciparum blood smears, showing an AUC of 0.93 or greater for both classification features. For in vivo investigations, the intensity-based metric was the best classifier, with an AUC of 0.91. This work demonstrates that size and intensity features of haemozoin absorbance signal collected by in vivo

Classification and reduction of pilot error

NASA Technical Reports Server (NTRS)

Rogers, W. H.; Logan, A. L.; Boley, G. D.

1989-01-01

Human error is a primary or contributing factor in about two-thirds of commercial aviation accidents worldwide. With the ultimate goal of reducing pilot error accidents, this contract effort is aimed at understanding the factors underlying error events and reducing the probability of certain types of errors by modifying underlying factors such as flight deck design and procedures. A review of the literature relevant to error classification was conducted. Classification includes categorizing types of errors, the information processing mechanisms and factors underlying them, and identifying factor-mechanism-error relationships. The classification scheme developed by Jens Rasmussen was adopted because it provided a comprehensive yet basic error classification shell or structure that could easily accommodate addition of details on domain-specific factors. For these purposes, factors specific to the aviation environment were incorporated. Hypotheses concerning the relationship of a small number of underlying factors, information processing mechanisms, and error types types identified in the classification scheme were formulated. ASRS data were reviewed and a simulation experiment was performed to evaluate and quantify the hypotheses.

Diagnosis of Lung Cancer in Small Biopsies and Cytology

PubMed Central

Travis, William D.; Brambilla, Elisabeth; Noguchi, Masayuki; Nicholson, Andrew G.; Geisinger, Kim; Yatabe, Yasushi; Ishikawa, Yuichi; Wistuba, Ignacio; Flieder, Douglas B.; Franklin, Wilbur; Gazdar, Adi; Hasleton, Philip S.; Henderson, Douglas W.; Kerr, Keith M.; Petersen, Iver; Roggli, Victor; Thunnissen, Erik; Tsao, Ming

2015-01-01

The new International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society lung adenocarcinoma classification provides, for the first time, standardized terminology for lung cancer diagnosis in small biopsies and cytology; this was not primarily addressed by previous World Health Organization classifications. Until recently there have been no therapeutic implications to further classification of NSCLC, so little attention has been given to the distinction of adenocarcinoma and squamous cell carcinoma in small tissue samples. This situation has changed dramatically in recent years with the discovery of several therapeutic options that are available only to patients with adenocarcinoma or NSCLC, not otherwise specified, rather than squamous cell carcinoma. This includes recommendation for use of special stains as an aid to diagnosis, particularly in the setting of poorly differentiated tumors that do not show clear differentiation by routine light microscopy. A limited diagnostic workup is recommended to preserve as much tissue for molecular testing as possible. Most tumors can be classified using a single adenocarcinoma marker (eg, thyroid transcription factor 1 or mucin) and a single squamous marker (eg, p40 or p63). Carcinomas lacking clear differentiation by morphology and special stains are classified as NSCLC, not otherwise specified. Not otherwise specified carcinomas that stain with adenocarcinoma markers are classified as NSCLC, favor adenocarcinoma, and tumors that stain only with squamous markers are classified as NSCLC, favor squamous cell carcinoma. The need for every institution to develop a multidisciplinary tissue management strategy to obtain these small specimens and process them, not only for diagnosis but also for molecular testing and evaluation of markers of resistance to therapy, is emphasized. PMID:22970842

Factors Accounting for a Missed Diagnosis of Cystic Fibrosis After Newborn Screening

PubMed Central

Rock, Michael J.; Levy, Hara; Zaleski, Christina; Farrell, Philip M.

2015-01-01

Summary Newborn screening is a public health policy program involving the centralized testing laboratory, infant and their family, primary care provider, and subspecialist for confirmatory testing and follow-up of abnormal results. Cystic fibrosis (CF) newborn screening has now been enacted in all 50 states and the District of Columbia and throughout many countries in the world. Although CF neonatal screening will identify the vast majority of infants with CF, there are many factors in the newborn screening system that can lead to a missed diagnosis of CF. To inform clinicians, this article summarizes the CF newborn screening system and highlights 14 factors that can account for a missed diagnosis of CF. Care providers should maintain a high suspicion for CF if there are compatible symptoms, regardless of the results of the newborn screening test. These factors in newborn screening programs leading to a missed diagnosis of CF present opportunities for quality improvement in specimen collection, laboratory analysis of immunoreactive tryspinogen (IRT) and CF mutation testing, communication, and sweat testing. PMID:22081556

Optimal classification for the diagnosis of duchenne muscular dystrophy images using support vector machines.

PubMed

Zhang, Ming-Huan; Ma, Jun-Shan; Shen, Ying; Chen, Ying

2016-09-01

This study aimed to investigate the optimal support vector machines (SVM)-based classifier of duchenne muscular dystrophy (DMD) magnetic resonance imaging (MRI) images. T1-weighted (T1W) and T2-weighted (T2W) images of the 15 boys with DMD and 15 normal controls were obtained. Textural features of the images were extracted and wavelet decomposed, and then, principal features were selected. Scale transform was then performed for MRI images. Afterward, SVM-based classifiers of MRI images were analyzed based on the radical basis function and decomposition levels. The cost (C) parameter and kernel parameter [Formula: see text] were used for classification. Then, the optimal SVM-based classifier, expressed as [Formula: see text]), was identified by performance evaluation (sensitivity, specificity and accuracy). Eight of 12 textural features were selected as principal features (eigenvalues [Formula: see text]). The 16 SVM-based classifiers were obtained using combination of (C, [Formula: see text]), and those with lower C and [Formula: see text] values showed higher performances, especially classifier of [Formula: see text]). The SVM-based classifiers of T1W images showed higher performance than T1W images at the same decomposition level. The T1W images in classifier of [Formula: see text]) at level 2 decomposition showed the highest performance of all, and its overall correct sensitivity, specificity, and accuracy reached 96.9, 97.3, and 97.1 %, respectively. The T1W images in SVM-based classifier [Formula: see text] at level 2 decomposition showed the highest performance of all, demonstrating that it was the optimal classification for the diagnosis of DMD.

Childbirth and Diagnosis Related Groups (DRGs): patient classification and hospital reimbursement in 11 European countries.

PubMed

Bellanger, Martine M; Quentin, Wilm; Tan, Siok Swan

2013-05-01

The study compares how Diagnosis-Related Group (DRG) based hospital payment systems in eleven European countries (Austria, England, Estonia, Finland, France, Germany, Ireland, Netherlands, Poland, Spain, and Sweden) deal with women giving birth in hospitals. It aims to assist gynaecologists and national authorities in optimizing their DRG systems. National or regional databases were used to identify childbirth cases. DRG grouping algorithms and indicators of resource consumption were compared for those DRGs which account for at least 1% of all childbirth cases in the respective database. Five standardized case vignettes were defined and quasi prices (i.e. administrative prices or tariffs) of hospital deliveries according to national DRG-based hospital payment systems were ascertained. European DRG systems classify childbirth cases according to different sets of variables (between one and eight variables) into diverging numbers of DRGs (between three and eight DRGs). The most complex DRG is valued 3.5 times more resource intensive than an index case in Ireland but only 1.1 times more resource intensive than an index case in The Netherlands. Comparisons of quasi prices for the vignettes show that hypothetical payments for the most complex case amount to only € 479 in Poland but to € 5532 in Ireland. Differences in the classification of hospital childbirth cases into DRGs raise concerns whether European systems rely on the most appropriate classification variables. Physicians, hospitals and national DRG authorities should consider how other countries' DRG systems classify cases to optimize their system and to ensure fair and appropriate reimbursement. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Patient classification tool in home health care.

PubMed

Pavasaris, B

1989-01-01

Medicare's system of diagnosis related groups for health care cost reimbursements is inadequate for the special requirements of home health care. A visiting nurses association's patient classification tool correlates a meticulous record of professional time spent per patient with patient diagnosis and level of care, aimed at helping policymakers develop a more equitable DRG-based prospective payment formula for home care costs.

Testicular cancer from diagnosis to epigenetic factors

PubMed Central

Boccellino, Mariarosaria; Vanacore, Daniela; Zappavigna, Silvia; Cavaliere, Carla; Rossetti, Sabrina; D’Aniello, Carmine; Chieffi, Paolo; Amler, Evzen; Buonerba, Carlo; Di Lorenzo, Giuseppe; Di Franco, Rossella; Izzo, Alessandro; Piscitelli, Raffaele; Iovane, Gelsomina; Muto, Paolo; Botti, Gerardo; Perdonà, Sisto; Caraglia, Michele; Facchini, Gaetano

2017-01-01

Testicular cancer (TC) is one of the most common neoplasms that occurs in male and includes germ cell tumors (GCT), sex cord-gonadal stromal tumors and secondary testicular tumors. Diagnosis of TC involves the evaluation of serum tumor markers alpha-fetoprotein, human chorionic gonadotropin and lactate dehydrogenase, but clinically several types of immunohistochemical markers are more useful and more sensitive in GCT, but not in teratoma. These new biomarkers are genes expressed in primordial germ cells/gonocytes and embryonic pluripotency-related cells but not in normal adult germ cells and they include PLAP, OCT3/4 (POU5F1), NANOG, SOX2, REX1, AP-2γ (TFAP2C) and LIN28. Gene expression in GCT is regulated, at least in part, by DNA and histone modifications, and the epigenetic profile of these tumours is characterised by genome-wide demethylation. There are different epigenetic modifications in TG-subtypes that reflect the normal developmental switch in primordial germ cells from an under- to normally methylated genome. The main purpose of this review is to illustrate the findings of recent investigations in the classification of male genital organs, the discoveries in the use of prognostic and diagnostic markers and the epigenetic aberrations mainly affecting the patterns of DNA methylation/histone modifications of genes (especially tumor suppressors) and microRNAs (miRNAs). PMID:29262668

Influence of nuclei segmentation on breast cancer malignancy classification

NASA Astrophysics Data System (ADS)

Jelen, Lukasz; Fevens, Thomas; Krzyzak, Adam

2009-02-01

Breast Cancer is one of the most deadly cancers affecting middle-aged women. Accurate diagnosis and prognosis are crucial to reduce the high death rate. Nowadays there are numerous diagnostic tools for breast cancer diagnosis. In this paper we discuss a role of nuclear segmentation from fine needle aspiration biopsy (FNA) slides and its influence on malignancy classification. Classification of malignancy plays a very important role during the diagnosis process of breast cancer. Out of all cancer diagnostic tools, FNA slides provide the most valuable information about the cancer malignancy grade which helps to choose an appropriate treatment. This process involves assessing numerous nuclear features and therefore precise segmentation of nuclei is very important. In this work we compare three powerful segmentation approaches and test their impact on the classification of breast cancer malignancy. The studied approaches involve level set segmentation, fuzzy c-means segmentation and textural segmentation based on co-occurrence matrix. Segmented nuclei were used to extract nuclear features for malignancy classification. For classification purposes four different classifiers were trained and tested with previously extracted features. The compared classifiers are Multilayer Perceptron (MLP), Self-Organizing Maps (SOM), Principal Component-based Neural Network (PCA) and Support Vector Machines (SVM). The presented results show that level set segmentation yields the best results over the three compared approaches and leads to a good feature extraction with a lowest average error rate of 6.51% over four different classifiers. The best performance was recorded for multilayer perceptron with an error rate of 3.07% using fuzzy c-means segmentation.

On-the-spot lung cancer differential diagnosis by label-free, molecular vibrational imaging and knowledge-based classification

NASA Astrophysics Data System (ADS)

Gao, Liang; Li, Fuhai; Thrall, Michael J.; Yang, Yaliang; Xing, Jiong; Hammoudi, Ahmad A.; Zhao, Hong; Massoud, Yehia; Cagle, Philip T.; Fan, Yubo; Wong, Kelvin K.; Wang, Zhiyong; Wong, Stephen T. C.

2011-09-01

We report the development and application of a knowledge-based coherent anti-Stokes Raman scattering (CARS) microscopy system for label-free imaging, pattern recognition, and classification of cells and tissue structures for differentiating lung cancer from non-neoplastic lung tissues and identifying lung cancer subtypes. A total of 1014 CARS images were acquired from 92 fresh frozen lung tissue samples. The established pathological workup and diagnostic cellular were used as prior knowledge for establishment of a knowledge-based CARS system using a machine learning approach. This system functions to separate normal, non-neoplastic, and subtypes of lung cancer tissues based on extracted quantitative features describing fibrils and cell morphology. The knowledge-based CARS system showed the ability to distinguish lung cancer from normal and non-neoplastic lung tissue with 91% sensitivity and 92% specificity. Small cell carcinomas were distinguished from nonsmall cell carcinomas with 100% sensitivity and specificity. As an adjunct to submitting tissue samples to routine pathology, our novel system recognizes the patterns of fibril and cell morphology, enabling medical practitioners to perform differential diagnosis of lung lesions in mere minutes. The demonstration of the strategy is also a necessary step toward in vivo point-of-care diagnosis of precancerous and cancerous lung lesions with a fiber-based CARS microendoscope.

Classification methods to detect sleep apnea in adults based on respiratory and oximetry signals: a systematic review.

PubMed

Uddin, M B; Chow, C M; Su, S W

2018-03-26

Sleep apnea (SA), a common sleep disorder, can significantly decrease the quality of life, and is closely associated with major health risks such as cardiovascular disease, sudden death, depression, and hypertension. The normal diagnostic process of SA using polysomnography is costly and time consuming. In addition, the accuracy of different classification methods to detect SA varies with the use of different physiological signals. If an effective, reliable, and accurate classification method is developed, then the diagnosis of SA and its associated treatment will be time-efficient and economical. This study aims to systematically review the literature and present an overview of classification methods to detect SA using respiratory and oximetry signals and address the automated detection approach. Sixty-two included studies revealed the application of single and multiple signals (respiratory and oximetry) for the diagnosis of SA. Both airflow and oxygen saturation signals alone were effective in detecting SA in the case of binary decision-making, whereas multiple signals were good for multi-class detection. In addition, some machine learning methods were superior to the other classification methods for SA detection using respiratory and oximetry signals. To deal with the respiratory and oximetry signals, a good choice of classification method as well as the consideration of associated factors would result in high accuracy in the detection of SA. An accurate classification method should provide a high detection rate with an automated (independent of human action) analysis of respiratory and oximetry signals. Future high-quality automated studies using large samples of data from multiple patient groups or record batches are recommended.

Human Error Assessment and Reduction Technique (HEART) and Human Factor Analysis and Classification System (HFACS)

NASA Technical Reports Server (NTRS)

Alexander, Tiffaney Miller

2017-01-01

Research results have shown that more than half of aviation, aerospace and aeronautics mishaps incidents are attributed to human error. As a part of Safety within space exploration ground processing operations, the identification and/or classification of underlying contributors and causes of human error must be identified, in order to manage human error. This research provides a framework and methodology using the Human Error Assessment and Reduction Technique (HEART) and Human Factor Analysis and Classification System (HFACS), as an analysis tool to identify contributing factors, their impact on human error events, and predict the Human Error probabilities (HEPs) of future occurrences. This research methodology was applied (retrospectively) to six (6) NASA ground processing operations scenarios and thirty (30) years of Launch Vehicle related mishap data. This modifiable framework can be used and followed by other space and similar complex operations.

Human Error Assessment and Reduction Technique (HEART) and Human Factor Analysis and Classification System (HFACS)

NASA Technical Reports Server (NTRS)

Alexander, Tiffaney Miller

2017-01-01

Research results have shown that more than half of aviation, aerospace and aeronautics mishaps/incidents are attributed to human error. As a part of Safety within space exploration ground processing operations, the identification and/or classification of underlying contributors and causes of human error must be identified, in order to manage human error. This research provides a framework and methodology using the Human Error Assessment and Reduction Technique (HEART) and Human Factor Analysis and Classification System (HFACS), as an analysis tool to identify contributing factors, their impact on human error events, and predict the Human Error probabilities (HEPs) of future occurrences. This research methodology was applied (retrospectively) to six (6) NASA ground processing operations scenarios and thirty (30) years of Launch Vehicle related mishap data. This modifiable framework can be used and followed by other space and similar complex operations.

Human Error Assessment and Reduction Technique (HEART) and Human Factor Analysis and Classification System (HFACS)

NASA Technical Reports Server (NTRS)

Alexander, Tiffaney Miller

2017-01-01

Research results have shown that more than half of aviation, aerospace and aeronautics mishaps incidents are attributed to human error. As a part of Quality within space exploration ground processing operations, the identification and or classification of underlying contributors and causes of human error must be identified, in order to manage human error.This presentation will provide a framework and methodology using the Human Error Assessment and Reduction Technique (HEART) and Human Factor Analysis and Classification System (HFACS), as an analysis tool to identify contributing factors, their impact on human error events, and predict the Human Error probabilities (HEPs) of future occurrences. This research methodology was applied (retrospectively) to six (6) NASA ground processing operations scenarios and thirty (30) years of Launch Vehicle related mishap data. This modifiable framework can be used and followed by other space and similar complex operations.

Inter- and intraobserver reliability of the Rockwood classification in acute acromioclavicular joint dislocations.

PubMed

Schneider, M M; Balke, M; Koenen, P; Fröhlich, M; Wafaisade, A; Bouillon, B; Banerjee, M

2016-07-01

The reliability of the Rockwood classification, the gold standard for acute acromioclavicular (AC) joint separations, has not yet been tested. The purpose of this study was to investigate the reliability of visual and measured AC joint lesion grades according to the Rockwood classification. Four investigators (two shoulder specialists and two second-year residents) examined radiographs (bilateral panoramic stress and axial views) in 58 patients and graded the injury according to the Rockwood classification using the following sequence: (1) visual classification of the AC joint lesion, (2) digital measurement of the coracoclavicular distance (CCD) and the horizontal dislocation (HD) with Osirix Dicom Viewer (Pixmeo, Switzerland), (3) classification of the AC joint lesion according to the measurements and (4) repetition of (1) and (2) after repeated anonymization by an independent physician. Visual and measured Rockwood grades as well as the CCD and HD of every patient were documented, and a CC index was calculated (CCD injured/CCD healthy). All records were then used to evaluate intra- and interobserver reliability. The disagreement between visual and measured diagnosis ranged from 6.9 to 27.6 %. Interobserver reliability for visual diagnosis was good (0.72-0.74) and excellent (0.85-0.93) for measured Rockwood grades. Intraobserver reliability was good to excellent (0.67-0.93) for visual diagnosis and excellent for measured diagnosis (0.90-0.97). The correlations between measurements of the axial view varied from 0.68 to 0.98 (good to excellent) for interobserver reliability and from 0.90 to 0.97 (excellent) for intraobserver reliability. Bilateral panoramic stress and axial radiographs are reliable examinations for grading AC joint injuries according to Rockwood's classification. Clinicians of all experience levels can precisely classify AC joint lesions according to the Rockwood classification. We recommend to grade acute ACG lesions by performing a digital

Neural attractor network for application in visual field data classification.

PubMed

Fink, Wolfgang

2004-07-07

The purpose was to introduce a novel method for computer-based classification of visual field data derived from perimetric examination, that may act as a 'counsellor', providing an independent 'second opinion' to the diagnosing physician. The classification system consists of a Hopfield-type neural attractor network that obtains its input data from perimetric examination results. An iterative relaxation process determines the states of the neurons dynamically. Therefore, even 'noisy' perimetric output, e.g., early stages of a disease, may eventually be classified correctly according to the predefined idealized visual field defect (scotoma) patterns, stored as attractors of the network, that are found with diseases of the eye, optic nerve and the central nervous system. Preliminary tests of the classification system on real visual field data derived from perimetric examinations have shown a classification success of over 80%. Some of the main advantages of the Hopfield-attractor-network-based approach over feed-forward type neural networks are: (1) network architecture is defined by the classification problem; (2) no training is required to determine the neural coupling strengths; (3) assignment of an auto-diagnosis confidence level is possible by means of an overlap parameter and the Hamming distance. In conclusion, the novel method for computer-based classification of visual field data, presented here, furnishes a valuable first overview and an independent 'second opinion' in judging perimetric examination results, pointing towards a final diagnosis by a physician. It should not be considered a substitute for the diagnosing physician. Thanks to the worldwide accessibility of the Internet, the classification system offers a promising perspective towards modern computer-assisted diagnosis in both medicine and tele-medicine, for example and in particular, with respect to non-ophthalmic clinics or in communities where perimetric expertise is not readily available.

Improved biliary detection and diagnosis through intelligent machine analysis.

PubMed

Logeswaran, Rajasvaran

2012-09-01

This paper reports on work undertaken to improve automated detection of bile ducts in magnetic resonance cholangiopancreatography (MRCP) images, with the objective of conducting preliminary classification of the images for diagnosis. The proposed I-BDeDIMA (Improved Biliary Detection and Diagnosis through Intelligent Machine Analysis) scheme is a multi-stage framework consisting of successive phases of image normalization, denoising, structure identification, object labeling, feature selection and disease classification. A combination of multiresolution wavelet, dynamic intensity thresholding, segment-based region growing, region elimination, statistical analysis and neural networks, is used in this framework to achieve good structure detection and preliminary diagnosis. Tests conducted on over 200 clinical images with known diagnosis have shown promising results of over 90% accuracy. The scheme outperforms related work in the literature, making it a viable framework for computer-aided diagnosis of biliary diseases. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Maternal psychosocial stress and children's ADHD diagnosis: a prospective birth cohort study.

PubMed

Okano, Lauren; Ji, Yuelong; Riley, Anne W; Wang, Xiaobin

2018-05-23

Examine the association of mothers' psychosocial stressors before and during pregnancy with their children's diagnosis of attention deficit hyperactivity disorder (ADHD). This study included 2140 mother-child pairs who had at least one postnatal pediatric visit at the Boston Medical Center between 2003 and 2015. Child ADHD was determined via International Classification of Diseases, Ninth Revision (ICD-9) codes documented in electronic medical records. Latent factors of maternal stress and social support and measures of the physical home environment and psychosocial adversities were constructed using exploratory factor analysis. The association between the latent factors and child ADHD diagnosis was examined using multiple logistic regression, controlling for known risk factors for ADHD. Children were 1.45 (95% CI: 1.06, 1.99) and 3.03 (95% CI: 2.19, 4.20) times more likely to receive an ADHD diagnosis if their mother experienced a major stressful event during pregnancy or reported a high level of perceived stress, respectively. The number of family adversities increases the risk of ADHD diagnosis [second quartile: OR = 1.90; CI (1.31, 2.77); third quartile: OR = 1.96 CI (1.34, 2.88); fourth quartile: OR = 2.89 CI (2.01, 4.16)] compared to first quartile. In this prospective, predominantly urban, low-income, minority birth cohort, mothers' psychosocial stress before and during pregnancy appears to be an independent risk factor for the development of ADHD in their children.

Radiographic classifications in Perthes disease

PubMed Central

Huhnstock, Stefan; Svenningsen, Svein; Merckoll, Else; Catterall, Anthony; Terjesen, Terje; Wiig, Ola

2017-01-01

Background and purpose Different radiographic classifications have been proposed for prediction of outcome in Perthes disease. We assessed whether the modified lateral pillar classification would provide more reliable interobserver agreement and prognostic value compared with the original lateral pillar classification and the Catterall classification. Patients and methods 42 patients (38 boys) with Perthes disease were included in the interobserver study. Their mean age at diagnosis was 6.5 (3–11) years. 5 observers classified the radiographs in 2 separate sessions according to the Catterall classification, the original and the modified lateral pillar classifications. Interobserver agreement was analysed using weighted kappa statistics. We assessed the associations between the classifications and femoral head sphericity at 5-year follow-up in 37 non-operatively treated patients in a crosstable analysis (Gamma statistics for ordinal variables, γ). Results The original lateral pillar and Catterall classifications showed moderate interobserver agreement (kappa 0.49 and 0.43, respectively) while the modified lateral pillar classification had fair agreement (kappa 0.40). The original lateral pillar classification was strongly associated with the 5-year radiographic outcome, with a mean γ correlation coefficient of 0.75 (95% CI: 0.61–0.95) among the 5 observers. The modified lateral pillar and Catterall classifications showed moderate associations (mean γ correlation coefficient 0.55 [95% CI: 0.38–0.66] and 0.64 [95% CI: 0.57–0.72], respectively). Interpretation The Catterall classification and the original lateral pillar classification had sufficient interobserver agreement and association to late radiographic outcome to be suitable for clinical use. Adding the borderline B/C group did not increase the interobserver agreement or prognostic value of the original lateral pillar classification. PMID:28613966

Advances in Psychiatric Diagnosis: Past, Present, and Future.

PubMed

North, Carol S; Surís, Alina M

2017-04-26

This editorial examines controversies identified by the articles in this special issue, which explore psychopathology in the broad history of the classification of selected psychiatric disorders and syndromes over time through current American criteria. Psychiatric diagnosis has a long history of scientific investigation and application, with periods of rapid change, instability, and heated controversy associated with it. The articles in this issue examine the history of psychiatric nomenclature and explore current and future directions in psychiatric diagnosis through the various versions of accepted diagnostic criteria and accompanying research literature addressing the criteria. The articles seek to guide readers in appreciating the complexities of psychiatric diagnosis as the field of psychiatry pushes forward toward future advancements in diagnosis. Despite efforts of many scientists to advance a diagnostic classification system that incorporates neuroscience and genetics, it has been argued that it may be premature to attempt to move to a biologically-based classification system, because psychiatric disorders cannot yet be fully distinguished by any specific biological markers. For now, the symptom-based criteria that the field has been using continue to serve many essential purposes, including selection of the most effective treatment, communication about disease with colleagues, education about psychiatric illness, and support for ongoing research.

Classification of L2 Vocabulary Learning Strategies: Evidence from Exploratory and Confirmatory Factor Analyses

ERIC Educational Resources Information Center

Zhang, Bo; Li, Changyu

2011-01-01

This research presents a classification theory for the L2 vocabulary learning strategies. Based on the exploratory and confirmatory factor analyses of strategies that adult Chinese English learners used, this theory identifies six categories, four of which are related to the cognitive process in lexical acquisition and the other two are…

Trialling diagnosis-related groups classification in the Iranian health system: a case study examining the feasibility of introducing casemix.

PubMed

Ghaffari, S; Doran, C; Wilson, A; Aisbett, C

2010-05-01

This paper examines the quality of routinely collected information in an Iranian hospital in a trial of casemix classification. Australian Refined Diagnosis Related Groups (AR-DRG) were used to classify patient episodes. There were 327 DRGs identified, of which 20% had only 1 case. The grouper program identified invalid records for 4% of total separations. Approximately 4.5% of cases were classified into error DRGs and 3.4% were ungroupable. No complication and comorbidity effects were identified with 93% of total cases. R2 (variance in length of stay explained) was 44% for untrimmed cases, increasing to 63%, 57% and 58% after trimming by L3H3, IQR and 10th-95th percentile methods respectively.

Accuracy of the all patient refined diagnosis related groups classification system in congenital heart surgery.

PubMed

Parnell, Aimee S; Shults, Justine; Gaynor, J William; Leonard, Mary B; Dai, Dingwei; Feudtner, Chris

2014-02-01

Administrative data are increasingly used to evaluate clinical outcomes and quality of care in pediatric congenital heart surgery (CHS) programs. Several published analyses of large pediatric administrative data sets have relied on the All Patient Refined Diagnosis Related Groups (APR-DRG, version 24) diagnostic classification system. The accuracy of this classification system for patients undergoing CHS is unclear. We performed a retrospective cohort study of all 14,098 patients 0 to 5 years of age undergoing any of six selected congenital heart operations, ranging in complexity from isolated closure of a ventricular septal defect to single-ventricle palliation, at 40 tertiary-care pediatric centers in the Pediatric Health Information Systems database between 2007 and 2010. Assigned APR-DRGs (cardiac versus noncardiac) were compared using χ2 or Fisher's exact tests between those patients admitted during the first day of life versus later and between those receiving extracorporeal membrane oxygenation support versus those not. Recursive partitioning was used to assess the greatest determinants of APR-DRG type in the model. Every patient admitted on day 1 of life was assigned to a noncardiac APR-DRG (p<0.001 for each procedure). Similarly, use of extracorporeal membrane oxygenation was highly associated with misclassification of CHS patients into a noncardiac APR-DRG (p<0.001 for each procedure). Cases misclassified into a noncardiac APR-DRG experienced a significantly increased mortality (p<0.001). In classifying patients undergoing CHS, APR-DRG coding has systematic misclassifications, which may result in inaccurate reporting of CHS case volumes and mortality. Copyright © 2014 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

Accuracy of the All Patient Refined Diagnosis Related Groups Classification System in Congenital Heart Surgery

PubMed Central

Parnell, Aimee S.; Shults, Justine; Gaynor, J. William; Leonard, Mary B.; Dai, Dingwei; Feudtner, Chris

2015-01-01

Background Administrative data is increasingly used to evaluate clinical outcomes and quality of care in pediatric congenital heart surgery (CHS) programs. Several published analyses of large pediatric administrative datasets have relied on the All Patient Refined Diagnosis Related Groups (APR-DRG, version 24) diagnostic classification system. The accuracy of this classification system for patients undergoing CHS is unclear. Methods We performed a retrospective cohort study of all 14,098 patients 0-5 years of age undergoing any of six selected congenital heart operations, ranging in complexity from isolated closure of a ventricular septal defect to single ventricle palliation, at 40 tertiary care pediatric centers in the Pediatric Health Information Systems database between 2007 and 2010. Assigned APR-DRGs (cardiac versus non-cardiac) were compared using chi-squared or Fisher's exact tests between those patients admitted during the first day of life versus later and between those receiving extracorporeal membrane oxygenation support versus not. Recursive partitioning was used to assess the greatest determinants of APR-DRG type in the model. Results Every patient admitted on day of life 1 was assigned to a non-cardiac APR-DRG (p < 0.001 for each procedure). Similarly, use of extracorporeal membrane oxygenation was highly associated with misclassification of congenital heart surgery patients into a non-cardiac APR-DRG (p < 0.001 for each procedure). Cases misclassified into a non-cardiac APR-DRG experienced a significantly increased mortality (p < 0.001). Conclusions In classifying patients undergoing congenital heart surgery, APR-DRG coding has systematic misclassifications, which may result in inaccurate reporting of CHS case volumes and mortality. PMID:24200398

The not guilty verdict: psychological reactions to a diagnosis of Asperger syndrome in adulthood.

PubMed

Punshon, Clare; Skirrow, Paul; Murphy, Glynis

2009-05-01

Asperger syndrome is a relatively new diagnostic classification. A number of factors make receiving a diagnosis of Asperger syndrome in adulthood a unique experience. This study used a phenomenological approach to examine the experiences of 10 adults receiving such a diagnosis. Results suggested that six major themes were associated with receiving a diagnosis of Asperger syndrome. Individuals discussed their negative life experiences and their experience of services prior to diagnosis, which led to individuals holding certain beliefs about the symptoms of Asperger syndrome. These beliefs had an effect on the formation of each individual's perceived self-identity. Participants made links between how they felt when they received the diagnosis and their current beliefs about both their ;symptoms' and themselves. Finally, participants highlighted the importance of the societal view of Asperger syndrome. The implications of these findings are reappraised in the context of previous research and the wider literature on identity formation.

MODEL-BASED CLUSTERING FOR CLASSIFICATION OF AQUATIC SYSTEMS AND DIAGNOSIS OF ECOLOGICAL STRESS

EPA Science Inventory

Clustering approaches were developed using the classification likelihood, the mixture likelihood, and also using a randomization approach with a model index. Using a clustering approach based on the mixture and classification likelihoods, we have developed an algorithm that...

Measurement of psychological disorders using cognitive diagnosis models.

PubMed

Templin, Jonathan L; Henson, Robert A

2006-09-01

Cognitive diagnosis models are constrained (multiple classification) latent class models that characterize the relationship of questionnaire responses to a set of dichotomous latent variables. Having emanated from educational measurement, several aspects of such models seem well suited to use in psychological assessment and diagnosis. This article presents the development of a new cognitive diagnosis model for use in psychological assessment--the DINO (deterministic input; noisy "or" gate) model--which, as an illustrative example, is applied to evaluate and diagnose pathological gamblers. As part of this example, a demonstration of the estimates obtained by cognitive diagnosis models is provided. Such estimates include the probability an individual meets each of a set of dichotomous Diagnostic and Statistical Manual of Mental Disorders (text revision [DSM-IV-TR]; American Psychiatric Association, 2000) criteria, resulting in an estimate of the probability an individual meets the DSM-IV-TR definition for being a pathological gambler. Furthermore, a demonstration of how the hypothesized underlying factors contributing to pathological gambling can be measured with the DINO model is presented, through use of a covariance structure model for the tetrachoric correlation matrix of the dichotomous latent variables representing DSM-IV-TR criteria. Copyright 2006 APA

Diagnosis of Specific Learning Disabilities and Prescriptive Teaching.

ERIC Educational Resources Information Center

Alonso, Lou; And Others

The recent trend in special education toward individualized teaching based on the diagnosis of specific learning disabilities is reviewed. The concern of educators for emphasis on psychoeducational diagnosis to determine learning and behavioral problems, and their remediation, rather than primarily on classification and categorization along…

Human error analysis of commercial aviation accidents: application of the Human Factors Analysis and Classification system (HFACS).

PubMed

Wiegmann, D A; Shappell, S A

2001-11-01

The Human Factors Analysis and Classification System (HFACS) is a general human error framework originally developed and tested within the U.S. military as a tool for investigating and analyzing the human causes of aviation accidents. Based on Reason's (1990) model of latent and active failures, HFACS addresses human error at all levels of the system, including the condition of aircrew and organizational factors. The purpose of the present study was to assess the utility of the HFACS framework as an error analysis and classification tool outside the military. The HFACS framework was used to analyze human error data associated with aircrew-related commercial aviation accidents that occurred between January 1990 and December 1996 using database records maintained by the NTSB and the FAA. Investigators were able to reliably accommodate all the human causal factors associated with the commercial aviation accidents examined in this study using the HFACS system. In addition, the classification of data using HFACS highlighted several critical safety issues in need of intervention research. These results demonstrate that the HFACS framework can be a viable tool for use within the civil aviation arena. However, additional research is needed to examine its applicability to areas outside the flight deck, such as aircraft maintenance and air traffic control domains.

Cirrhosis Diagnosis and Liver Fibrosis Staging: Transient Elastometry Versus Cirrhosis Blood Test.

PubMed

Calès, Paul; Boursier, Jérôme; Oberti, Frédéric; Bardou, Derek; Zarski, Jean-Pierre; de Lédinghen, Victor

2015-07-01

Elastometry is more accurate than blood tests for cirrhosis diagnosis. However, blood tests were developed for significant fibrosis, with the exception of CirrhoMeter developed for cirrhosis. We compared the performance of Fibroscan and CirrhoMeter, and classic binary cirrhosis diagnosis versus new fibrosis staging for cirrhosis diagnosis. The diagnostic population included 679 patients with hepatitis C and liver biopsy (Metavir staging and morphometry), Fibroscan, and CirrhoMeter. The prognostic population included 1110 patients with chronic liver disease and both tests. Binary diagnosis: AUROCs for cirrhosis were: Fibroscan: 0.905; CirrhoMeter: 0.857; and P=0.041. Accuracy (Youden cutoff) was: Fibroscan: 85.4%; CirrhoMeter: 79.2%; and P<0.001. Fibrosis classification provided 6 classes (F0/1, F1/2, F2±1, F3±1, F3/4, and F4). Accuracy was: Fibroscan: 88.2%; CirrhoMeter: 88.8%; and P=0.77. A simplified fibrosis classification comprised 3 categories: discrete (F1±1), moderate (F2±1), and severe (F3/4) fibrosis. Using this simplified classification, CirrhoMeter predicted survival better than Fibroscan (respectively, χ=37.9 and 19.7 by log-rank test), but both predicted it well (P<0.001 by log-rank test). Comparison: binary diagnosis versus fibrosis classification, respectively, overall accuracy: CirrhoMeter: 79.2% versus 88.8% (P<0.001); Fibroscan: 85.4% versus 88.2% (P=0.127); positive predictive value for cirrhosis by Fibroscan: Youden cutoff (11.1 kPa): 49.1% versus cutoffs of F3/4 (17.6 kPa): 67.6% and F4 classes (25.7 kPa): 82.4%. Fibroscan's usual binary cutoffs for cirrhosis diagnosis are not sufficiently accurate. Fibrosis classification should be preferred over binary diagnosis. A cirrhosis-specific blood test markedly attenuates the accuracy deficit for cirrhosis diagnosis of usual blood tests versus transient elastometry, and may offer better prognostication.

Neuropsychological Test Selection for Cognitive Impairment Classification: A Machine Learning Approach

PubMed Central

Williams, Jennifer A.; Schmitter-Edgecombe, Maureen; Cook, Diane J.

2016-01-01

Introduction Reducing the amount of testing required to accurately detect cognitive impairment is clinically relevant. The aim of this research was to determine the fewest number of clinical measures required to accurately classify participants as healthy older adult, mild cognitive impairment (MCI) or dementia using a suite of classification techniques. Methods Two variable selection machine learning models (i.e., naive Bayes, decision tree), a logistic regression, and two participant datasets (i.e., clinical diagnosis, clinical dementia rating; CDR) were explored. Participants classified using clinical diagnosis criteria included 52 individuals with dementia, 97 with MCI, and 161 cognitively healthy older adults. Participants classified using CDR included 154 individuals CDR = 0, 93 individuals with CDR = 0.5, and 25 individuals with CDR = 1.0+. Twenty-seven demographic, psychological, and neuropsychological variables were available for variable selection. Results No significant difference was observed between naive Bayes, decision tree, and logistic regression models for classification of both clinical diagnosis and CDR datasets. Participant classification (70.0 – 99.1%), geometric mean (60.9 – 98.1%), sensitivity (44.2 – 100%), and specificity (52.7 – 100%) were generally satisfactory. Unsurprisingly, the MCI/CDR = 0.5 participant group was the most challenging to classify. Through variable selection only 2 – 9 variables were required for classification and varied between datasets in a clinically meaningful way. Conclusions The current study results reveal that machine learning techniques can accurately classifying cognitive impairment and reduce the number of measures required for diagnosis. PMID:26332171

An efficient ensemble learning method for gene microarray classification.

PubMed

Osareh, Alireza; Shadgar, Bita

2013-01-01

The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. However, it has been also revealed that the basic classification techniques have intrinsic drawbacks in achieving accurate gene classification and cancer diagnosis. On the other hand, classifier ensembles have received increasing attention in various applications. Here, we address the gene classification issue using RotBoost ensemble methodology. This method is a combination of Rotation Forest and AdaBoost techniques which in turn preserve both desirable features of an ensemble architecture, that is, accuracy and diversity. To select a concise subset of informative genes, 5 different feature selection algorithms are considered. To assess the efficiency of the RotBoost, other nonensemble/ensemble techniques including Decision Trees, Support Vector Machines, Rotation Forest, AdaBoost, and Bagging are also deployed. Experimental results have revealed that the combination of the fast correlation-based feature selection method with ICA-based RotBoost ensemble is highly effective for gene classification. In fact, the proposed method can create ensemble classifiers which outperform not only the classifiers produced by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods, that is, Bagging and AdaBoost.

Socializing the human factors analysis and classification system: incorporating social psychological phenomena into a human factors error classification system.

PubMed

Paletz, Susannah B F; Bearman, Christopher; Orasanu, Judith; Holbrook, Jon

2009-08-01

The presence of social psychological pressures on pilot decision making was assessed using qualitative analyses of critical incident interviews. Social psychological phenomena have long been known to influence attitudes and behavior but have not been highlighted in accident investigation models. Using a critical incident method, 28 pilots who flew in Alaska were interviewed. The participants were asked to describe a situation involving weather when they were pilot in command and found their skills challenged. They were asked to describe the incident in detail but were not explicitly asked to identify social pressures. Pressures were extracted from transcripts in a bottom-up manner and then clustered into themes. Of the 28 pilots, 16 described social psychological pressures on their decision making, specifically, informational social influence, the foot-in-the-door persuasion technique, normalization of deviance, and impression management and self-consistency motives. We believe accident and incident investigations can benefit from explicit inclusion of common social psychological pressures. We recommend specific ways of incorporating these pressures into theHuman Factors Analysis and Classification System.

From diagnosis to social diagnosis.

PubMed

Brown, Phil; Lyson, Mercedes; Jenkins, Tania

2011-09-01

In the past two decades, research on the sociology of diagnosis has attained considerable influence within medical sociology. Analyzing the process and factors that contribute to making a diagnosis amidst uncertainty and contestation, as well as the diagnostic encounter itself, are topics rich for sociological investigation. This paper provides a reformulation of the sociology of diagnosis by proposing the concept of 'social diagnosis' which helps us recognize the interplay between larger social structures and individual or community illness manifestations. By outlining a conceptual frame, exploring how social scientists, medical professionals and laypeople contribute to social diagnosis, and providing a case study of how the North American Mohawk Akwesasne reservation dealt with rising obesity prevalence to further illustrate the social diagnosis idea, we embark on developing a cohesive and updated framework for a sociology of diagnosis. This approach is useful not just for sociological research, but has direct implications for the fields of medicine and public health. Approaching diagnosis from this integrated perspective potentially provides a broader context for practitioners and researchers to understand extra-medical factors, which in turn has consequences for patient care and health outcomes. Copyright © 2011 Elsevier Ltd. All rights reserved.

Measurement of Psychological Disorders Using Cognitive Diagnosis Models

ERIC Educational Resources Information Center

Templin, Jonathan L.; Henson, Robert A.

2006-01-01

Cognitive diagnosis models are constrained (multiple classification) latent class models that characterize the relationship of questionnaire responses to a set of dichotomous latent variables. Having emanated from educational measurement, several aspects of such models seem well suited to use in psychological assessment and diagnosis. This article…

A novel approach to malignant-benign classification of pulmonary nodules by using ensemble learning classifiers.

PubMed

Tartar, A; Akan, A; Kilic, N

2014-01-01

Computer-aided detection systems can help radiologists to detect pulmonary nodules at an early stage. In this paper, a novel Computer-Aided Diagnosis system (CAD) is proposed for the classification of pulmonary nodules as malignant and benign. The proposed CAD system using ensemble learning classifiers, provides an important support to radiologists at the diagnosis process of the disease, achieves high classification performance. The proposed approach with bagging classifier results in 94.7 %, 90.0 % and 77.8 % classification sensitivities for benign, malignant and undetermined classes (89.5 % accuracy), respectively.

Missed and Delayed Diagnosis of Dementia in Primary Care: Prevalence and Contributing Factors

PubMed Central

Bradford, Andrea; Kunik, Mark E.; Schulz, Paul; Williams, Susan P.; Singh, Hardeep

2009-01-01

Dementia is a growing public health problem for which early detection may be beneficial. Currently, the diagnosis of dementia in primary care is dependent mostly on clinical suspicion based on patient symptomsor caregivers’ concerns and is prone to be missed or delayed. We conducted a systematic review of the literature to ascertain the prevalence and contributing factors for missed and delayed dementia diagnoses in primary care. Prevalence of missed and delayed diagnosis was estimated by abstracting quantitative data from studies of diagnostic sensitivity among primary care providers. Possible predictors and contributory factors were determined from the text of quantitative and qualitative studies of patient-, caregiver-, provider-, and system-related barriers. Overall estimates of diagnostic sensitivity varied among studies and appeared to be in part a function of dementia severity, degree of patient impairment, dementia subtype, and frequency of patient-provider contact. Major contributory factors included problems with attitudes and patient-provider communication, educational deficits, and system resource constraints. The true prevalence of missed and delayed diagnoses of dementia is unknown but appears to be high. Until the case for dementia screening becomes more compelling, efforts to promote timely detection should focus on removing barriers to diagnosis. PMID:19568149

Diagnosis of Parkinson's disease on the basis of clinical and genetic classification: a population-based modelling study.

PubMed

Nalls, Mike A; McLean, Cory Y; Rick, Jacqueline; Eberly, Shirley; Hutten, Samantha J; Gwinn, Katrina; Sutherland, Margaret; Martinez, Maria; Heutink, Peter; Williams, Nigel M; Hardy, John; Gasser, Thomas; Brice, Alexis; Price, T Ryan; Nicolas, Aude; Keller, Margaux F; Molony, Cliona; Gibbs, J Raphael; Chen-Plotkin, Alice; Suh, Eunran; Letson, Christopher; Fiandaca, Massimo S; Mapstone, Mark; Federoff, Howard J; Noyce, Alastair J; Morris, Huw; Van Deerlin, Vivianna M; Weintraub, Daniel; Zabetian, Cyrus; Hernandez, Dena G; Lesage, Suzanne; Mullins, Meghan; Conley, Emily Drabant; Northover, Carrie A M; Frasier, Mark; Marek, Ken; Day-Williams, Aaron G; Stone, David J; Ioannidis, John P A; Singleton, Andrew B

2015-10-01

Accurate diagnosis and early detection of complex diseases, such as Parkinson's disease, has the potential to be of great benefit for researchers and clinical practice. We aimed to create a non-invasive, accurate classification model for the diagnosis of Parkinson's disease, which could serve as a basis for future disease prediction studies in longitudinal cohorts. We developed a model for disease classification using data from the Parkinson's Progression Marker Initiative (PPMI) study for 367 patients with Parkinson's disease and phenotypically typical imaging data and 165 controls without neurological disease. Olfactory function, genetic risk, family history of Parkinson's disease, age, and gender were algorithmically selected by stepwise logistic regression as significant contributors to our classifying model. We then tested the model with data from 825 patients with Parkinson's disease and 261 controls from five independent cohorts with varying recruitment strategies and designs: the Parkinson's Disease Biomarkers Program (PDBP), the Parkinson's Associated Risk Study (PARS), 23andMe, the Longitudinal and Biomarker Study in PD (LABS-PD), and the Morris K Udall Parkinson's Disease Research Center of Excellence cohort (Penn-Udall). Additionally, we used our model to investigate patients who had imaging scans without evidence of dopaminergic deficit (SWEDD). In the population from PPMI, our initial model correctly distinguished patients with Parkinson's disease from controls at an area under the curve (AUC) of 0·923 (95% CI 0·900-0·946) with high sensitivity (0·834, 95% CI 0·711-0·883) and specificity (0·903, 95% CI 0·824-0·946) at its optimum AUC threshold (0·655). All Hosmer-Lemeshow simulations suggested that when parsed into random subgroups, the subgroup data matched that of the overall cohort. External validation showed good classification of Parkinson's disease, with AUCs of 0·894 (95% CI 0·867-0·921) in the PDBP cohort, 0·998 (0·992-1·000

Human error analysis of commercial aviation accidents using the human factors analysis and classification system (HFACS)

DOT National Transportation Integrated Search

2001-02-01

The Human Factors Analysis and Classification System (HFACS) is a general human error framework : originally developed and tested within the U.S. military as a tool for investigating and analyzing the human : causes of aviation accidents. Based upon ...

Computer vision for microscopy diagnosis of malaria.

PubMed

Tek, F Boray; Dempster, Andrew G; Kale, Izzet

2009-07-13

This paper reviews computer vision and image analysis studies aiming at automated diagnosis or screening of malaria infection in microscope images of thin blood film smears. Existing works interpret the diagnosis problem differently or propose partial solutions to the problem. A critique of these works is furnished. In addition, a general pattern recognition framework to perform diagnosis, which includes image acquisition, pre-processing, segmentation, and pattern classification components, is described. The open problems are addressed and a perspective of the future work for realization of automated microscopy diagnosis of malaria is provided.

Evaluation of a Human Factors Analysis and Classification System as used by simulated mishap boards.

PubMed

O'Connor, Paul; Walker, Peter

2011-01-01

The reliability of the Department of Defense Human Factors Analysis and Classification System (DOD-HFACS) has been examined when used by individuals working alone to classify the causes of summary, or partial, information about a mishap. However, following an actual mishap a team of investigators would work together to gather and analyze a large amount of information before identifying the causal factors and coding them with DOD-HFACS. There were 204 military Aviation Safety Officer students who were divided into 30 groups. Each group was provided with evidence collected from one of two military aviation mishaps. DOD-HFACS was used to classify the mishap causal factors. Averaged across the two mishaps, acceptable levels of reliability were only achieved for 56.9% of nanocodes. There were high levels of agreement regarding the factors that did not contribute to the incident (a mean agreement of 50% or greater between groups for 91.0% of unselected nanocodes); the level of agreement on the factors that did cause the incident as classified using DOD-HFACS were low (a mean agreement of 50% or greater between the groups for 14.6% of selected nanocodes). Despite using teams to carry out the classification, the findings from this study are consistent with other studies of DOD-HFACS reliability with individuals. It is suggested that in addition to simplifying DOD-HFACS itself, consideration should be given to involving a human factors/organizational psychologist in mishap investigations to ensure the human factors issues are identified and classified in a consistent and reliable manner.

Recognizing systemic sclerosis: comparative analysis of various sets of classification criteria

PubMed Central

Romanowska-Próchnicka, Katarzyna; Olesińska, Marzena

2016-01-01

Systemic sclerosis is a complex disease characterized by autoimmunity, vasculopathy and tissue fibrosis. Although most patients present with some degree of skin sclerosis, which is a distinguishing hallmark, the clinical presentation vary greatly complicating the diagnosis. In this regard, new classification criteria were jointly published in 2013 by American College of Rheumatology (ACR) and European League Against Rheumatism (EULAR). A recent major development in the classification criteria is improved sensitivity, particularly for detecting early disease. The new criteria allow more cases to be classified as having systemic sclerosis (SSc), which leads to earlier treatment. Moreover it is clinically beneficial in preventing the disease progression with its irreversible fibrosis and organ damage. The aim of this review is to give insight into new classification criteria and current trends in the diagnosis of systemic sclerosis. PMID:28115780

Automatic migraine classification via feature selection committee and machine learning techniques over imaging and questionnaire data.

PubMed

Garcia-Chimeno, Yolanda; Garcia-Zapirain, Begonya; Gomez-Beldarrain, Marian; Fernandez-Ruanova, Begonya; Garcia-Monco, Juan Carlos

2017-04-13

Feature selection methods are commonly used to identify subsets of relevant features to facilitate the construction of models for classification, yet little is known about how feature selection methods perform in diffusion tensor images (DTIs). In this study, feature selection and machine learning classification methods were tested for the purpose of automating diagnosis of migraines using both DTIs and questionnaire answers related to emotion and cognition - factors that influence of pain perceptions. We select 52 adult subjects for the study divided into three groups: control group (15), subjects with sporadic migraine (19) and subjects with chronic migraine and medication overuse (18). These subjects underwent magnetic resonance with diffusion tensor to see white matter pathway integrity of the regions of interest involved in pain and emotion. The tests also gather data about pathology. The DTI images and test results were then introduced into feature selection algorithms (Gradient Tree Boosting, L1-based, Random Forest and Univariate) to reduce features of the first dataset and classification algorithms (SVM (Support Vector Machine), Boosting (Adaboost) and Naive Bayes) to perform a classification of migraine group. Moreover we implement a committee method to improve the classification accuracy based on feature selection algorithms. When classifying the migraine group, the greatest improvements in accuracy were made using the proposed committee-based feature selection method. Using this approach, the accuracy of classification into three types improved from 67 to 93% when using the Naive Bayes classifier, from 90 to 95% with the support vector machine classifier, 93 to 94% in boosting. The features that were determined to be most useful for classification included are related with the pain, analgesics and left uncinate brain (connected with the pain and emotions). The proposed feature selection committee method improved the performance of migraine diagnosis

Classification of cancerous cells based on the one-class problem approach

NASA Astrophysics Data System (ADS)

Murshed, Nabeel A.; Bortolozzi, Flavio; Sabourin, Robert

1996-03-01

One of the most important factors in reducing the effect of cancerous diseases is the early diagnosis, which requires a good and a robust method. With the advancement of computer technologies and digital image processing, the development of a computer-based system has become feasible. In this paper, we introduce a new approach for the detection of cancerous cells. This approach is based on the one-class problem approach, through which the classification system need only be trained with patterns of cancerous cells. This reduces the burden of the training task by about 50%. Based on this approach, a computer-based classification system is developed, based on the Fuzzy ARTMAP neural networks. Experimental results were performed using a set of 542 patterns taken from a sample of breast cancer. Results of the experiment show 98% correct identification of cancerous cells and 95% correct identification of non-cancerous cells.

Analysis of the factors linked to a diagnosis of attention deficit hyperactivity disorder in children.

PubMed

Rivas-Juesas, C; de Dios, J G; Benac-Prefaci, M; Colomer-Revuelta, J

2017-09-01

Attention deficit hyperactivity disorder (ADHD) is a neuropsychiatric disorder originating from multiple factors. The aim of this study is to determine the percentage of patients with ADHD out of all patients referred to our clinic for assessment, and to explore the epidemiological and clinical factors linked to this diagnosis. retrospective analytical study of a sample of patients under 15 years old sent to the paediatric neurology clinic for suspected ADHD. DSM-IV criteria were used for diagnosis. We completed a binary logistic regression analysis to determine which risk factors were associated with the diagnosis. Of the 280 selected patients, 224 were male (male/female ratio 4:1); mean age (SD) was 8.4 (3.08) years. Almost half (49%) of the patients were referred by their schools and 64.9% were born in the second half of the year, but this tendency was more marked in girls than in boys. Assessment according to DSM-IV criteria resulted in diagnosis of 139 subjects (49.7%). The risk factors linked to diagnosis were male sex, parents with ADHD, associated sleep disorders, tics, and absence of neurodevelopmental delay. Only half of the children referred for suspected ADHD were diagnosed with that condition, and most were among the youngest in their classes, which suggests that suspected ADHD is overestimated. An exhaustive clinical interview investigating the family's psychological disorders and the patient's sleep disorders and tics is needed to improve the diagnostic process. Copyright © 2016 Sociedad Española de Neurología. Publicado por Elsevier España, S.L.U. All rights reserved.

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

PubMed

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

2017-03-09

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

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

PubMed Central

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

2017-01-01

Intelligent condition monitoring and fault diagnosis by analyzing the sensor data can assure the safety of machinery. Conventional fault diagnosis and classification methods usually implement pretreatments to decrease noise and extract some time domain or frequency domain features from raw time series sensor data. Then, some classifiers are utilized to make diagnosis. However, these conventional fault diagnosis approaches suffer from the expertise of feature selection and they do not consider the temporal coherence of time series data. This paper proposes a fault diagnosis model based on Deep Neural Networks (DNN). The model can directly recognize raw time series sensor data without feature selection and signal processing. It also takes advantage of the temporal coherence of the data. Firstly, raw time series training data collected by sensors are used to train the DNN until the cost function of DNN gets the minimal value; Secondly, test data are used to test the classification accuracy of the DNN on local time series data. Finally, fault diagnosis considering temporal coherence with former time series data is implemented. Experimental results show that the classification accuracy of bearing faults can get 100%. The proposed fault diagnosis approach is effective in recognizing the type of bearing faults. PMID:28282936

Culture, cultural factors and psychiatric diagnosis: review and projections.

PubMed

Alarcón, Renato D

2009-10-01

This paper aims to provide conceptual justifications for the inclusion of culture and cultural factors in psychiatric diagnosis, and logistic suggestions as to the content and use of this approach. A discussion of the scope and limitations of current diagnostic practice, criticisms from different quarters, and the role and relevance of culture in the diagnostic encounter, precede the examination of advantages and disadvantages of the approach. The cultural content of psychiatric diagnosis should include the main, well-recognized cultural variables, adequate family data, explanatory models, and strengths and weaknesses of every individual patient. The practical aspects include the acceptance of "cultural discordances" as a component of an updated definition of mental disorder, and the use of a refurbished cultural formulation. Clinical "telescoping" strategies to obtain relevant cultural data during the diagnostic interview, and areas of future research (including field trials on the cultural formulation and on "culture bound syndromes"), are outlined.

Tuberculosis disease diagnosis using artificial immune recognition system.

PubMed

Shamshirband, Shahaboddin; Hessam, Somayeh; Javidnia, Hossein; Amiribesheli, Mohsen; Vahdat, Shaghayegh; Petković, Dalibor; Gani, Abdullah; Kiah, Miss Laiha Mat

2014-01-01

There is a high risk of tuberculosis (TB) disease diagnosis among conventional methods. This study is aimed at diagnosing TB using hybrid machine learning approaches. Patient epicrisis reports obtained from the Pasteur Laboratory in the north of Iran were used. All 175 samples have twenty features. The features are classified based on incorporating a fuzzy logic controller and artificial immune recognition system. The features are normalized through a fuzzy rule based on a labeling system. The labeled features are categorized into normal and tuberculosis classes using the Artificial Immune Recognition Algorithm. Overall, the highest classification accuracy reached was for the 0.8 learning rate (α) values. The artificial immune recognition system (AIRS) classification approaches using fuzzy logic also yielded better diagnosis results in terms of detection accuracy compared to other empirical methods. Classification accuracy was 99.14%, sensitivity 87.00%, and specificity 86.12%.

Examining transgender health through the International Classification of Functioning, Disability, and Health's (ICF) Contextual Factors.

PubMed

Jacob, Melissa; Cox, Steven R

2017-12-01

For many transgender individuals, medical intervention is necessary to live as their desired gender. However, little is known about Contextual Factors (i.e., Environmental and Personal) that may act as facilitators and barriers in the health of transgender individuals. Therefore, this paper sought to examine Contextual Factors of the World Health Organization's International Classification of Functioning, Disability, and Health that may facilitate or negatively impact the physical, psychological, and social functioning of transgender individuals. A literature review was conducted to identify Environmental and Personal Factors that may influence transgender individuals' physical, psychological, and social functioning. Seven electronic databases were searched. In total, 154 records were reviewed, and 41 articles and other records met inclusion criteria. Three general themes emerged for Environmental Factors: family and social networks, education, and health care. Three general themes also emerged for Personal Factors: socioeconomic status, race, and age. Transgender individuals benefit from gender-affirming services, improved family and social support systems, and competent provider care. Educational training programs, including medical curricula or workshops, might provide the greatest benefit in improving transgender health by increasing the knowledge and cultural competency of health professionals working with this population. Given the diversity of gender expression, differences in lived experiences, and potential for enduring persistent "double discrimination" due to the intersectional relationships between socioeconomic status, race, and/or age, health professionals must approach transgender health using a holistic lens such as the World Health Organization's International Classification of Functioning, Disability, and Health.

Heuristic Classification. Technical Report Number 12.

ERIC Educational Resources Information Center

Clancey, William J.

A broad range of well-structured problems--embracing forms of diagnosis, catalog selection, and skeletal planning--are solved in expert computer systems by the method of heuristic classification. These programs have a characteristic inference structure that systematically relates data to a pre-enumerated set of solutions by abstraction, heuristic…

Diagnostic Classification of Mental Health and Developmental Disorders of Infancy and Early Childhood. Diagnostic Classification: 0-3.

ERIC Educational Resources Information Center

Wieder, Serena, Ed.

The diagnostic framework presented in this manual seeks to address the need for a systematic, multidisciplinary, developmentally based approach to the classification of mental health and developmental difficulties in the first 4 years of life. An introduction discusses clinical approaches to assessment and diagnosis, gives an overview of the…

TNM: evolution and relation to other prognostic factors.

PubMed

Sobin, Leslie H

2003-01-01

The TNM Classification describes the anatomic extent of cancer. TNM's ability to separately classify the individual tumor (T), node (N), and metastasis (M) elements and then group them into stages differs from other cancer staging classifications (e.g., Dukes), which are only concerned with summarized groups. The objectives of the TNM Classification are to aid the clinician in the planning of treatment, give some indication of prognosis, assist in the evaluation of the results of treatment, and facilitate the exchange of information. During the past 50 years, the TNM system has evolved under the influence of advances in diagnosis and treatment. Radiographic imaging (e.g., endoscopic ultrasound for the depth of invasion of esophageal and rectal tumors) has improved the accuracy of the clinical T, N, and M classifications. Advances in treatment have necessitated more detail in some T4 categories. Developments in multimodality therapy have increased the importance of the "y" symbol and the R (residual tumor) classification. New surgical techniques have resulted in the elaboration of the sentinel node (sn) symbol. The use of immunohistochemistry has resulted in the classification of isolated tumor cells and their distinction from micrometastasis. The most important challenge facing users of the TNM Classification is how it should interface with the large number of non-anatomic prognostic factors that are currently in use or under study. As non-anatomic prognostic factors become widely used, the TNM system provides an inviting foundation upon which to build a prognostic classification; however, this carries a risk that the system will be overwhelmed by a variety of prognostic data. An anatomic extent-of-disease classification is needed to aid practitioners in selecting the initial therapeutic approach, stratifying patients for therapeutic studies, evaluating non-anatomic prognostic factors at specific anatomic stages, comparing the weight of non-anatomic factors with

Phenotype at diagnosis predicts recurrence rates in Crohn's disease.

PubMed

Wolters, F L; Russel, M G; Sijbrandij, J; Ambergen, T; Odes, S; Riis, L; Langholz, E; Politi, P; Qasim, A; Koutroubakis, I; Tsianos, E; Vermeire, S; Freitas, J; van Zeijl, G; Hoie, O; Bernklev, T; Beltrami, M; Rodriguez, D; Stockbrügger, R W; Moum, B

2006-08-01

In Crohn's disease (CD), studies associating phenotype at diagnosis and subsequent disease activity are important for patient counselling and health care planning. To calculate disease recurrence rates and to correlate these with phenotypic traits at diagnosis. A prospectively assembled uniformly diagnosed European population based inception cohort of CD patients was classified according to the Vienna classification for disease phenotype at diagnosis. Surgical and non-surgical recurrence rates throughout a 10 year follow up period were calculated. Multivariate analysis was performed to classify risk factors present at diagnosis for recurrent disease. A total of 358 were classified for phenotype at diagnosis, of whom 262 (73.2%) had a first recurrence and 113 patients (31.6%) a first surgical recurrence during the first 10 years after diagnosis. Patients with upper gastrointestinal disease at diagnosis had an excess risk of recurrence (hazard ratio 1.54 (95% confidence interval (CI) 1.13-2.10)) whereas age >/=40 years at diagnosis was protective (hazard ratio 0.82 (95% CI 0.70-0.97)). Colonic disease was a protective characteristic for resective surgery (hazard ratio 0.38 (95% CI 0.21-0.69)). More frequent resective surgical recurrences were reported from Copenhagen (hazard ratio 3.23 (95% CI 1.32-7.89)). A mild course of disease in terms of disease recurrence was observed in this European cohort. Phenotype at diagnosis had predictive value for disease recurrence with upper gastrointestinal disease being the most important positive predictor. A phenotypic North-South gradient in CD may be present, illustrated by higher surgery risks in some of the Northern European centres.

Hierarchical classification strategy for Phenotype extraction from epidermal growth factor receptor endocytosis screening.

PubMed

Cao, Lu; Graauw, Marjo de; Yan, Kuan; Winkel, Leah; Verbeek, Fons J

2016-05-03

Endocytosis is regarded as a mechanism of attenuating the epidermal growth factor receptor (EGFR) signaling and of receptor degradation. There is increasing evidence becoming available showing that breast cancer progression is associated with a defect in EGFR endocytosis. In order to find related Ribonucleic acid (RNA) regulators in this process, high-throughput imaging with fluorescent markers is used to visualize the complex EGFR endocytosis process. Subsequently a dedicated automatic image and data analysis system is developed and applied to extract the phenotype measurement and distinguish different developmental episodes from a huge amount of images acquired through high-throughput imaging. For the image analysis, a phenotype measurement quantifies the important image information into distinct features or measurements. Therefore, the manner in which prominent measurements are chosen to represent the dynamics of the EGFR process becomes a crucial step for the identification of the phenotype. In the subsequent data analysis, classification is used to categorize each observation by making use of all prominent measurements obtained from image analysis. Therefore, a better construction for a classification strategy will support to raise the performance level in our image and data analysis system. In this paper, we illustrate an integrated analysis method for EGFR signalling through image analysis of microscopy images. Sophisticated wavelet-based texture measurements are used to obtain a good description of the characteristic stages in the EGFR signalling. A hierarchical classification strategy is designed to improve the recognition of phenotypic episodes of EGFR during endocytosis. Different strategies for normalization, feature selection and classification are evaluated. The results of performance assessment clearly demonstrate that our hierarchical classification scheme combined with a selected set of features provides a notable improvement in the temporal

[Attention deficit hyperactivity disorder in adults. Benchmarking diagnosis using the Wender-Reimherr adult rating scale].

PubMed

Rösler, M; Retz, W; Retz-Junginger, P; Stieglitz, R D; Kessler, H; Reimherr, F; Wender, P H

2008-03-01

making an ADHD diagnosis but in certain cases move beyond the DSM-IV. Of the patients 56% had ADHD diagnoses according to all three classification instruments. Examining the factor structure of the ADHD psychopathology represented by seven WRI and three ADHD-SR subscales, we found a two-factor solution explaining for 63% of the variance. Factor 1 was designated by impulsivity, affective lability, hyperactivity, and hot temper; factor 2 consisted of inattention, disorganisation, and overreactivity.

A systematic review and development of a classification framework for factors associated with missing patient-reported outcome data.

PubMed

Palmer, Michael J; Mercieca-Bebber, Rebecca; King, Madeleine; Calvert, Melanie; Richardson, Harriet; Brundage, Michael

2018-02-01

Missing patient-reported outcome data can lead to biased results, to loss of power to detect between-treatment differences, and to research waste. Awareness of factors may help researchers reduce missing patient-reported outcome data through study design and trial processes. The aim was to construct a Classification Framework of factors associated with missing patient-reported outcome data in the context of comparative studies. The first step in this process was informed by a systematic review. Two databases (MEDLINE and CINAHL) were searched from inception to March 2015 for English articles. Inclusion criteria were (a) relevant to patient-reported outcomes, (b) discussed missing data or compliance in prospective medical studies, and (c) examined predictors or causes of missing data, including reasons identified in actual trial datasets and reported on cover sheets. Two reviewers independently screened titles and abstracts. Discrepancies were discussed with the research team prior to finalizing the list of eligible papers. In completing the systematic review, four particular challenges to synthesizing the extracted information were identified. To address these challenges, operational principles were established by consensus to guide the development of the Classification Framework. A total of 6027 records were screened. In all, 100 papers were eligible and included in the review. Of these, 57% focused on cancer, 23% did not specify disease, and 20% reported for patients with a variety of non-cancer conditions. In total, 40% of the papers offered a descriptive analysis of possible factors associated with missing data, but some papers used other methods. In total, 663 excerpts of text (units), each describing a factor associated with missing patient-reported outcome data, were extracted verbatim. Redundant units were identified and sequestered. Similar units were grouped, and an iterative process of consensus among the investigators was used to reduce these units to a

The 2015 World Health Organization Classification of Lung Tumors: Impact of Genetic, Clinical and Radiologic Advances Since the 2004 Classification.

PubMed

Travis, William D; Brambilla, Elisabeth; Nicholson, Andrew G; Yatabe, Yasushi; Austin, John H M; Beasley, Mary Beth; Chirieac, Lucian R; Dacic, Sanja; Duhig, Edwina; Flieder, Douglas B; Geisinger, Kim; Hirsch, Fred R; Ishikawa, Yuichi; Kerr, Keith M; Noguchi, Masayuki; Pelosi, Giuseppe; Powell, Charles A; Tsao, Ming Sound; Wistuba, Ignacio

2015-09-01

The 2015 World Health Organization (WHO) Classification of Tumors of the Lung, Pleura, Thymus and Heart has just been published with numerous important changes from the 2004 WHO classification. The most significant changes in this edition involve (1) use of immunohistochemistry throughout the classification, (2) a new emphasis on genetic studies, in particular, integration of molecular testing to help personalize treatment strategies for advanced lung cancer patients, (3) a new classification for small biopsies and cytology similar to that proposed in the 2011 Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society classification, (4) a completely different approach to lung adenocarcinoma as proposed by the 2011 Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society classification, (5) restricting the diagnosis of large cell carcinoma only to resected tumors that lack any clear morphologic or immunohistochemical differentiation with reclassification of the remaining former large cell carcinoma subtypes into different categories, (6) reclassifying squamous cell carcinomas into keratinizing, nonkeratinizing, and basaloid subtypes with the nonkeratinizing tumors requiring immunohistochemistry proof of squamous differentiation, (7) grouping of neuroendocrine tumors together in one category, (8) adding NUT carcinoma, (9) changing the term sclerosing hemangioma to sclerosing pneumocytoma, (10) changing the name hamartoma to "pulmonary hamartoma," (11) creating a group of PEComatous tumors that include (a) lymphangioleiomyomatosis, (b) PEComa, benign (with clear cell tumor as a variant) and (c) PEComa, malignant, (12) introducing the entity pulmonary myxoid sarcoma with an EWSR1-CREB1 translocation, (13) adding the entities myoepithelioma and myoepithelial carcinomas, which can show EWSR1 gene rearrangements, (14) recognition of usefulness of WWTR1-CAMTA1 fusions in diagnosis of epithelioid

Diagnostic Classification 0-3: Diagnostic Classification of Mental Health and Developmental Disorders of Infancy and Early Childhood.

ERIC Educational Resources Information Center

Zero to Three: National Center for Infants, Toddlers and Families, Washington, DC.

The diagnostic framework presented in this manual seeks to address the need for a systematic, multi-disciplinary, developmentally based approach to the classification of mental health and developmental difficulties in the first 4 years of life. An introduction discusses clinical approaches to assessment and diagnosis, gives an overview of the…

Support vector machines-based fault diagnosis for turbo-pump rotor

NASA Astrophysics Data System (ADS)

Yuan, Sheng-Fa; Chu, Fu-Lei

2006-05-01

Most artificial intelligence methods used in fault diagnosis are based on empirical risk minimisation principle and have poor generalisation when fault samples are few. Support vector machines (SVM) is a new general machine-learning tool based on structural risk minimisation principle that exhibits good generalisation even when fault samples are few. Fault diagnosis based on SVM is discussed. Since basic SVM is originally designed for two-class classification, while most of fault diagnosis problems are multi-class cases, a new multi-class classification of SVM named 'one to others' algorithm is presented to solve the multi-class recognition problems. It is a binary tree classifier composed of several two-class classifiers organised by fault priority, which is simple, and has little repeated training amount, and the rate of training and recognition is expedited. The effectiveness of the method is verified by the application to the fault diagnosis for turbo pump rotor.

An Integrative Dimensional Classification of Personality Disorder

ERIC Educational Resources Information Center

Widiger, Thomas A.; Livesley, W. John; Clark, Lee Anna

2009-01-01

Psychological assessment research concerns how to describe psychological dysfunction in ways that are both valid and useful. Recent advances in assessment research hold the promise of facilitating significant improvements in description and diagnosis. One such contribution is in the classification of personality disorder symptomatology. The…

14 CFR 1203.406 - Additional classification factors.

Code of Federal Regulations, 2010 CFR

2010-01-01

... Section 1203.406 Aeronautics and Space NATIONAL AERONAUTICS AND SPACE ADMINISTRATION INFORMATION SECURITY... other Government departments and agencies should be considered. Classification of official information... exists elsewhere for the information under consideration which would make it necessary to assign a higher...

Classification of glioblastoma and metastasis for neuropathology intraoperative diagnosis: a multi-resolution textural approach to model the background

NASA Astrophysics Data System (ADS)

Ahmad Fauzi, Mohammad Faizal; Gokozan, Hamza Numan; Elder, Brad; Puduvalli, Vinay K.; Otero, Jose J.; Gurcan, Metin N.

2014-03-01

Brain cancer surgery requires intraoperative consultation by neuropathology to guide surgical decisions regarding the extent to which the tumor undergoes gross total resection. In this context, the differential diagnosis between glioblastoma and metastatic cancer is challenging as the decision must be made during surgery in a short time-frame (typically 30 minutes). We propose a method to classify glioblastoma versus metastatic cancer based on extracting textural features from the non-nuclei region of cytologic preparations. For glioblastoma, these regions of interest are filled with glial processes between the nuclei, which appear as anisotropic thin linear structures. For metastasis, these regions correspond to a more homogeneous appearance, thus suitable texture features can be extracted from these regions to distinguish between the two tissue types. In our work, we use the Discrete Wavelet Frames to characterize the underlying texture due to its multi-resolution capability in modeling underlying texture. The textural characterization is carried out in primarily the non-nuclei regions after nuclei regions are segmented by adapting our visually meaningful decomposition segmentation algorithm to this problem. k-nearest neighbor method was then used to classify the features into glioblastoma or metastasis cancer class. Experiment on 53 images (29 glioblastomas and 24 metastases) resulted in average accuracy as high as 89.7% for glioblastoma, 87.5% for metastasis and 88.7% overall. Further studies are underway to incorporate nuclei region features into classification on an expanded dataset, as well as expanding the classification to more types of cancers.

[Definition and classification of pulmonary arterial hypertension].

PubMed

Nakanishi, Norifumi

2008-11-01

Pulmonary hypertension(PH) is a disorder that may occur either in the setting of a variety of underlying medical conditions or as a disease that uniquely affects the pulmonary vasculature. Because an accurate diagnosis of PH in a patient is essential to establish an effective treatment, a classification of PH has been helpful. The first classification, established at WHO Symposium in 1973, classified PH into groups based on the known cause and defined primary pulmonary hypertension (PPH) as a separate entity of unknown cause. In 1998, the second World Symposium on PPH was held in Evian. Evian classification introduced the concept of conditions that directly affected the pulmonary vasculature (i.e., PAH), which included PPH. In 2003, the third World Symposium on PAH convened in Venice. In Venice classification, the term 'PPH' was abandoned in favor of 'idiopathic' within the group of disease known as 'PAH'.

Computer aided diagnosis system for the Alzheimer's disease based on partial least squares and random forest SPECT image classification.

PubMed

Ramírez, J; Górriz, J M; Segovia, F; Chaves, R; Salas-Gonzalez, D; López, M; Alvarez, I; Padilla, P

2010-03-19

This letter shows a computer aided diagnosis (CAD) technique for the early detection of the Alzheimer's disease (AD) by means of single photon emission computed tomography (SPECT) image classification. The proposed method is based on partial least squares (PLS) regression model and a random forest (RF) predictor. The challenge of the curse of dimensionality is addressed by reducing the large dimensionality of the input data by downscaling the SPECT images and extracting score features using PLS. A RF predictor then forms an ensemble of classification and regression tree (CART)-like classifiers being its output determined by a majority vote of the trees in the forest. A baseline principal component analysis (PCA) system is also developed for reference. The experimental results show that the combined PLS-RF system yields a generalization error that converges to a limit when increasing the number of trees in the forest. Thus, the generalization error is reduced when using PLS and depends on the strength of the individual trees in the forest and the correlation between them. Moreover, PLS feature extraction is found to be more effective for extracting discriminative information from the data than PCA yielding peak sensitivity, specificity and accuracy values of 100%, 92.7%, and 96.9%, respectively. Moreover, the proposed CAD system outperformed several other recently developed AD CAD systems. Copyright 2010 Elsevier Ireland Ltd. All rights reserved.

Swapping horses midstream: factors related to physicians' changing their minds about a diagnosis.

PubMed

Eva, Kevin W; Link, Carol L; Lutfey, Karen E; McKinlay, John B

2010-07-01

Premature closure has been identified as the single most common cause of diagnostic error. This factorial experiment explored which variables exert an unconfounded influence on physicians' diagnostic flexibility (changing their minds about the most likely diagnosis during a clinical case presentation). In 2007-2008, 256 practicing physicians viewed a clinically authentic vignette simulating a patient presenting with possible coronary heart disease (CHD) and provided their initial impression midway through the case. At the end, they answered questions about the case, indicated how they would continue their clinical investigation, and made a final diagnosis. The authors used general linear models to determine which patient factors (age, gender, socioeconomic status, race), physician factors (gender, age/experience), and process variables were related to the likelihood of physicians' changing their minds about the most likely diagnosis. Physicians who had less experience, those who named a non-CHD diagnosis as their initial impression, and those who did not ask for information about the patient's prior cardiac disease history were the most likely to change their minds. Participants' certainty in their initial diagnosis, the additional information desired, the diagnostic hypotheses generated, and the follow-up intended were not related to the likelihood of change in diagnostic hypotheses. Although efforts encouraging physicians to avoid cognitive biases and to reason in a more analytic manner may yield some benefit, this study suggests that experience is a more important determinant of diagnostic flexibility than is the consideration of additional diagnoses or the amount of additional information collected.

Swapping Horses Midstream: Factors Related to Physicians’ Changing Their Minds About a Diagnosis

PubMed Central

Eva, Kevin W.; Link, Carol L.; Lutfey, Karen E.; McKinlay, John B.

2013-01-01

Purpose Premature closure has been identified as the single most common cause of diagnostic error. The authors conducted a factorial experiment to explore which variables exert an unconfounded influence on physicians’ diagnostic flexibility (changing their minds about the most likely diagnosis during a clinical case presentation). Methods In 2007–2008, 256 practicing physicians viewed a clinically authentic vignette simulating a patient presenting with possible coronary heart disease (CHD), provided their initial impression midway through the case, answered questions about the case, indicated how they would continue their clinical investigation, and made a final diagnosis. The authors used general linear models to determine which patient factors (age, gender, socioeconomic status, race), physician factors (gender, age/experience), and process variables were related to the likelihood of physicians’ changing their minds about the most likely diagnosis. Results Physicians who had less experience, those who named a non-CHD diagnosis as their initial impression, and those who did not ask for information about the patient’s prior cardiac disease history were the most likely to change their minds. Participants’ certainty in their initial diagnosis, the additional information desired, the diagnostic hypotheses generated, and the follow-up intended were not related to the likelihood of change in diagnostic hypotheses. Discussion While efforts encouraging physicians to avoid cognitive biases and to reason in a more analytic manner may yield some benefit, this study suggests that experience is a more important determinant of diagnostic flexibility than is the consideration of additional diagnoses or the amount of additional information collected. PMID:20592506

[Factors linked to delayed diagnosis of tuberculosis in Conakry (Guinea)].

PubMed

Camara, A; Diallo, A; Camara, L M; Fielding, K; Sow, O Y; Chaperon, J

2006-03-01

Untreated smear-positive pulmonary tuberculosis constitutes a reservoir of infection which is highly contagious. The present study was conducted in Conakry, Guinea, to determine the different options which are available when seeking treatment or care, and to ascertain the average delay in diagnosis of pulmonary tuberculosis and the main factors linked to the delay in diagnosis after the initial onset of symptoms. Through a cross-sectional study, 113 consecutive patients with smear-positive pulmonary tuberculosis were interviewed through the use of a questionnaire. The median total delay from the onset of symptoms of pulmonary tuberculosis until the diagnosis was 11 weeks. This delay period exceeded 4 weeks for 90 of the patients (80%). The average delay linked to the conventional health care system was double that of the one at the fault of the patient (6 weeks versus 3 weeks, respectively). 54% of the patients had initially resorted to non-conventional care. To shorten this mean delay period, it is necessary to both strengthen the professional abilities and skills which train for one to better to detect tuberculosis and to sensitize the population to the subject matter and information on the illness and its symptoms.

The groningen laryngomalacia classification system--based on systematic review and dynamic airway changes.

PubMed

van der Heijden, Martijn; Dikkers, Frederik G; Halmos, Gyorgy B

2015-12-01

Laryngomalacia is the most common cause of dyspnea and stridor in newborn infants. Laryngomalacia is a dynamic change of the upper airway based on abnormally pliable supraglottic structures, which causes upper airway obstruction. In the past, different classification systems have been introduced. Until now no classification system is widely accepted and applied. Our goal is to provide a simple and complete classification system based on systematic literature search and our experiences. Retrospective cohort study with literature review. All patients with laryngomalacia under the age of 5 at time of diagnosis were included. Photo and video documentation was used to confirm diagnosis and characteristics of dynamic airway change. Outcome was compared with available classification systems in literature. Eighty-five patients were included. In contrast to other classification systems, only three typical different dynamic changes have been identified in our series. Two existing classification systems covered 100% of our findings, but there was an unnecessary overlap between different types in most of the systems. Based on our finding, we propose a new a classification system for laryngomalacia, which is purely based on dynamic airway changes. The groningen laryngomalacia classification is a new, simplified classification system with three types, based on purely dynamic laryngeal changes, tested in a tertiary referral center: Type 1: inward collapse of arytenoids cartilages, Type 2: medial displacement of aryepiglottic folds, and Type 3: posterocaudal displacement of epiglottis against the posterior pharyngeal wall. © 2015 Wiley Periodicals, Inc.

Factors contributing to delayed diagnosis of cancer among Aboriginal people in Australia: a qualitative study

PubMed Central

Shahid, Shaouli; Teng, Tiew-Hwa Katherine; Bessarab, Dawn; Aoun, Samar; Baxi, Siddhartha; Thompson, Sandra C

2016-01-01

Background/objectives Delayed presentation of symptomatic cancer is associated with poorer survival. Aboriginal patients with cancer have higher rates of distant metastases at diagnosis compared with non-Aboriginal Australians. This paper examined factors contributing to delayed diagnosis of cancer among Aboriginal Australians from patient and service providers' perspectives. Methods In-depth, open-ended interviews were conducted in two stages (2006–2007 and 2011). Inductive thematic analysis was assisted by use of NVivo looking around delays in presentation, diagnosis and referral for cancer. Participants Aboriginal patients with cancer/family members (n=30) and health service providers (n=62) were recruited from metropolitan Perth and six rural/remote regions of Western Australia. Results Three broad themes of factors were identified: (1) Contextual factors such as intergenerational impact of colonisation and racism and socioeconomic deprivation have negatively impacted on Aboriginal Australians' trust of the healthcare professionals; (2) health service-related factors included low accessibility to health services, long waiting periods, inadequate numbers of Aboriginal professionals and high staff turnover; (3) patient appraisal of symptoms and decision-making, fear of cancer and denial of symptoms were key reasons patients procrastinated in seeking help. Elements of shame, embarrassment, shyness of seeing the doctor, psychological ‘fear of the whole health system’, attachment to the land and ‘fear of leaving home’ for cancer treatment in metropolitan cities were other deterrents for Aboriginal people. Manifestation of masculinity and the belief that ‘health is women's domain’ emerged as a reason why Aboriginal men were reluctant to receive health checks. Conclusions Solutions to improved Aboriginal cancer outcomes include focusing on the primary care sector encouraging general practitioners to be proactive to suspicion of symptoms with appropriate

Missed opportunities for diagnosis of female genital mutilation.

PubMed

Abdulcadir, Jasmine; Dugerdil, Adeline; Boulvain, Michel; Yaron, Michal; Margairaz, Christiane; Irion, Olivier; Petignat, Patrick

2014-06-01

To investigate missed opportunities for diagnosing female genital mutilation (FGM) at an obstetrics and gynecology (OB/GYN) department in Switzerland. In a retrospective study, we included 129 consecutive women with FGM who attended the FGM outpatient clinic at the Department of Gynecology and Obstetrics at the University Hospitals of Geneva between 2010 and 2012. The medical files of all women who had undergone at least 1 previous gynecologic exam performed by an OB/GYN doctor or a midwife at the study institution were reviewed. The type of FGM reported in the files was considered correct if it corresponded to that reported by the specialized gynecologist at the FGM clinic, according to WHO classification. In 48 (37.2%) cases, FGM was not mentioned in the medical file. In 34 (26.4%) women, the diagnosis was correct. FGM was identified but erroneously classified in 28 (21.7%) cases. There were no factors (women's characteristics or FGM type) associated with missed diagnosis. Opportunities to identify FGM are frequently missed. Measures should be taken to improve FGM diagnosis and care. Copyright © 2014 International Federation of Gynecology and Obstetrics. Published by Elsevier Ireland Ltd. All rights reserved.

A classification on human factor accident/incident of China civil aviation in recent twelve years.

PubMed

Luo, Xiao-li

2004-10-01

To study human factor accident/incident occurred during 1990-2001 using new classification standard. The human factor accident/incident classification standard is developed on the basis of Reason's Model, combining with CAAC's traditional classifying method, and applied to the classified statistical analysis for 361 flying incidents and 35 flight accidents of China civil aviation, which is induced by human factors and occurred from 1990 to 2001. 1) the incident percentage of taxi and cruise is higher than that of takeoff, climb and descent. 2) The dominating type of flight incidents is diverging of runway, overrunning, near-miss, tail/wingtip/engine strike and ground obstacle impacting. 3) The top three accidents are out of control caused by crew, mountain collision and over runway. 4) Crew's basic operating skill is lower than what we imagined, the mostly representation is poor correcting ability when flight error happened. 5) Crew errors can be represented by incorrect control, regulation and procedure violation, disorientation and diverging percentage of correct flight level. The poor CRM skill is the dominant factor impacting China civil aviation safety, this result has a coincidence with previous study, but there is much difference and distinct characteristic in top incident phase, the type of crew error and behavior performance compared with that of advanced countries. We should strengthen CRM training for all of pilots aiming at the Chinese pilot behavior characteristic in order to improve the safety level of China civil aviation.

Personal and contextual factors related to delayed HIV diagnosis among men who have sex with men

PubMed Central

Nelson, Kimberly M.; Thiede, Hanne; Jenkins, Richard A.; Carey, James W.; Hutcheson, Rebecca; Golden, Matthew R.

2014-01-01

Delayed HIV diagnosis among men who have sex with men (MSM) in the United States continues to be a significant personal and public health issue. Using qualitative and quantitative data from 75 recently tested, HIV-seropositive MSM (38 delayed and 37 non-delayed testers) we sought to further elucidate potential personal and contextual factors that may contribute to delayed HIV diagnosis among MSM. Our findings indicate MSM who experience multiple life stressors, whether personal or contextual, have an increased likelihood of delaying HIV diagnosis. Further, MSM experiencing multiple life stressors without the scaffolding of social support, stable mental health, and self-efficacy to engage in protective health behaviors may be particularly vulnerable to delaying diagnosis. Interventions targeting these factors as well as structural interventions targeting physiological and safety concerns are needed to help MSM handle their life stressors more effectively and seek HIV testing in a timelier manner. PMID:24694326

[Combining speech sample and feature bilateral selection algorithm for classification of Parkinson's disease].

PubMed

Zhang, Xiaoheng; Wang, Lirui; Cao, Yao; Wang, Pin; Zhang, Cheng; Yang, Liuyang; Li, Yongming; Zhang, Yanling; Cheng, Oumei

2018-02-01

Diagnosis of Parkinson's disease (PD) based on speech data has been proved to be an effective way in recent years. However, current researches just care about the feature extraction and classifier design, and do not consider the instance selection. Former research by authors showed that the instance selection can lead to improvement on classification accuracy. However, no attention is paid on the relationship between speech sample and feature until now. Therefore, a new diagnosis algorithm of PD is proposed in this paper by simultaneously selecting speech sample and feature based on relevant feature weighting algorithm and multiple kernel method, so as to find their synergy effects, thereby improving classification accuracy. Experimental results showed that this proposed algorithm obtained apparent improvement on classification accuracy. It can obtain mean classification accuracy of 82.5%, which was 30.5% higher than the relevant algorithm. Besides, the proposed algorithm detected the synergy effects of speech sample and feature, which is valuable for speech marker extraction.

Multi-factorial analysis of class prediction error: estimating optimal number of biomarkers for various classification rules.

PubMed

Khondoker, Mizanur R; Bachmann, Till T; Mewissen, Muriel; Dickinson, Paul; Dobrzelecki, Bartosz; Campbell, Colin J; Mount, Andrew R; Walton, Anthony J; Crain, Jason; Schulze, Holger; Giraud, Gerard; Ross, Alan J; Ciani, Ilenia; Ember, Stuart W J; Tlili, Chaker; Terry, Jonathan G; Grant, Eilidh; McDonnell, Nicola; Ghazal, Peter

2010-12-01

Machine learning and statistical model based classifiers have increasingly been used with more complex and high dimensional biological data obtained from high-throughput technologies. Understanding the impact of various factors associated with large and complex microarray datasets on the predictive performance of classifiers is computationally intensive, under investigated, yet vital in determining the optimal number of biomarkers for various classification purposes aimed towards improved detection, diagnosis, and therapeutic monitoring of diseases. We investigate the impact of microarray based data characteristics on the predictive performance for various classification rules using simulation studies. Our investigation using Random Forest, Support Vector Machines, Linear Discriminant Analysis and k-Nearest Neighbour shows that the predictive performance of classifiers is strongly influenced by training set size, biological and technical variability, replication, fold change and correlation between biomarkers. Optimal number of biomarkers for a classification problem should therefore be estimated taking account of the impact of all these factors. A database of average generalization errors is built for various combinations of these factors. The database of generalization errors can be used for estimating the optimal number of biomarkers for given levels of predictive accuracy as a function of these factors. Examples show that curves from actual biological data resemble that of simulated data with corresponding levels of data characteristics. An R package optBiomarker implementing the method is freely available for academic use from the Comprehensive R Archive Network (http://www.cran.r-project.org/web/packages/optBiomarker/).

Application of texture analysis method for mammogram density classification

NASA Astrophysics Data System (ADS)

Nithya, R.; Santhi, B.

2017-07-01

Mammographic density is considered a major risk factor for developing breast cancer. This paper proposes an automated approach to classify breast tissue types in digital mammogram. The main objective of the proposed Computer-Aided Diagnosis (CAD) system is to investigate various feature extraction methods and classifiers to improve the diagnostic accuracy in mammogram density classification. Texture analysis methods are used to extract the features from the mammogram. Texture features are extracted by using histogram, Gray Level Co-Occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), Gray Level Difference Matrix (GLDM), Local Binary Pattern (LBP), Entropy, Discrete Wavelet Transform (DWT), Wavelet Packet Transform (WPT), Gabor transform and trace transform. These extracted features are selected using Analysis of Variance (ANOVA). The features selected by ANOVA are fed into the classifiers to characterize the mammogram into two-class (fatty/dense) and three-class (fatty/glandular/dense) breast density classification. This work has been carried out by using the mini-Mammographic Image Analysis Society (MIAS) database. Five classifiers are employed namely, Artificial Neural Network (ANN), Linear Discriminant Analysis (LDA), Naive Bayes (NB), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). Experimental results show that ANN provides better performance than LDA, NB, KNN and SVM classifiers. The proposed methodology has achieved 97.5% accuracy for three-class and 99.37% for two-class density classification.

Feature selection and classification model construction on type 2 diabetic patients' data.

PubMed

Huang, Yue; McCullagh, Paul; Black, Norman; Harper, Roy

2007-11-01

Diabetes affects between 2% and 4% of the global population (up to 10% in the over 65 age group), and its avoidance and effective treatment are undoubtedly crucial public health and health economics issues in the 21st century. The aim of this research was to identify significant factors influencing diabetes control, by applying feature selection to a working patient management system to assist with ranking, classification and knowledge discovery. The classification models can be used to determine individuals in the population with poor diabetes control status based on physiological and examination factors. The diabetic patients' information was collected by Ulster Community and Hospitals Trust (UCHT) from year 2000 to 2004 as part of clinical management. In order to discover key predictors and latent knowledge, data mining techniques were applied. To improve computational efficiency, a feature selection technique, feature selection via supervised model construction (FSSMC), an optimisation of ReliefF, was used to rank the important attributes affecting diabetic control. After selecting suitable features, three complementary classification techniques (Naïve Bayes, IB1 and C4.5) were applied to the data to predict how well the patients' condition was controlled. FSSMC identified patients' 'age', 'diagnosis duration', the need for 'insulin treatment', 'random blood glucose' measurement and 'diet treatment' as the most important factors influencing blood glucose control. Using the reduced features, a best predictive accuracy of 95% and sensitivity of 98% was achieved. The influence of factors, such as 'type of care' delivered, the use of 'home monitoring', and the importance of 'smoking' on outcome can contribute to domain knowledge in diabetes control. In the care of patients with diabetes, the more important factors identified: patients' 'age', 'diagnosis duration' and 'family history', are beyond the control of physicians. Treatment methods such as 'insulin', 'diet

SVM feature selection based rotation forest ensemble classifiers to improve computer-aided diagnosis of Parkinson disease.

PubMed

Ozcift, Akin

2012-08-01

Parkinson disease (PD) is an age-related deterioration of certain nerve systems, which affects movement, balance, and muscle control of clients. PD is one of the common diseases which affect 1% of people older than 60 years. A new classification scheme based on support vector machine (SVM) selected features to train rotation forest (RF) ensemble classifiers is presented for improving diagnosis of PD. The dataset contains records of voice measurements from 31 people, 23 with PD and each record in the dataset is defined with 22 features. The diagnosis model first makes use of a linear SVM to select ten most relevant features from 22. As a second step of the classification model, six different classifiers are trained with the subset of features. Subsequently, at the third step, the accuracies of classifiers are improved by the utilization of RF ensemble classification strategy. The results of the experiments are evaluated using three metrics; classification accuracy (ACC), Kappa Error (KE) and Area under the Receiver Operating Characteristic (ROC) Curve (AUC). Performance measures of two base classifiers, i.e. KStar and IBk, demonstrated an apparent increase in PD diagnosis accuracy compared to similar studies in literature. After all, application of RF ensemble classification scheme improved PD diagnosis in 5 of 6 classifiers significantly. We, numerically, obtained about 97% accuracy in RF ensemble of IBk (a K-Nearest Neighbor variant) algorithm, which is a quite high performance for Parkinson disease diagnosis.

Classification and disease prediction via mathematical programming

NASA Astrophysics Data System (ADS)

Lee, Eva K.; Wu, Tsung-Lin

2007-11-01

In this chapter, we present classification models based on mathematical programming approaches. We first provide an overview on various mathematical programming approaches, including linear programming, mixed integer programming, nonlinear programming and support vector machines. Next, we present our effort of novel optimization-based classification models that are general purpose and suitable for developing predictive rules for large heterogeneous biological and medical data sets. Our predictive model simultaneously incorporates (1) the ability to classify any number of distinct groups; (2) the ability to incorporate heterogeneous types of attributes as input; (3) a high-dimensional data transformation that eliminates noise and errors in biological data; (4) the ability to incorporate constraints to limit the rate of misclassification, and a reserved-judgment region that provides a safeguard against over-training (which tends to lead to high misclassification rates from the resulting predictive rule) and (5) successive multi-stage classification capability to handle data points placed in the reserved judgment region. To illustrate the power and flexibility of the classification model and solution engine, and its multigroup prediction capability, application of the predictive model to a broad class of biological and medical problems is described. Applications include: the differential diagnosis of the type of erythemato-squamous diseases; predicting presence/absence of heart disease; genomic analysis and prediction of aberrant CpG island meythlation in human cancer; discriminant analysis of motility and morphology data in human lung carcinoma; prediction of ultrasonic cell disruption for drug delivery; identification of tumor shape and volume in treatment of sarcoma; multistage discriminant analysis of biomarkers for prediction of early atherosclerois; fingerprinting of native and angiogenic microvascular networks for early diagnosis of diabetes, aging, macular

Factor Structure of Attention Deficit Hyperactivity Disorder Symptoms for Children Age 3 to 5 Years

ERIC Educational Resources Information Center

McGoey, Kara E.; Schreiber, James; Venesky, Lindsey; Westwood, Wendy; McGuirk, Lindsay; Schaffner, Kristen

2015-01-01

The diagnosis of attention deficit hyperactivity disorder (ADHD) distinguishes two dimensions of symptoms, inattention and hyperactivity-impulsivity for ages 3 to adulthood. Currently, no separate classification for preschool-age children exists, whereas preliminary research suggests that the two-factor structure of ADHD may not match the…

Cutaneous Lupus Erythematosus: Diagnosis and treatment

PubMed Central

Okon, Lauren G.; Werth, Victoria P.

2013-01-01

Cutaneous lupus erythematosus encompasses a wide range of dermatologic manifestations, which may or may not be associated with the development of systemic disease. Cutaneous lupus is divided into several subtypes, including acute cutaneous lupus erythematosus, subacute cutaneous lupus erythematosus, and chronic cutaneous lupus erythematosus. Chronic cutaneous lupus erythematosus includes discoid lupus erythematosus, lupus erythematosus profundus, chilblain cutaneous lupus, and lupus tumidus. Diagnosis of these diseases requires proper classification of the subtype, through a combination of physical exam, laboratory studies, histology, antibody serology, and occasionally direct immunofluorescence, while ensuring to exclude systemic disease. Treatment of cutaneous lupus consists of patient education on proper sun protection along with appropriate topical and systemic agents. Systemic agents are indicated in cases of widespread, scarring, or treatment-refractory disease. In this review, we discuss issues in classification and diagnosis of the various subtypes of CLE, as well as provide an update on therapeutic management. PMID:24238695

Bone involvement at diagnosis as a predictive factor in children with acute lymphoblastic leukemia.

PubMed

Tragiannidis, A; Vasileiou, E; Papageorgiou, M; Damianidou, L; Hatzipantelis, E; Gombakis, N; Giannopoulos, A

2016-01-01

Bone involvement represents a common symptom at diagnosis in children with acute lymphoblastic leukemia, and its prognostic value is not entirely clarified. The aim of this study was to evaluate bone involvement at diagnosis in children with acute lymphoblastic leukemia as a predictive factor and to correlate its presence with other demographic, clinical, and laboratory findings. We retrospectively reviewed the medical records of 97 children with acute lymphoblastic leukemia diagnosed from January 2005 to December 2014. The mean age of patients was 5.7 years, and 83 (85.6 %) of them were diagnosed with B-acute lymphoblastic leukemia. Among the 97 children, 46 (47.4 %) reported bone involvement at the time of diagnosis. Among children with B-acute lymphoblastic leukemia 43/83 (51.8 %) reported bone involvement, while among children with T-acute lymphoblastic leukemia only 3/14 (21.4 %) (p =0.04). Bone involvement was registered more frequently among males (30/59; 50.8 %) in comparison to females (16/38; 42.2 %) (p =0.414). The mean white blood cell count at diagnosis was lower among children with bone involvement (109,800/mm 3 vs. 184,700/mm 3 ) (p =0.092). The mean age of patients with bone involvement was four years, which differs significantly from those without bone involvement (p =0.029). Moreover, children with bone involvement at diagnosis were prednisone "good responders" (79.5 %) when compared with those without bone involvement (58.8 %) (p =0.046). Additionally, mean serum phosphate values were higher at diagnosis among children with bone involvement (5.3 mg/dl vs. 4.8 mg/dl, p =0.035). The presence of bone involvement at diagnosis is related with immunophenotype of B-acute lymphoblastic leukemia, lower mean age, lower mean white blood cell count and good prednisone response. According to presented data, we conclude that the presence of bone involvement at diagnosis represents a positive predictive factor for outcome/survival. Hippokratia 2016, 20(3): 227-230.

Bone involvement at diagnosis as a predictive factor in children with acute lymphoblastic leukemia

PubMed Central

Tragiannidis, A; Vasileiou, E; Papageorgiou, M; Damianidou, L; Hatzipantelis, E; Gombakis, N; Giannopoulos, A

2016-01-01

Background: Bone involvement represents a common symptom at diagnosis in children with acute lymphoblastic leukemia, and its prognostic value is not entirely clarified. The aim of this study was to evaluate bone involvement at diagnosis in children with acute lymphoblastic leukemia as a predictive factor and to correlate its presence with other demographic, clinical, and laboratory findings. Methods: We retrospectively reviewed the medical records of 97 children with acute lymphoblastic leukemia diagnosed from January 2005 to December 2014. The mean age of patients was 5.7 years, and 83 (85.6 %) of them were diagnosed with B-acute lymphoblastic leukemia. Results: Among the 97 children, 46 (47.4 %) reported bone involvement at the time of diagnosis. Among children with B-acute lymphoblastic leukemia 43/83 (51.8 %) reported bone involvement, while among children with T-acute lymphoblastic leukemia only 3/14 (21.4 %) (p =0.04). Bone involvement was registered more frequently among males (30/59; 50.8 %) in comparison to females (16/38; 42.2 %) (p =0.414). The mean white blood cell count at diagnosis was lower among children with bone involvement (109,800/mm3 vs. 184,700/mm3) (p =0.092). The mean age of patients with bone involvement was four years, which differs significantly from those without bone involvement (p =0.029). Moreover, children with bone involvement at diagnosis were prednisone “good responders” (79.5 %) when compared with those without bone involvement (58.8 %) (p =0.046). Additionally, mean serum phosphate values were higher at diagnosis among children with bone involvement (5.3 mg/dl vs. 4.8 mg/dl, p =0.035). Conclusions: The presence of bone involvement at diagnosis is related with immunophenotype of B-acute lymphoblastic leukemia, lower mean age, lower mean white blood cell count and good prednisone response. According to presented data, we conclude that the presence of bone involvement at diagnosis represents a positive predictive factor for

The Universal Decimal Classification: Some Factors Concerning Its Origins, Development, and Influence.

ERIC Educational Resources Information Center

McIlwaine, I. C.

1997-01-01

Discusses the history and development of the Universal Decimal Classification (UDC). Topics include the relationship with Dewey Decimal Classification; revision process; structure; facet analysis; lack of standard rules for application; application in automated systems; influence of UDC on classification development; links with thesauri; and use…

Automatic classification of hyperactive children: comparing multiple artificial intelligence approaches.

PubMed

Delavarian, Mona; Towhidkhah, Farzad; Gharibzadeh, Shahriar; Dibajnia, Parvin

2011-07-12

Automatic classification of different behavioral disorders with many similarities (e.g. in symptoms) by using an automated approach will help psychiatrists to concentrate on correct disorder and its treatment as soon as possible, to avoid wasting time on diagnosis, and to increase the accuracy of diagnosis. In this study, we tried to differentiate and classify (diagnose) 306 children with many similar symptoms and different behavioral disorders such as ADHD, depression, anxiety, comorbid depression and anxiety and conduct disorder with high accuracy. Classification was based on the symptoms and their severity. With examining 16 different available classifiers, by using "Prtools", we have proposed nearest mean classifier as the most accurate classifier with 96.92% accuracy in this research. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

A four-tier classification system of pulmonary artery metrics on computed tomography for the diagnosis and prognosis of pulmonary hypertension.

PubMed

Truong, Quynh A; Bhatia, Harpreet Singh; Szymonifka, Jackie; Zhou, Qing; Lavender, Zachary; Waxman, Aaron B; Semigran, Marc J; Malhotra, Rajeev

We aimed to develop a severity classification system of the main pulmonary artery diameter (mPA) and its ratio to the ascending aorta diameter (ratio PA) for the diagnosis and prognosis of pulmonary hypertension (PH) on computed tomography (CT) scans. In 228 patients (136 with PH) undergoing right heart catheterization (RHC) and CT for dyspnea, we measured mPA and ratio PA. In a derivation cohort (n = 114), we determined cutpoints for a four-tier severity grading system that would maximize sensitivity and specificity, and validated it in a separate cohort (n = 114). Cutpoints for mPA were defined with ≤27 mm(F) and ≤29 mm(M) as the normal reference range; mild as >27 to <31 mm(F) and >29 to <31 mm(M); moderate≥31-34 mm; and severe>34 mm. Cutpoints for ratio PA were defined as normal ≤0.9; mild>0.9 to 1.0; moderate>1.0 to 1.1; and severe>1.1. Sensitivities for normal tier were 99% for mPA and 93% for ratio PA; while specificities for severe tier were 98% for mPA>34 mm and 100% for ratio PA>1.1. C-statistics for four-tier mPA and ratio PA were both 0.90 (derivation) and both 0.85 (validation). Severity of mPA and ratio PA corresponded to hemodynamics by RHC and echocardiography (both p < 0.001). Moderate-severe mPA values of ≥31 mm and ratio PA>1.1 had worse survival than normal values (all p ≤ 0.01). A CT-based four-tier severity classification system of PA diameter and its ratio to the aortic diameter has high accuracy for PH diagnosis with increased mortality in patients with moderate-severe severity grades. These results may support clinical utilization on chest and cardiac CT reports. Copyright © 2018 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.

The Value of Ensari's Proposal in Evaluating the Mucosal Pathology of Childhood Celiac Disease: Old Classification versus New Version.

PubMed

Güreşci, Servet; Hızlı, Samil; Simşek, Gülçin Güler

2012-09-01

Small intestinal biopsy remains the gold standard in diagnosing celiac disease (CD); however, the wide spectrum of histopathological states and differential diagnosis of CD is still a diagnostic problem for pathologists. Recently, Ensari reviewed the literature and proposed an update of the histopathological diagnosis and classification for CD. In this study, the histopathological materials of 54 children in whom CD was diagnosed at our hospital were reviewed to compare the previous Marsh and Modified Marsh-Oberhuber classifications with this new proposal. In this study, we show that the Ensari classification is as accurate as the Marsh and Modified Marsh classifications in describing the consecutive states of mucosal damage seen in CD. Ensari's classification is simple, practical and facilitative in diagnosing and subtyping of mucosal pathology of CD.

Phenotype at diagnosis predicts recurrence rates in Crohn's disease

PubMed Central

Wolters, F L; Russel, M G; Sijbrandij, J; Ambergen, T; Odes, S; Riis, L; Langholz, E; Politi, P; Qasim, A; Koutroubakis, I; Tsianos, E; Vermeire, S; Freitas, J; van Zeijl, G; Hoie, O; Bernklev, T; Beltrami, M; Rodriguez, D; Stockbrügger, R W; Moum, B

2006-01-01

Background In Crohn's disease (CD), studies associating phenotype at diagnosis and subsequent disease activity are important for patient counselling and health care planning. Aims To calculate disease recurrence rates and to correlate these with phenotypic traits at diagnosis. Methods A prospectively assembled uniformly diagnosed European population based inception cohort of CD patients was classified according to the Vienna classification for disease phenotype at diagnosis. Surgical and non‐surgical recurrence rates throughout a 10 year follow up period were calculated. Multivariate analysis was performed to classify risk factors present at diagnosis for recurrent disease. Results A total of 358 were classified for phenotype at diagnosis, of whom 262 (73.2%) had a first recurrence and 113 patients (31.6%) a first surgical recurrence during the first 10 years after diagnosis. Patients with upper gastrointestinal disease at diagnosis had an excess risk of recurrence (hazard ratio 1.54 (95% confidence interval (CI) 1.13–2.10)) whereas age ⩾40 years at diagnosis was protective (hazard ratio 0.82 (95% CI 0.70–0.97)). Colonic disease was a protective characteristic for resective surgery (hazard ratio 0.38 (95% CI 0.21–0.69)). More frequent resective surgical recurrences were reported from Copenhagen (hazard ratio 3.23 (95% CI 1.32–7.89)). Conclusions A mild course of disease in terms of disease recurrence was observed in this European cohort. Phenotype at diagnosis had predictive value for disease recurrence with upper gastrointestinal disease being the most important positive predictor. A phenotypic North‐South gradient in CD may be present, illustrated by higher surgery risks in some of the Northern European centres. PMID:16361306

Heterogeneity of European DRG systems and potentials for a common EuroDRG system Comment on "Cholecystectomy and Diagnosis-Related Groups (DRGs): patient classification and hospital reimbursement in 11 European countries".

PubMed

Geissler, Alexander; Quentin, Wilm; Busse, Reinhard

2015-03-05

Diagnosis-Related Group (DRG) systems across Europe are very heterogeneous, in particular because of different classification variables and algorithms as well as costing methodologies. But, given the challenge of increasing patient mobility within Europe, health systems are forced to incorporate a common patient classification language in order to compare and identify similar patients e.g. for reimbursement purposes. Beside the national adoption of DRGs for a wide range of purposes (measuring hospital activity vs. paying hospitals), a common DRG system can serve as an international communication basis among health administrators and can reduce the national development efforts as it is demonstrated by the NordDRG consortium. © 2015 by Kerman University of Medical Sciences.

A Power Transformers Fault Diagnosis Model Based on Three DGA Ratios and PSO Optimization SVM

NASA Astrophysics Data System (ADS)

Ma, Hongzhe; Zhang, Wei; Wu, Rongrong; Yang, Chunyan

2018-03-01

In order to make up for the shortcomings of existing transformer fault diagnosis methods in dissolved gas-in-oil analysis (DGA) feature selection and parameter optimization, a transformer fault diagnosis model based on the three DGA ratios and particle swarm optimization (PSO) optimize support vector machine (SVM) is proposed. Using transforming support vector machine to the nonlinear and multi-classification SVM, establishing the particle swarm optimization to optimize the SVM multi classification model, and conducting transformer fault diagnosis combined with the cross validation principle. The fault diagnosis results show that the average accuracy of test method is better than the standard support vector machine and genetic algorithm support vector machine, and the proposed method can effectively improve the accuracy of transformer fault diagnosis is proved.

Comparison of the prevalence of malnutrition diagnosis in head and neck, gastrointestinal and lung cancer patients by three classification methods

PubMed Central

Platek, Mary E.; Popp KPf, Johann V.; Possinger, Candi S.; DeNysschen, Carol A.; Horvath, Peter; Brown, Jean K.

2011-01-01

Background Malnutrition is prevalent among patients within certain cancer types. There is lack of universal standard of care for nutrition screening, lack of agreement on an operational definition and on validity of malnutrition indicators. Objective In a secondary data analysis, we investigated prevalence of malnutrition diagnosis by three classification methods using data from medical records of a National Cancer Institute (NCI)-designated comprehensive cancer center. Interventions/Methods Records of 227 patients hospitalized during 1998 with head and neck, gastrointestinal or lung cancer were reviewed for malnutrition based on three methods: 1) physician diagnosed malnutrition related ICD-9 codes; 2) in-hospital nutritional assessment summary conducted by Registered Dietitians; and 3) body mass index (BMI). For patients with multiple admissions, only data from the first hospitalization was included. Results Prevalence of malnutrition diagnosis ranged from 8.8% based on BMI to approximately 26% of all cases based on dietitian assessment. Kappa coefficients between any methods indicated a weak (kappa=0.23, BMI and Dietitians and kappa=0.28, Dietitians and Physicians) to fair strength of agreement (kappa=0.38, BMI and Physicians). Conclusions Available methods to identify patients with malnutrition in an NCI designated comprehensive cancer center resulted in varied prevalence of malnutrition diagnosis. Universal standard of care for nutrition screening that utilizes validated tools is needed. Implications for Practice The Joint Commission on the Accreditation of Healthcare Organizations requires nutritional screening of patients within 24 hours of admission. For this purpose, implementation of a validated tool that can be used by various healthcare practitioners, including nurses, needs to be considered. PMID:21242767

Tree Classification Software

NASA Technical Reports Server (NTRS)

Buntine, Wray

1993-01-01

This paper introduces the IND Tree Package to prospective users. IND does supervised learning using classification trees. This learning task is a basic tool used in the development of diagnosis, monitoring and expert systems. The IND Tree Package was developed as part of a NASA project to semi-automate the development of data analysis and modelling algorithms using artificial intelligence techniques. The IND Tree Package integrates features from CART and C4 with newer Bayesian and minimum encoding methods for growing classification trees and graphs. The IND Tree Package also provides an experimental control suite on top. The newer features give improved probability estimates often required in diagnostic and screening tasks. The package comes with a manual, Unix 'man' entries, and a guide to tree methods and research. The IND Tree Package is implemented in C under Unix and was beta-tested at university and commercial research laboratories in the United States.

Sociocultural factors and breast cancer in sub-Saharan Africa: implications for diagnosis and management.

PubMed

Tetteh, Dinah A; Faulkner, Sandra L

2016-01-01

The incidence of breast cancer is on the rise in sub-Saharan Africa (SSA) and efforts at early diagnosis have not been very successful because the public has scant knowledge about the disease, a large percentage of breast cancer cases are diagnosed late and mainly rural SSA women's practice of breast self-examination is poor. In this paper, we argue that an examination of the social and cultural contexts of SSA that influence breast cancer diagnosis and management in the region is needed. We discuss the implications of sociocultural factors, such as gender roles and spirituality, on breast cancer diagnosis and management in SSA.

A cautionary note on the use of the Analysis of Covariance (ANCOVA) in classification designs with and without within-subject factors

PubMed Central

Schneider, Bruce A.; Avivi-Reich, Meital; Mozuraitis, Mindaugas

2015-01-01

A number of statistical textbooks recommend using an analysis of covariance (ANCOVA) to control for the effects of extraneous factors that might influence the dependent measure of interest. However, it is not generally recognized that serious problems of interpretation can arise when the design contains comparisons of participants sampled from different populations (classification designs). Designs that include a comparison of younger and older adults, or a comparison of musicians and non-musicians are examples of classification designs. In such cases, estimates of differences among groups can be contaminated by differences in the covariate population means across groups. A second problem of interpretation will arise if the experimenter fails to center the covariate measures (subtracting the mean covariate score from each covariate score) whenever the design contains within-subject factors. Unless the covariate measures on the participants are centered, estimates of within-subject factors are distorted, and significant increases in Type I error rates, and/or losses in power can occur when evaluating the effects of within-subject factors. This paper: (1) alerts potential users of ANCOVA of the need to center the covariate measures when the design contains within-subject factors, and (2) indicates how they can avoid biases when one cannot assume that the expected value of the covariate measure is the same for all of the groups in a classification design. PMID:25954230

The debate over diagnosis related groups.

PubMed

Spiegel, A D; Kavaler, F

1985-01-01

With the advent of the Prospective Payment System (PPS) using Diagnosis Related Groups (DRGs) as a classification method, the pros and cons of that mechanism have been sharply debated. Grouping the comments into categories related to administration/management, DRG system and quality of care, a review of relevant literature highlights the pertinent attitudes and views of professionals and organizations. Points constantly argued include data utilization, meaningful medical classifications, resource use, gaming, profit centers, patient homogeneity, severity of illness, length of stay, technology limitations and the erosion of standards.

Factors Influencing Time-to-diagnosis of Biliary Atresia.

PubMed

Harpavat, Sanjiv; Lupo, Philip J; Liwanag, Loriel; Hollier, John; Brandt, Mary L; Finegold, Milton J; Shneider, Benjamin L

2018-06-01

Diagnosing biliary atresia (BA) quickly is critical, because earlier treatment correlates with delayed or reduced need for liver transplantation. However, diagnosing BA quickly is also difficult, with infants usually treated after 60 days of life. In this study, we aim to accelerate BA diagnosis and treatment, by better understanding factors influencing the diagnostic timeline. Infants born between 2007 and 2014 and diagnosed with BA at our institution were included (n = 65). Two periods were examined retrospectively: P1, the time from birth to specialist referral, and P2, the time from specialist referral to treatment. How sociodemographic factors associate with P1 and P2 were analyzed with Kaplan-Meier curves and Cox proportional hazard models. In addition, to better characterize P2, laboratory results and early tissue histology were studied. P1 associated with race/ethnicity, with shorter times in non-Hispanic white infants compared to non-Hispanic black and Hispanic infants (P = 0.007 and P = 0.004, respectively). P2 associated with referral age, with shorter times in infants referred after 30, 45, or 60 days of life (P < 0.001, P < 0.001, and P = 0.001, respectively). One potential reason for longer P2 in infants referred ≤30 days is that aminotransferase levels were normal or near-normal. However, despite reassuring laboratory values, tissue histology in early cases showed key features of BA. Our findings suggest 2 opportunities to accelerate BA diagnosis and treatment. First, to achieve prompt referrals for all races/ethnicities, universal screening strategies should be considered. Second, to ensure efficient evaluations independent of age, algorithms designed to detect early features of BA can be developed.

Automated classification of multiphoton microscopy images of ovarian tissue using deep learning.

PubMed

Huttunen, Mikko J; Hassan, Abdurahman; McCloskey, Curtis W; Fasih, Sijyl; Upham, Jeremy; Vanderhyden, Barbara C; Boyd, Robert W; Murugkar, Sangeeta

2018-06-01

Histopathological image analysis of stained tissue slides is routinely used in tumor detection and classification. However, diagnosis requires a highly trained pathologist and can thus be time-consuming, labor-intensive, and potentially risk bias. Here, we demonstrate a potential complementary approach for diagnosis. We show that multiphoton microscopy images from unstained, reproductive tissues can be robustly classified using deep learning techniques. We fine-train four pretrained convolutional neural networks using over 200 murine tissue images based on combined second-harmonic generation and two-photon excitation fluorescence contrast, to classify the tissues either as healthy or associated with high-grade serous carcinoma with over 95% sensitivity and 97% specificity. Our approach shows promise for applications involving automated disease diagnosis. It could also be readily applied to other tissues, diseases, and related classification problems. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Melanoma Diagnosis

NASA Astrophysics Data System (ADS)

Horsch, Alexander

The chapter deals with the diagnosis of the malignant melanoma of the skin. This aggressive type of cancer with steadily growing incidence in white populations can hundred percent be cured if it is detected in an early stage. Imaging techniques, in particular dermoscopy, have contributed significantly to improvement of diagnostic accuracy in clinical settings, achieving sensitivities for melanoma experts of beyond 95% at specificities of 90% and more. Automatic computer analysis of dermoscopy images has, in preliminary studies, achieved classification rates comparable to those of experts. However, the diagnosis of melanoma requires a lot of training and experience, and at the time being, average numbers of lesions excised per histology-proven melanoma are around 30, a number which clearly is too high. Further improvements in computer dermoscopy systems and their competent use in clinical settings certainly have the potential to support efforts of improving this situation. In the chapter, medical basics, current state of melanoma diagnosis, image analysis methods, commercial dermoscopy systems, evaluation of systems, and methods and future directions are presented.

Review of the diagnosis and management of gastrointestinal bezoars

PubMed Central

Iwamuro, Masaya; Okada, Hiroyuki; Matsueda, Kazuhiro; Inaba, Tomoki; Kusumoto, Chiaki; Imagawa, Atsushi; Yamamoto, Kazuhide

2015-01-01

The formation of a bezoar is a relatively infrequent disorder that affects the gastrointestinal system. Bezoars are mainly classified into four types depending on the material constituting the indigestible mass of the bezoar: phytobezoars, trichobezoars, pharmacobezoars, and lactobezoars. Gastric bezoars often cause ulcerative lesions in the stomach and subsequent bleeding, whereas small intestinal bezoars present with small bowel obstruction and ileus. A number of articles have emphasized the usefulness of Coca-Cola® administration for the dissolution of phytobezoars. However, persimmon phytobezoars may be resistant to such dissolution treatment because of their harder consistency compared to other types of phytobezoars. Better understanding of the etiology and epidemiology of each type of bezoar will facilitate prompt diagnosis and management. Here we provide an overview of the prevalence, classification, predisposing factors, and manifestations of bezoars. Diagnosis and management strategies are also discussed, reviewing mainly our own case series. Recent progress in basic research regarding persimmon phytobezoars is also briefly reviewed. PMID:25901212

Pancreatic abnormalities detected by endoscopic ultrasound (EUS) in patients without clinical signs of pancreatic disease: any difference between standard and Rosemont classification scoring?

PubMed

Petrone, Maria Chiara; Terracciano, Fulvia; Perri, Francesco; Carrara, Silvia; Cavestro, Giulia Martina; Mariani, Alberto; Testoni, Pier Alberto; Arcidiacono, Paolo Giorgio

2014-01-01

The prevalence of nine EUS features of chronic pancreatitis (CP) according to the standard Wiersema classification has been investigated in 489 patients undergoing EUS for an indication not related to pancreatico-biliary disease. We showed that 82 subjects (16.8%) had at least one ductular or parenchymal abnormality. Among them, 18 (3.7% of study population) had ≥3 Wiersema criteria suggestive of CP. Recently, a new classification (Rosemont) of EUS findings consistent, suggestive or indeterminate for CP has been proposed. To stratify healthy subjects into different subgroups on the basis of EUS features of CP according to the Wiersema and Rosemont classifications and to evaluate the agreement in the diagnosis of CP with the two scoring systems. Weighted kappa statistics was computed to evaluate the strength of agreement between the two scoring systems. Univariate and multivariate analysis between any EUS abnormality and habits were performed. Eighty-two EUS videos were reviewed. Using the Wiersema classification, 18 subjects showed ≥3 EUS features suggestive of CP. The EUS diagnosis of CP in these 18 subjects was considered as consistent in only one patient, according to Rosemont classification. Weighted Kappa statistics was 0.34 showing that the strength of agreement was 'fair'. Alcohol use and smoking were identified as risk factors for having pancreatic abnormalities on EUS. The prevalence of EUS features consistent or suggestive of CP in healthy subjects according to the Rosemont classification is lower than that assessed by Wiersema criteria. In that regard the Rosemont classification seems to be more accurate in excluding clinically relevant CP. Overall agreement between the two classifications is fair. Copyright © 2014 IAP and EPC. Published by Elsevier B.V. All rights reserved.

Nonlinear programming for classification problems in machine learning

NASA Astrophysics Data System (ADS)

Astorino, Annabella; Fuduli, Antonio; Gaudioso, Manlio

2016-10-01

We survey some nonlinear models for classification problems arising in machine learning. In the last years this field has become more and more relevant due to a lot of practical applications, such as text and web classification, object recognition in machine vision, gene expression profile analysis, DNA and protein analysis, medical diagnosis, customer profiling etc. Classification deals with separation of sets by means of appropriate separation surfaces, which is generally obtained by solving a numerical optimization model. While linear separability is the basis of the most popular approach to classification, the Support Vector Machine (SVM), in the recent years using nonlinear separating surfaces has received some attention. The objective of this work is to recall some of such proposals, mainly in terms of the numerical optimization models. In particular we tackle the polyhedral, ellipsoidal, spherical and conical separation approaches and, for some of them, we also consider the semisupervised versions.

On-board multispectral classification study

NASA Technical Reports Server (NTRS)

Ewalt, D.

1979-01-01

The factors relating to onboard multispectral classification were investigated. The functions implemented in ground-based processing systems for current Earth observation sensors were reviewed. The Multispectral Scanner, Thematic Mapper, Return Beam Vidicon, and Heat Capacity Mapper were studied. The concept of classification was reviewed and extended from the ground-based image processing functions to an onboard system capable of multispectral classification. Eight different onboard configurations, each with varying amounts of ground-spacecraft interaction, were evaluated. Each configuration was evaluated in terms of turnaround time, onboard processing and storage requirements, geometric and classification accuracy, onboard complexity, and ancillary data required from the ground.

Diagnosis of streamflow prediction skills in Oregon using Hydrologic Landscape Classification

EPA Science Inventory

A complete understanding of why rainfall-runoff models provide good streamflow predictions at catchments in some regions, but fail to do so in other regions, has still not been achieved. Here, we argue that a hydrologic classification system is a robust conceptual tool that is w...

The new WHO 2016 classification of brain tumors-what neurosurgeons need to know.

PubMed

Banan, Rouzbeh; Hartmann, Christian

2017-03-01

The understanding of molecular alterations of tumors has severely changed the concept of classification in all fields of pathology. The availability of high-throughput technologies such as next-generation sequencing allows for a much more precise definition of tumor entities. Also in the field of brain tumors a dramatic increase of knowledge has occurred over the last years partially calling into question the purely morphologically based concepts that were used as exclusive defining criteria in the WHO 2007 classification. Review of the WHO 2016 classification of brain tumors as well as a search and review of publications in the literature relevant for brain tumor classification from 2007 up to now. The idea of incorporating the molecular features in classifying tumors of the central nervous system led the authors of the new WHO 2016 classification to encounter inevitable conceptual problems, particularly with respect to linking morphology to molecular alterations. As a solution they introduced the concept of a "layered diagnosis" to the classification of brain tumors that still allows at a lower level a purely morphologically based diagnosis while partially forcing the incorporation of molecular characteristics for an "integrated diagnosis" at the highest diagnostic level. In this context the broad availability of molecular assays was debated. On the one hand molecular antibodies specifically targeting mutated proteins should be available in nearly all neuropathological laboratories. On the other hand, different high-throughput assays are accessible only in few first-world neuropathological institutions. As examples oligodendrogliomas are now primarily defined by molecular characteristics since the required assays are generally established, whereas molecular grouping of ependymomas, found to clearly outperform morphologically based tumor interpretation, was rejected from inclusion in the WHO 2016 classification because the required assays are currently only

Effective Diagnosis of Alzheimer's Disease by Means of Association Rules

NASA Astrophysics Data System (ADS)

Chaves, R.; Ramírez, J.; Górriz, J. M.; López, M.; Salas-Gonzalez, D.; Illán, I.; Segovia, F.; Padilla, P.

In this paper we present a novel classification method of SPECT images for the early diagnosis of the Alzheimer's disease (AD). The proposed method is based on Association Rules (ARs) aiming to discover interesting associations between attributes contained in the database. The system uses firstly voxel-as-features (VAF) and Activation Estimation (AE) to find tridimensional activated brain regions of interest (ROIs) for each patient. These ROIs act as inputs to secondly mining ARs between activated blocks for controls, with a specified minimum support and minimum confidence. ARs are mined in supervised mode, using information previously extracted from the most discriminant rules for centering interest in the relevant brain areas, reducing the computational requirement of the system. Finally classification process is performed depending on the number of previously mined rules verified by each subject, yielding an up to 95.87% classification accuracy, thus outperforming recent developed methods for AD diagnosis.

I-CAN: the classification and prediction of support needs.

PubMed

Arnold, Samuel R C; Riches, Vivienne C; Stancliffe, Roger J

2014-03-01

Since 1992, the diagnosis and classification of intellectual disability has been dependent upon three constructs: intelligence, adaptive behaviour and support needs (Luckasson et al. 1992. Mental Retardation: Definition, Classification and Systems of Support. American Association on Intellectual and Developmental Disability, Washington, DC). While the methods and instruments to measure intelligence and adaptive behaviour are well established and generally accepted, the measurement and classification of support needs is still in its infancy. This article explores the measurement and classification of support needs. A study is presented comparing scores on the ICF (WHO, 2001) based I-CAN v4.2 support needs assessment and planning tool with expert clinical judgment using a proposed classification of support needs. A logical classification algorithm was developed and validated on a separate sample. Good internal consistency (range 0.73-0.91, N = 186) and criterion validity (κ = 0.94, n = 49) were found. Further advances in our understanding and measurement of support needs could change the way we assess, describe and classify disability. © 2013 John Wiley & Sons Ltd.

Head Lice: Diagnosis

MedlinePlus

... Treatment FAQs Malathion FAQs Epidemiology & Risk Factors Disease Biology Diagnosis Treatment Prevention & Control Resources for Health Professionals ... Frequently Asked Questions (FAQs) Epidemiology & Risk Factors Disease Biology Diagnosis Treatment Prevention & Control Resources for Health Professionals ...

Body Lice Diagnosis

MedlinePlus

... Treatment FAQs Malathion FAQs Epidemiology & Risk Factors Disease Biology Diagnosis Treatment Prevention & Control Resources for Health Professionals ... Frequently Asked Questions (FAQs) Epidemiology & Risk Factors Disease Biology Diagnosis Treatment Prevention & Control Resources for Health Professionals ...

Association between gastric cancer and the Kyoto classification of gastritis.

PubMed

Shichijo, Satoki; Hirata, Yoshihiro; Niikura, Ryota; Hayakawa, Yoku; Yamada, Atsuo; Koike, Kazuhiko

2017-09-01

Histological gastritis is associated with gastric cancer, but its diagnosis requires biopsy. Many classifications of endoscopic gastritis are available, but not all are useful for risk stratification of gastric cancer. The Kyoto Classification of Gastritis was proposed at the 85th Congress of the Japan Gastroenterological Endoscopy Society. This cross-sectional study evaluated the usefulness of the Kyoto Classification of Gastritis for risk stratification of gastric cancer. From August 2013 to September 2014, esophagogastroduodenoscopy was performed and the gastric findings evaluated according to the Kyoto Classification of Gastritis in a total of 4062 patients. The following five endoscopic findings were selected based on previous reports: atrophy, intestinal metaplasia, enlarged folds, nodularity, and diffuse redness. A total of 3392 patients (1746 [51%] men and 1646 [49%] women) were analyzed. Among them, 107 gastric cancers were diagnosed. Atrophy was found in 2585 (78%) and intestinal metaplasia in 924 (27%). Enlarged folds, nodularity, and diffuse redness were found in 197 (5.8%), 22 (0.6%), and 573 (17%), respectively. In univariate analyses, the severity of atrophy, intestinal metaplasia, diffuse redness, age, and male sex were associated with gastric cancer. In a multivariate analysis, atrophy and male sex were found to be independent risk factors. Younger age and severe atrophy were determined to be associated with diffuse-type gastric cancer. Endoscopic detection of atrophy was associated with the risk of gastric cancer. Thus, patients with severe atrophy should be examined carefully and may require intensive follow-up. © 2017 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

An improved arteriovenous classification method for the early diagnostics of various diseases in retinal image.

PubMed

Xu, Xiayu; Ding, Wenxiang; Abràmoff, Michael D; Cao, Ruofan

2017-04-01

Retinal artery and vein classification is an important task for the automatic computer-aided diagnosis of various eye diseases and systemic diseases. This paper presents an improved supervised artery and vein classification method in retinal image. Intra-image regularization and inter-subject normalization is applied to reduce the differences in feature space. Novel features, including first-order and second-order texture features, are utilized to capture the discriminating characteristics of arteries and veins. The proposed method was tested on the DRIVE dataset and achieved an overall accuracy of 0.923. This retinal artery and vein classification algorithm serves as a potentially important tool for the early diagnosis of various diseases, including diabetic retinopathy and cardiovascular diseases. Copyright © 2017 Elsevier B.V. All rights reserved.

Classification of worldwide bovine tuberculosis risk factors in cattle: a stratified approach

PubMed Central

Humblet, Marie-France; Boschiroli, Maria Laura; Saegerman, Claude

2009-01-01

The worldwide status of bovine tuberculosis (bTB) as a zoonosis remains of great concern. This article reviews the main risk factors for bTB in cattle based on a three-level classification: animal, herd and region/country level. A distinction is also made, whenever possible, between situations in developed and developing countries as the difference of context might have consequences in terms of risk of bTB. Recommendations are suggested to animal health professionals and scientists directly involved in the control and prevention of bTB in cattle. The determination of Millenium Development Goals for bTB is proposed to improve the control/eradication of the disease worldwide. PMID:19497258

Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding

PubMed Central

Wang, Xiang; Zheng, Yuan; Zhao, Zhenzhou; Wang, Jinping

2015-01-01

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. PMID:26153771

Evaluation of the ACR and SLICC classification criteria in juvenile-onset systemic lupus erythematosus: a longitudinal analysis.

PubMed

Lythgoe, H; Morgan, T; Heaf, E; Lloyd, O; Al-Abadi, E; Armon, K; Bailey, K; Davidson, J; Friswell, M; Gardner-Medwin, J; Haslam, K; Ioannou, Y; Leahy, A; Leone, V; Pilkington, C; Rangaraj, S; Riley, P; Tizard, E J; Wilkinson, N; Beresford, M W

2017-10-01

Objectives The Systemic Lupus International Collaborating Clinics (SLICC) group proposed revised classification criteria for systemic lupus erythematosus (SLICC-2012 criteria). This study aimed to compare these criteria with the well-established American College of Rheumatology classification criteria (ACR-1997 criteria) in a national cohort of juvenile-onset systemic lupus erythematosus (JSLE) patients and evaluate how patients' classification criteria evolved over time. Methods Data from patients in the UK JSLE Cohort Study with a senior clinician diagnosis of probable evolving, or definite JSLE, were analyzed. Patients were assessed using both classification criteria within 1 year of diagnosis and at latest follow up (following a minimum 12-month follow-up period). Results A total of 226 patients were included. The SLICC-2012 was more sensitive than ACR-1997 at diagnosis (92.9% versus 84.1% p < 0.001) and after follow up (100% versus 92.0% p < 0.001). Most patients meeting the SLICC-2012 criteria and not the ACR-1997 met more than one additional criterion on the SLICC-2012. Conclusions The SLICC-2012 was better able to classify patients with JSLE than the ACR-1997 and did so at an earlier stage in their disease course. SLICC-2012 should be considered for classification of JSLE patients in observational studies and clinical trial eligibility.

[To represent needs of nursing care using nursing diagnoses: potentials and restrictions of the NANDA classification and ICNP].

PubMed

Schilder, Michael

2005-03-01

Nursing diagnoses represent individual reactions to existing or potential changes in one's state of health. They are result of a diagnostic process, which is part of the dynamic nursing care process in its whole. Thus, as a basis of nursing interventions diagnoses have to be proved continuously. The classification of the North American Nursing Diagnosis Association (NANDA) as well as the International Classification for Nursing Practice (ICNP) can be account to the international well-known classifications of nursing diagnoses. Comparing their structures, some fundamental differences between both classifications become obvious. While the NANDA classification represents a systematic structured body of nursing knowledge with regard to human health reactions patterns, the ICNP reflects a more comprehensive part of the nursing reality, since it also contains nursing interventions and outcomes. Until the latest changes by establishing the taxonomy II, NANDA diagnoses have primarily focused deficits. But in contrast to the diagnoses of the ICNP they also comprise etiological factors. To prove the applicability of both classifications to nursing practice, they have been applied to a case study of a female resident living in a nursing home. The results of analysis show that because of their different structures the NANDA classification and ICNP have their own possibilities and limitations in covering the resident's individual needs of nursing care. These characteristic potentials and restrictions have to be taken into account when one of the classification systems is going to be implemented into nursing practice.

Transforaminal epidural steroid injections influence Mechanical Diagnosis and Therapy (MDT) pain response classification in candidates for lumbar herniated disc surgery.

PubMed

van Helvoirt, Hans; Apeldoorn, Adri T; Knol, Dirk L; Arts, Mark P; Kamper, Steven J; van Tulder, Maurits W; Ostelo, Raymond W

2016-04-27

Prospective cohort study. Although lumbar radiculopathy is regarded as a specific diagnosis, the most effective treatment strategy is unclear. Commonly used treatments include transforaminal epidural steroid injections (TESIs) and Mechanical Diagnosis & Therapy (MDT), but no studies have investigated the effectiveness of this combination. MDT differentiates pain centralization (C) from non-centralization (NC), which indicates good vs. poor prognostic validity respectively. The main aims were 1) to determine changes in Mechanical Diagnosis and Therapy (MDT) pain response classifications after transforaminal epidural steroid injections (TESIs) in candidates for lumbar herniated disc surgery and 2) to evaluate differences in short and long term outcomes for patients with different pain response classifications. Candidates for lumbar herniated disc surgery were assessed with a MDT protocol and their pain response classified as centralizing or peripheralizing. For this study,only patients were eligible who showed a peripheralizing pain response at intake. All patients then received TESIs and were reassessed and classified using the MDT protocol, into groups according to pain response (resolved, centralizing, peripheralizing with less pain and peripheralising with severe pain). After receiving targeted treatment based on pain response after TESIs, ranging from advice, MDT or surgery, follow-up assessments were completed at discharge and at 12 months. The primary outcomes were disability (Roland-Morris Disability Questionnaire [RMDQ] for Sciatica), pain severity in leg (visual analogue scale [VAS], 0-100) and global perceived effect (GPE). Linear mixed-models were used to determine between-groups differences in outcome. A total of 77 patients with lumbar disc herniation and peripheralizing symptoms were included. Patients received an average of 2 (SD 0.7) TESIs. After TESIs, 17 patients (22%) were classified as peripheralizing with continuing severe pain.These patients

Dystonia: an update on phenomenology, classification, pathogenesis and treatment.

PubMed

Balint, Bettina; Bhatia, Kailash P

2014-08-01

This article will highlight recent advances in dystonia with focus on clinical aspects such as the new classification, syndromic approach, new gene discoveries and genotype-phenotype correlations. Broadening of phenotype of some of the previously described hereditary dystonias and environmental risk factors and trends in treatment will be covered. Based on phenomenology, a new consensus update on the definition, phenomenology and classification of dystonia and a syndromic approach to guide diagnosis have been proposed. Terminology has changed and 'isolated dystonia' is used wherein dystonia is the only motor feature apart from tremor, and the previously called heredodegenerative dystonias and dystonia plus syndromes are now subsumed under 'combined dystonia'. The recently discovered genes ANO3, GNAL and CIZ1 appear not to be a common cause of adult-onset cervical dystonia. Clinical and genetic heterogeneity underlie myoclonus-dystonia, dopa-responsive dystonia and deafness-dystonia syndrome. ALS2 gene mutations are a newly recognized cause for combined dystonia. The phenotypic and genotypic spectra of ATP1A3 mutations have considerably broadened. Two new genome-wide association studies identified new candidate genes. A retrospective analysis suggested complicated vaginal delivery as a modifying risk factor in DYT1. Recent studies confirm lasting therapeutic effects of deep brain stimulation in isolated dystonia, good treatment response in myoclonus-dystonia, and suggest that early treatment correlates with a better outcome. Phenotypic classification continues to be important to recognize particular forms of dystonia and this includes syndromic associations. There are a number of genes underlying isolated or combined dystonia and there will be further new discoveries with the advances in genetic technologies such as exome and whole-genome sequencing. The identification of new genes will facilitate better elucidation of pathogenetic mechanisms and possible corrective

Cryptogenic stroke. A non-diagnosis.

PubMed

Gutiérrez-Zúñiga, Raquel; Fuentes, Blanca; Díez-Tejedor, Exuperio

2018-04-30

The term cryptogenic stroke refers to a stroke for which there is no specific attributable cause after a comprehensive evaluation. However, there are differences between the diagnostic criteria of etiological classifications used in clinical practice. An improvement in diagnostic tools such advances in monitoring for atrial fibrillation, advances in vascular imaging and evidence regarding the implication of patent foramen oval on the risk of stroke specially in young patients are reducing the proportion of stroke patients without etiological diagnosis. We carried out a critical review of the current concept of cryptogenic stroke, as a non-diagnosis, avoiding the simplification of it and reviewing the different entities that could fall under this diagnosis and reviewing the different entities that could fall under this diagnosis; and therefore avoid the same treatment for differents entities with uncertains results. Copyright © 2018 Elsevier España, S.L.U. All rights reserved.

A Deep Learning Approach for Fault Diagnosis of Induction Motors in Manufacturing

NASA Astrophysics Data System (ADS)

Shao, Si-Yu; Sun, Wen-Jun; Yan, Ru-Qiang; Wang, Peng; Gao, Robert X.

2017-11-01

Extracting features from original signals is a key procedure for traditional fault diagnosis of induction motors, as it directly influences the performance of fault recognition. However, high quality features need expert knowledge and human intervention. In this paper, a deep learning approach based on deep belief networks (DBN) is developed to learn features from frequency distribution of vibration signals with the purpose of characterizing working status of induction motors. It combines feature extraction procedure with classification task together to achieve automated and intelligent fault diagnosis. The DBN model is built by stacking multiple-units of restricted Boltzmann machine (RBM), and is trained using layer-by-layer pre-training algorithm. Compared with traditional diagnostic approaches where feature extraction is needed, the presented approach has the ability of learning hierarchical representations, which are suitable for fault classification, directly from frequency distribution of the measurement data. The structure of the DBN model is investigated as the scale and depth of the DBN architecture directly affect its classification performance. Experimental study conducted on a machine fault simulator verifies the effectiveness of the deep learning approach for fault diagnosis of induction motors. This research proposes an intelligent diagnosis method for induction motor which utilizes deep learning model to automatically learn features from sensor data and realize working status recognition.

A new epileptic seizure classification based exclusively on ictal semiology.

PubMed

Lüders, H; Acharya, J; Baumgartner, C; Benbadis, S; Bleasel, A; Burgess, R; Dinner, D S; Ebner, A; Foldvary, N; Geller, E; Hamer, H; Holthausen, H; Kotagal, P; Morris, H; Meencke, H J; Noachtar, S; Rosenow, F; Sakamoto, A; Steinhoff, B J; Tuxhorn, I; Wyllie, E

1999-03-01

Historically, seizure semiology was the main feature in the differential diagnosis of epileptic syndromes. With the development of clinical EEG, the definition of electroclinical complexes became an essential tool to define epileptic syndromes, particularly focal epileptic syndromes. Modern advances in diagnostic technology, particularly in neuroimaging and molecular biology, now permit better definitions of epileptic syndromes. At the same time detailed studies showed that there does not necessarily exist a one-to-one relationship between epileptic seizures or electroclinical complexes and epileptic syndromes. These developments call for the reintroduction of an epileptic seizure classification based exclusively on clinical semiology, similar to the seizure classifications which were used by neurologists before the introduction of the modern diagnostic methods. This classification of epileptic seizures should always be complemented by an epileptic syndrome classification based on all the available clinical information (clinical history, neurological exam, ictal semiology, EEG, anatomical and functional neuroimaging, etc.). Such an approach is more consistent with mainstream clinical neurology and would avoid the current confusion between the classification of epileptic seizures (which in the International Seizure Classification is actually a classification of electroclinical complexes) and the classification of epileptic syndromes.

Automatic classification of schizophrenia using resting-state functional language network via an adaptive learning algorithm

NASA Astrophysics Data System (ADS)

Zhu, Maohu; Jie, Nanfeng; Jiang, Tianzi

2014-03-01

A reliable and precise classification of schizophrenia is significant for its diagnosis and treatment of schizophrenia. Functional magnetic resonance imaging (fMRI) is a novel tool increasingly used in schizophrenia research. Recent advances in statistical learning theory have led to applying pattern classification algorithms to access the diagnostic value of functional brain networks, discovered from resting state fMRI data. The aim of this study was to propose an adaptive learning algorithm to distinguish schizophrenia patients from normal controls using resting-state functional language network. Furthermore, here the classification of schizophrenia was regarded as a sample selection problem where a sparse subset of samples was chosen from the labeled training set. Using these selected samples, which we call informative vectors, a classifier for the clinic diagnosis of schizophrenia was established. We experimentally demonstrated that the proposed algorithm incorporating resting-state functional language network achieved 83.6% leaveone- out accuracy on resting-state fMRI data of 27 schizophrenia patients and 28 normal controls. In contrast with KNearest- Neighbor (KNN), Support Vector Machine (SVM) and l1-norm, our method yielded better classification performance. Moreover, our results suggested that a dysfunction of resting-state functional language network plays an important role in the clinic diagnosis of schizophrenia.

Diesel Engine Valve Clearance Fault Diagnosis Based on Features Extraction Techniques and FastICA-SVM

NASA Astrophysics Data System (ADS)

Jing, Ya-Bing; Liu, Chang-Wen; Bi, Feng-Rong; Bi, Xiao-Yang; Wang, Xia; Shao, Kang

2017-07-01

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.

Patient casemix classification for medicare psychiatric prospective payment.

PubMed

Drozd, Edward M; Cromwell, Jerry; Gage, Barbara; Maier, Jan; Greenwald, Leslie M; Goldman, Howard H

2006-04-01

For a proposed Medicare prospective payment system for inpatient psychiatric facility treatment, the authors developed a casemix classification to capture differences in patients' real daily resource use. Primary data on patient characteristics and daily time spent in various activities were collected in a survey of 696 patients from 40 inpatient psychiatric facilities. Survey data were combined with Medicare claims data to estimate intensity-adjusted daily cost. Classification and Regression Trees (CART) analysis of average daily routine and ancillary costs yielded several hierarchical classification groupings. Regression analysis was used to control for facility and day-of-stay effects in order to compare hierarchical models with models based on the recently proposed payment system of the Centers for Medicare & Medicaid Services. CART analysis identified a small set of patient characteristics strongly associated with higher daily costs, including age, psychiatric diagnosis, deficits in daily living activities, and detox or ECT use. A parsimonious, 16-group, fully interactive model that used five major DSM-IV categories and stratified by age, illness severity, deficits in daily living activities, dangerousness, and use of ECT explained 40% (out of a possible 76%) of daily cost variation not attributable to idiosyncratic daily changes within patients. A noninteractive model based on diagnosis-related groups, age, and medical comorbidity had explanatory power of only 32%. A regression model with 16 casemix groups restricted to using "appropriate" payment variables (i.e., those with clinical face validity and low administrative burden that are easily validated and provide proper care incentives) produced more efficient and equitable payments than did a noninteractive system based on diagnosis-related groups.

From Classification to Epilepsy Ontology and Informatics

PubMed Central

Zhang, Guo-Qiang; Sahoo, Satya S; Lhatoo, Samden D

2012-01-01

Summary The 2010 International League Against Epilepsy (ILAE) classification and terminology commission report proposed a much needed departure from previous classifications to incorporate advances in molecular biology, neuroimaging, and genetics. It proposed an interim classification and defined two key requirements that need to be satisfied. The first is the ability to classify epilepsy in dimensions according to a variety of purposes including clinical research, patient care, and drug discovery. The second is the ability of the classification system to evolve with new discoveries. Multi-dimensionality and flexibility are crucial to the success of any future classification. In addition, a successful classification system must play a central role in the rapidly growing field of epilepsy informatics. An epilepsy ontology, based on classification, will allow information systems to facilitate data-intensive studies and provide a proven route to meeting the two foregoing key requirements. Epilepsy ontology will be a structured terminology system that accommodates proposed and evolving ILAE classifications, the NIH/NINDS Common Data Elements, the ICD systems and explicitly specifies all known relationships between epilepsy concepts in a proper framework. This will aid evidence based epilepsy diagnosis, investigation, treatment and research for a diverse community of clinicians and researchers. Benefits range from systematization of electronic patient records to multi-modal data repositories for research and training manuals for those involved in epilepsy care. Given the complexity, heterogeneity and pace of research advances in the epilepsy domain, such an ontology must be collaboratively developed by key stakeholders in the epilepsy community and experts in knowledge engineering and computer science. PMID:22765502

Ensemble Sparse Classification of Alzheimer’s Disease

PubMed Central

Liu, Manhua; Zhang, Daoqiang; Shen, Dinggang

2012-01-01

The high-dimensional pattern classification methods, e.g., support vector machines (SVM), have been widely investigated for analysis of structural and functional brain images (such as magnetic resonance imaging (MRI)) to assist the diagnosis of Alzheimer’s disease (AD) including its prodromal stage, i.e., mild cognitive impairment (MCI). Most existing classification methods extract features from neuroimaging data and then construct a single classifier to perform classification. However, due to noise and small sample size of neuroimaging data, it is challenging to train only a global classifier that can be robust enough to achieve good classification performance. In this paper, instead of building a single global classifier, we propose a local patch-based subspace ensemble method which builds multiple individual classifiers based on different subsets of local patches and then combines them for more accurate and robust classification. Specifically, to capture the local spatial consistency, each brain image is partitioned into a number of local patches and a subset of patches is randomly selected from the patch pool to build a weak classifier. Here, the sparse representation-based classification (SRC) method, which has shown effective for classification of image data (e.g., face), is used to construct each weak classifier. Then, multiple weak classifiers are combined to make the final decision. We evaluate our method on 652 subjects (including 198 AD patients, 225 MCI and 229 normal controls) from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database using MR images. The experimental results show that our method achieves an accuracy of 90.8% and an area under the ROC curve (AUC) of 94.86% for AD classification and an accuracy of 87.85% and an AUC of 92.90% for MCI classification, respectively, demonstrating a very promising performance of our method compared with the state-of-the-art methods for AD/MCI classification using MR images. PMID:22270352

Developing a dementia-specific health state classification system for a new preference-based instrument AD-5D.

PubMed

Nguyen, Kim-Huong; Mulhern, Brendan; Kularatna, Sanjeewa; Byrnes, Joshua; Moyle, Wendy; Comans, Tracy

2017-01-25

With an ageing population, the number of people with dementia is rising. The economic impact on the health care system is considerable and new treatment methods and approaches to dementia care must be cost effective. Economic evaluation requires valid patient reported outcome measures, and this study aims to develop a dementia-specific health state classification system based on the Quality of Life for Alzheimer's disease (QOL-AD) instrument (nursing home version). This classification system will subsequently be valued to generate a preference-based measure for use in the economic evaluation of interventions for people with dementia. We assessed the dimensionality of the QOL-AD to develop a new classification system. This was done using exploratory and confirmatory factor analysis and further assessment of the structure of the measure to ensure coverage of the key areas of quality of life. Secondly, we used Rasch analysis to test the psychometric performance of the items, and select item(s) to describe each dimension. This was done on 13 items of the QOL-AD (excluding two general health items) using a sample of 284 residents living in long-term care facilities in Australia who had a diagnosis of dementia. A five dimension classification system is proposed resulting from the three factor structure (defined as 'interpersonal environment', 'physical health' and 'self-functioning') derived from the factor analysis and two factors ('memory' and 'mood') from the accompanying review. For the first three dimensions, Rasch analysis selected three questions of the QOL-AD ('living situation', 'physical health', and 'do fun things') with memory and mood questions representing their own dimensions. The resulting classification system (AD-5D) includes many of the health-related quality of life dimensions considered important to people with dementia, including mood, global function and skill in daily living. The development of the AD-5D classification system is an important step

An exudate detection method for diagnosis risk of diabetic macular edema in retinal images using feature-based and supervised classification.

PubMed

Marin, D; Gegundez-Arias, M E; Ponte, B; Alvarez, F; Garrido, J; Ortega, C; Vasallo, M J; Bravo, J M

2018-01-10

The present paper aims at presenting the methodology and first results of a detection system of risk of diabetic macular edema (DME) in fundus images. The system is based on the detection of retinal exudates (Ex), whose presence in the image is clinically used for an early diagnosis of the disease. To do so, the system applies digital image processing algorithms to the retinal image in order to obtain a set of candidate regions to be Ex, which are validated by means of feature extraction and supervised classification techniques. The diagnoses provided by the system on 1058 retinographies of 529 diabetic patients at risk of having DME show that the system can operate at a level of sensitivity comparable to that of ophthalmological specialists: it achieved 0.9000 sensitivity per patient against 0.7733, 0.9133 and 0.9000 of several specialists, where the false negatives were mild clinical cases of the disease. In addition, the level of specificity reached by the system was 0.6939, high enough to screen about 70% of the patients with no evidence of DME. These values show that the system fulfils the requirements for its possible integration into a complete diabetic retinopathy pre-screening tool for the automated management of patients within a screening programme. Graphical Abstract Diagnosis system of risk of diabetic macular edema (DME) based on exudate (Ex) detection in fundus images.

Pattern classification of brain activation during emotional processing in subclinical depression: psychosis proneness as potential confounding factor.

PubMed

Modinos, Gemma; Mechelli, Andrea; Pettersson-Yeo, William; Allen, Paul; McGuire, Philip; Aleman, Andre

2013-01-01

We used Support Vector Machine (SVM) to perform multivariate pattern classification based on brain activation during emotional processing in healthy participants with subclinical depressive symptoms. Six-hundred undergraduate students completed the Beck Depression Inventory II (BDI-II). Two groups were subsequently formed: (i) subclinical (mild) mood disturbance (n = 17) and (ii) no mood disturbance (n = 17). Participants also completed a self-report questionnaire on subclinical psychotic symptoms, the Community Assessment of Psychic Experiences Questionnaire (CAPE) positive subscale. The functional magnetic resonance imaging (fMRI) paradigm entailed passive viewing of negative emotional and neutral scenes. The pattern of brain activity during emotional processing allowed correct group classification with an overall accuracy of 77% (p = 0.002), within a network of regions including the amygdala, insula, anterior cingulate cortex and medial prefrontal cortex. However, further analysis suggested that the classification accuracy could also be explained by subclinical psychotic symptom scores (correlation with SVM weights r = 0.459, p = 0.006). Psychosis proneness may thus be a confounding factor for neuroimaging studies in subclinical depression.

Non-negative matrix factorization in texture feature for classification of dementia with MRI data

NASA Astrophysics Data System (ADS)

Sarwinda, D.; Bustamam, A.; Ardaneswari, G.

2017-07-01

This paper investigates applications of non-negative matrix factorization as feature selection method to select the features from gray level co-occurrence matrix. The proposed approach is used to classify dementia using MRI data. In this study, texture analysis using gray level co-occurrence matrix is done to feature extraction. In the feature extraction process of MRI data, we found seven features from gray level co-occurrence matrix. Non-negative matrix factorization selected three features that influence of all features produced by feature extractions. A Naïve Bayes classifier is adapted to classify dementia, i.e. Alzheimer's disease, Mild Cognitive Impairment (MCI) and normal control. The experimental results show that non-negative factorization as feature selection method able to achieve an accuracy of 96.4% for classification of Alzheimer's and normal control. The proposed method also compared with other features selection methods i.e. Principal Component Analysis (PCA).

Evaluation of the Melanocytic Pathology Assessment Tool and Hierarchy for Diagnosis (MPATH-Dx) classification scheme for diagnosis of cutaneous melanocytic neoplasms: Results from the International Melanoma Pathology Study Group.

PubMed

Lott, Jason P; Elmore, Joann G; Zhao, Ge A; Knezevich, Stevan R; Frederick, Paul D; Reisch, Lisa M; Chu, Emily Y; Cook, Martin G; Duncan, Lyn M; Elenitsas, Rosalie; Gerami, Pedram; Landman, Gilles; Lowe, Lori; Messina, Jane L; Mihm, Martin C; van den Oord, Joost J; Rabkin, Michael S; Schmidt, Birgitta; Shea, Christopher R; Yun, Sook Jung; Xu, George X; Piepkorn, Michael W; Elder, David E; Barnhill, Raymond L

2016-08-01

Pathologists use diverse terminology when interpreting melanocytic neoplasms, potentially compromising quality of care. We sought to evaluate the Melanocytic Pathology Assessment Tool and Hierarchy for Diagnosis (MPATH-Dx) scheme, a 5-category classification system for melanocytic lesions. Participants (n = 16) of the 2013 International Melanoma Pathology Study Group Workshop provided independent case-level diagnoses and treatment suggestions for 48 melanocytic lesions. Individual diagnoses (including, when necessary, least and most severe diagnoses) were mapped to corresponding MPATH-Dx classes. Interrater agreement and correlation between MPATH-Dx categorization and treatment suggestions were evaluated. Most participants were board-certified dermatopathologists (n = 15), age 50 years or older (n = 12), male (n = 9), based in the United States (n = 11), and primary academic faculty (n = 14). Overall, participants generated 634 case-level diagnoses with treatment suggestions. Mean weighted kappa coefficients for diagnostic agreement after MPATH-Dx mapping (assuming least and most severe diagnoses, when necessary) were 0.70 (95% confidence interval 0.68-0.71) and 0.72 (95% confidence interval 0.71-0.73), respectively, whereas correlation between MPATH-Dx categorization and treatment suggestions was 0.91. This was a small sample size of experienced pathologists in a testing situation. Varying diagnostic nomenclature can be classified into a concise hierarchy using the MPATH-Dx scheme. Further research is needed to determine whether this classification system can facilitate diagnostic concordance in general pathology practice and improve patient care. Copyright © 2016 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

Leishmania infections: Molecular targets and diagnosis.

PubMed

Akhoundi, Mohammad; Downing, Tim; Votýpka, Jan; Kuhls, Katrin; Lukeš, Julius; Cannet, Arnaud; Ravel, Christophe; Marty, Pierre; Delaunay, Pascal; Kasbari, Mohamed; Granouillac, Bruno; Gradoni, Luigi; Sereno, Denis

2017-10-01

Progress in the diagnosis of leishmaniases depends on the development of effective methods and the discovery of suitable biomarkers. We propose firstly an update classification of Leishmania species and their synonymies. We demonstrate a global map highlighting the geography of known endemic Leishmania species pathogenic to humans. We summarize a complete list of techniques currently in use and discuss their advantages and limitations. The available data highlights the benefits of molecular markers in terms of their sensitivity and specificity to quantify variation from the subgeneric level to species complexes, (sub) species within complexes, and individual populations and infection foci. Each DNA-based detection method is supplied with a comprehensive description of markers and primers and proposal for a classification based on the role of each target and primer in the detection, identification and quantification of leishmaniasis infection. We outline a genome-wide map of genes informative for diagnosis that have been used for Leishmania genotyping. Furthermore, we propose a classification method based on the suitability of well-studied molecular markers for typing the 21 known Leishmania species pathogenic to humans. This can be applied to newly discovered species and to hybrid strains originating from inter-species crosses. Developing more effective and sensitive diagnostic methods and biomarkers is vital for enhancing Leishmania infection control programs. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

An automated cirrus classification

NASA Astrophysics Data System (ADS)

Gryspeerdt, Edward; Quaas, Johannes; Sourdeval, Odran; Goren, Tom

2017-04-01

Cirrus clouds play an important role in determining the radiation budget of the earth, but our understanding of the lifecycle and controls on cirrus clouds remains incomplete. Cirrus clouds can have very different properties and development depending on their environment, particularly during their formation. However, the relevant factors often cannot be distinguished using commonly retrieved satellite data products (such as cloud optical depth). In particular, the initial cloud phase has been identified as an important factor in cloud development, but although back-trajectory based methods can provide information on the initial cloud phase, they are computationally expensive and depend on the cloud parametrisations used in re-analysis products. In this work, a classification system (Identification and Classification of Cirrus, IC-CIR) is introduced. Using re-analysis and satellite data, cirrus clouds are separated in four main types: frontal, convective, orographic and in-situ. The properties of these classes show that this classification is able to provide useful information on the properties and initial phase of cirrus clouds, information that could not be provided by instantaneous satellite retrieved cloud properties alone. This classification is designed to be easily implemented in global climate models, helping to improve future comparisons between observations and models and reducing the uncertainty in cirrus clouds properties, leading to improved cloud parametrisations.

Developing a case mix classification for child and adolescent mental health services: the influence of presenting problems, complexity factors and service providers on number of appointments.

PubMed

Martin, Peter; Davies, Roger; Macdougall, Amy; Ritchie, Benjamin; Vostanis, Panos; Whale, Andy; Wolpert, Miranda

2017-09-01

Case-mix classification is a focus of international attention in considering how best to manage and fund services, by providing a basis for fairer comparison of resource utilization. Yet there is little evidence of the best ways to establish case mix for child and adolescent mental health services (CAMHS). To develop a case mix classification for CAMHS that is clinically meaningful and predictive of number of appointments attended and to investigate the influence of presenting problems, context and complexity factors and provider variation. We analysed 4573 completed episodes of outpatient care from 11 English CAMHS. Cluster analysis, regression trees and a conceptual classification based on clinical best practice guidelines were compared regarding their ability to predict number of appointments, using mixed effects negative binomial regression. The conceptual classification is clinically meaningful and did as well as data-driven classifications in accounting for number of appointments. There was little evidence for effects of complexity or context factors, with the possible exception of school attendance problems. Substantial variation in resource provision between providers was not explained well by case mix. The conceptually-derived classification merits further testing and development in the context of collaborative decision making.

TFOS DEWS II Definition and Classification Report.

PubMed

Craig, Jennifer P; Nichols, Kelly K; Akpek, Esen K; Caffery, Barbara; Dua, Harminder S; Joo, Choun-Ki; Liu, Zuguo; Nelson, J Daniel; Nichols, Jason J; Tsubota, Kazuo; Stapleton, Fiona

2017-07-01

The goals of the TFOS DEWS II Definition and Classification Subcommittee were to create an evidence-based definition and a contemporary classification system for dry eye disease (DED). The new definition recognizes the multifactorial nature of dry eye as a disease where loss of homeostasis of the tear film is the central pathophysiological concept. Ocular symptoms, as a broader term that encompasses reports of discomfort or visual disturbance, feature in the definition and the key etiologies of tear film instability, hyperosmolarity, and ocular surface inflammation and damage were determined to be important for inclusion in the definition. In the light of new data, neurosensory abnormalities were also included in the definition for the first time. In the classification of DED, recent evidence supports a scheme based on the pathophysiology where aqueous deficient and evaporative dry eye exist as a continuum, such that elements of each are considered in diagnosis and management. Central to the scheme is a positive diagnosis of DED with signs and symptoms, and this is directed towards management to restore homeostasis. The scheme also allows consideration of various related manifestations, such as non-obvious disease involving ocular surface signs without related symptoms, including neurotrophic conditions where dysfunctional sensation exists, and cases where symptoms exist without demonstrable ocular surface signs, including neuropathic pain. This approach is not intended to override clinical assessment and judgment but should prove helpful in guiding clinical management and research. Copyright © 2017 Elsevier Inc. All rights reserved.

Retinal vasculature classification using novel multifractal features

NASA Astrophysics Data System (ADS)

Ding, Y.; Ward, W. O. C.; Duan, Jinming; Auer, D. P.; Gowland, Penny; Bai, L.

2015-11-01

Retinal blood vessels have been implicated in a large number of diseases including diabetic retinopathy and cardiovascular diseases, which cause damages to retinal blood vessels. The availability of retinal vessel imaging provides an excellent opportunity for monitoring and diagnosis of retinal diseases, and automatic analysis of retinal vessels will help with the processes. However, state of the art vascular analysis methods such as counting the number of branches or measuring the curvature and diameter of individual vessels are unsuitable for the microvasculature. There has been published research using fractal analysis to calculate fractal dimensions of retinal blood vessels, but so far there has been no systematic research extracting discriminant features from retinal vessels for classifications. This paper introduces new methods for feature extraction from multifractal spectra of retinal vessels for classification. Two publicly available retinal vascular image databases are used for the experiments, and the proposed methods have produced accuracies of 85.5% and 77% for classification of healthy and diabetic retinal vasculatures. Experiments show that classification with multiple fractal features produces better rates compared with methods using a single fractal dimension value. In addition to this, experiments also show that classification accuracy can be affected by the accuracy of vessel segmentation algorithms.

Advances in the diagnosis and treatment of tumor necrosis factor receptor-associated periodic syndrome.

PubMed

Aguado-Gil, L; Irarrazaval-Armendáriz, I; Pretel-Irazabal, M

2013-09-01

Tumor necrosis factor receptor-associated periodic syndrome (TRAPS) is a rare autosomal dominant disease included in the group of autoinflammatory syndromes. It is characterized by recurrent episodes of fever and inflammation in different regions of the body. The main clinical manifestations are myalgia, migratory erythematous rash, periorbital edema, and abdominal pain. The diagnosis is reached using gene analysis and prognosis depends on the appearance of amyloidosis secondary to the recurrent episodes of inflammation. Tumor necrosis factor inhibitors and corticosteroids are the most widely used treatments. In recent years, significant advances have been made in the diagnosis and treatment of TRAPS, thanks to a better understanding of its pathogenesis. Dermatologists must be aware that the skin manifestations of TRAPS are particularly important, as they are often diagnostic. Copyright © 2012 Elsevier España, S.L. and AEDV. All rights reserved.

The research on medical image classification algorithm based on PLSA-BOW model.

PubMed

Cao, C H; Cao, H L

2016-04-29

With the rapid development of modern medical imaging technology, medical image classification has become more important for medical diagnosis and treatment. To solve the existence of polysemous words and synonyms problem, this study combines the word bag model with PLSA (Probabilistic Latent Semantic Analysis) and proposes the PLSA-BOW (Probabilistic Latent Semantic Analysis-Bag of Words) model. In this paper we introduce the bag of words model in text field to image field, and build the model of visual bag of words model. The method enables the word bag model-based classification method to be further improved in accuracy. The experimental results show that the PLSA-BOW model for medical image classification can lead to a more accurate classification.

The Oxfordshire Community Stroke Project classification: correlation with imaging, associated complications, and prediction of outcome in acute ischemic stroke.

PubMed

Pittock, Sean J; Meldrum, Dara; Hardiman, Orla; Thornton, John; Brennan, Paul; Moroney, Joan T

2003-01-01

This preliminary study investigates the risk factor profile, post stroke complications, and outcome for four OCSP (Oxfordshire Community Stroke Project Classification) subtypes. One hundred seventeen consecutive ischemic stroke patients were clinically classified into 1 of 4 subtypes: total anterior (TACI), partial anterior (PACI), lacunar (LACI), and posterior (POCI) circulation infarcts. Study evaluations were performed at admission, 2 weeks, and 6 months. There was a good correlation between clinical classification and radiological diagnosis if a negative CT head was considered consistent with a lacunar infarction. No significant difference in risk factor profile was observed between subtypes. The TACI group had significantly higher mortality (P < .001), morbidity (P < .001, as per disability scales), length of hospital stay (P < .001), and complications (respiratory tract infection and seizures [P < .01]) as compared to the other three groups which were all similar at the different time points. The only significant difference found was the higher rate of stroke recurrence within the first 6 months in the POCI group (P < .001). The OCSP classification identifies two major groups (TACI and other 3 groups combined) who behave differently with respect to post stroke outcome. Further study with larger numbers of patients and thus greater power will be required to allow better discrimination of OCSP subtypes in respect of risk factors, complications, and outcomes if the OCSP is to be used to stratify patients in clinical trials.

Analysis of framelets for breast cancer diagnosis.

PubMed

Thivya, K S; Sakthivel, P; Venkata Sai, P M

2016-01-01

Breast cancer is the second threatening tumor among the women. The effective way of reducing breast cancer is its early detection which helps to improve the diagnosing process. Digital mammography plays a significant role in mammogram screening at earlier stage of breast carcinoma. Even though, it is very difficult to find accurate abnormality in prevalent screening by radiologists. But the possibility of precise breast cancer screening is encouraged by predicting the accurate type of abnormality through Computer Aided Diagnosis (CAD) systems. The two most important indicators of breast malignancy are microcalcifications and masses. In this study, framelet transform, a multiresolutional analysis is investigated for the classification of the above mentioned two indicators. The statistical and co-occurrence features are extracted from the framelet decomposed mammograms with different resolution levels and support vector machine is employed for classification with k-fold cross validation. This system achieves 94.82% and 100% accuracy in normal/abnormal classification (stage I) and benign/malignant classification (stage II) of mass classification system and 98.57% and 100% for microcalcification system when using the MIAS database.

Neuromuscular disease classification system

NASA Astrophysics Data System (ADS)

Sáez, Aurora; Acha, Begoña; Montero-Sánchez, Adoración; Rivas, Eloy; Escudero, Luis M.; Serrano, Carmen

2013-06-01

Diagnosis of neuromuscular diseases is based on subjective visual assessment of biopsies from patients by the pathologist specialist. A system for objective analysis and classification of muscular dystrophies and neurogenic atrophies through muscle biopsy images of fluorescence microscopy is presented. The procedure starts with an accurate segmentation of the muscle fibers using mathematical morphology and a watershed transform. A feature extraction step is carried out in two parts: 24 features that pathologists take into account to diagnose the diseases and 58 structural features that the human eye cannot see, based on the assumption that the biopsy is considered as a graph, where the nodes are represented by each fiber, and two nodes are connected if two fibers are adjacent. A feature selection using sequential forward selection and sequential backward selection methods, a classification using a Fuzzy ARTMAP neural network, and a study of grading the severity are performed on these two sets of features. A database consisting of 91 images was used: 71 images for the training step and 20 as the test. A classification error of 0% was obtained. It is concluded that the addition of features undetectable by the human visual inspection improves the categorization of atrophic patterns.

Case-based statistical learning applied to SPECT image classification

NASA Astrophysics Data System (ADS)

Górriz, Juan M.; Ramírez, Javier; Illán, I. A.; Martínez-Murcia, Francisco J.; Segovia, Fermín.; Salas-Gonzalez, Diego; Ortiz, A.

2017-03-01

Statistical learning and decision theory play a key role in many areas of science and engineering. Some examples include time series regression and prediction, optical character recognition, signal detection in communications or biomedical applications for diagnosis and prognosis. This paper deals with the topic of learning from biomedical image data in the classification problem. In a typical scenario we have a training set that is employed to fit a prediction model or learner and a testing set on which the learner is applied to in order to predict the outcome for new unseen patterns. Both processes are usually completely separated to avoid over-fitting and due to the fact that, in practice, the unseen new objects (testing set) have unknown outcomes. However, the outcome yields one of a discrete set of values, i.e. the binary diagnosis problem. Thus, assumptions on these outcome values could be established to obtain the most likely prediction model at the training stage, that could improve the overall classification accuracy on the testing set, or keep its performance at least at the level of the selected statistical classifier. In this sense, a novel case-based learning (c-learning) procedure is proposed which combines hypothesis testing from a discrete set of expected outcomes and a cross-validated classification stage.

Tongue Images Classification Based on Constrained High Dispersal Network.

PubMed

Meng, Dan; Cao, Guitao; Duan, Ye; Zhu, Minghua; Tu, Liping; Xu, Dong; Xu, Jiatuo

2017-01-01

Computer aided tongue diagnosis has a great potential to play important roles in traditional Chinese medicine (TCM). However, the majority of the existing tongue image analyses and classification methods are based on the low-level features, which may not provide a holistic view of the tongue. Inspired by deep convolutional neural network (CNN), we propose a novel feature extraction framework called constrained high dispersal neural networks (CHDNet) to extract unbiased features and reduce human labor for tongue diagnosis in TCM. Previous CNN models have mostly focused on learning convolutional filters and adapting weights between them, but these models have two major issues: redundancy and insufficient capability in handling unbalanced sample distribution. We introduce high dispersal and local response normalization operation to address the issue of redundancy. We also add multiscale feature analysis to avoid the problem of sensitivity to deformation. Our proposed CHDNet learns high-level features and provides more classification information during training time, which may result in higher accuracy when predicting testing samples. We tested the proposed method on a set of 267 gastritis patients and a control group of 48 healthy volunteers. Test results show that CHDNet is a promising method in tongue image classification for the TCM study.

Lung texture classification using bag of visual words

NASA Astrophysics Data System (ADS)

Asherov, Marina; Diamant, Idit; Greenspan, Hayit

2014-03-01

Interstitial lung diseases (ILD) refer to a group of more than 150 parenchymal lung disorders. High-Resolution Computed Tomography (HRCT) is the most essential imaging modality of ILD diagnosis. Nonetheless, classification of various lung tissue patterns caused by ILD is still regarded as a challenging task. The current study focuses on the classification of five most common categories of lung tissues of ILD in HRCT images: normal, emphysema, ground glass, fibrosis and micronodules. The objective of the research is to classify an expert-given annotated region of interest (AROI) using a bag of visual words (BoVW) framework. The images are divided into small patches and a collection of representative patches are defined as visual words. This procedure, termed dictionary construction, is performed for each individual lung texture category. The assumption is that different lung textures are represented by a different visual word distribution. The classification is performed using an SVM classifier with histogram intersection kernel. In the experiments, we use a dataset of 1018 AROIs from 95 patients. Classification using a leave-one-patient-out cross validation (LOPO CV) is used. Current classification accuracy obtained is close to 80%.

A new hybrid method based on fuzzy-artificial immune system and k-nn algorithm for breast cancer diagnosis.

PubMed

Sahan, Seral; Polat, Kemal; Kodaz, Halife; Güneş, Salih

2007-03-01

The use of machine learning tools in medical diagnosis is increasing gradually. This is mainly because the effectiveness of classification and recognition systems has improved in a great deal to help medical experts in diagnosing diseases. Such a disease is breast cancer, which is a very common type of cancer among woman. As the incidence of this disease has increased significantly in the recent years, machine learning applications to this problem have also took a great attention as well as medical consideration. This study aims at diagnosing breast cancer with a new hybrid machine learning method. By hybridizing a fuzzy-artificial immune system with k-nearest neighbour algorithm, a method was obtained to solve this diagnosis problem via classifying Wisconsin Breast Cancer Dataset (WBCD). This data set is a very commonly used data set in the literature relating the use of classification systems for breast cancer diagnosis and it was used in this study to compare the classification performance of our proposed method with regard to other studies. We obtained a classification accuracy of 99.14%, which is the highest one reached so far. The classification accuracy was obtained via 10-fold cross validation. This result is for WBCD but it states that this method can be used confidently for other breast cancer diagnosis problems, too.

The Value of Ensari’s Proposal in Evaluating the Mucosal Pathology of Childhood Celiac Disease: Old Classification versus New Version

PubMed Central

Güreşci, Servet; Hızlı, Şamil; Şimşek, Gülçin Güler

2012-01-01

Objective: Small intestinal biopsy remains the gold standard in diagnosing celiac disease (CD); however, the wide spectrum of histopathological states and differential diagnosis of CD is still a diagnostic problem for pathologists. Recently, Ensari reviewed the literature and proposed an update of the histopathological diagnosis and classification for CD. Materials and Methods: In this study, the histopathological materials of 54 children in whom CD was diagnosed at our hospital were reviewed to compare the previous Marsh and Modified Marsh-Oberhuber classifications with this new proposal. Results: In this study, we show that the Ensari classification is as accurate as the Marsh and Modified Marsh classifications in describing the consecutive states of mucosal damage seen in CD. Conclusions: Ensari’s classification is simple, practical and facilitative in diagnosing and subtyping of mucosal pathology of CD. PMID:25207015

Differential diagnosis of the scalp hair folliculitis.

PubMed

Lugović-Mihić, Liborija; Barisić, Freja; Bulat, Vedrana; Buljan, Marija; Situm, Mirna; Bradić, Lada; Mihić, Josip

2011-09-01

Scalp hair folliculitis is a relatively common condition in dermatological practice and a major diagnostic and therapeutic challenge due to the lack of exact guidelines. Generally, inflammatory diseases of the pilosebaceous follicle of the scalp most often manifest as folliculitis. There are numerous infective agents that may cause folliculitis, including bacteria, viruses and fungi, as well as many noninfective causes. Several noninfectious diseases may present as scalp hair folliculitis, such as folliculitis decalvans capillitii, perifolliculitis capitis abscendens et suffodiens, erosive pustular dermatitis, lichen planopilaris, eosinophilic pustular folliculitis, etc. The classification of folliculitis is both confusing and controversial. There are many different forms of folliculitis and several classifications. According to the considerable variability of histologic findings, there are three groups of folliculitis: infectious folliculitis, noninfectious folliculitis and perifolliculitis. The diagnosis of folliculitis occasionally requires histologic confirmation and cannot be based solely on clinical appearance of scalp lesions. This article summarizes prominent variants of inflammatory diseases of the scalp hair follicle with differential diagnosis and appertaining histological features.

Centered Kernel Alignment Enhancing Neural Network Pretraining for MRI-Based Dementia Diagnosis

PubMed Central

Cárdenas-Peña, David; Collazos-Huertas, Diego; Castellanos-Dominguez, German

2016-01-01

Dementia is a growing problem that affects elderly people worldwide. More accurate evaluation of dementia diagnosis can help during the medical examination. Several methods for computer-aided dementia diagnosis have been proposed using resonance imaging scans to discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and healthy controls (NC). Nonetheless, the computer-aided diagnosis is especially challenging because of the heterogeneous and intermediate nature of MCI. We address the automated dementia diagnosis by introducing a novel supervised pretraining approach that takes advantage of the artificial neural network (ANN) for complex classification tasks. The proposal initializes an ANN based on linear projections to achieve more discriminating spaces. Such projections are estimated by maximizing the centered kernel alignment criterion that assesses the affinity between the resonance imaging data kernel matrix and the label target matrix. As a result, the performed linear embedding allows accounting for features that contribute the most to the MCI class discrimination. We compare the supervised pretraining approach to two unsupervised initialization methods (autoencoders and Principal Component Analysis) and against the best four performing classification methods of the 2014 CADDementia challenge. As a result, our proposal outperforms all the baselines (7% of classification accuracy and area under the receiver-operating-characteristic curve) at the time it reduces the class biasing. PMID:27148392

Vascular Anomalies (Part I): Classification and Diagnostics of Vascular Anomalies.

PubMed

Sadick, Maliha; Müller-Wille, René; Wildgruber, Moritz; Wohlgemuth, Walter A

2018-06-06

 Vascular anomalies are a diagnostic and therapeutic challenge. They require dedicated interdisciplinary management. Optimal patient care relies on integral medical evaluation and a classification system established by experts in the field, to provide a better understanding of these complex vascular entities.  A dedicated classification system according to the International Society for the Study of Vascular Anomalies (ISSVA) and the German Interdisciplinary Society of Vascular Anomalies (DiGGefA) is presented. The vast spectrum of diagnostic modalities, ranging from ultrasound with color Doppler, conventional X-ray, CT with 4 D imaging and MRI as well as catheter angiography for appropriate assessment is discussed.  Congenital vascular anomalies are comprised of vascular tumors, based on endothelial cell proliferation and vascular malformations with underlying mesenchymal and angiogenetic disorder. Vascular tumors tend to regress with patient's age, vascular malformations increase in size and are subdivided into capillary, venous, lymphatic, arterio-venous and combined malformations, depending on their dominant vasculature. According to their appearance, venous malformations are the most common representative of vascular anomalies (70 %), followed by lymphatic malformations (12 %), arterio-venous malformations (8 %), combined malformation syndromes (6 %) and capillary malformations (4 %).  The aim is to provide an overview of the current classification system and diagnostic characterization of vascular anomalies in order to facilitate interdisciplinary management of vascular anomalies.   · Vascular anomalies are comprised of vascular tumors and vascular malformations, both considered to be rare diseases.. · Appropriate treatment depends on correct classification and diagnosis of vascular anomalies, which is based on established national and international classification systems, recommendations and guidelines.. · In the classification

[Facilitating the diagnosis of depression and burnout by identifying demographic and work-related risk and protective factors among nurses].

PubMed

Ádám, Szilvia; Nistor, Anikó; Nistor, Katalin; Cserháti, Zoltán; Mészáros, Veronika

2015-08-09

Depression and burnout are frequent comorbidities among nurses. Despite similar symptoms, their management differ. Therefore, their timely diagnosis is essential. To identify demographic and work-related risk and protective factors of burnout and depression, and facilitate their diagnosis. A cross-sectional study among 1,713 nurses was carried out. Depression and burnout were assessed by the shortened Beck Depression Questionnaire and Maclach Burnout Inventory, respectively. Risk and protective factors were explored using t-tests and analysis of variance. The prevalence of depression and moderate-to-high burnout was 35.1% and 34-74%, respectively. Having a partner/child and longer employment in the outpatient setting protected from burnout. Lack of a partner and male sex emerged as risk factors of depression and depersonalisation, respectively. High prevalence of depression and burnout among nurses poses a significant public health issue. Familiarity with the disease-specific risk and protective factors identified in this research may facilitate timely diagnosis and effective disease management.

Fault diagnosis for diesel valve trains based on time frequency images

NASA Astrophysics Data System (ADS)

Wang, Chengdong; Zhang, Youyun; Zhong, Zhenyuan

2008-11-01

In this paper, the Wigner-Ville distributions (WVD) of vibration acceleration signals which were acquired from the cylinder head in eight different states of valve train were calculated and displayed in grey images; and the probabilistic neural networks (PNN) were directly used to classify the time-frequency images after the images were normalized. By this way, the fault diagnosis of valve train was transferred to the classification of time-frequency images. As there is no need to extract further fault features (such as eigenvalues or symptom parameters) from time-frequency distributions before classification, the fault diagnosis process is highly simplified. The experimental results show that the faults of diesel valve trains can be classified accurately by the proposed methods.

[A philosophical information leaflet to accompany a DSM classification].

PubMed

Kraaijenbrink, J; Kuipers, T; van der Laan, B; Kremer, S

2016-01-01

The introduction of the dsm-5 has re-ignited discussion about the classification of mental disorders. The public may have misconceptions with regard to the nature of the information contained in a dsm-classification. To bring about a conceptual switch so that the professional user of a classification sees it as an aid to diagnosis rather than as a definition of a problem or illness. We devised a 'thought experiment' to serve as a support for dsm classifications. The 'thought experiment' led us to devise a medicine package containing a 'philosophical' information leaflet. This 'thought-experiment', the information leaflet and the medicine package were presented to both students and trainee doctors at the ucp in Groningen and to clinicians at the fpc dr. S. van Mesdag Clinic. Students and trainee doctors were able to make the desired conceptual switch as a result of 'the thought experiment' and with the help they received from the medicine packaging containing the 'philosophical' information leaflet.

Classification of cardiac patient states using artificial neural networks

PubMed Central

Kannathal, N; Acharya, U Rajendra; Lim, Choo Min; Sadasivan, PK; Krishnan, SM

2003-01-01

Electrocardiogram (ECG) is a nonstationary signal; therefore, the disease indicators may occur at random in the time scale. This may require the patient be kept under observation for long intervals in the intensive care unit of hospitals for accurate diagnosis. The present study examined the classification of the states of patients with certain diseases in the intensive care unit using their ECG and an Artificial Neural Networks (ANN) classification system. The states were classified into normal, abnormal and life threatening. Seven significant features extracted from the ECG were fed as input parameters to the ANN for classification. Three neural network techniques, namely, back propagation, self-organizing maps and radial basis functions, were used for classification of the patient states. The ANN classifier in this case was observed to be correct in approximately 99% of the test cases. This result was further improved by taking 13 features of the ECG as input for the ANN classifier. PMID:19649222

Risk Factors, Etiological Classification, Topographical Location, and Outcome in Medullary Infarctions.

PubMed

Gökçal, Elif; Baran, Gözde; Niftaliyev, Elvin; Güzel, Vildan; Asil, Talip

2017-07-01

An understanding of the etiological mechanisms is important for therapeutic decisions and prognostic evaluation of patients with ischemic stroke. The object of this study was to evaluate the risk factors, etiological subtypes, and topography of lesion in patients with medullary infarctions (MIs). Besides, we also investigated early neurological deterioration, new vascular events, and functional outcome of all patients at 3-month follow-up. We analyzed our database consisting of patients who were diagnosed with acute MI and who were admitted within 24 hours of onset. Etiological classification of stroke was made on the basis of the Trial of Org 1972 in Acute Stroke Treatment criteria. All of the infarctions were grouped into anteromedial, anterolateral, lateral, and posterior arterial territories and also categorized into those involving the upper, middle, or lower medulla oblongata. Early neurological deterioration, major vascular events within the first 3 months of follow-up and modified Rankin Score at 3 months were reviewed. A total of 65 patients with medullary infarctions were reviewed. Involved arterial territories differed according to the etiological classification. Large artery atherosclerosis was the most common etiological subtype; however, small vessel disease was the most common subtype in medial MIs. The lesions involving the anteromedial territory were common in the upper medullary region, whereas the lesions involving the posterior and lateral territories were common in the lower medulla oblangata. Recurrent stroke was seen in the posterior and lateral territories; however, early progression and poor functional outcome were mostly seen in lesions involving the anteromedial territories.

Use of Classification Agreement Analyses to Evaluate RTI Implementation

ERIC Educational Resources Information Center

VanDerHeyden, Amanda

2010-01-01

RTI as a framework for decision making has implications for the diagnosis of specific learning disabilities. Any diagnostic tool must meet certain standards to demonstrate that its use leads to predictable decisions with minimal risk. Classification agreement analyses are described as optimal for demonstrating the technical adequacy of RTI…

Pubic "Crab" Lice Diagnosis

MedlinePlus

... Treatment FAQs Malathion FAQs Epidemiology & Risk Factors Disease Biology Diagnosis Treatment Prevention & Control Resources for Health Professionals ... Frequently Asked Questions (FAQs) Epidemiology & Risk Factors Disease Biology Diagnosis Treatment Prevention & Control Resources for Health Professionals ...

Diagnosis and classification of Addison's disease (autoimmune adrenalitis).

PubMed

Brandão Neto, Rodrigo Antonio; de Carvalho, Jozélio Freire

2014-01-01

Autoimmune adrenalitis, or autoimmune Addison disease (AAD), is the most prevalent cause of primary adrenal insufficiency in the developed world. AAD is rare and can easily be misdiagnosed as other conditions. The diagnosis depends on demonstrating inappropriately low cortisol production and the presence of high titers of adrenal cortex autoantibodies (ACAs), along with excluding other causes of adrenal failure using other tests as necessary. The treatment corticosteroid replacement, and the prognosis following the treatment is the same as the normal population. Spontaneous recovery of adrenal function has been described but is rare. Copyright © 2014 Elsevier B.V. All rights reserved.

[Risk factors associated with the diagnosis of chronic Chagasic miocardiopathy in seropositive individuals from Barinas state, Venezuela].

PubMed

González, Beatriz; Silva, Martha; Al-Atrache, Yusra; Delgado, Yelitze; Serrano, José Luis; Doccimo, Angelina; Hernández, Huber; Verde, Juan; Morillo, Daniela; Marín, Jaime; Concepción, Juan Luis; Bonfante-Cabarcas, Rafael; Rodríguez-Bonfante, Claudina

2014-06-01

This study evaluates the risk factors associated with the diagnosis of chronic chagasic miocardiopathy (CChM) in 115 seropositive individuals to anti-Trypanosoma cruzi antibodies, in Barinas state, Venezuela. Serology was performed with ELISA and MABA; while the CChM diagnosis was established by electrocardiography and echocardiography. A complete clinical history including epidemiological, personal/familiar antecedents and psychobiological habits, plus socioeconomic, psychosocial and alimentary habits interviews were performed for each individual. Risk factors were determined through binary logistic regression. Results showed that 81 patients (70,4%; CI 95% = 66.4-74.4) had criteria for CChM, of which 74 (64.4%; IC 95% = 60.2-68.6) were in phase II; while 34 (29.6%; IC 95% = 25.5-33.5) were in phase I of the disease and 7 (6.1%; IC 95% = 4.0-8.2) in phase III. In a one year period, two patients in phase III died of heart failure. The diagnosis of CChM was associated with hunting practice, maternal history of cardiopathies, chewing chimó, medical history of hypertension and apex beat visible; it was negatively associated with canned and preserved foods ingest. In conclusion the CChM diagnosis has high frequency in seropositive individuals in Barinas and heart failure prevention must be based on an early medical attention and educative strategies in order to control risk factors.

[Analysis of dietary pattern and diabetes mellitus influencing factors identified by classification tree model in adults of Fujian].

PubMed

Yu, F L; Ye, Y; Yan, Y S

2017-05-10

Objective: To find out the dietary patterns and explore the relationship between environmental factors (especially dietary patterns) and diabetes mellitus in the adults of Fujian. Methods: Multi-stage sampling method were used to survey residents aged ≥18 years by questionnaire, physical examination and laboratory detection in 10 disease surveillance points in Fujian. Factor analysis was used to identify the dietary patterns, while logistic regression model was applied to analyze relationship between dietary patterns and diabetes mellitus, and classification tree model was adopted to identify the influencing factors for diabetes mellitus. Results: There were four dietary patterns in the population, including meat, plant, high-quality protein, and fried food and beverages patterns. The result of logistic analysis showed that plant pattern, which has higher factor loading of fresh fruit-vegetables and cereal-tubers, was a protective factor for non-diabetes mellitus. The risk of diabetes mellitus in the population at T2 and T3 levels of factor score were 0.727 (95 %CI: 0.561-0.943) times and 0.736 (95 %CI : 0.573-0.944) times higher, respectively, than those whose factor score was in lowest quartile. Thirteen influencing factors and eleven group at high-risk for diabetes mellitus were identified by classification tree model. The influencing factors were dyslipidemia, age, family history of diabetes, hypertension, physical activity, career, sex, sedentary time, abdominal adiposity, BMI, marital status, sleep time and high-quality protein pattern. Conclusion: There is a close association between dietary patterns and diabetes mellitus. It is necessary to promote healthy and reasonable diet, strengthen the monitoring and control of blood lipids, blood pressure and body weight, and have good lifestyle for the prevention and control of diabetes mellitus.

Discrepancy between the clinical and histopathologic diagnosis of soft tissue vascular malformations.

PubMed

Horbach, Sophie E R; Utami, Amalia M; Meijer-Jorna, Lorine B; Sillevis Smitt, J H; Spuls, Phyllis I; van der Horst, Chantal M A M; van der Wal, Allard C

2017-11-01

Soft tissue vascular malformations are generally diagnosed clinically, according to the International Society for the Study of Vascular Anomalies (ISSVA) classification. Diagnostic histopathologic examination is rarely performed. We sought to evaluate the validity of the current diagnostic workup without routinely performed diagnostic histopathology. We retrospectively determined whether there were discrepancies between clinical and histopathologic diagnoses of patients with clinically diagnosed vascular malformations undergoing therapeutic surgical resections in our center (2000-2015). Beforehand, a pathologist revised the histopathologic diagnoses according to the ISSVA classification. Clinical and histopathologic diagnoses were discrepant in 57% of 142 cases. In these cases, the pathologist indicated the diagnosis was not at all a vascular malformation (n = 24; 17%), a completely different type of vascular malformation (n = 26; 18%), or a partially different type with regard to the combination of vessel-types involved (n = 31; 22%). Possible factors associated with the discrepancies were both clinician-related (eg, diagnostic uncertainty) and pathology-related (eg, lack of immunostaining). Retrospective analysis of a subgroup of patients undergoing surgery. The large discrepancy between clinical and histopathologic diagnoses raises doubt about the validity of the current diagnostic workup for vascular malformations. Clear clinical and histopathologic diagnostic criteria might be essential for a uniform diagnosis. Copyright © 2017 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

2008 International Conference on Ectodermal Dysplasias Classification Conference Report

PubMed Central

Salinas, Carlos F.; Jorgenson, Ronald J.; Wright, J. Timothy; DiGiovanna, John J.; Fete, Mary D.

2009-01-01

There are many ways to classify ectodermal dysplasia syndromes. Clinicians in practice use a list of syndromes from which to choose a potential diagnosis, paging through a volume, such as Freire-Maia and Pinheiro's corpus, matching their patient's findings to listed syndromes. Medical researchers may want a list of syndromes that share one (monothetic system) or several (polythetic system) traits in order to focus research on a narrowly defined group. Special interest groups may want a list from which they can choose constituencies, and insurance companies and government agencies may want a list to determine for whom to provide (or deny) health care coverage. Furthermore, various molecular biologists are now promoting classification systems based on gene mutation (e.g. TP63 associated syndromes) or common molecular pathways. The challenge will be to balance comprehensiveness within the classification with usability and accessibility so that the benefits truly serve the needs of researchers, health care providers and ultimately the individuals and families directly affected by ectodermal dysplasias. It is also recognized that a new classification approach is an ongoing process and will require periodical reviews or updates. Whatever scheme is developed, however, will have far-reaching application for other groups of disorders for which classification is complicated by the number of interested parties and advances in diagnostic acumen. Consensus among interested parties is necessary for optimizing communication among the diverse groups whether it be for equitable distribution of funds, correctness of diagnosis and treatment, or focusing research efforts. PMID:19681152

Correlation of Clinicoserologic and Pathologic Classifications of Inflammatory Myopathies

PubMed Central

Fernandez, Carla; Bardin, Nathalie; De Paula, André Maues; Salort-Campana, Emmanuelle; Benyamine, Audrey; Franques, Jérôme; Schleinitz, Nicolas; Weiller, Pierre-Jean; Pouget, Jean; Pellissier, Jean-François; Figarella-Branger, Dominique

2013-01-01

Abstract The idiopathic inflammatory myopathies (IIM) are acquired muscle diseases characterized by muscle weakness and inflammation on muscle biopsy. Clinicoserologic classifications do not take muscle histology into account to distinguish the subsets of IIM. Our objective was to determine the pathologic features of each serologic subset of IIM and to correlate muscle biopsy results with the clinicoserologic classification defined by Troyanov et al, and with the final diagnoses. We retrospectively studied a cohort of 178 patients with clinicopathologic features suggestive of IIM with the exclusion of inclusion body myositis. At the end of follow-up, 156 of 178 cases were still categorized as IIM: pure dermatomyositis, n = 44; pure polymyositis, n = 14; overlap myositis, n = 68; necrotizing autoimmune myopathy, n = 8; cancer-associated myositis, n = 18; and unclassified IIM, n = 4. The diagnosis of IIM was ruled out in the 22 remaining cases. Pathologic dermatomyositis was the most frequent histologic picture in all serologic subsets of IIM, with the exception of patients with anti-Ku or anti-SRP autoantibodies, suggesting that it supports the histologic diagnosis of pure dermatomyositis, but also myositis of connective tissue diseases and cancer-associated myositis. Unspecified myositis was the second most frequent histologic pattern. It frequently correlated with overlap myositis, especially with anti-Ku or anti-PM-Scl autoantibodies. Pathologic polymyositis was rare and more frequently correlated with myositis mimickers than true polymyositis. The current study shows that clinicoserologic and pathologic data are complementary and must be taken into account when classifying patients with IIM patients. We propose guidelines for diagnosis according to both clinicoserologic and pathologic classifications, to be used in clinical practice. PMID:23269233

Classification of childhood epilepsies in a tertiary pediatric neurology clinic using a customized classification scheme from the international league against epilepsy 2010 report.

PubMed

Khoo, Teik-Beng

2013-01-01

In its 2010 report, the International League Against Epilepsy Commission on Classification and Terminology had made a number of changes to the organization, terminology, and classification of seizures and epilepsies. This study aims to test the usefulness of this revised classification scheme on children with epilepsies aged between 0 and 18 years old. Of 527 patients, 75.1% only had 1 type of seizure and the commonest was focal seizure (61.9%). A specific electroclinical syndrome diagnosis could be made in 27.5%. Only 2.1% had a distinctive constellation. In this cohort, 46.9% had an underlying structural, metabolic, or genetic etiology. Among the important causes were pre-/perinatal insults, malformation of cortical development, intracranial infections, and neurocutaneous syndromes. However, 23.5% of the patients in our cohort were classified as having "epilepsies of unknown cause." The revised classification scheme is generally useful for pediatric patients. To make it more inclusive and clinically meaningful, some local customizations are required.

Neural network and wavelet average framing percentage energy for atrial fibrillation classification.

PubMed

Daqrouq, K; Alkhateeb, A; Ajour, M N; Morfeq, A

2014-03-01

ECG signals are an important source of information in the diagnosis of atrial conduction pathology. Nevertheless, diagnosis by visual inspection is a difficult task. This work introduces a novel wavelet feature extraction method for atrial fibrillation derived from the average framing percentage energy (AFE) of terminal wavelet packet transform (WPT) sub signals. Probabilistic neural network (PNN) is used for classification. The presented method is shown to be a potentially effective discriminator in an automated diagnostic process. The ECG signals taken from the MIT-BIH database are used to classify different arrhythmias together with normal ECG. Several published methods were investigated for comparison. The best recognition rate selection was obtained for AFE. The classification performance achieved accuracy 97.92%. It was also suggested to analyze the presented system in an additive white Gaussian noise (AWGN) environment; 55.14% for 0dB and 92.53% for 5dB. It was concluded that the proposed approach of automating classification is worth pursuing with larger samples to validate and extend the present study. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Automated Diagnosis Coding with Combined Text Representations.

PubMed

Berndorfer, Stefan; Henriksson, Aron

2017-01-01

Automated diagnosis coding can be provided efficiently by learning predictive models from historical data; however, discriminating between thousands of codes while allowing a variable number of codes to be assigned is extremely difficult. Here, we explore various text representations and classification models for assigning ICD-9 codes to discharge summaries in MIMIC-III. It is shown that the relative effectiveness of the investigated representations depends on the frequency of the diagnosis code under consideration and that the best performance is obtained by combining models built using different representations.

Hyperspectral imaging with wavelet transform for classification of colon tissue biopsy samples

NASA Astrophysics Data System (ADS)

Masood, Khalid

2008-08-01

Automatic classification of medical images is a part of our computerised medical imaging programme to support the pathologists in their diagnosis. Hyperspectral data has found its applications in medical imagery. Its usage is increasing significantly in biopsy analysis of medical images. In this paper, we present a histopathological analysis for the classification of colon biopsy samples into benign and malignant classes. The proposed study is based on comparison between 3D spectral/spatial analysis and 2D spatial analysis. Wavelet textural features in the wavelet domain are used in both these approaches for classification of colon biopsy samples. Experimental results indicate that the incorporation of wavelet textural features using a support vector machine, in 2D spatial analysis, achieve best classification accuracy.

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

NASA Astrophysics Data System (ADS)

Chen, Junde; Zhang, Defu

2018-04-01

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

Expert Systems: Implications for the Diagnosis and Treatment of Learning Disabilities.

ERIC Educational Resources Information Center

Hofmeister, Alan M.; Lubke, Margaret M.

1986-01-01

Expert systems are briefly reviewed and applications in special education diagnosis and classification are described. Future applications are noted to include text interpretation and pupil performance monitoring. (CL)

Cosmetic sequelae after oncoplastic surgery of the breast. Classification and factors for prevention.

PubMed

Acea Nebril, Benigno; Cereijo Garea, Carmen; García Novoa, Alejandra

2015-02-01

Oncoplastic surgery is an essential tool in the surgical approach to women with breast cancer. These techniques are not absolute guarantee for a good cosmetic result and therefore some patients will have cosmetic sequelae secondary to poor surgical planning, the effects of adjuvant treatments or the need for resection greater than originally planned. The high frequency of these cosmetic sequelae in oncology practice makes it necessary to classify them for optimal surgical planning. The aim of this paper is to present a classification of cosmetic sequelae after oncoplastic procedures to identify those factors that are crucial to its prevention. This classification contains 4 groups: breast contour deformities, asymmetries, alterations in nipple-aréola complex (NAC) and defects in the three dimensional structure of the breast. A significant group of these sequelae (asymmetries and deformities) are associated with breast irradiation and need an accurate information process with patients to set realistic expectations about cosmetic results. Finally, there is another group of sequelae (NAC disorders and three-dimensional structure) that are related to poor planning and deficiencies in surgical approach, therfore specific training is essential for learning these surgical techniques. Copyright © 2014 AEC. Publicado por Elsevier España, S.L.U. All rights reserved.

Factors affecting breeding soundness classification of beef bulls examined at the Western College of Veterinary Medicine.

PubMed

Barth, Albert D; Waldner, Cheryl L

2002-04-01

Breeding soundness evaluation records from 2110 beef bulls, for the period of 1986 to 1999, were analyzed to determine the prevalence and importance of factors affecting breeding soundness classification. The percentage of all bulls classified as satisfactory ranged from 49.0% in January to 73.3% in May. The percentage of physically normal bulls with satisfactory semen quality ranged from 65.7% in January to 87.5% in June. Poor body condition or excessive body condition, below average or below the recommended minimum scrotal circumference, lameness, and severe scrotal frostbite significantly reduced the probability of a satisfactory breeding soundness classification. The percentage of sperm with midpiece defects declined significantly and the percentage of sperm with head defects increased significantly with the approach of summer. Photoperiod, cold stress, poor or excessive body condition, and reduced feed quality may interact to reduce semen quality in the winter months.

Burning mouth syndrome: a review on diagnosis and treatment

PubMed Central

Coculescu, EC; Radu, A; Coculescu, BI

2014-01-01

Burning mouth syndrome (BMS) is defined as a chronic pain condition characterized by a burning sensation in the clinically healthy oral mucosa. It is difficult to diagnose BMS because there is a discrepancy between the severity, extensive objective pain felt by the patient and the absence of any clinical changes of the oral mucosa. This review presents some aspects of BMS, including its clinical diagnosis, classification, differential diagnosis, general treatment, evolution and prognosis. PMID:25713611

Burning mouth syndrome: a review on diagnosis and treatment.

PubMed

Coculescu, E C; Radu, A; Coculescu, B I

2014-01-01

Burning mouth syndrome (BMS) is defined as a chronic pain condition characterized by a burning sensation in the clinically healthy oral mucosa. It is difficult to diagnose BMS because there is a discrepancy between the severity, extensive objective pain felt by the patient and the absence of any clinical changes of the oral mucosa. This review presents some aspects of BMS, including its clinical diagnosis, classification, differential diagnosis, general treatment, evolution and prognosis.

Prototype diagnosis of psychiatric syndromes

PubMed Central

WESTEN, DREW

2012-01-01

The method of diagnosing patients used since the early 1980s in psychiatry, which involves evaluating each of several hundred symptoms for their presence or absence and then applying idiosyncratic rules for combining them for each of several hundred disorders, has led to great advances in research over the last 30 years. However, its problems have become increasingly apparent, particularly for clinical practice. An alternative approach, designed to maximize clinical utility, is prototype matching. Instead of counting symptoms of a disorder and determining whether they cross an arbitrary cutoff, the task of the diagnostician is to gauge the extent to which a patient’s clinical presentation matches a paragraph-length description of the disorder using a simple 5-point scale, from 1 (“little or no match”) to 5 (“very good match”). The result is both a dimensional diagnosis that captures the extent to which the patient “has” the disorder and a categorical diagnosis, with ratings of 4 and 5 corresponding to presence of the disorder and a rating of 3 indicating “subthreshold” or “clinically significant features”. The disorders and criteria woven into the prototypes can be identified empirically, so that the prototypes are both scientifically grounded and clinically useful. Prototype diagnosis has a number of advantages: it better captures the way humans naturally classify novel and complex stimuli; is clinically helpful, reliable, and easy to use in everyday practice; facilitates both dimensional and categorical diagnosis and dramatically reduces the number of categories required for classification; allows for clinically richer, empirically derived, and culturally relevant classification; reduces the gap between research criteria and clinical knowledge, by allowing clinicians in training to learn a small set of standardized prototypes and to develop richer mental representations of the disorders over time through clinical experience; and can help

Classification techniques on computerized systems to predict and/or to detect Apnea: A systematic review.

PubMed

Pombo, Nuno; Garcia, Nuno; Bousson, Kouamana

2017-03-01

Sleep apnea syndrome (SAS), which can significantly decrease the quality of life is associated with a major risk factor of health implications such as increased cardiovascular disease, sudden death, depression, irritability, hypertension, and learning difficulties. Thus, it is relevant and timely to present a systematic review describing significant applications in the framework of computational intelligence-based SAS, including its performance, beneficial and challenging effects, and modeling for the decision-making on multiple scenarios. This study aims to systematically review the literature on systems for the detection and/or prediction of apnea events using a classification model. Forty-five included studies revealed a combination of classification techniques for the diagnosis of apnea, such as threshold-based (14.75%) and machine learning (ML) models (85.25%). In addition, the ML models, were clustered in a mind map, include neural networks (44.26%), regression (4.91%), instance-based (11.47%), Bayesian algorithms (1.63%), reinforcement learning (4.91%), dimensionality reduction (8.19%), ensemble learning (6.55%), and decision trees (3.27%). A classification model should provide an auto-adaptive and no external-human action dependency. In addition, the accuracy of the classification models is related with the effective features selection. New high-quality studies based on randomized controlled trials and validation of models using a large and multiple sample of data are recommended. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Chemical factor analysis of skin cancer FTIR-FEW spectroscopic data

NASA Astrophysics Data System (ADS)

Bruch, Reinhard F.; Sukuta, Sydney

2002-03-01

Chemical Factor Analysis (CFA) algorithms were applied to transform complex Fourier transform infrared fiberoptical evanescent wave (FTIR-FEW) normal and malignant skin tissue spectra into factor spaces for analysis and classification. The factor space approach classified melanoma beyond prior pathological classifications related to specific biochemical alterations to health states in cluster diagrams allowing diagnosis with more biochemical specificity, resolving biochemical component spectra and employing health state eigenvector angular configurations as disease state sensors. This study demonstrated a wealth of new information from in vivo FTIR-FEW spectral tissue data, without extensive a priori information or clinically invasive procedures. In particular, we employed a variety of methods used in CFA to select the rank of spectroscopic data sets of normal benign and cancerous skin tissue. We used the Malinowski indicator function (IND), significance level and F-Tests to rank our data matrices. Normal skin tissue, melanoma and benign tumors were modeled by four, two and seven principal abstract factors, respectively. We also showed that the spectrum of the first eigenvalue was equivalent to the mean spectrum. The graphical depiction of angular disparities between the first abstract factors can be adopted as a new way to characterize and diagnose melanoma cancer.

A Fault Alarm and Diagnosis Method Based on Sensitive Parameters and Support Vector Machine

NASA Astrophysics Data System (ADS)

Zhang, Jinjie; Yao, Ziyun; Lv, Zhiquan; Zhu, Qunxiong; Xu, Fengtian; Jiang, Zhinong

2015-08-01

Study on the extraction of fault feature and the diagnostic technique of reciprocating compressor is one of the hot research topics in the field of reciprocating machinery fault diagnosis at present. A large number of feature extraction and classification methods have been widely applied in the related research, but the practical fault alarm and the accuracy of diagnosis have not been effectively improved. Developing feature extraction and classification methods to meet the requirements of typical fault alarm and automatic diagnosis in practical engineering is urgent task. The typical mechanical faults of reciprocating compressor are presented in the paper, and the existing data of online monitoring system is used to extract fault feature parameters within 15 types in total; the inner sensitive connection between faults and the feature parameters has been made clear by using the distance evaluation technique, also sensitive characteristic parameters of different faults have been obtained. On this basis, a method based on fault feature parameters and support vector machine (SVM) is developed, which will be applied to practical fault diagnosis. A better ability of early fault warning has been proved by the experiment and the practical fault cases. Automatic classification by using the SVM to the data of fault alarm has obtained better diagnostic accuracy.

Diagnostic Principles of Peri-Implantitis: a Systematic Review and Guidelines for Peri-Implantitis Diagnosis Proposal.

PubMed

Ramanauskaite, Ausra; Juodzbalys, Gintaras

2016-01-01

To review and summarize the literature concerning peri-implantitis diagnostic parameters and to propose guidelines for peri-implantitis diagnosis. An electronic literature search was conducted of the MEDLINE (Ovid) and EMBASE databases for articles published between 2011 and 2016. Sequential screening at the title/abstract and full-text levels was performed. Systematic reviews/guidelines of consensus conferences proposing classification or suggesting diagnostic parameters for peri-implantitis in the English language were included. The review was recorded on PROSPERO system with the code CRD42016033287. The search resulted in 10 articles that met the inclusion criteria. Four were papers from consensus conferences, two recommended diagnostic guidelines, three proposed classification of peri-implantitis, and one suggested an index for implant success. The following parameters were suggested to be used for peri-implantitis diagnosis: pain, mobility, bleeding on probing, probing depth, suppuration/exudate, and radiographic bone loss. In all of the papers, different definitions of peri-implantitis or implant success, as well as different thresholds for the above mentioned clinical and radiographical parameters, were used. Current evidence rationale for the diagnosis of peri-implantitis and classification based on consecutive evaluation of soft-tissue conditions and the amount of bone loss were suggested. Currently there is no single uniform definition of peri-implantitis or the parameters that should be used. Rationale for diagnosis and prognosis of peri-implantitis as well as classification of the disease is proposed.

Support vector machine and principal component analysis for microarray data classification

NASA Astrophysics Data System (ADS)

Astuti, Widi; Adiwijaya

2018-03-01

Cancer is a leading cause of death worldwide although a significant proportion of it can be cured if it is detected early. In recent decades, technology called microarray takes an important role in the diagnosis of cancer. By using data mining technique, microarray data classification can be performed to improve the accuracy of cancer diagnosis compared to traditional techniques. The characteristic of microarray data is small sample but it has huge dimension. Since that, there is a challenge for researcher to provide solutions for microarray data classification with high performance in both accuracy and running time. This research proposed the usage of Principal Component Analysis (PCA) as a dimension reduction method along with Support Vector Method (SVM) optimized by kernel functions as a classifier for microarray data classification. The proposed scheme was applied on seven data sets using 5-fold cross validation and then evaluation and analysis conducted on term of both accuracy and running time. The result showed that the scheme can obtained 100% accuracy for Ovarian and Lung Cancer data when Linear and Cubic kernel functions are used. In term of running time, PCA greatly reduced the running time for every data sets.

Multimodal Classification of Alzheimer’s Disease and Mild Cognitive Impairment

PubMed Central

Zhang, Daoqiang; Wang, Yaping; Zhou, Luping; Yuan, Hong; Shen, Dinggang

2011-01-01

Effective and accurate diagnosis of Alzheimer’s disease (AD), as well as its prodromal stage (i.e., mild cognitive impairment (MCI)), has attracted more and more attentions recently. So far, multiple biomarkers have been shown sensitive to the diagnosis of AD and MCI, i.e., structural MR imaging (MRI) for brain atrophy measurement, functional imaging (e.g., FDG-PET) for hypometabolism quantification, and cerebrospinal fluid (CSF) for quantification of specific proteins. However, most existing research focuses on only a single modality of biomarkers for diagnosis of AD and MCI, although recent studies have shown that different biomarkers may provide complementary information for diagnosis of AD and MCI. In this paper, we propose to combine three modalities of biomarkers, i.e., MRI, FDG-PET, and CSF biomarkers, to discriminate between AD (or MCI) and healthy controls, using a kernel combination method. Specifically, ADNI baseline MRI, FDG-PET, and CSF data from 51 AD patients, 99 MCI patients (including 43 MCI converters who had converted to AD within 18 months and 56 MCI non-converters who had not converted to AD within 18 months), and 52 healthy controls are used for development and validation of our proposed multimodal classification method. In particular, for each MR or FDG-PET image, 93 volumetric features are extracted from the 93 regions of interest (ROIs), automatically labeled by an atlas warping algorithm. For CSF biomarkers, their original values are directly used as features. Then, a linear support vector machine (SVM) is adopted to evaluate the classification accuracy, using a 10-fold cross-validation. As a result, for classifying AD from healthy controls, we achieve a classification accuracy of 93.2% (with a sensitivity of 93% and a specificity of 93.3%) when combining all three modalities of biomarkers, and only 86.5% when using even the best individual modality of biomarkers. Similarly, for classifying MCI from healthy controls, we achieve a

Influence of Texture and Colour in Breast TMA Classification

PubMed Central

Fernández-Carrobles, M. Milagro; Bueno, Gloria; Déniz, Oscar; Salido, Jesús; García-Rojo, Marcial; González-López, Lucía

2015-01-01

Breast cancer diagnosis is still done by observation of biopsies under the microscope. The development of automated methods for breast TMA classification would reduce diagnostic time. This paper is a step towards the solution for this problem and shows a complete study of breast TMA classification based on colour models and texture descriptors. The TMA images were divided into four classes: i) benign stromal tissue with cellularity, ii) adipose tissue, iii) benign and benign anomalous structures, and iv) ductal and lobular carcinomas. A relevant set of features was obtained on eight different colour models from first and second order Haralick statistical descriptors obtained from the intensity image, Fourier, Wavelets, Multiresolution Gabor, M-LBP and textons descriptors. Furthermore, four types of classification experiments were performed using six different classifiers: (1) classification per colour model individually, (2) classification by combination of colour models, (3) classification by combination of colour models and descriptors, and (4) classification by combination of colour models and descriptors with a previous feature set reduction. The best result shows an average of 99.05% accuracy and 98.34% positive predictive value. These results have been obtained by means of a bagging tree classifier with combination of six colour models and the use of 1719 non-correlated (correlation threshold of 97%) textural features based on Statistical, M-LBP, Gabor and Spatial textons descriptors. PMID:26513238

Key factors associated with postoperative complications in patients undergoing colorectal surgery.

PubMed

Manilich, E; Vogel, J D; Kiran, R P; Church, J M; Seyidova-Khoshknabi, Dilara; Remzi, F H

2013-01-01

Surgical outcomes are determined by complex interactions among a variety of factors including patient characteristics, diagnosis, and type of procedure. The aim of this study was to prioritize the effect and relative importance of the surgeon (in terms of identity of a surgeon and surgeon volume), patient characteristics, and the intraoperative details on complications of colorectal surgery including readmission, reoperation, sepsis, anastomotic leak, small-bowel obstruction, surgical site infection, abscess, need for transfusion, and portal and deep vein thrombosis. This study uses a novel classification methodology to measure the influence of various risk factors on postoperative complications in a large outcomes database. Using prospectively collected information from the departmental outcomes database from 2010 to 2011, we examined the records of 3552 patients who underwent colorectal surgery. Instead of traditional statistical methods, we used a family of 7000 bootstrap classification models to examine and quantify the impact of various factors on the most common serious surgical complications. For each complication, an ensemble of multivariate classification models was designed to determine the relative importance of potential factors that may influence outcomes of surgery. This is a new technique for analyzing outcomes data that produces more accurate results and a more reliable ranking of study variables in order of their importance in producing complications. Patients who underwent colorectal surgery in 2010 and 2011 were included. This study was conducted at a tertiary referral department at a major medical center. Postoperative complications were the primary outcomes measured. Factors sorted themselves into 2 groups: a highly important group (operative time, BMI, age, identity of the surgeon, type of surgery) and a group of low importance (sex, comorbidity, laparoscopy, and emergency). ASA score and diagnosis were of intermediate importance. The outcomes

Ecological Factors of Being Bullied Among Adolescents: a Classification and Regression Tree Approach

PubMed Central

Moon, Sung Seek; Kim, Heeyoung; Seay, Kristen; Small, Eusebius; Kim, Youn Kyoung

2015-01-01

Being bullied is a well-recognized trauma for adolescents. Bullying can best be understood through an ecological framework since bullying or being bullied involves risk factors at multiple contextual levels. The purpose of the study was to identify the risk and protective factors that best differentiate groups along with the outcome variable of interest (being bullied) using Classification and Regression Tree (CART) analysis. The study used the Health Behavior in School-Aged Children (HBSC) data collected from a nationally representative sample of students in grades six through ten during the 2005–2006 school years. This study identified that for adolescents 12 and younger, lower parental support is a critical risk factor associated with bullying and among those 13 to 14 with lower parent support, adolescent with higher academic pressure reported experiencing more bullying. For the older group of adolescents (aged 15 and older), school related factors were identified to increase the risk level of being bullied. There was a critical age (15 years old) for implementing victimization interventions to reduce the damage from being bullied. Service providers working with adolescents aged 14 and less should focus more on family-oriented intervention and those working with adolescents aged 15 and more should offer peer- or school-related interventions. PMID:27617043

The Classification of Hysteria and Related Disorders: Historical and Phenomenological Considerations

PubMed Central

North, Carol S.

2015-01-01

This article examines the history of the conceptualization of dissociative, conversion, and somatoform syndromes in relation to one another, chronicles efforts to classify these and other phenomenologically-related psychopathology in the American diagnostic system for mental disorders, and traces the subsequent divergence in opinions of dissenting sectors on classification of these disorders. This article then considers the extensive phenomenological overlap across these disorders in empirical research, and from this foundation presents a new model for the conceptualization of these disorders. The classification of disorders formerly known as hysteria and phenomenologically-related syndromes has long been contentious and unsettled. Examination of the long history of the conceptual difficulties, which remain inherent in existing classification schemes for these disorders, can help to address the continuing controversy. This review clarifies the need for a major conceptual revision of the current classification of these disorders. A new phenomenologically-based classification scheme for these disorders is proposed that is more compatible with the agnostic and atheoretical approach to diagnosis of mental disorders used by the current classification system. PMID:26561836

Diagnosis of Parkinson’s disease on the basis of clinical–genetic classification: a population-based modelling study

PubMed Central

Nalls, Mike A.; McLean, Cory Y.; Rick, Jacqueline; Eberly, Shirley; Hutten, Samantha J.; Gwinn, Katrina; Sutherland, Margaret; Martinez, Maria; Heutink, Peter; Williams, Nigel; Hardy, John; Gasser, Thomas; Brice, Alexis; Price, T. Ryan; Nicolas, Aude; Keller, Margaux F.; Molony, Cliona; Gibbs, J. Raphael; Chen-Plotkin, Alice; Suh, Eunran; Letson, Christopher; Fiandaca, Massimo S.; Mapstone, Mark; Federoff, Howard J.; Noyce, Alastair J; Morris, Huw; Van Deerlin, Vivianna M.; Weintraub, Daniel; Zabetian, Cyrus; Hernandez, Dena G.; Lesage, Suzanne; Mullins, Meghan; Conley, Emily Drabant; Northover, Carrie; Frasier, Mark; Marek, Ken; Day-Williams, Aaron G.; Stone, David J.; Ioannidis, John P. A.; Singleton, Andrew B.

2015-01-01

Background Accurate diagnosis and early detection of complex disease has the potential to be of enormous benefit to clinical trialists, patients, and researchers alike. We sought to create a non-invasive, low-cost, and accurate classification model for diagnosing Parkinson’s disease risk to serve as a basis for future disease prediction studies in prospective longitudinal cohorts. Methods We developed a simple disease classifying model within 367 patients with Parkinson’s disease and phenotypically typical imaging data and 165 controls without neurological disease of the Parkinson’s Progression Marker Initiative (PPMI) study. Olfactory function, genetic risk, family history of PD, age and gender were algorithmically selected as significant contributors to our classifying model. This model was developed using the PPMI study then tested in 825 patients with Parkinson’s disease and 261 controls from five independent studies with varying recruitment strategies and designs including the Parkinson’s Disease Biomarkers Program (PDBP), Parkinson’s Associated Risk Study (PARS), 23andMe, Longitudinal and Biomarker Study in PD (LABS-PD), and Morris K. Udall Parkinson’s Disease Research Center of Excellence (Penn-Udall). Findings Our initial model correctly distinguished patients with Parkinson’s disease from controls at an area under the curve (AUC) of 0.923 (95% CI = 0.900 – 0.946) with high sensitivity (0.834, 95% CI = 0.711 – 0.883) and specificity (0.903, 95% CI = 0.824 – 0.946) in PPMI at its optimal AUC threshold (0.655). The model is also well-calibrated with all Hosmer-Lemeshow simulations suggesting that when parsed into random subgroups, the actual data mirrors that of the larger expected data, demonstrating that our model is robust and fits well. Likewise external validation shows excellent classification of PD with AUCs of 0.894 in PDBP, 0.998 in PARS, 0.955 in 23andMe, 0.929 in LABS-PD, and 0.939 in Penn-Udall. Additionally, when our model

The Nursing Diagnosis Disturbed Thought Processes: An Integrative Review.

PubMed

Escalada-Hermández, Paula; Marín-Fernández, Blanca

2017-09-08

To analyze and synthetize the existing scientific literature in relation to the nursing diagnosis disturbed thought processes (DTPs) (00130). An integrative review was developed, identifying relevant papers through a search of international and Spanish databases and the examination of key manuals. Theoretical papers propose modifications for the nursing diagnosis DTPs. Most of the research papers offer data about its frequency in different clinical settings. There exists an interest in the nursing diagnosis DTPs. However, the available evidence is not very extensive and further work is necessary in order to refine this nursing diagnosis. The re-inclusion of DTPs in the NANDA-I classification will specially contribute to increment its utility in mental healthcare. © 2017 NANDA International, Inc.

Pathohistological classification systems in gastric cancer: Diagnostic relevance and prognostic value

PubMed Central

Berlth, Felix; Bollschweiler, Elfriede; Drebber, Uta; Hoelscher, Arnulf H; Moenig, Stefan

2014-01-01

Several pathohistological classification systems exist for the diagnosis of gastric cancer. Many studies have investigated the correlation between the pathohistological characteristics in gastric cancer and patient characteristics, disease specific criteria and overall outcome. It is still controversial as to which classification system imparts the most reliable information, and therefore, the choice of system may vary in clinical routine. In addition to the most common classification systems, such as the Laurén and the World Health Organization (WHO) classifications, other authors have tried to characterize and classify gastric cancer based on the microscopic morphology and in reference to the clinical outcome of the patients. In more than 50 years of systematic classification of the pathohistological characteristics of gastric cancer, there is no sole classification system that is consistently used worldwide in diagnostics and research. However, several national guidelines for the treatment of gastric cancer refer to the Laurén or the WHO classifications regarding therapeutic decision-making, which underlines the importance of a reliable classification system for gastric cancer. The latest results from gastric cancer studies indicate that it might be useful to integrate DNA- and RNA-based features of gastric cancer into the classification systems to establish prognostic relevance. This article reviews the diagnostic relevance and the prognostic value of different pathohistological classification systems in gastric cancer. PMID:24914328

Psychopathology, biopsychosocial factors, crime characteristics, and classification of 25 homicidal youths.

PubMed

Myers, W C; Scott, K; Burgess, A W; Burgess, A G

1995-11-01

This study investigates diagnostic, behavioral, offense, and classification characteristics of juvenile murderers. Twenty-five homicidal children and adolescents were assessed using the Diagnostic Interview for Children and Adolescents, clinical interviews, record review, and all available collateral data. DSM-III-R psychopathology was found in 96% of these youths, and one half of them had experienced suicidal ideation at some point in their lives. Nevertheless, only 17% had ever received mental health treatment. Family and school dysfunction were present in virtually all subjects. Histories of abuse, prior violence, arrests, and promiscuous sexual behavior were typical. Motives were equally divided between crime-based and conflict-based causes. A weapon was used in 96% of cases. Significant differences were found between crime classification groups and victim age, physical abuse, IQ, and victim relationship. In addition, those who committed sexual homicide were significantly more likely to have engaged in overkill, used a knife, and been armed beforehand. Ten profile characteristics present in at least 70% of these juveniles were identified. All murders were readily classified according to the FBI Crime Classification Manual (CCM). These findings support juvenile murderers as being an inadequately treated, emotionally and behaviorally disturbed population with profound social problems. The CCM proved to be a useful instrument for the classification of this sample.

Factors associated with late diagnosis of HIV infection and missed opportunities for earlier testing.

PubMed

Gullón, Alejandra; Verdejo, José; de Miguel, Rosa; Gómez, Ana; Sanz, Jesús

2016-10-01

Late diagnosis (LD) of human immunodeficiency virus (HIV) infection continues to be a significant problem that increases disease burden both for patients and for the public health system. Guidelines have been updated in order to facilitate earlier HIV diagnosis, introducing "indicator condition-guided HIV testing". In this study, we analysed the frequency of LD and associated risk factors. We retrospectively identified those cases that could be considered missed opportunities for an earlier diagnosis. All patients newly diagnosed with HIV infection who attended Hospital La Princesa, Madrid (Spain) between 2007 and 2014 were analysed. We collected epidemiological, clinical and immunological data. We also reviewed electronic medical records from the year before the HIV diagnosis to search for medical consultations due to clinical indicators. HIV infection was diagnosed in 354 patients. The median CD4 count at presentation was 352 cells/mm(3). Overall, 158 patients (50%) met the definition of LD, and 97 (30.7%) the diagnosis of advanced disease. LD was associated with older age and was more frequent amongst immigrants. Heterosexual relations and injection drug use were more likely to be the reasons for LD than relations between men who have sex with men. During the year preceding the diagnosis, 46.6% of the patients had sought medical advice owing to the presence of clinical indicators that should have led to HIV testing. Of those, 24 cases (14.5%) were classified as missed opportunities for earlier HIV diagnosis because testing was not performed. According to these results, all health workers should pursue early HIV diagnosis through the proper implementation of HIV testing guidelines. Such an approach would prove directly beneficial to the patient and indirectly beneficial to the general population through the reduction in the risk of transmission.

Central Sensitization-Based Classification for Temporomandibular Disorders: A Pathogenetic Hypothesis

PubMed Central

Cattaneo, Ruggero; Marci, Maria Chiara; Pietropaoli, Davide; Ortu, Eleonora

2017-01-01

Dysregulation of Autonomic Nervous System (ANS) and central pain pathways in temporomandibular disorders (TMD) is a growing evidence. Authors include some forms of TMD among central sensitization syndromes (CSS), a group of pathologies characterized by central morphofunctional alterations. Central Sensitization Inventory (CSI) is useful for clinical diagnosis. Clinical examination and CSI cannot identify the central site(s) affected in these diseases. Ultralow frequency transcutaneous electrical nerve stimulation (ULFTENS) is extensively used in TMD and in dental clinical practice, because of its effects on descending pain modulation pathways. The Diagnostic Criteria for TMD (DC/TMD) are the most accurate tool for diagnosis and classification of TMD. However, it includes CSI to investigate central aspects of TMD. Preliminary data on sensory ULFTENS show it is a reliable tool for the study of central and autonomic pathways in TMD. An alternative classification based on the presence of Central Sensitization and on individual response to sensory ULFTENS is proposed. TMD may be classified into 4 groups: (a) TMD with Central Sensitization ULFTENS Responders; (b) TMD with Central Sensitization ULFTENS Nonresponders; (c) TMD without Central Sensitization ULFTENS Responders; (d) TMD without Central Sensitization ULFTENS Nonresponders. This pathogenic classification of TMD may help to differentiate therapy and aetiology. PMID:28932132

Predictive factors for the diagnosis of irritable bowel syndrome in a large cohort of 440,822 young adults.

PubMed

Carter, Dan; Beer-Gabel, Marc; Tzur, Dorit; Levy, Gad; Derazne, Estela; Novis, Ben; Afek, Arnon

2015-04-01

The prevalence of irritable bowel syndrome (IBS) in the community has been reported in numerous cross-sectional surveys. However, little is known about the incidence and predictive factors for the clinical diagnosis of IBS. We examined the association of socioeconomic, anthropometric, and occupational factors with the incidence of IBS in a cohort of 440,822 young Israeli adults aged 18 to 39 who served in active military service during the years 2005 to 2011. During the follow-up of 1,925,003 person-years, IBS was diagnosed de novo in 976 patients, giving an incidence rate of 221:100,000 (0.2%) person-years for the diagnosis of IBS. On multivariable Cox analysis, higher socioeconomic status [hazard ratio (HR) 1.629; 95% confidence interval (CI), 1.328-1.999; P<0.0001], Israeli birth (HR 1.362; 95% CI, 1.084-1.712; P=0.008), Jewish ethnicity (HR 2.089; 95% CI, 1.344-3.248; P=0.001), education ≥than 11 years (HR 1.674; 95% CI, 1.019-2.751; P=0.042), and a noncombat military position (HR 1.196; 95% CI, 1.024-1.397; P=0.024) were found to be risk factors for the diagnosis or for the worsening of IBS. Overweight (HR 0.744; 95% CI, 0.589-0.941; P=0.014), obesity (HR 0.698; 95% CI, 0.510-0.95; P=0.025), living in a rural settlement (HR 0.705; 95% CI, 0.561-0.886; P=0.003), and Middle Eastern (HR 0.739; 95% CI, 0.617-0.884; P=0.001,) or North African and Ethiopian origin (HR 0.702; 95% CI, 0.585-0.842; P<0.001) were found to be protective for the diagnosis or the worsening of IBS. This study provides novel data on the socioeconomic, anthropometric, and occupational factors predictive for IBS development. The predictive factors for IBS diagnosis may point to the fact that stress had a lower impact on IBS incidence in our study cohort.

Total motile sperm count: a better indicator for the severity of male factor infertility than the WHO sperm classification system.

PubMed

Hamilton, J A M; Cissen, M; Brandes, M; Smeenk, J M J; de Bruin, J P; Kremer, J A M; Nelen, W L D M; Hamilton, C J C M

2015-05-01

Does the prewash total motile sperm count (TMSC) have a better predictive value for spontaneous ongoing pregnancy (SOP) than the World Health Organization (WHO) classification system? The prewash TMSC shows a better correlation with the spontaneous ongoing pregnancy rate (SOPR) than the WHO 2010 classification system. According to the WHO classification system, an abnormal semen analysis can be diagnosed as oligozoospermia, astenozoospermia, teratozoospermia or combinations of these and azoospermia. This classification is based on the fifth percentile cut-off values of a cohort of 1953 men with proven fertility. Although this classification suggests accuracy, the relevance for the prognosis of an infertile couple and the choice of treatment is questionable. The TMSC is obtained by multiplying the sample volume by the density and the percentage of A and B motility spermatozoa. We analyzed data from a longitudinal cohort study among unselected infertile couples who were referred to three Dutch hospitals between January 2002 and December 2006. Of the total cohort of 2476 infertile couples, only the couples with either male infertility as a single diagnosis or unexplained infertility were included (n = 1177) with a follow-up period of 3 years. In all couples a semen analysis was performed. Based on the best semen analysis if more tests were performed, couples were grouped according to the WHO classification system and the TMSC range, as described in the Dutch national guidelines for male infertility. The primary outcome measure was the SOPR, which occurred before, during or after treatments, including expectant management, intrauterine insemination, in vitro fertilization or intracytoplasmic sperm injection. After adjustment for the confounding factors (female and male age, duration and type of infertility and result of the postcoital test) the odd ratios (ORs) for risk of SOP for each WHO and TMSC group were calculated. The couples with unexplained infertility were

Structure constrained semi-nonnegative matrix factorization for EEG-based motor imagery classification.

PubMed

Lu, Na; Li, Tengfei; Pan, Jinjin; Ren, Xiaodong; Feng, Zuren; Miao, Hongyu

2015-05-01

Electroencephalogram (EEG) provides a non-invasive approach to measure the electrical activities of brain neurons and has long been employed for the development of brain-computer interface (BCI). For this purpose, various patterns/features of EEG data need to be extracted and associated with specific events like cue-paced motor imagery. However, this is a challenging task since EEG data are usually non-stationary time series with a low signal-to-noise ratio. In this study, we propose a novel method, called structure constrained semi-nonnegative matrix factorization (SCS-NMF), to extract the key patterns of EEG data in time domain by imposing the mean envelopes of event-related potentials (ERPs) as constraints on the semi-NMF procedure. The proposed method is applicable to general EEG time series, and the extracted temporal features by SCS-NMF can also be combined with other features in frequency domain to improve the performance of motor imagery classification. Real data experiments have been performed using the SCS-NMF approach for motor imagery classification, and the results clearly suggest the superiority of the proposed method. Comparison experiments have also been conducted. The compared methods include ICA, PCA, Semi-NMF, Wavelets, EMD and CSP, which further verified the effectivity of SCS-NMF. The SCS-NMF method could obtain better or competitive performance over the state of the art methods, which provides a novel solution for brain pattern analysis from the perspective of structure constraint. Copyright © 2015 Elsevier Ltd. All rights reserved.

Online Learning for Classification of Alzheimer Disease based on Cortical Thickness and Hippocampal Shape Analysis.

PubMed

Lee, Ga-Young; Kim, Jeonghun; Kim, Ju Han; Kim, Kiwoong; Seong, Joon-Kyung

2014-01-01

Mobile healthcare applications are becoming a growing trend. Also, the prevalence of dementia in modern society is showing a steady growing trend. Among degenerative brain diseases that cause dementia, Alzheimer disease (AD) is the most common. The purpose of this study was to identify AD patients using magnetic resonance imaging in the mobile environment. We propose an incremental classification for mobile healthcare systems. Our classification method is based on incremental learning for AD diagnosis and AD prediction using the cortical thickness data and hippocampus shape. We constructed a classifier based on principal component analysis and linear discriminant analysis. We performed initial learning and mobile subject classification. Initial learning is the group learning part in our server. Our smartphone agent implements the mobile classification and shows various results. With use of cortical thickness data analysis alone, the discrimination accuracy was 87.33% (sensitivity 96.49% and specificity 64.33%). When cortical thickness data and hippocampal shape were analyzed together, the achieved accuracy was 87.52% (sensitivity 96.79% and specificity 63.24%). In this paper, we presented a classification method based on online learning for AD diagnosis by employing both cortical thickness data and hippocampal shape analysis data. Our method was implemented on smartphone devices and discriminated AD patients for normal group.

Diagnosis of vulvovaginitis: comparison of clinical and microbiological diagnosis.

PubMed

Esim Buyukbayrak, Esra; Kars, Bulent; Karsidag, Ayse Yasemin Karageyim; Karadeniz, Bernan Ilkay; Kaymaz, Ozge; Gencer, Serap; Pirimoglu, Zehra Meltem; Unal, Orhan; Turan, Mehmet Cem

2010-11-01

The purpose of the present study was to compare the current diagnostic clinical and laboratory approaches to women with vulvovaginal discharge complaint. The secondary outcomes were to determine the prevalence of infections in our setting and to look for the relation between vulvovaginal infections and predisposing factors if present. Premenopausal women applying to our gynecology outpatient clinic with vaginal discharge complaint were enrolled prospectively into the study. Each patient evaluated clinically with direct observation of vaginal secretions, wet mount examination, whiff test, vaginal pH testing and chlamydia rapid antigen test. Each patient also evaluated microbiologically with vaginal discharge culture and gram staining. Clinical diagnosis was compared with the microbiological diagnosis (the gold standard). Diagnostic accuracy was measured with sensitivity, specificity, positive (ppv) and negative predictive values (npv). 460 patients were included in the study. 89.8% of patients received a clinical diagnosis whereas only 36% of them had microbiological diagnosis. The sensitivity, specificity, ppv, npv of clinical diagnosis over microbiological culture results were 95, 13, 38, 82%, respectively. The most commonly encountered microorganisms by culture were Candida species (17.4%) and Gardnerella vaginalis (10.2%). Clinically, the most commonly made diagnoses were mixed infection (34.1%), bacterial vaginosis (32.4%) and fungal infection (14.1%). Symptoms did not predict laboratory results. Predisposing factors (DM, vaginal douching practice, presence of IUD and usage of oral contraceptive pills) were not found to be statistically important influencing factors for vaginal infections. Clinical diagnosis based on combining symptoms with office-based testing improves diagnostic accuracy but is insufficient. The most effective approach also incorporates laboratory testing as an adjunct when a diagnosis is in question or treatment is failing.

Factors That Affect Large Subunit Ribosomal DNA Amplicon Sequencing Studies of Fungal Communities: Classification Method, Primer Choice, and Error

PubMed Central

Porter, Teresita M.; Golding, G. Brian

2012-01-01

Nuclear large subunit ribosomal DNA is widely used in fungal phylogenetics and to an increasing extent also amplicon-based environmental sequencing. The relatively short reads produced by next-generation sequencing, however, makes primer choice and sequence error important variables for obtaining accurate taxonomic classifications. In this simulation study we tested the performance of three classification methods: 1) a similarity-based method (BLAST + Metagenomic Analyzer, MEGAN); 2) a composition-based method (Ribosomal Database Project naïve Bayesian classifier, NBC); and, 3) a phylogeny-based method (Statistical Assignment Package, SAP). We also tested the effects of sequence length, primer choice, and sequence error on classification accuracy and perceived community composition. Using a leave-one-out cross validation approach, results for classifications to the genus rank were as follows: BLAST + MEGAN had the lowest error rate and was particularly robust to sequence error; SAP accuracy was highest when long LSU query sequences were classified; and, NBC runs significantly faster than the other tested methods. All methods performed poorly with the shortest 50–100 bp sequences. Increasing simulated sequence error reduced classification accuracy. Community shifts were detected due to sequence error and primer selection even though there was no change in the underlying community composition. Short read datasets from individual primers, as well as pooled datasets, appear to only approximate the true community composition. We hope this work informs investigators of some of the factors that affect the quality and interpretation of their environmental gene surveys. PMID:22558215

Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System

PubMed Central

Hosseini, Monireh Sheikh; Zekri, Maryam

2012-01-01

Image classification is an issue that utilizes image processing, pattern recognition and classification methods. Automatic medical image classification is a progressive area in image classification, and it is expected to be more developed in the future. Because of this fact, automatic diagnosis can assist pathologists by providing second opinions and reducing their workload. This paper reviews the application of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier in medical image classification during the past 16 years. ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of an FIS with the learning power of artificial neural networks. The objective of ANFIS is to integrate the best features of fuzzy systems and neural networks. A brief comparison with other classifiers, main advantages and drawbacks of this classifier are investigated. PMID:23493054

42 CFR 412.60 - DRG classification and weighting factors.

Code of Federal Regulations, 2014 CFR

2014-10-01

... discharge is based, as appropriate, on the patient's age, sex, principal diagnosis (that is, the diagnosis... whether a change in the DRG assignment is appropriate. If the intermediary decides that a higher-weighted... (b) of this section at least annually to reflect changes in treatment patterns, technology, and other...

[Prostate cancer. Epidemiology. Risk factors. Pathology].

PubMed

Fournier, G; Valeri, A; Mangin, P; Cussenot, O

2004-10-01

Prostate cancer (prostate adenocarcinoma) has become an important concern in terms of public health these past fifteen years; recent French epidemiological data revealed 10,104 deaths due to this disease in 2000. The two main factors involved are the serum prostatic specific antigen (PSA), routinely used since late 1980's and which allows early diagnosis (before symptom onset), and the lengthened duration of life. Such cancer is rare before the age of 50, but its frequency increases with age, making it the most frequent type of cancer in French men. Although the aetiology of this disease is unknown, the ethnic origin, and a familial history of prostate or breast cancer are known risk factors. Predisposing genes to such hereditary types remain to be identified. Other genetic factors (polymorphisms), combined with environmental factors such as nutrition, have been incriminated, which is likely to explain the geographical variations of this affection. At the molecular level, the mechanisms involved in the tumoral initiation and progression remain unclear. Various genetic alterations have been identified among the genome of cancerous cells, at various stages of the affection: intraepithelial neoplasia, localized, locally advanced, metastatic or hormone refractory stage -, hormonal escape). However, the precise sequence and nature of the complex molecular events remain to be determined prior to their routine utilisation in the determination of subjects at risk, or as prognostic factors, and even as therapeutic targets. The anatomopathology is a key for the diagnosis. Intraepithelial neoplasia is the pre-cancerous lesion observed in most adenocarcinomas; these are localized in the peripheral part of the prostate gland in 70% of the cases. Gleason's classification is the current gold standard for the determination of tumoral aggressiveness and categorisation of the adenocarcinomas which are basically heterogeneous (coexistence of tumors cells with different degrees of

EXTENDING AQUATIC CLASSIFICATION TO THE LANDSCAPE SCALE HYDROLOGY-BASED STRATEGIES

EPA Science Inventory

Aquatic classification of single water bodies (lakes, wetlands, estuaries) is often based on geologic origin, while stream classification has relied on multiple factors related to landform, geomorphology, and soils. We have developed an approach to aquatic classification based o...

Aided diagnosis methods of breast cancer based on machine learning

NASA Astrophysics Data System (ADS)

Zhao, Yue; Wang, Nian; Cui, Xiaoyu

2017-08-01

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%.

[Sequencing technology in gene diagnosis and its application].

PubMed

Yibin, Guo

2014-11-01

The study of gene mutation is one of the hot topics in the field of life science nowadays, and the related detection methods and diagnostic technology have been developed rapidly. Sequencing technology plays an indispensable role in the definite diagnosis and classification of genetic diseases. In this review, we summarize the research progress in sequencing technology, evaluate the advantages and disadvantages of 1(st) ~3(rd) generation of sequencing technology, and describe its application in gene diagnosis. Also we made forecasts and prospects on its development trend.

Diagnosis and classification of Goodpasture's disease (anti-GBM).

PubMed

Hellmark, Thomas; Segelmark, Mårten

2014-01-01

Goodpasture's disease or anti-glomerular basement membrane disease (anti-GBM-disease) is included among immune complex small vessel vasculitides. The definition of anti-GBM disease is a vasculitis affecting glomerular capillaries, pulmonary capillaries, or both, with GBM deposition of anti-GBM autoantibodies. The disease is a prototype of autoimmune disease, where the patients develop autoantibodies that bind to the basement membranes and activate the classical pathway of the complement system, which start a neutrophil dependent inflammation. The diagnosis of anti-GBM disease relies on the detection of anti-GBM antibodies in conjunction with glomerulonephritis and/or alveolitis. Overt clinical symptoms are most prominent in the glomeruli where the inflammation usually results in a severe rapidly progressive glomerulonephritis. Despite modern treatment less than one third of the patients survive with a preserved kidney function after 6 months follow-up. Frequencies vary from 0.5 to 1 cases per million inhabitants per year and there is a strong genetic linkage to HLA-DRB1(∗)1501 and DRB1(∗)1502. Essentially, anti-GBM disease is now a preferred term for what was earlier called Goodpasture's syndrome or Goodpasture's disease; anti-GBM disease is now classified as small vessel vasculitis caused by in situ immune complex formation; the diagnosis relies on the detection of anti-GBM in tissues or circulation in conjunction with alveolar or glomerular disease; therapy is effective only when detected at an early stage, making a high degree of awareness necessary to find these rare cases; 20-35% have anti-GBM and MPO-ANCA simultaneously, which necessitates testing for anti-GBM whenever acute test for ANCA is ordered in patients with renal disease. Copyright © 2014 Elsevier Ltd. All rights reserved.

A Qualitative Analysis of Physician Perspectives on Missed and Delayed Outpatient Diagnosis: The Focus on System-Related Factors.

PubMed

Sarkar, Urmimala; Simchowitz, Brett; Bonacum, Doug; Strull, William; Lopez, Andrea; Rotteau, Leahora; Shojania, Kaveh G

2014-10-01

Delayed and missed diagnoses lead to significant patient harm. Because physician actions are fundamental to the outpatient diagnostic process, a study was conducted to explore physician perspectives on diagnosis. As part of a quality improvement initiative, an integrated health system conducted six physician focus groups in 2004 and 2005. The focus groups included questions about the process of diagnosis, specific factors contributing to missed diagnosis, use of guidelines, atypical vs. typical presentations of disease, diagnostic tools, and follow-up, all with regard to delays in the diagnostic process. The interviews were analyzed (1) deductively, with application of the Systems Engineering Initiative for Patient Safety (SEIPS) model, which addresses systems design, quality management, job design, and technology implementations that affect safety-related patient and organizational and/or staff outcomes, and (2) inductively, with identification of novel themes using content analysis. A total of 25 physicians participated in the six focus groups, which yielded 12 hours of discussion. Providers identified multiple barriers to timely and accurate diagnosis, including organizational culture, information availability, and communication factors. Multiple themes relating to each of the participants in the diagnostic process-health system, provider, and patient-emerged. Concerns about health system structure and providers' interactions with one another and with patients far exceeded discussion of the cognitive factors that might affect the diagnostic process. The results suggest that, at least in physicians' views, improving the diagnostic process requires attention to the organization of the health system in addition to the cognitive aspects of diagnosis.

A classification of psychological factors leading to violent behavior in posttraumatic stress disorder.

PubMed

Silva, J A; Derecho, D V; Leong, G B; Weinstock, R; Ferrari, M M

2001-03-01

Posttraumatic stress disorder has long been linked to violent behavior. However, the exact nature of that association remains poorly characterized due to the limitations of knowledge in the area of phenomenology, contextual factors, the biology, and the nature of the aggression involved in the disorder. A clear understanding of the genesis of violence in posttraumatic stress disorder can be helpful to those involved in assessing psychiatric-legal issues relevant to the disorder and in its therapeutic management. In this article, we review the potential psychological links between posttraumatic stress disorder secondary to combat exposure and violent behavior and suggest a tentative classification of the main psychological causes of violence in that syndrome.

Classification and identification of molecules through factor analysis method based on terahertz spectroscopy

NASA Astrophysics Data System (ADS)

Huang, Jianglou; Liu, Jinsong; Wang, Kejia; Yang, Zhengang; Liu, Xiaming

2018-06-01

By means of factor analysis approach, a method of molecule classification is built based on the measured terahertz absorption spectra of the molecules. A data matrix can be obtained by sampling the absorption spectra at different frequency points. The data matrix is then decomposed into the product of two matrices: a weight matrix and a characteristic matrix. By using the K-means clustering to deal with the weight matrix, these molecules can be classified. A group of samples (spirobenzopyran, indole, styrene derivatives and inorganic salts) has been prepared, and measured via a terahertz time-domain spectrometer. These samples are classified with 75% accuracy compared to that directly classified via their molecular formulas.

Effects of externally rated job demand and control on depression diagnosis claims in an industrial cohort.

PubMed

DeSanto Iennaco, Joanne; Cullen, Mark R; Cantley, Linda; Slade, Martin D; Fiellin, Martha; Kasl, Stanislav V

2010-02-01

This study examined whether externally rated job demand and control were associated with depression diagnosis claims in a heavy industrial cohort. The retrospective cohort sample consisted of 7,566 hourly workers aged 18-64 years who were actively employed at 11 US plants between January 1, 1996, and December 31, 2003, and free of depression diagnosis claims during an initial 2-year run-in period. Logistic regression analysis was used to model the effect of tertiles of demand and control exposure on depression diagnosis claims. Demand had a significant positive association with depression diagnosis claims in bivariate models and models adjusted for demographic (age, gender, race, education, job grade, tenure) and lifestyle (smoking status, body mass index, cholesterol level) variables (high demand odds ratio = 1.39, 95% confidence interval: 1.04, 1.86). Control was associated with greater risk of depression diagnosis at moderate levels in unadjusted models only (odds ratio = 1.47, 95% confidence interval: 1.12, 1.93), while low control, contrary to expectation, was not associated with depression. The effects of the externally rated demand exposure were lost with adjustment for location. This may reflect differences in measurement or classification of exposure, differences in depression diagnosis by location, or other location-specific factors.

Effects of Externally Rated Job Demand and Control on Depression Diagnosis Claims in an Industrial Cohort

PubMed Central

DeSanto Iennaco, Joanne; Cullen, Mark R.; Cantley, Linda; Slade, Martin D.; Fiellin, Martha; Kasl, Stanislav V.

2010-01-01

This study examined whether externally rated job demand and control were associated with depression diagnosis claims in a heavy industrial cohort. The retrospective cohort sample consisted of 7,566 hourly workers aged 18–64 years who were actively employed at 11 US plants between January 1, 1996, and December 31, 2003, and free of depression diagnosis claims during an initial 2-year run-in period. Logistic regression analysis was used to model the effect of tertiles of demand and control exposure on depression diagnosis claims. Demand had a significant positive association with depression diagnosis claims in bivariate models and models adjusted for demographic (age, gender, race, education, job grade, tenure) and lifestyle (smoking status, body mass index, cholesterol level) variables (high demand odds ratio = 1.39, 95% confidence interval: 1.04, 1.86). Control was associated with greater risk of depression diagnosis at moderate levels in unadjusted models only (odds ratio = 1.47, 95% confidence interval: 1.12, 1.93), while low control, contrary to expectation, was not associated with depression. The effects of the externally rated demand exposure were lost with adjustment for location. This may reflect differences in measurement or classification of exposure, differences in depression diagnosis by location, or other location-specific factors. PMID:20035011

Salivary fistula: Blue dye testing as part of an algorithm for early diagnosis

PubMed Central

Kiong, Kimberley L.; Tan, Ngian Chye; Skanthakumar, Thakshayeni; Teo, Constance E.H.; Soo, Khee Chee; Tan, Hiang Khoon; Roche, Elizabeth; Yee, Kaisin

2017-01-01

Objective Orocutaneous and pharyngocutaneous fistula (OPCF) is a debilitating complication of head and neck surgery for squamous cell carcinoma (SCC), resulting in delayed adjuvant treatment and prolonged hospitalization. As yet, there is no established test that can help in prompt and accurate diagnosis of OPCF. This study aims to determine the accuracy of bedside blue dye testing and its role as part of an algorithm for early diagnosis. We also analyze the risk factors predisposing to OPCF. Study Design Retrospective cohort study from 2012 to 2014. Methods Patients with head and neck SCC who underwent major resection and reconstruction, at risk of OPCF, were included. Results of blue‐dye and video‐fluoroscopic swallow‐studies (VFSS) testing for OPCF were recorded. For the patients that were noted to develop OPCF, the length of time to diagnosis of fistula and subsequent mode of management were examined. Results Of the 93 patients in this study, 25 (26.9%) developed OPCF. Advanced T‐classification (T3/T4) was the only significant predisposing risk factor (p = 0.013). The sensitivity and specificity of the bedside blue dye testing was found to be 36.4% and 100%, respectively. The test positive patients were diagnosed with OPCF at a median of postoperative day (POD) 9.5 as compared to POD 13 for the test negative patients (p = 0.001). Early diagnosis was associated with faster fistula resolution with treatment. Conclusion Blue dye testing is a simple bedside test that can assist in the early diagnosis of OPCF in patients, allowing treatment to be instituted earlier with improved outcomes. Level of Evidence 3 PMID:29299509

Autism Diagnosis and Screening: Factors to Consider in Differential Diagnosis

ERIC Educational Resources Information Center

Matson, Johnny L.; Beighley, Jennifer; Turygin, Nicole

2012-01-01

There has been an exponential growth in assessment methods to diagnose disorders on the autism spectrum. Many reasons for this trend exist and include advancing knowledge on how to make a diagnosis, the heterogeneity of the spectrum, the realization that different methods may be needed based on age and intellectual disability. Other factors…

Large-scale optimization-based classification models in medicine and biology.

PubMed

Lee, Eva K

2007-06-01

We present novel optimization-based classification models that are general purpose and suitable for developing predictive rules for large heterogeneous biological and medical data sets. Our predictive model simultaneously incorporates (1) the ability to classify any number of distinct groups; (2) the ability to incorporate heterogeneous types of attributes as input; (3) a high-dimensional data transformation that eliminates noise and errors in biological data; (4) the ability to incorporate constraints to limit the rate of misclassification, and a reserved-judgment region that provides a safeguard against over-training (which tends to lead to high misclassification rates from the resulting predictive rule); and (5) successive multi-stage classification capability to handle data points placed in the reserved-judgment region. To illustrate the power and flexibility of the classification model and solution engine, and its multi-group prediction capability, application of the predictive model to a broad class of biological and medical problems is described. Applications include: the differential diagnosis of the type of erythemato-squamous diseases; predicting presence/absence of heart disease; genomic analysis and prediction of aberrant CpG island meythlation in human cancer; discriminant analysis of motility and morphology data in human lung carcinoma; prediction of ultrasonic cell disruption for drug delivery; identification of tumor shape and volume in treatment of sarcoma; discriminant analysis of biomarkers for prediction of early atherosclerois; fingerprinting of native and angiogenic microvascular networks for early diagnosis of diabetes, aging, macular degeneracy and tumor metastasis; prediction of protein localization sites; and pattern recognition of satellite images in classification of soil types. In all these applications, the predictive model yields correct classification rates ranging from 80 to 100%. This provides motivation for pursuing its use as a

Interobserver variability for the WHO classification of pulmonary carcinoids.

PubMed

Swarts, Dorian R A; van Suylen, Robert-Jan; den Bakker, Michael A; van Oosterhout, Matthijs F M; Thunnissen, Frederik B J M; Volante, Marco; Dingemans, Anne-Marie C; Scheltinga, Marc R M; Bootsma, Gerben P; Pouwels, Harry M M; van den Borne, Ben E E M; Ramaekers, Frans C S; Speel, Ernst-Jan M

2014-10-01

Pulmonary carcinoids are neuroendocrine tumors histopathologically subclassified into typical (TC; no necrosis, <2 mitoses per 2 mm) and atypical (AC; necrosis or 2 to 10 mitoses per 2 mm). The reproducibility of lung carcinoid classification, however, has not been extensively studied and may be hampered by the presence of pyknotic apoptosis mimicking mitotic figures. Furthermore, prediction of prognosis based on histopathology varies, especially for ACs. We examined the presence of interobserver variation between 5 experienced pulmonary pathologists who reviewed 123 originally diagnosed pulmonary carcinoid cases. The tumors were subsequently redistributed over 3 groups: unanimously classified cases, consensus cases (4/5 pathologists rendered identical diagnosis), and disagreement cases (divergent diagnosis by ≥2 assessors). κ-values were calculated, and results were correlated with clinical follow-up and molecular data. When focusing on the 114/123 cases unanimously classified as pulmonary carcinoids, the interobserver agreement was only fair (κ=0.32). Of these 114 cases, 55% were unanimously classified, 25% reached consensus classification, and for 19% there was no consensus. ACs were significantly more often in the latter category (P=0.00038). The designation of TCs and ACs by ≥3 assessors was not associated with prognosis (P=0.11). However, when disagreement cases were allocated on the basis of Ki-67 proliferative index (<5%; ≥5%) or nuclear orthopedia homeobox immunostaining (+; -), correlation with prognosis improved significantly (P=0.00040 and 0.0024, respectively). In conclusion, there is a considerable interobserver variation in the histopathologic classification of lung carcinoids, in particular concerning ACs. Additional immunomarkers such as Ki-67 or orthopedia homeobox may improve classification and prediction of prognosis.

Factors influencing delay in the diagnosis of colorectal cancer: a study protocol

PubMed Central

Esteva, Magdalena; Ramos, Maria; Cabeza, Elena; Llobera, Joan; Ruiz, Amador; Pita, Salvador; Segura, Josep M; Cortés, Jose M; González-Lujan, Luis

2007-01-01

Background Colorectal cancer (CRC) is the second most frequent tumor in developed countries. Since survival from CRC depends mostly on disease stage at the time of diagnosis, individuals with symptoms or signs suspicious of CRC should be examined without delay. Many factors, however, intervene between symptom onset and diagnosis. This study was designed to: 1) Describe the diagnostic process of CRC from the onset of first symptoms to diagnosis and treatment. 2) Establish the time interval from initial symptoms to diagnosis and treatment, globally and considering patient's and doctors' delay, with the latter due to family physician and/or hospital services. 3) Identify the factors related to defined types of delay. 4) Assess the concordance between information included in primary health care and hospital clinical records regarding onset of first symptoms. Methods/Design Descriptive study, coordinated, with 5 participant groups of 5 different Spanish regions (Balearic Islands, Galicia, Catalunya, Aragón and Valencia Health Districts), with a total of 8 acute public hospitals and 140 primary care centers. Incident cases of CRC during the study period, as identified from pathology services at the involved hospitals. A sample size of 896 subjects has been estimated, 150 subjects for each participant group. Information will be collected through patient interviews and primary health care and hospital clinical records. Patient variables will include sociodemographic variables, family history of cancer, symptom perception, and confidence in the family physician; tumor variables will include tumor site, histological type, grade and stage; symptom variables will include date of onset, type and number of symptoms; health system variables will include number of patient contacts with family physician, type and content of the referral, hospital services attending the patient, diagnostic modalities and results; and delay intervals, including global delays and delays attributed to

Performance of the new ACR/EULAR classification criteria for systemic sclerosis in clinical practice.

PubMed

Jordan, Suzana; Maurer, Britta; Toniolo, Martin; Michel, Beat; Distler, Oliver

2015-08-01

The preliminary classification criteria for SSc lack sensitivity for mild/early SSc patients, therefore, the new ACR/EULAR classification criteria for SSc were developed. The objective of this study was to evaluate the performance of the new classification criteria for SSc in clinical practice in a cohort of mild/early patients. Consecutive patients with a clinical diagnosis of SSc, based on expert opinion, were prospectively recruited and assessed according to the EULAR Scleroderma Trials and Research group (EUSTAR) and very early diagnosis of SSc (VEDOSS) recommendations. In some patients, missing values were retrieved retrospectively from the patient's records. Patients were grouped into established SSc (fulfilling the old ACR criteria) and mild/early SSc (not fulfilling the old ACR criteria). The new ACR/EULAR criteria were applied to all patients. Of the 304 patients available for the final analysis, 162/304 (53.3%) had established SSc and 142/304 (46.7%) had mild/early SSc. All 162 established SSc patients fulfilled the new ACR/EULAR classification criteria. The remaining 142 patients had mild/early SSc. Eighty of these 142 patients (56.3%) fulfilled the new ACR/EULAR classification criteria. Patients with mild/early SSc not fulfilling the new classification criteria were most often suffering from RP, had SSc-characteristic autoantibodies and had an SSc pattern on nailfold capillaroscopy. Taken together, the sensitivity of the new ACR/EULAR classification criteria for the overall cohort was 242/304 (79.6%) compared with 162/304 (53.3%) for the ACR criteria. In this cohort with a focus on mild/early SSc, the new ACR/EULAR classification criteria showed higher sensitivity and classified more patients as definite SSc patients than the ACR criteria. © The Author 2015. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

The Differential Diagnosis of Functional Symptoms in Adolescence.

ERIC Educational Resources Information Center

Silber, Thomas J.

1982-01-01

Functional complaints constitute the major reason why adolescents visit the physician's office. These complaints may coexist with organic illness of minor or major significance. Proposes a definition of functional disorders, sets forth a classification of the differential diagnosis of these disorders and suggests techniques for their management.…

The revised WHO dengue case classification: does the system need to be modified?

PubMed

Hadinegoro, Sri Rezeki S

2012-05-01

There has been considerable debate regarding the value of both the 1997 and 2009 World Health Organization (WHO) dengue case classification criteria for its diagnosis and management. Differentiation between classic dengue fever (DF) and dengue haemorrhagic fever (DHF) or severe dengue is a key aspect of dengue case classification. The geographic expansion of dengue and its increased incidence in older age groups have contributed to the limited applicability of the 1997 case definitions. Clinical experience of dengue suggests that the illness presents as a spectrum of disease instead of distinct phases. However, despite the rigid grouping of dengue into DF, DHF and dengue shock syndrome (DSS), overlap between the different manifestations has often been observed, which has affected clinical management and triage of patients. The findings of the DENCO study evaluating the 1997 case definitions formed the basis of the revised 2009 WHO case definitions, which classified the illness into dengue with and without warning signs and severe dengue. Although the revised scheme is more sensitive to the diagnosis of severe dengue, and beneficial to triage and case management, there remain issues with its applicability. It is considered by many to be too broad, requiring more specific definition of warning signs. Quantitative research into the predictive value of these warning signs on patient outcomes and the cost-effectiveness of the new classification system is required to ascertain whether the new classification system requires further modification, or whether elements of both classification systems can be combined.

The use of ecological classification in management

Treesearch

Constance A. Carpenter; Wolf-Dieter Busch; David T. Cleland; Juan Gallegos; Rick Harris; ray Holm; Chris Topik; Al Williamson

1999-01-01

Ecological classificafion systems range over a variety of scales and reflect a variety of scientific viewpoints. They incorporate or emphasize varied arrays of environmental factors. Ecological classifications have been developed for marine, wetland, lake, stream, and terrestrial ecosystems. What are the benefits of ecological classification for natural resource...

Classification and description of world formation types

Treesearch

D. Faber-Langendoen; T. Keeler-Wolf; D. Meidinger; C. Josse; A. Weakley; D. Tart; G. Navarro; B. Hoagland; S. Ponomarenko; G. Fults; Eileen Helmer

2016-01-01

An ecological vegetation classification approach has been developed in which a combination of vegetation attributes (physiognomy, structure, and floristics) and their response to ecological and biogeographic factors are used as the basis for classifying vegetation types. This approach can help support international, national, and subnational classification efforts. The...

Application of Sal classification to parotid gland fine-needle aspiration cytology: 10-year retrospective analysis of 312 patients.

PubMed

Kilavuz, Ahmet Erdem; Songu, Murat; İmre, Abdulkadir; Arslanoğlu, Secil; Özkul, Yilmaz; Pinar, Ercan; Ateş, Düzgün

2018-05-01

The accuracy of fine-needle aspiration biopsy (FNAB) is controversial in parotid tumors. We aimed to compare FNAB results with the final histopathological diagnosis and to apply the "Sal classification" to our data and discuss its results and its place in parotid gland cytology. The FNAB cytological findings and final histological diagnosis were assessed retrospectively in 2 different scenarios based on the distribution of nondefinitive cytology, and we applied the Sal classification and determined malignancy rate, sensitivity, and specificity for each category. In 2 different scenarios FNAB sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were found to be 81%, 87%, 54.7%, and 96.1%; and 65.3%, 100%, 100%, and 96.1%, respectively. The malignancy rates and sensitivity and specificity were also calculated and discussed for each Sal category. We believe that the Sal classification has a great potential to be a useful tool in classification of parotid gland cytology. © 2018 Wiley Periodicals, Inc.

Primary progressive aphasia: classification of variants in 100 consecutive Brazilian cases

PubMed Central

Senaha, Mirna Lie Hosogi; Caramelli, Paulo; Brucki, Sonia M.D.; Smid, Jerusa; Takada, Leonel T.; Porto, Claudia S.; César, Karolina G.; Matioli, Maria Niures P.; Soares, Roger T.; Mansur, Letícia L.; Nitrini, Ricardo

2013-01-01

Primary progressive aphasia (PPA) is a neurodegenerative clinical syndrome characterized primarily by progressive language impairment. Recently, consensus diagnostic criteria were published for the diagnosis and classification of variants of PPA. The currently recognized variants are nonfluent/agrammatic (PPA-G), logopenic (PPA-L) and semantic (PPA-S). OBJECTIVE To analyze the demographic data and the clinical classification of 100 PPA cases. METHODS Data from 100 PPA patients who were consecutively evaluated between 1999 and 2012 were analyzed. The patients underwent neurological, cognitive and language evaluation. The cases were classified according to the proposed variants, using predominantly the guidelines proposed in the consensus diagnostic criteria from 2011. RESULTS The sample consisted of 57 women and 43 men, aged at onset 67.2±8.1 years (range of between 53 and 83 years). Thirty-five patients presented PPA-S, 29 PPA-G and 16 PPA-L. It was not possible to classify 20% of the cases into any one of the proposed variants. CONCLUSION It was possible to classify 80% of the sample into one of the three PPA variants proposed. Perhaps the consensus classification requires some adjustments to accommodate cases that do not fit into any of the variants and to avoid overlap where cases fit more than one variant. Nonetheless, the established current guidelines are a useful tool to address the classification and diagnosis of PPA and are also of great value in standardizing terminologies to improve consistency across studies from different research centers. PMID:29213827

Accuracy of Diagnosis Codes to Identify Febrile Young Infants Using Administrative Data

PubMed Central

Aronson, Paul L.; Williams, Derek J.; Thurm, Cary; Tieder, Joel S.; Alpern, Elizabeth R.; Nigrovic, Lise E.; Schondelmeyer, Amanda C.; Balamuth, Fran; Myers, Angela L.; McCulloh, Russell J.; Alessandrini, Evaline A.; Shah, Samir S.; Browning, Whitney L.; Hayes, Katie L.; Feldman, Elana A.; Neuman, Mark I.

2015-01-01

Background Administrative data can be used to determine optimal management of febrile infants and aid clinical practice guideline development. Objective Determine the most accurate International Classification of Diseases, 9th revision (ICD-9) diagnosis coding strategies for identification of febrile infants. Design Retrospective cross-sectional study. Setting Eight emergency departments in the Pediatric Health Information System. Patients Infants age < 90 days evaluated between July 1, 2012 and June 30, 2013 were randomly selected for medical record review from one of four ICD-9 diagnosis code groups: 1) discharge diagnosis of fever, 2) admission diagnosis of fever without discharge diagnosis of fever, 3) discharge diagnosis of serious infection without diagnosis of fever, and 4) no diagnosis of fever or serious infection. Exposure The ICD-9 diagnosis code groups were compared in four case-identification algorithms to a reference standard of fever ≥ 100.4°F documented in the medical record. Measurements Algorithm predictive accuracy was measured using sensitivity, specificity, negative and positive predictive values. Results Among 1790 medical records reviewed, 766 (42.8%) infants had fever. Discharge diagnosis of fever demonstrated high specificity (98.2%, 95% confidence interval [CI]: 97.8-98.6) but low sensitivity (53.2%, 95% CI: 50.0-56.4). A case-identification algorithm of admission or discharge diagnosis of fever exhibited higher sensitivity (71.1%, 95% CI: 68.2-74.0), similar specificity (97.7%, 95% CI: 97.3-98.1), and the highest positive predictive value (86.9%, 95% CI: 84.5-89.3). Conclusions A case-identification strategy that includes admission or discharge diagnosis of fever should be considered for febrile infant studies using administrative data, though under-classification of patients is a potential limitation. PMID:26248691

Diagnosis of Epilepsy and Related Episodic Disorders.

PubMed

St Louis, Erik K; Cascino, Gregory D

2016-02-01

This review identifies the diverse and variable clinical presentations associated with epilepsy that may create challenges in diagnosis and treatment. Epilepsy has recently been redefined as a disease characterized by one or more seizures with a relatively high recurrence risk (ie, 60% or greater likelihood). The implication of this definition for therapy is that antiepileptic drug therapy may be initiated following a first seizure in certain situations.EEG remains the most commonly used study in the evaluation of people with epilepsy. Routine EEG may assist in diagnosis, classification of seizure type(s), identification of treatment, and monitoring the efficacy of therapy. Video-EEG monitoring permits seizure classification, assessment of psychogenic nonepileptic seizures, and evaluation of candidacy for epilepsy surgery. MRI is pivotal in elucidating the etiology of the seizure disorder and in suggesting the localization of seizure onset. This article reviews the new International League Against Epilepsy practical clinical definition for epilepsy and the differential diagnosis of other physiologic paroxysmal spells, including syncope, parasomnias, transient ischemic attacks, and migraine, as well as psychogenic nonepileptic seizures. The initial investigational approaches to new-onset epilepsy are considered, including neuroimaging and neurophysiologic investigations with interictal and ictal video-EEG. Neurologists should maintain a high index of suspicion for epilepsy when children or adults present with a single paroxysmal spell or recurrent episodic events.

A System for Heart Sounds Classification

PubMed Central

Redlarski, Grzegorz; Gradolewski, Dawid; Palkowski, Aleksander

2014-01-01

The future of quick and efficient disease diagnosis lays in the development of reliable non-invasive methods. As for the cardiac diseases – one of the major causes of death around the globe – a concept of an electronic stethoscope equipped with an automatic heart tone identification system appears to be the best solution. Thanks to the advancement in technology, the quality of phonocardiography signals is no longer an issue. However, appropriate algorithms for auto-diagnosis systems of heart diseases that could be capable of distinguishing most of known pathological states have not been yet developed. The main issue is non-stationary character of phonocardiography signals as well as a wide range of distinguishable pathological heart sounds. In this paper a new heart sound classification technique, which might find use in medical diagnostic systems, is presented. It is shown that by combining Linear Predictive Coding coefficients, used for future extraction, with a classifier built upon combining Support Vector Machine and Modified Cuckoo Search algorithm, an improvement in performance of the diagnostic system, in terms of accuracy, complexity and range of distinguishable heart sounds, can be made. The developed system achieved accuracy above 93% for all considered cases including simultaneous identification of twelve different heart sound classes. The respective system is compared with four different major classification methods, proving its reliability. PMID:25393113

Discriminative non-negative matrix factorization (DNMF) and its application to the fault diagnosis of diesel engine

NASA Astrophysics Data System (ADS)

Yang, Yong-sheng; Ming, An-bo; Zhang, You-yun; Zhu, Yong-sheng

2017-10-01

Diesel engines, widely used in engineering, are very important for the running of equipments and their fault diagnosis have attracted much attention. In the past several decades, the image based fault diagnosis methods have provided efficient ways for the diesel engine fault diagnosis. By introducing the class information into the traditional non-negative matrix factorization (NMF), an improved NMF algorithm named as discriminative NMF (DNMF) was developed and a novel imaged based fault diagnosis method was proposed by the combination of the DNMF and the KNN classifier. Experiments performed on the fault diagnosis of diesel engine were used to validate the efficacy of the proposed method. It is shown that the fault conditions of diesel engine can be efficiently classified by the proposed method using the coefficient matrix obtained by DNMF. Compared with the original NMF (ONMF) and principle component analysis (PCA), the DNMF can represent the class information more efficiently because the class characters of basis matrices obtained by the DNMF are more visible than those in the basis matrices obtained by the ONMF and PCA.

Deep learning for brain tumor classification

NASA Astrophysics Data System (ADS)

Paul, Justin S.; Plassard, Andrew J.; Landman, Bennett A.; Fabbri, Daniel

2017-03-01

Recent research has shown that deep learning methods have performed well on supervised machine learning, image classification tasks. The purpose of this study is to apply deep learning methods to classify brain images with different tumor types: meningioma, glioma, and pituitary. A dataset was publicly released containing 3,064 T1-weighted contrast enhanced MRI (CE-MRI) brain images from 233 patients with either meningioma, glioma, or pituitary tumors split across axial, coronal, or sagittal planes. This research focuses on the 989 axial images from 191 patients in order to avoid confusing the neural networks with three different planes containing the same diagnosis. Two types of neural networks were used in classification: fully connected and convolutional neural networks. Within these two categories, further tests were computed via the augmentation of the original 512×512 axial images. Training neural networks over the axial data has proven to be accurate in its classifications with an average five-fold cross validation of 91.43% on the best trained neural network. This result demonstrates that a more general method (i.e. deep learning) can outperform specialized methods that require image dilation and ring-forming subregions on tumors.

[Landscape classification: research progress and development trend].

PubMed

Liang, Fa-Chao; Liu, Li-Ming

2011-06-01

Landscape classification is the basis of the researches on landscape structure, process, and function, and also, the prerequisite for landscape evaluation, planning, protection, and management, directly affecting the precision and practicability of landscape research. This paper reviewed the research progress on the landscape classification system, theory, and methodology, and summarized the key problems and deficiencies of current researches. Some major landscape classification systems, e. g. , LANMAP and MUFIC, were introduced and discussed. It was suggested that a qualitative and quantitative comprehensive classification based on the ideology of functional structure shape and on the integral consideration of landscape classification utility, landscape function, landscape structure, physiogeographical factors, and human disturbance intensity should be the major research directions in the future. The integration of mapping, 3S technology, quantitative mathematics modeling, computer artificial intelligence, and professional knowledge to enhance the precision of landscape classification would be the key issues and the development trend in the researches of landscape classification.

Ensemble based on static classifier selection for automated diagnosis of Mild Cognitive Impairment.

PubMed

Nanni, Loris; Lumini, Alessandra; Zaffonato, Nicolò

2018-05-15

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.

Patient prognosis based on feature extraction, selection and classification of EEG periodic activity.

PubMed

Sánchez-González, Alain; García-Zapirain, Begoña; Maestro Saiz, Iratxe; Yurrebaso Santamaría, Izaskun

2015-01-01

Periodic activity in electroencephalography (PA-EEG) is shown as comprising a series of repetitive wave patterns that may appear in different cerebral regions and are due to many different pathologies. The diagnosis based on PA-EEG is an arduous task for experts in Clinical Neurophysiology, being mainly based on other clinical features of patients. Considering this difficulty in the diagnosis it is also very complicated to establish the prognosis of patients who present PA-EEG. The goal of this paper is to propose a method capable of determining patient prognosis based on characteristics of the PA-EEG activity. The approach, based on a parallel classification architecture and a majority vote system has proven successful by obtaining a success rate of 81.94% in the classification of patient prognosis of our database.

Modified Bat Algorithm for Feature Selection with the Wisconsin Diagnosis Breast Cancer (WDBC) Dataset

PubMed

Jeyasingh, Suganthi; Veluchamy, Malathi

2017-05-01

Early diagnosis of breast cancer is essential to save lives of patients. Usually, medical datasets include a large variety of data that can lead to confusion during diagnosis. The Knowledge Discovery on Database (KDD) process helps to improve efficiency. It requires elimination of inappropriate and repeated data from the dataset before final diagnosis. This can be done using any of the feature selection algorithms available in data mining. Feature selection is considered as a vital step to increase the classification accuracy. This paper proposes a Modified Bat Algorithm (MBA) for feature selection to eliminate irrelevant features from an original dataset. The Bat algorithm was modified using simple random sampling to select the random instances from the dataset. Ranking was with the global best features to recognize the predominant features available in the dataset. The selected features are used to train a Random Forest (RF) classification algorithm. The MBA feature selection algorithm enhanced the classification accuracy of RF in identifying the occurrence of breast cancer. The Wisconsin Diagnosis Breast Cancer Dataset (WDBC) was used for estimating the performance analysis of the proposed MBA feature selection algorithm. The proposed algorithm achieved better performance in terms of Kappa statistic, Mathew’s Correlation Coefficient, Precision, F-measure, Recall, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE) and Root Relative Squared Error (RRSE). Creative Commons Attribution License

Differential diagnosis of pleural mesothelioma using Logic Learning Machine.

PubMed

Parodi, Stefano; Filiberti, Rosa; Marroni, Paola; Libener, Roberta; Ivaldi, Giovanni Paolo; Mussap, Michele; Ferrari, Enrico; Manneschi, Chiara; Montani, Erika; Muselli, Marco

2015-01-01

Tumour markers are standard tools for the differential diagnosis of cancer. However, the occurrence of nonspecific symptoms and different malignancies involving the same cancer site may lead to a high proportion of misclassifications. Classification accuracy can be improved by combining information from different markers using standard data mining techniques, like Decision Tree (DT), Artificial Neural Network (ANN), and k-Nearest Neighbour (KNN) classifier. Unfortunately, each method suffers from some unavoidable limitations. DT, in general, tends to show a low classification performance, whereas ANN and KNN produce a "black-box" classification that does not provide biological information useful for clinical purposes. Logic Learning Machine (LLM) is an innovative method of supervised data analysis capable of building classifiers described by a set of intelligible rules including simple conditions in their antecedent part. It is essentially an efficient implementation of the Switching Neural Network model and reaches excellent classification accuracy while keeping low the computational demand. LLM was applied to data from a consecutive cohort of 169 patients admitted for diagnosis to two pulmonary departments in Northern Italy from 2009 to 2011. Patients included 52 malignant pleural mesotheliomas (MPM), 62 pleural metastases (MTX) from other tumours and 55 benign diseases (BD) associated with pleurisies. Concentration of three tumour markers (CEA, CYFRA 21-1 and SMRP) was measured in the pleural fluid of each patient and a cytological examination was also carried out. The performance of LLM and that of three competing methods (DT, KNN and ANN) was assessed by leave-one-out cross-validation. LLM outperformed all other considered methods. Global accuracy was 77.5% for LLM, 72.8% for DT, 54.4% for KNN, and 63.9% for ANN, respectively. In more details, LLM correctly classified 79% of MPM, 66% of MTX and 89% of BD. The corresponding figures for DT were: MPM = 83%, MTX

PSG-EXPERT. An expert system for the diagnosis of sleep disorders.

PubMed

Fred, A; Filipe, J; Partinen, M; Paiva, T

2000-01-01

This paper describes PSG-EXPERT, an expert system in the domain of sleep disorders exploring polysomnographic data. The developed software tool is addressed from two points of view: (1)--as an integrated environment for the development of diagnosis-oriented expert systems; (2)--as an auxiliary diagnosis tool in the particular domain of sleep disorders. Developed over a Windows platform, this software tool extends one of the most popular shells--CLIPS (C Language Integrated Production System) with the following features: backward chaining engine; graph-based explanation facilities; knowledge editor including a fuzzy fact editor and a rules editor, with facts-rules integrity checking; belief revision mechanism; built-in case generator and validation module. It therefore provides graphical support for knowledge acquisition, edition, explanation and validation. From an application domain point of view, PSG-Expert is an auxiliary diagnosis system for sleep disorders based on polysomnographic data, that aims at assisting the medical expert in his diagnosis task by providing automatic analysis of polysomnographic data, summarising the results of this analysis in terms of a report of major findings and possible diagnosis consistent with the polysomnographic data. Sleep disorders classification follows the International Classification of Sleep Disorders. Major features of the system include: browsing on patients data records; structured navigation on Sleep Disorders descriptions according to ASDA definitions; internet links to related pages; diagnosis consistent with polysomnographic data; graphical user-interface including graph-based explanatory facilities; uncertainty modelling and belief revision; production of reports; connection to remote databases.

Cystic fibrosis liver disease - from diagnosis to risk factors.

PubMed

Ciucă, Ioana Mihaiela; Pop, Liviu; Tămaş, Liviu; Tăban, Sorina

2014-01-01

Cystic fibrosis (CF) is the most frequent monogenic genetic disease, autosomal recessive transmitted, characterized by an impressive clinical polymorphism and appreciative fatal prospective. Liver disease is the second non-pulmonary cause of death in cystic fibrosis, which, with increasing life expectancy, became an important management problem. Predisposing factors like male gender, pancreatic insufficiency, meconium ileus and severe mutation are incriminated to influence the occurrence of cystic fibrosis associated liver disease (CFLD). Our study included 174 patients with CF, monitored in the National Cystic Fibrosis Centre, Timisoara, Romania. They were routinely followed-up by clinical assessment, liver biochemical tests, ultrasound examinations and other methods like transient elastography, biopsy, in selected cases. Sixty-six patients, with median age at diagnosis 4.33 years, diagnosed with CFLD, without significant gender gap. CFLD was frequent in patients aged over eight years, with meconium ileus history, carriers of severe mutations (p=0.002). Pancreatic insufficiency, although present in 75% of patients with CFLD was not confirmed as risk factor, not male gender, in our study. CF children older than eight years, carriers of a severe genotype, with a positive history of meconium ileus, were more likely predisposed to CFLD.

Human factors analysis and classification system applied to civil aircraft accidents in India.

PubMed

Gaur, Deepak

2005-05-01

The Human Factors Analysis and Classification System (HFACS) has gained wide acceptance as a tool to classify human factors in aircraft accidents and incidents. This study on application of HFACS to civil aircraft accident reports at Directorate General Civil of Aviation (DGCA), India, was conducted to ascertain the practicability of applying HFACS to existing investigation reports and to analyze the trends of human factor causes of civil aircraft accidents. Accident investigation reports held at DGCA, New Delhi, for the period 1990--99 were scrutinized. In all, 83 accidents occurred during this period, of which 48 accident reports were evaluated in this study. One or more human factors contributed to 37 of the 48 (77.1%) accidents. The commonest unsafe act was 'skill based errors' followed by 'decision errors.' Violations of laid down rules were contributory in 16 cases (33.3%). 'Preconditions for unsafe acts' were seen in 23 of the 48 cases (47.9%). A fairly large number (52.1%) had 'organizational influences' contributing to the accident. These results are in consonance with larger studies of accidents in the U.S. Navy and general aviation. Such a high percentage of 'organizational influences' has not been reported in other studies. This is a healthy sign for Indian civil aviation, provided effective remedial action for the same is undertaken.

Individual Patient Diagnosis of AD and FTD via High-Dimensional Pattern Classification of MRI

PubMed Central

Davatzikos, C.; Resnick, S. M.; Wu, X.; Parmpi, P.; Clark, C. M.

2008-01-01

The purpose of this study is to determine the diagnostic accuracy of MRI-based high-dimensional pattern classification in differentiating between patients with Alzheimer’s Disease (AD), Frontotemporal Dementia (FTD), and healthy controls, on an individual patient basis. MRI scans of 37 patients with AD and 37 age-matched cognitively normal elderly individuals, as well as 12 patients with FTD and 12 age-matched cognitively normal elderly individuals, were analyzed using voxel-based analysis and high-dimensional pattern classification. Diagnostic sensitivity and specificity of spatial patterns of regional brain atrophy found to be characteristic of AD and FTD were determined via cross-validation and via split-sample methods. Complex spatial patterns of relatively reduced brain volumes were identified, including temporal, orbitofrontal, parietal and cingulate regions, which were predominantly characteristic of either AD or FTD. These patterns provided 100% diagnostic accuracy, when used to separate AD or FTD from healthy controls. The ability to correctly distinguish AD from FTD averaged 84.3%. All estimates of diagnostic accuracy were determined via cross-validation. In conclusion, AD- and FTD-specific patterns of brain atrophy can be detected with high accuracy using high-dimensional pattern classification of MRI scans obtained in a typical clinical setting. PMID:18474436

Round Cell Tumors: Classification and Immunohistochemistry.

PubMed

Sharma, Shweta; Kamala, R; Nair, Divya; Ragavendra, T Raju; Mhatre, Swapnil; Sabharwal, Robin; Choudhury, Basanta Kumar; Rana, Vivek

2017-01-01

Round cell tumors as the name suggest are comprised round cells with increased nuclear-cytoplasmic ratio. This group of tumor includes entities such as peripheral neuroectodermal tumor, rhabdomyosarcoma, synovial sarcoma, non-Hodgkin's lymphoma, neuroblastoma, hepatoblastoma, Wilms' tumor, and desmoplastic small round cell tumor. These round cells tumors are characterized by typical histological pattern, immunohistochemical, and electron microscopic features that can help in differential diagnosis. The present article describes the classification and explains the histopathology and immunohistochemistry of some important round cell tumors.

On the convergence of nanotechnology and Big Data analysis for computer-aided diagnosis.

PubMed

Rodrigues, Jose F; Paulovich, Fernando V; de Oliveira, Maria Cf; de Oliveira, Osvaldo N

2016-04-01

An overview is provided of the challenges involved in building computer-aided diagnosis systems capable of precise medical diagnostics based on integration and interpretation of data from different sources and formats. The availability of massive amounts of data and computational methods associated with the Big Data paradigm has brought hope that such systems may soon be available in routine clinical practices, which is not the case today. We focus on visual and machine learning analysis of medical data acquired with varied nanotech-based techniques and on methods for Big Data infrastructure. Because diagnosis is essentially a classification task, we address the machine learning techniques with supervised and unsupervised classification, making a critical assessment of the progress already made in the medical field and the prospects for the near future. We also advocate that successful computer-aided diagnosis requires a merge of methods and concepts from nanotechnology and Big Data analysis.

What clues are available for differential diagnosis of headaches in emergency settings?

PubMed

Mert, Ertan; Ozge, Aynur; Taşdelen, Bahar; Yilmaz, Arda; Bilgin, Nursel G

2008-04-01

The correct diagnosis of headache disorders in an emergency room is important for developing early management strategies and determining optimal emergency room activities. This prospective clinical based study was performed in order to determine demographic and clinical clues for differential diagnosis of primary and secondary headache disorders and also to obtain a classification plot for the emergency room practitioners. This study included 174 patients older than 15 years of age presenting in the emergency room with a chief complaint of headache. Definite headache diagnoses were made according to ICHD-II criteria. Classification and regression tree was used as new method for the statistical analysis of the differential diagnostic process. Our 174 patients with headache were diagnosed as basically primary (72.9%) and secondary (27.1%) headaches. Univariate analysis with cross tabs showed three important results. First, unilateral pain location caused 1.431-fold increase in the primary headache risk (p = 0.006). Second, having any triggers caused 1.440-fold increase in the primary headache risk (p = 0.001). Third, having associated co-morbid medical disorders caused 4.643-fold increase in the secondary headache risk (p < 0.001). It was concluded that the presence of comorbidity, the patient's age, the existence of trigger and relaxing factors, the pain in other body parts that accompanies headache and the quality of pain in terms of location and duration were all important clues for physicians in making an accurate differentiation between primary and secondary headaches.

Diagnosis and management of neurotrophic keratitis

PubMed Central

Sacchetti, Marta; Lambiase, Alessandro

2014-01-01

Neurotrophic keratitis (NK) is a degenerative disease characterized by corneal sensitivity reduction, spontaneous epithelium breakdown, and impairment of corneal healing. Several causes of NK, including herpetic keratitis, diabetes, and ophthalmic and neurosurgical procedures, share the common mechanism of trigeminal damage. Diagnosis of NK requires accurate investigation of clinical ocular and systemic history, complete eye examination, and assessment of corneal sensitivity. All diagnostic procedures to achieve correct diagnosis and classification of NK, including additional examinations such as in vivo confocal microscopy, are reviewed. NK can be classified according to severity of corneal damage, ie, epithelial alterations (stage 1), persistent epithelial defect (stage 2), and corneal ulcer (stage 3). Management of NK should be based on clinical severity, and aimed at promoting corneal healing and preventing progression of the disease to stromal melting and perforation. Concomitant ocular diseases, such as exposure keratitis, dry eye, and limbal stem cell deficiency, negatively influence the outcome of NK and should be treated. Currently, no specific medical treatment exists, and surgical approaches, such as amniotic membrane transplantation and conjunctival flap, are effective in preserving eye integrity, without ameliorating corneal sensitivity or visual function. This review describes experimental and clinical reports showing several novel and potential therapies for NK, including growth factors and metalloprotease inhibitors, as well as three ongoing Phase II clinical trials. PMID:24672223

Medical image classification based on multi-scale non-negative sparse coding.

PubMed

Zhang, Ruijie; Shen, Jian; Wei, Fushan; Li, Xiong; Sangaiah, Arun Kumar

2017-11-01

With the rapid development of modern medical imaging technology, medical image classification has become more and more important in medical diagnosis and clinical practice. Conventional medical image classification algorithms usually neglect the semantic gap problem between low-level features and high-level image semantic, which will largely degrade the classification performance. To solve this problem, we propose a multi-scale non-negative sparse coding based medical image classification algorithm. Firstly, Medical images are decomposed into multiple scale layers, thus diverse visual details can be extracted from different scale layers. Secondly, for each scale layer, the non-negative sparse coding model with fisher discriminative analysis is constructed to obtain the discriminative sparse representation of medical images. Then, the obtained multi-scale non-negative sparse coding features are combined to form a multi-scale feature histogram as the final representation for a medical image. Finally, SVM classifier is combined to conduct medical image classification. The experimental results demonstrate that our proposed algorithm can effectively utilize multi-scale and contextual spatial information of medical images, reduce the semantic gap in a large degree and improve medical image classification performance. Copyright © 2017 Elsevier B.V. All rights reserved.

Three-Way Analysis of Spectrospatial Electromyography Data: Classification and Interpretation

PubMed Central

Kauppi, Jukka-Pekka; Hahne, Janne; Müller, Klaus-Robert; Hyvärinen, Aapo

2015-01-01

Classifying multivariate electromyography (EMG) data is an important problem in prosthesis control as well as in neurophysiological studies and diagnosis. With modern high-density EMG sensor technology, it is possible to capture the rich spectrospatial structure of the myoelectric activity. We hypothesize that multi-way machine learning methods can efficiently utilize this structure in classification as well as reveal interesting patterns in it. To this end, we investigate the suitability of existing three-way classification methods to EMG-based hand movement classification in spectrospatial domain, as well as extend these methods by sparsification and regularization. We propose to use Fourier-domain independent component analysis as preprocessing to improve classification and interpretability of the results. In high-density EMG experiments on hand movements across 10 subjects, three-way classification yielded higher average performance compared with state-of-the art classification based on temporal features, suggesting that the three-way analysis approach can efficiently utilize detailed spectrospatial information of high-density EMG. Phase and amplitude patterns of features selected by the classifier in finger-movement data were found to be consistent with known physiology. Thus, our approach can accurately resolve hand and finger movements on the basis of detailed spectrospatial information, and at the same time allows for physiological interpretation of the results. PMID:26039100

Comparative analysis of image classification methods for automatic diagnosis of ophthalmic images

NASA Astrophysics Data System (ADS)

Wang, Liming; Zhang, Kai; Liu, Xiyang; Long, Erping; Jiang, Jiewei; An, Yingying; Zhang, Jia; Liu, Zhenzhen; Lin, Zhuoling; Li, Xiaoyan; Chen, Jingjing; Cao, Qianzhong; Li, Jing; Wu, Xiaohang; Wang, Dongni; Li, Wangting; Lin, Haotian

2017-01-01

There are many image classification methods, but it remains unclear which methods are most helpful for analyzing and intelligently identifying ophthalmic images. We select representative slit-lamp images which show the complexity of ocular images as research material to compare image classification algorithms for diagnosing ophthalmic diseases. To facilitate this study, some feature extraction algorithms and classifiers are combined to automatic diagnose pediatric cataract with same dataset and then their performance are compared using multiple criteria. This comparative study reveals the general characteristics of the existing methods for automatic identification of ophthalmic images and provides new insights into the strengths and shortcomings of these methods. The relevant methods (local binary pattern +SVMs, wavelet transformation +SVMs) which achieve an average accuracy of 87% and can be adopted in specific situations to aid doctors in preliminarily disease screening. Furthermore, some methods requiring fewer computational resources and less time could be applied in remote places or mobile devices to assist individuals in understanding the condition of their body. In addition, it would be helpful to accelerate the development of innovative approaches and to apply these methods to assist doctors in diagnosing ophthalmic disease.

Java-Based Diabetes Type 2 Prediction Tool for Better Diagnosis

PubMed Central

Odedra, Devang; Mallick, Medhavi; Shukla, Prateek; Samanta, Subir; Vidyarthi, Ambarish S.

2012-01-01

Abstract Background The concept of classification of clinical data can be utilized in the development of an effective diagnosis system by taking the advantage of computational intelligence. Diabetes disease diagnosis via proper interpretation of the diabetes data is an important problem in neural networks. Unfortunately, although several classification studies have been carried out with significant performance, many of the current methods often fail to reach out to patients. Graphical user interface-enabled tools need to be developed through which medical practitioners can simply enter the health profiles of their patients and receive an instant diabetes prediction with an acceptable degree of confidence. Methods In this study, the neural network approach was used for a dataset of 768 persons from a Pima Indian population living near Phoenix, AZ. A neural network mixture of experts model was trained with these data using the expectation-minimization algorithm. Results The mixture of experts method was used to train the algorithm with 97% accuracy. A graphical user interface was developed that would work in conjunction with the trained network to provide the output in a presentable format. Conclusions This study provides a machine-implementable approach that can be used by physicians and patients to minimize the extent of error in diagnosis. The authors are hopeful that replication of results of this study in other populations may lead to improved diagnosis. Physicians can simply enter the health profile of patients and get the diagnosis for diabetes type 2. PMID:22059431

Inter- and intrarater reliability of the Chicago Classification in pediatric high-resolution esophageal manometry recordings.

PubMed

Singendonk, M M J; Smits, M J; Heijting, I E; van Wijk, M P; Nurko, S; Rosen, R; Weijenborg, P W; Abu-Assi, R; Hoekman, D R; Kuizenga-Wessel, S; Seiboth, G; Benninga, M A; Omari, T I; Kritas, S

2015-02-01

The Chicago Classification (CC) facilitates interpretation of high-resolution manometry (HRM) recordings. Application of this adult based algorithm to the pediatric population is unknown. We therefore assessed intra and interrater reliability of software-based CC diagnosis in a pediatric cohort. Thirty pediatric solid state HRM recordings (13M; mean age 12.1 ± 5.1 years) assessing 10 liquid swallows per patient were analyzed twice by 11 raters (six experts, five non-experts). Software-placed anatomical landmarks required manual adjustment or removal. Integrated relaxation pressure (IRP4s), distal contractile integral (DCI), contractile front velocity (CFV), distal latency (DL) and break size (BS), and an overall CC diagnosis were software-generated. In addition, raters provided their subjective CC diagnosis. Reliability was calculated with Cohen's and Fleiss' kappa (κ) and intraclass correlation coefficient (ICC). Intra- and interrater reliability of software-generated CC diagnosis after manual adjustment of landmarks was substantial (mean κ = 0.69 and 0.77 respectively) and moderate-substantial for subjective CC diagnosis (mean κ = 0.70 and 0.58 respectively). Reliability of both software-generated and subjective diagnosis of normal motility was high (κ = 0.81 and κ = 0.79). Intra- and interrater reliability were excellent for IRP4s, DCI, and BS. Experts had higher interrater reliability than non-experts for DL (ICC = 0.65 vs ICC = 0.36 respectively) and the software-generated diagnosis diffuse esophageal spasm (DES, κ = 0.64 vs κ = 0.30). Among experts, the reliability for the subjective diagnosis of achalasia and esophageal gastric junction outflow obstruction was moderate-substantial (κ = 0.45-0.82). Inter- and intrarater reliability of software-based CC diagnosis of pediatric HRM recordings was high overall. However, experience was a factor influencing the diagnosis of some motility disorders, particularly DES and achalasia. © 2014 John Wiley & Sons

Interactions between factors related to the decision of sex offenders to confess during police interrogation: a classification-tree approach.

PubMed

Beauregard, Eric; Deslauriers-Varin, Nadine; St-Yves, Michel

2010-09-01

Most studies of confessions have looked at the influence of individual factors, neglecting the potential interactions between these factors and their impact on the decision to confess or not during an interrogation. Classification and regression tree analyses conducted on a sample of 624 convicted sex offenders showed that certain factors related to the offenders (e.g., personality, criminal career), victims (e.g., sex, relationship to offender), and case (e.g., time of day of the crime) were related to the decision to confess or not during the police interrogation. Several interactions were also observed between these factors. Results will be discussed in light of previous findings and interrogation strategies for sex offenders.

A New Classification of Endodontic-Periodontal Lesions

PubMed Central

Al-Fouzan, Khalid S.

2014-01-01

The interrelationship between periodontal and endodontic disease has always aroused confusion, queries, and controversy. Differentiating between a periodontal and an endodontic problem can be difficult. A symptomatic tooth may have pain of periodontal and/or pulpal origin. The nature of that pain is often the first clue in determining the etiology of such a problem. Radiographic and clinical evaluation can help clarify the nature of the problem. In some cases, the influence of pulpal pathology may cause the periodontal involvement and vice versa. The simultaneous existence of pulpal problems and inflammatory periodontal disease can complicate diagnosis and treatment planning. An endo-perio lesion can have a varied pathogenesis which ranges from simple to relatively complex one. The differential diagnosis of endodontic and periodontal diseases can sometimes be difficult, but it is of vital importance to make a correct diagnosis for providing the appropriate treatment. This paper aims to discuss a modified clinical classification to be considered for accurately diagnosing and treating endo-perio lesion. PMID:24829580

A new classification of endodontic-periodontal lesions.

PubMed

Al-Fouzan, Khalid S

2014-01-01

The interrelationship between periodontal and endodontic disease has always aroused confusion, queries, and controversy. Differentiating between a periodontal and an endodontic problem can be difficult. A symptomatic tooth may have pain of periodontal and/or pulpal origin. The nature of that pain is often the first clue in determining the etiology of such a problem. Radiographic and clinical evaluation can help clarify the nature of the problem. In some cases, the influence of pulpal pathology may cause the periodontal involvement and vice versa. The simultaneous existence of pulpal problems and inflammatory periodontal disease can complicate diagnosis and treatment planning. An endo-perio lesion can have a varied pathogenesis which ranges from simple to relatively complex one. The differential diagnosis of endodontic and periodontal diseases can sometimes be difficult, but it is of vital importance to make a correct diagnosis for providing the appropriate treatment. This paper aims to discuss a modified clinical classification to be considered for accurately diagnosing and treating endo-perio lesion.

Structural MRI-based detection of Alzheimer's disease using feature ranking and classification error.

PubMed

Beheshti, Iman; Demirel, Hasan; Farokhian, Farnaz; Yang, Chunlan; Matsuda, Hiroshi

2016-12-01

This paper presents an automatic computer-aided diagnosis (CAD) system based on feature ranking for detection of Alzheimer's disease (AD) using structural magnetic resonance imaging (sMRI) data. The proposed CAD system is composed of four systematic stages. First, global and local differences in the gray matter (GM) of AD patients compared to the GM of healthy controls (HCs) are analyzed using a voxel-based morphometry technique. The aim is to identify significant local differences in the volume of GM as volumes of interests (VOIs). Second, the voxel intensity values of the VOIs are extracted as raw features. Third, the raw features are ranked using a seven-feature ranking method, namely, statistical dependency (SD), mutual information (MI), information gain (IG), Pearson's correlation coefficient (PCC), t-test score (TS), Fisher's criterion (FC), and the Gini index (GI). The features with higher scores are more discriminative. To determine the number of top features, the estimated classification error based on training set made up of the AD and HC groups is calculated, with the vector size that minimized this error selected as the top discriminative feature. Fourth, the classification is performed using a support vector machine (SVM). In addition, a data fusion approach among feature ranking methods is introduced to improve the classification performance. The proposed method is evaluated using a data-set from ADNI (130 AD and 130 HC) with 10-fold cross-validation. The classification accuracy of the proposed automatic system for the diagnosis of AD is up to 92.48% using the sMRI data. An automatic CAD system for the classification of AD based on feature-ranking method and classification errors is proposed. In this regard, seven-feature ranking methods (i.e., SD, MI, IG, PCC, TS, FC, and GI) are evaluated. The optimal size of top discriminative features is determined by the classification error estimation in the training phase. The experimental results indicate that

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

PubMed Central

Klin, Ami; Klaiman, Cheryl; Jones, Warren

2015-01-01

Summary Autism spectrum disorder (autism) is a highly prevalent and heterogeneous family of neurodevelopmental disorders of genetic origins with potentially devastating implications for child, family, health and educational systems. Despite advances in paper-and-pencil screening and in standardization of diagnostic procedures, diagnosis of autism in the US still hovers around the ages of four or five years, later still in disadvantaged communities, and several years after the age of two to three years when the condition can be reliably diagnosed by expert clinicians. As early detection and treatment are two of the most important factors optimizing outcome, and given that diagnosis is typically a necessary condition for families to have access to early treatment, reducing age of diagnosis has become one of the greatest priorities of the field. Recent advances in developmental social neuroscience promise the advent of cost-effective and community-viable, performance-based procedures, and suggest a complementary method for promoting universal screening and much greater access to the diagnosis process. Small but critical studies have already reported on experiments that differentiate groups of children at risk for autism from controls, and at least one study so far could predict diagnostic classification and level of disability on the basis of a brief experiment. Although the road to translating such procedures into effective devices for screening diagnosis is still a long one, and premature claims should be avoided, this effort could be critical in addressing this worldwide public health challenge. PMID:25726820

Factors Associated with Mood Disorder Diagnosis Among a Population Based Cohort of Men and Women Living With and Without HIV in British Columbia Between 1998 and 2012.

PubMed

Closson, Kalysha; Osborne, Chuck; Smith, Danielle M; Kesselring, Sarah; Eyawo, Oghenowede; Card, Kiffer; Sereda, Paul; Jabbari, Shahab; Franco-Villalobos, Conrado; Ahmed, Tareq; Gabler, Karyn; Patterson, Thomas; Hull, Mark; Montaner, Julio S G; Hogg, Robert S

2018-05-01

Using data from the Comparison of Outcomes and Service Utilization Trends (COAST) study we examined factors associated with mood disorder diagnosis (MDD) among people living with HIV (PLHIV) and HIV-negative individuals in British Columbia, Canada. MDD cases were identified between 1998 and 2012 using International Classification of Disease 9 and 10 codes. A total of 491,796 individuals were included and 1552 (23.7%) and 60,097 (12.4%) cases of MDD were identified among the HIV-positive and HIV-negative populations, respectively. Results showed HIV status was associated with greater odds of MDD among men and lower odds among women. Among PLHIV, MDD was significantly associated with: identifying as gay, bisexual or other men who have sex with men compared to heterosexuals; higher viral load; history of injection drug use; and concurrent anxiety, dysthymia, and substance use disorders. Findings highlight the need for comprehensive and holistic HIV and mental health care.

Shape Classification Using Wasserstein Distance for Brain Morphometry Analysis.

PubMed

Su, Zhengyu; Zeng, Wei; Wang, Yalin; Lu, Zhong-Lin; Gu, Xianfeng

2015-01-01

Brain morphometry study plays a fundamental role in medical imaging analysis and diagnosis. This work proposes a novel framework for brain cortical surface classification using Wasserstein distance, based on uniformization theory and Riemannian optimal mass transport theory. By Poincare uniformization theorem, all shapes can be conformally deformed to one of the three canonical spaces: the unit sphere, the Euclidean plane or the hyperbolic plane. The uniformization map will distort the surface area elements. The area-distortion factor gives a probability measure on the canonical uniformization space. All the probability measures on a Riemannian manifold form the Wasserstein space. Given any 2 probability measures, there is a unique optimal mass transport map between them, the transportation cost defines the Wasserstein distance between them. Wasserstein distance gives a Riemannian metric for the Wasserstein space. It intrinsically measures the dissimilarities between shapes and thus has the potential for shape classification. To the best of our knowledge, this is the first. work to introduce the optimal mass transport map to general Riemannian manifolds. The method is based on geodesic power Voronoi diagram. Comparing to the conventional methods, our approach solely depends on Riemannian metrics and is invariant under rigid motions and scalings, thus it intrinsically measures shape distance. Experimental results on classifying brain cortical surfaces with different intelligence quotients demonstrated the efficiency and efficacy of our method.

Shape Classification Using Wasserstein Distance for Brain Morphometry Analysis

PubMed Central

Su, Zhengyu; Zeng, Wei; Wang, Yalin; Lu, Zhong-Lin; Gu, Xianfeng

2015-01-01

Brain morphometry study plays a fundamental role in medical imaging analysis and diagnosis. This work proposes a novel framework for brain cortical surface classification using Wasserstein distance, based on uniformization theory and Riemannian optimal mass transport theory. By Poincare uniformization theorem, all shapes can be conformally deformed to one of the three canonical spaces: the unit sphere, the Euclidean plane or the hyperbolic plane. The uniformization map will distort the surface area elements. The area-distortion factor gives a probability measure on the canonical uniformization space. All the probability measures on a Riemannian manifold form the Wasserstein space. Given any 2 probability measures, there is a unique optimal mass transport map between them, the transportation cost defines the Wasserstein distance between them. Wasserstein distance gives a Riemannian metric for the Wasserstein space. It intrinsically measures the dissimilarities between shapes and thus has the potential for shape classification. To the best of our knowledge, this is the first work to introduce the optimal mass transport map to general Riemannian manifolds. The method is based on geodesic power Voronoi diagram. Comparing to the conventional methods, our approach solely depends on Riemannian metrics and is invariant under rigid motions and scalings, thus it intrinsically measures shape distance. Experimental results on classifying brain cortical surfaces with different intelligence quotients demonstrated the efficiency and efficacy of our method. PMID:26221691

[Diagnosis of acute heart failure and relevance of biomarkers in elderly patients].

PubMed

Ruiz Ortega, Raúl Antonio; Manzano, Luis; Montero-Pérez-Barquero, Manuel

2014-03-01

Diagnosis of acute heart failure (HF) is difficult in elderly patients with multiple comorbidities. Risk scales and classification criteria based exclusively on clinical manifestations, such as the Framingham scales, lack sufficient specificity. In addition to clinical manifestations, diagnosis should be based on two key factors: natriuretic peptides and echocardiographic study. When there is clinical suspicion of acute HF, a normal natriuretic peptide level will rule out this process. When a consistent clinical suspicion is present, an echocardiographic study should also be performed. Diagnosis of HF with preserved ejection fraction (HF/pEF) requires detection of an enlarged left atrium or the presence of parameters of diastolic dysfunction. Elevation of cardiac biomarkers seems to be due to myocardial injury and the compensatory mechanisms of the body against this injury (hormone and inflammatory response and repair mechanisms). Elevation of markers of cardiac damage (troponins and natriuretic peptides) have been shown to be useful both in the diagnosis of acute HF and in prediction of outcome. MMP-2 could be useful in the diagnosis of HF/pEF. In addition to biomarkers with diagnostic value, other biomarkers are helpful in prognosis in the acute phase of HF, such as biomarkers of renal failure (eGFR, cystatin and urea), inflammation (cytokines and CRP), and the cell regeneration marker, galectin-3. A promising idea that is under investigation is the use of panels of biomarkers, which could allow more accurate diagnosis and prognosis of acute HF. Copyright © 2014 Elsevier España, S.L. All rights reserved.

Diagnosis as a social determinant: the development of prosocial behaviour before and after an autism spectrum diagnosis.

PubMed

Russell, Ginny; Kelly, Susan E; Ford, Tamsin; Steer, Colin

2012-11-01

Jutel and Nettleton (2011) discuss diagnosis as not only a major classification tool for medicine but also an interactive social process that itself may have ramifications for health. Consideration of diagnosis as a social determinant of health outcomes led to the formulation of our research question: Can we detect a change in the development of prosocial symptoms before and after an Autism Spectrum Disorder (ASD) diagnosis? We examined the developmental trajectory of prosocial skills of children, as impairment in social skills is given as a core symptom for children with ASD. We used a validated scale measuring prosocial behaviour for a sample of 57 children where the measure was repeatedly recorded over ten years. We plotted the developmental trajectory of the prosocial trait in this sample who were enrolled in a longitudinal birth cohort study based in South West England. Multi-factorial fixed effect modelling suggests that the developmental trajectory of this measure of behaviour was not significantly altered by ASD diagnosis, or the consequences of diagnosis, either for better or worse. Further analysis was conducted on a subset of 33 of the children who had both pre-diagnosis and post-diagnosis information, and the same result obtained. The results indicate that prosocial behaviours may be resistant to typical 'treatments': provision of educational and specialist health services triggered by a clinical ASD diagnosis. The implications of this for considering diagnosis as a social determinant are discussed. Copyright © 2012 Elsevier Ltd. All rights reserved.

Classification tree for the assessment of sedentary lifestyle among hypertensive.

PubMed

Castelo Guedes Martins, Larissa; Venícios de Oliveira Lopes, Marcos; Gomes Guedes, Nirla; Paixão de Menezes, Angélica; de Oliveira Farias, Odaleia; Alves Dos Santos, Naftale

2016-04-01

To develop a classification tree of clinical indicators for the correct prediction of the nursing diagnosis "Sedentary lifestyle" (SL) in people with high blood pressure (HTN). A cross-sectional study conducted in an outpatient care center specializing in high blood pressure and Mellitus diabetes located in northeastern Brazil. The sample consisted of 285 people between 19 and 59 years old diagnosed with high blood pressure and was applied an interview and physical examination, obtaining socio-demographic information, related factors and signs and symptoms that made the defining characteristics for the diagnosis under study. The tree was generated using the CHAID algorithm (Chi-square Automatic Interaction Detection). The construction of the decision tree allowed establishing the interactions between clinical indicators that facilitate a probabilistic analysis of multiple situations allowing quantify the probability of an individual presenting a sedentary lifestyle. The tree included the clinical indicator Choose daily routine without exercise as the first node. People with this indicator showed a probability of 0.88 of presenting the SL. The second node was composed of the indicator Does not perform physical activity during leisure, with 0.99 probability of presenting the SL with these two indicators. The predictive capacity of the tree was established at 69.5%. Decision trees help nurses who care HTN people in decision-making in assessing the characteristics that increase the probability of SL nursing diagnosis, optimizing the time for diagnostic inference.

DIMETER: A Haptic Master Device for Tremor Diagnosis in Neurodegenerative Diseases

PubMed Central

González, Roberto; Barrientos, Antonio; del Cerro, Jaime; Coca, Benito

2014-01-01

In this study, a device based on patient motion capture is developed for the reliable and non-invasive diagnosis of neurodegenerative diseases. The primary objective of this study is the classification of differential diagnosis between Parkinson's disease (PD) and essential tremor (ET). The DIMETER system has been used in the diagnoses of a significant number of patients at two medical centers in Spain. Research studies on classification have primarily focused on the use of well-known and reliable diagnosis criteria developed by qualified personnel. Here, we first present a literature review of the methods used to detect and evaluate tremor; then, we describe the DIMETER device in terms of the software and hardware used and the battery of tests developed to obtain the best diagnoses. All of the tests are classified and described in terms of the characteristics of the data obtained. A list of parameters obtained from the tests is provided, and the results obtained using multilayer perceptron (MLP) neural networks are presented and analyzed. PMID:24608001

Intra- and interobserver agreement in the classification and treatment of distal third clavicle fractures.

PubMed

Bishop, Julie Y; Jones, Grant L; Lewis, Brian; Pedroza, Angela

2015-04-01

In treatment of distal third clavicle fractures, the Neer classification system, based on the location of the fracture in relation to the coracoclavicular ligaments, has traditionally been used to determine fracture pattern stability. To determine the intra- and interobserver reliability in the classification of distal third clavicle fractures via standard plain radiographs and the intra- and interobserver agreement in the preferred treatment of these fractures. Cohort study (Diagnosis); Level of evidence, 3. Thirty radiographs of distal clavicle fractures were randomly selected from patients treated for distal clavicle fractures between 2006 and 2011. The radiographs were distributed to 22 shoulder/sports medicine fellowship-trained orthopaedic surgeons. Fourteen surgeons responded and took part in the study. The evaluators were asked to measure the size of the distal fragment, classify the fracture pattern as stable or unstable, assign the Neer classification, and recommend operative versus nonoperative treatment. The radiographs were reordered and redistributed 3 months later. Inter- and intrarater agreement was determined for the distal fragment size, stability of the fracture, Neer classification, and decision to operate. Single variable logistic regression was performed to determine what factors could most accurately predict the decision for surgery. Interrater agreement was fair for distal fragment size, moderate for stability, fair for Neer classification, slight for type IIB and III fractures, and moderate for treatment approach. Intrarater agreement was moderate for distal fragment size categories (κ = 0.50, P < .001) and Neer classification (κ = 0.42, P < .001) and substantial for stable fracture (κ = 0.65, P < .001) and decision to operate (κ = 0.65, P < .001). Fracture stability was the best predictor of treatment, with 89% accuracy (P < .001). Fracture stability determination and the decision to operate had the highest interobserver agreement

CHANGING OUR DIAGNOSTIC PARADIGM: MOVEMENT SYSTEM DIAGNOSTIC CLASSIFICATION

PubMed Central

Kamonseki, Danilo H.; Staker, Justin L.; Lawrence, Rebekah L.; Braman, Jonathan P.

2017-01-01

Proper diagnosis is a first step in applying best available treatments, and prognosticating outcomes for clients. Currently, the majority of musculoskeletal diagnoses are classified according to pathoanatomy. However, the majority of physical therapy treatments are applied toward movement system impairments or pain. While advocated within the physical therapy profession for over thirty years, diagnostic classification within a movement system framework has not been uniformly developed or adopted. We propose a basic framework and rationale for application of a movement system diagnostic classification for atraumatic shoulder pain conditions, as a case for the broader development of movement system diagnostic labels. Shifting our diagnostic paradigm has potential to enhance communication, improve educational efficiency, facilitate research, directly link to function, improve clinical care, and accelerate preventive interventions. PMID:29158950

[The possible drawbacks of applying the DRG classification].

PubMed

Huber, Zofia Swinarski