Global classification and coding of hypersensitivity diseases - An EAACI - WAO survey, strategic paper and review.

PubMed

Demoly, P; Tanno, L K; Akdis, C A; Lau, S; Calderon, M A; Santos, A F; Sanchez-Borges, M; Rosenwasser, L J; Pawankar, R; Papadopoulos, N G

2014-05-01

Hypersensitivity diseases are not adequately coded in the International Coding of Diseases (ICD)-10 resulting in misclassification, leading to low visibility of these conditions and general accuracy of official statistics. To call attention to the inadequacy of the ICD-10 in relation to allergic and hypersensitivity diseases and to contribute to improvements to be made in the forthcoming revision of ICD, a web-based global survey of healthcare professionals' attitudes toward allergic disorders classification was proposed to the members of European Academy of Allergy and Clinical Immunology (EAACI) (individuals) and World Allergy Organization (WAO) (representative responding on behalf of the national society), launched via internet and circulated for 6 week. As a result, we had 612 members of 144 countries from all six World Health Organization (WHO) global regions who answered the survey. ICD-10 is the most used classification worldwide, but it was not considered appropriate in clinical practice by the majority of participants. The majority indicated the EAACI-WAO classification as being easier and more accurate in the daily practice. They saw the need for a diagnostic system useful for nonallergists and endorsed the possibility of a global, cross-culturally applicable classification system of allergic disorders. This first and most broadly international survey ever conducted of health professionals' attitudes toward allergic disorders classification supports the need to update the current classifications of allergic diseases and can be useful to the WHO in improving the clinical utility of the classification and its global acceptability for the revised ICD-11. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons 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.

The Advantages and Limitations of International Classification of Diseases, Injuries and Causes of Death from Aspect of Existing Health Care System of Bosnia and Herzegovina

PubMed Central

Kurbasic, Izeta; Pandza, Haris; Masic, Izet; Huseinagic, Senad; Tandir, Salih; Alicajic, Fredi; Toromanovic, Selim

2008-01-01

CONFLICT OF INTEREST: NONE DECLARED Introduction The International classification of diseases (ICD) is the most important classification in medicine. It is used by all medical professionals. Concept The basic concept of ICD is founded on the standardization of the nomenclature for the names of diseases and their basic systematization in the hierarchically structured category. Advantages and disadvantages The health care provider institutions such as hospitals are subjects that should facilitate implementation of medical applications that follows the patient medical condition and facts connected with him. The definitive diagnosis that can be coded using ICD can be achieved after several visits of patient and rarely during the first visit. Conclusion The ICD classification is one of the oldest and most important classifications in medicine. In the scope of ICD are all fields of medicine. It is used in statistical purpose and as a coding system in medical databases. PMID:24109155

The Self-Directed Violence Classification System and the Columbia Classification Algorithm for Suicide Assessment: A Crosswalk

ERIC Educational Resources Information Center

Matarazzo, Bridget B.; Clemans, Tracy A.; Silverman, Morton M.; Brenner, Lisa A.

2013-01-01

The lack of a standardized nomenclature for suicide-related thoughts and behaviors prompted the Centers for Disease Control and Prevention, with the Veterans Integrated Service Network 19 Mental Illness Research Education and Clinical Center, to create the Self-Directed Violence Classification System (SDVCS). SDVCS has been adopted by the…

Discovering disease-disease associations by fusing systems-level molecular data

PubMed Central

Žitnik, Marinka; Janjić, Vuk; Larminie, Chris; Zupan, Blaž; Pržulj, Nataša

2013-01-01

The advent of genome-scale genetic and genomic studies allows new insight into disease classification. Recently, a shift was made from linking diseases simply based on their shared genes towards systems-level integration of molecular data. Here, we aim to find relationships between diseases based on evidence from fusing all available molecular interaction and ontology data. We propose a multi-level hierarchy of disease classes that significantly overlaps with existing disease classification. In it, we find 14 disease-disease associations currently not present in Disease Ontology and provide evidence for their relationships through comorbidity data and literature curation. Interestingly, even though the number of known human genetic interactions is currently very small, we find they are the most important predictor of a link between diseases. Finally, we show that omission of any one of the included data sources reduces prediction quality, further highlighting the importance in the paradigm shift towards systems-level data fusion. PMID:24232732

Discovering disease-disease associations by fusing systems-level molecular data.

PubMed

Žitnik, Marinka; Janjić, Vuk; Larminie, Chris; Zupan, Blaž; Pržulj, Nataša

2013-11-15

The advent of genome-scale genetic and genomic studies allows new insight into disease classification. Recently, a shift was made from linking diseases simply based on their shared genes towards systems-level integration of molecular data. Here, we aim to find relationships between diseases based on evidence from fusing all available molecular interaction and ontology data. We propose a multi-level hierarchy of disease classes that significantly overlaps with existing disease classification. In it, we find 14 disease-disease associations currently not present in Disease Ontology and provide evidence for their relationships through comorbidity data and literature curation. Interestingly, even though the number of known human genetic interactions is currently very small, we find they are the most important predictor of a link between diseases. Finally, we show that omission of any one of the included data sources reduces prediction quality, further highlighting the importance in the paradigm shift towards systems-level data fusion.

Approach for a Clinically Useful Comprehensive Classification of Vascular and Neural Aspects of Diabetic Retinal Disease

PubMed Central

Abramoff, Michael D.; Fort, Patrice E.; Han, Ian C.; Jayasundera, K. Thiran; Sohn, Elliott H.; Gardner, Thomas W.

2018-01-01

The Early Treatment Diabetic Retinopathy Study (ETDRS) and other standardized classification schemes have laid a foundation for tremendous advances in the understanding and management of diabetic retinopathy (DR). However, technological advances in optics and image analysis, especially optical coherence tomography (OCT), OCT angiography (OCTa), and ultra-widefield imaging, as well as new discoveries in diabetic retinal neuropathy (DRN), are exposing the limitations of ETDRS and other classification systems to completely characterize retinal changes in diabetes, which we term diabetic retinal disease (DRD). While it may be most straightforward to add axes to existing classification schemes, as diabetic macular edema (DME) was added as an axis to earlier DR classifications, doing so may make these classifications increasingly complicated and thus clinically intractable. Therefore, we propose future research efforts to develop a new, comprehensive, and clinically useful classification system that will identify multimodal biomarkers to reflect the complex pathophysiology of DRD and accelerate the development of therapies to prevent vision-threatening DRD. PMID:29372250

Approach for a Clinically Useful Comprehensive Classification of Vascular and Neural Aspects of Diabetic Retinal Disease.

PubMed

Abramoff, Michael D; Fort, Patrice E; Han, Ian C; Jayasundera, K Thiran; Sohn, Elliott H; Gardner, Thomas W

2018-01-01

The Early Treatment Diabetic Retinopathy Study (ETDRS) and other standardized classification schemes have laid a foundation for tremendous advances in the understanding and management of diabetic retinopathy (DR). However, technological advances in optics and image analysis, especially optical coherence tomography (OCT), OCT angiography (OCTa), and ultra-widefield imaging, as well as new discoveries in diabetic retinal neuropathy (DRN), are exposing the limitations of ETDRS and other classification systems to completely characterize retinal changes in diabetes, which we term diabetic retinal disease (DRD). While it may be most straightforward to add axes to existing classification schemes, as diabetic macular edema (DME) was added as an axis to earlier DR classifications, doing so may make these classifications increasingly complicated and thus clinically intractable. Therefore, we propose future research efforts to develop a new, comprehensive, and clinically useful classification system that will identify multimodal biomarkers to reflect the complex pathophysiology of DRD and accelerate the development of therapies to prevent vision-threatening DRD.

Can a Smartphone Diagnose Parkinson Disease? A Deep Neural Network Method and Telediagnosis System Implementation.

PubMed

Zhang, Y N

2017-01-01

Parkinson's disease (PD) is primarily diagnosed by clinical examinations, such as walking test, handwriting test, and MRI diagnostic. In this paper, we propose a machine learning based PD telediagnosis method for smartphone. Classification of PD using speech records is a challenging task owing to the fact that the classification accuracy is still lower than doctor-level. Here we demonstrate automatic classification of PD using time frequency features, stacked autoencoders (SAE), and K nearest neighbor (KNN) classifier. KNN classifier can produce promising classification results from useful representations which were learned by SAE. Empirical results show that the proposed method achieves better performance with all tested cases across classification tasks, demonstrating machine learning capable of classifying PD with a level of competence comparable to doctor. It concludes that a smartphone can therefore potentially provide low-cost PD diagnostic care. This paper also gives an implementation on browser/server system and reports the running time cost. Both advantages and disadvantages of the proposed telediagnosis system are discussed.

Can a Smartphone Diagnose Parkinson Disease? A Deep Neural Network Method and Telediagnosis System Implementation

PubMed Central

2017-01-01

Parkinson's disease (PD) is primarily diagnosed by clinical examinations, such as walking test, handwriting test, and MRI diagnostic. In this paper, we propose a machine learning based PD telediagnosis method for smartphone. Classification of PD using speech records is a challenging task owing to the fact that the classification accuracy is still lower than doctor-level. Here we demonstrate automatic classification of PD using time frequency features, stacked autoencoders (SAE), and K nearest neighbor (KNN) classifier. KNN classifier can produce promising classification results from useful representations which were learned by SAE. Empirical results show that the proposed method achieves better performance with all tested cases across classification tasks, demonstrating machine learning capable of classifying PD with a level of competence comparable to doctor. It concludes that a smartphone can therefore potentially provide low-cost PD diagnostic care. This paper also gives an implementation on browser/server system and reports the running time cost. Both advantages and disadvantages of the proposed telediagnosis system are discussed. PMID:29075547

Development of a database of health insurance claims: standardization of disease classifications and anonymous record linkage.

PubMed

Kimura, Shinya; Sato, Toshihiko; Ikeda, Shunya; Noda, Mitsuhiko; Nakayama, Takeo

2010-01-01

Health insurance claims (ie, receipts) record patient health care treatments and expenses and, although created for the health care payment system, are potentially useful for research. Combining different types of receipts generated for the same patient would dramatically increase the utility of these receipts. However, technical problems, including standardization of disease names and classifications, and anonymous linkage of individual receipts, must be addressed. In collaboration with health insurance societies, all information from receipts (inpatient, outpatient, and pharmacy) was collected. To standardize disease names and classifications, we developed a computer-aided post-entry standardization method using a disease name dictionary based on International Classification of Diseases (ICD)-10 classifications. We also developed an anonymous linkage system by using an encryption code generated from a combination of hash values and stream ciphers. Using different sets of the original data (data set 1: insurance certificate number, name, and sex; data set 2: insurance certificate number, date of birth, and relationship status), we compared the percentage of successful record matches obtained by using data set 1 to generate key codes with the percentage obtained when both data sets were used. The dictionary's automatic conversion of disease names successfully standardized 98.1% of approximately 2 million new receipts entered into the database. The percentage of anonymous matches was higher for the combined data sets (98.0%) than for data set 1 (88.5%). The use of standardized disease classifications and anonymous record linkage substantially contributed to the construction of a large, chronologically organized database of receipts. This database is expected to aid in epidemiologic and health services research using receipt information.

[Serpiginous calcifications in breast filariasis: A descriptor not included in the BI-RADS classification system].

PubMed

Mora-Encinas, J P; Martín-Martín, B; Martín-Martín, L; Mora-Monago, R

2015-01-01

Filariasis is a parasitic disease with a benign course caused by nematodes. Filariasis is endemic in some tropical regions, and immigration has made it increasingly common in some centers in Spain. The death of the parasites can lead to calcifications that are visible in mammograms; these calcifications have specific characteristics and should not be confused with those arising in other diseases. However, the appearance of calcifications due to filariasis is not included in the most common systems used for the classification of calcifications on mammograms (BI-RADS), and this can lead to confusion. In this article, we discuss the need to update classification systems and warn radiologists about the appearance of these calcifications to ensure their correct diagnosis and avoid confusion with other diseases. Copyright © 2014 SERAM. Published by Elsevier España, S.L.U. All rights reserved.

Nursing interventions for rehabilitation in Parkinson's disease: cross mapping of terms

PubMed Central

Tosin, Michelle Hyczy de Siqueira; Campos, Débora Moraes; de Andrade, Leonardo Tadeu; de Oliveira, Beatriz Guitton Renaud Baptista; Santana, Rosimere Ferreira

2016-01-01

ABSTRACT Objective: to perform a cross-term mapping of nursing language in the patient record with the Nursing Interventions Classification system, in rehabilitation patients with Parkinson's disease. Method: a documentary research study to perform cross mapping. A probabilistic, simple random sample composed of 67 records of patients with Parkinson's disease who participated in a rehabilitation program, between March of 2009 and April of 2013. The research was conducted in three stages, in which the nursing terms were mapped to natural language and crossed with the Nursing Interventions Classification. Results: a total of 1,077 standard interventions that, after crossing with the taxonomy and refinement performed by the experts, resulted in 32 interventions equivalent to the Nursing Interventions Classification (NIC) system. The NICs, "Education: The process of the disease.", "Contract with the patient", and "Facilitation of Learning" were present in 100% of the records. For these interventions, 40 activities were described, representing 13 activities by intervention. Conclusion: the cross mapping allowed for the identification of corresponding terms with the nursing interventions used every day in rehabilitation nursing, and compared them to the Nursing Interventions Classification. PMID:27508903

National Library of Medicine Classification: A Scheme for the Shelf Arrangement of Books in a Field of Medicine and Its Related Sciences. Fourth Edition.

ERIC Educational Resources Information Center

Wiggins, Emilie, Ed.

Outlined is the National Library of Medicine classification system for medicine and related sciences. In this system each preclinical science, such as human anatomy, biochemistry or pathology, and each medical subject, such as infectious diseases or pediatrics, receives a two-letter classification. Under each of these main headings numbered minor…

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.

Diagnostic discrepancies in retinopathy of prematurity classification

PubMed Central

Campbell, J. Peter; Ryan, Michael C.; Lore, Emily; Tian, Peng; Ostmo, Susan; Jonas, Karyn; Chan, R.V. Paul; Chiang, Michael F.

2016-01-01

Objective To identify the most common areas for discrepancy in retinopathy of prematurity (ROP) classification between experts. Design Prospective cohort study. Subjects, Participants, and/or Controls 281 infants were identified as part of a multi-center, prospective, ROP cohort study from 7 participating centers. Each site had participating ophthalmologists who provided the clinical classification after routine examination using binocular indirect ophthalmoscopy (BIO), and obtained wide-angle retinal images, which were independently classified by two study experts. Methods Wide-angle retinal images (RetCam; Clarity Medical Systems, Pleasanton, CA) were obtained from study subjects, and two experts evaluated each image using a secure web-based module. Image-based classifications for zone, stage, plus disease, overall disease category (no ROP, mild ROP, Type II or pre-plus, and Type I) were compared between the two experts, and to the clinical classification obtained by BIO. Main Outcome Measures Inter-expert image-based agreement and image-based vs. ophthalmoscopic diagnostic agreement using absolute agreement and weighted kappa statistic. Results 1553 study eye examinations from 281 infants were included in the study. Experts disagreed on the stage classification in 620/1553 (40%) of comparisons, plus disease classification (including pre-plus) in 287/1553 (18%), zone in 117/1553 (8%), and overall ROP category in 618/1553 (40%). However, agreement for presence vs. absence of type 1 disease was >95%. There were no differences between image-based and clinical classification except for zone III disease. Conclusions The most common area of discrepancy in ROP classification is stage, although inter-expert agreement for clinically-significant disease such as presence vs. absence of type 1 and type 2 disease is high. There were no differences between image-based grading and the clinical exam in the ability to detect clinically-significant disease. This study provides additional evidence that image-based classification of ROP reliably detects clinically significant levels of ROP with high accuracy compared to the clinical exam. PMID:27238376

78 FR 9055 - National Center for Health Statistics (NCHS), Classifications and Public Health Data Standards...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-07

... DEPARTMENT OF HEALTH AND HUMAN SERVICES Centers for Disease Control and Prevention National Center for Health Statistics (NCHS), Classifications and Public Health Data Standards Staff, Announces the..., Medical Systems Administrator, Classifications and Public Health Data Standards Staff, NCHS, 3311 Toledo...

Proposal for a functional classification system of heart failure in patients with end-stage renal disease: proceedings of the acute dialysis quality initiative (ADQI) XI workgroup.

PubMed

Chawla, Lakhmir S; Herzog, Charles A; Costanzo, Maria Rosa; Tumlin, James; Kellum, John A; McCullough, Peter A; Ronco, Claudio

2014-04-08

Structural heart disease is highly prevalent in patients with chronic kidney disease requiring dialysis. More than 80% of patients with end-stage renal disease (ESRD) are reported to have cardiovascular disease. This observation has enormous clinical relevance because the leading causes of death for patients with ESRD are of cardiovascular disease etiology, including heart failure, myocardial infarction, and sudden cardiac death. The 2 systems most commonly used to classify the severity of heart failure are the New York Heart Association (NYHA) functional classification and the American Heart Association (AHA)/American College of Cardiology (ACC) staging system. With rare exceptions, patients with ESRD who do not receive renal replacement therapy (RRT) develop signs and symptoms of heart failure, including dyspnea and edema due to inability of the severely diseased kidneys to excrete sodium and water. Thus, by definition, nearly all patients with ESRD develop a symptomatology consistent with heart failure if fluid removal by RRT is delayed. Neither the AHA/ACC heart failure staging nor the NYHA functional classification system identifies the variable symptomatology that patients with ESRD experience depending upon whether evaluation occurs before or after fluid removal by RRT. Consequently, the incidence, severity, and outcomes of heart failure in patients with ESRD are poorly characterized. The 11th Acute Dialysis Quality Initiative has identified this issue as a critical unmet need for the proper evaluation and treatment of heart failure in patients with ESRD. We propose a classification schema based on patient-reported dyspnea assessed both pre- and post-ultrafiltration, in conjunction with echocardiography. Copyright © 2014 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Prognostic Performance and Reproducibility of the 1973 and 2004/2016 World Health Organization Grading Classification Systems in Non-muscle-invasive Bladder Cancer: A European Association of Urology Non-muscle Invasive Bladder Cancer Guidelines Panel Systematic Review.

PubMed

Soukup, Viktor; Čapoun, Otakar; Cohen, Daniel; Hernández, Virginia; Babjuk, Marek; Burger, Max; Compérat, Eva; Gontero, Paolo; Lam, Thomas; MacLennan, Steven; Mostafid, A Hugh; Palou, Joan; van Rhijn, Bas W G; Rouprêt, Morgan; Shariat, Shahrokh F; Sylvester, Richard; Yuan, Yuhong; Zigeuner, Richard

2017-11-01

Tumour grade is an important prognostic indicator in non-muscle-invasive bladder cancer (NMIBC). Histopathological classifications are limited by interobserver variability (reproducibility), which may have prognostic implications. European Association of Urology NMIBC guidelines suggest concurrent use of both 1973 and 2004/2016 World Health Organization (WHO) classifications. To compare the prognostic performance and reproducibility of the 1973 and 2004/2016 WHO grading systems for NMIBC. A systematic literature search was undertaken incorporating Medline, Embase, and the Cochrane Library. Studies were critically appraised for risk of bias (QUIPS). For prognosis, the primary outcome was progression to muscle-invasive or metastatic disease. Secondary outcomes were disease recurrence, and overall and cancer-specific survival. For reproducibility, the primary outcome was interobserver variability between pathologists. Secondary outcome was intraobserver variability (repeatability) by the same pathologist. Of 3593 articles identified, 20 were included in the prognostic review; three were eligible for the reproducibility review. Increasing tumour grade in both classifications was associated with higher disease progression and recurrence rates. Progression rates in grade 1 patients were similar to those in low-grade patients; progression rates in grade 3 patients were higher than those in high-grade patients. Survival data were limited. Reproducibility of the 2004/2016 system was marginally better than that of the 1973 system. Two studies on repeatability showed conflicting results. Most studies had a moderate to high risk of bias. Current grading classifications in NMIBC are suboptimal. The 1973 system identifies more aggressive tumours. Intra- and interobserver variability was slightly less in the 2004/2016 classification. We could not confirm that the 2004/2016 classification outperforms the 1973 classification in prediction of recurrence and progression. This article summarises the utility of two different grading systems for non-muscle-invasive bladder cancer. Both systems predict progression and recurrence, although pathologists vary in their reporting; suggestions for further improvements are made. Copyright © 2017 European Association of Urology. Published by Elsevier B.V. All rights reserved.

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.

Policy Agenda for the Next Decade: Creating a Path for Graceful Evolution and Harmonized Classifications and Terminologies Used for Encoding Health Information in Electronic Environments

PubMed Central

Foley, Margaret M; Glenn, Regina M; Meli, Peggy L; Scichilone, Rita A

2009-01-01

Introduction Health information management (HIM) professionals' involvement with disease classification and nomenclature in the United States can be traced back to the early 20th century. In 1914, Grace Whiting Myers, the founder of the association known today as the American Health Information Management Association (AHIMA), served on the Committee on Uniform Nomenclature, which developed a disease classification system based upon etiological groupings. The profession's expertise and leadership in the collection, classification, and reporting of health data has continued since then. For example, in the early 1960s, another HIM professional (a medical record librarian) served as the associate editor of the fifth edition of the Standard Nomenclature of Disease (SNDO), a forerunner of the widely used clinical terminology, Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT). During the same period in history, the medical record professionals working in hospitals throughout the country were responsible for manually collecting and reporting disease and procedure information from medical records using SNDO.1 Because coded data have played a pivotal role in the ability to record and share health information through the years, creating the appropriate policy framework for the graceful evolution and harmonization of classification systems and clinical terminologies is essential. PMID:20169015

[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.

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

A systematic review of definitions and classification systems of adjacent segment pathology.

PubMed

Kraemer, Paul; Fehlings, Michael G; Hashimoto, Robin; Lee, Michael J; Anderson, Paul A; Chapman, Jens R; Raich, Annie; Norvell, Daniel C

2012-10-15

Systematic review. To undertake a systematic review to determine how "adjacent segment degeneration," "adjacent segment disease," or clinical pathological processes that serve as surrogates for adjacent segment pathology are classified and defined in the peer-reviewed literature. Adjacent segment degeneration and adjacent segment disease are terms referring to degenerative changes known to occur after reconstructive spine surgery, most commonly at an immediately adjacent functional spinal unit. These can include disc degeneration, instability, spinal stenosis, facet degeneration, and deformity. The true incidence and clinical impact of degenerative changes at the adjacent segment is unclear because there is lack of a universally accepted classification system that rigorously addresses clinical and radiological issues. A systematic review of the English language literature was undertaken and articles were classified using the Grades of Recommendation Assessment, Development, and Evaluation criteria. RESULTS.: Seven classification systems of spinal degeneration, including degeneration at the adjacent segment, were identified. None have been evaluated for reliability or validity specific to patients with degeneration at the adjacent segment. The ways in which terms related to adjacent segment "degeneration" or "disease" are defined in the peer-reviewed literature are highly variable. On the basis of the systematic review presented in this article, no formal classification system for either cervical or thoracolumbar adjacent segment disorders currently exists. No recommendations regarding the use of current classification of degeneration at any segments can be made based on the available literature. A new comprehensive definition for adjacent segment pathology (ASP, the now preferred terminology) has been proposed in this Focus Issue, which reflects the diverse pathology observed at functional spinal units adjacent to previous spinal reconstruction and balances detailed stratification with clinical utility. A comprehensive classification system is being developed through expert opinion and will require validation as well as peer review. Strength of Statement: Strong.

Toward a Reasoned Classification of Diseases Using Physico-Chemical Based Phenotypes

PubMed Central

Schwartz, Laurent; Lafitte, Olivier; da Veiga Moreira, Jorgelindo

2018-01-01

Background: Diseases and health conditions have been classified according to anatomical site, etiological, and clinical criteria. Physico-chemical mechanisms underlying the biology of diseases, such as the flow of energy through cells and tissues, have been often overlooked in classification systems. Objective: We propose a conceptual framework toward the development of an energy-oriented classification of diseases, based on the principles of physical chemistry. Methods: A review of literature on the physical chemistry of biological interactions in a number of diseases is traced from the point of view of the fluid and solid mechanics, electricity, and chemistry. Results: We found consistent evidence in literature of decreased and/or increased physical and chemical forces intertwined with biological processes of numerous diseases, which allowed the identification of mechanical, electric and chemical phenotypes of diseases. Discussion: Biological mechanisms of diseases need to be evaluated and integrated into more comprehensive theories that should account with principles of physics and chemistry. A hypothetical model is proposed relating the natural history of diseases to mechanical stress, electric field, and chemical equilibria (ATP) changes. The present perspective toward an innovative disease classification may improve drug-repurposing strategies in the future. PMID:29541031

Classification of electroencephalograph signals using time-frequency decomposition and linear discriminant analysis

NASA Astrophysics Data System (ADS)

Szuflitowska, B.; Orlowski, P.

2017-08-01

Automated detection system consists of two key steps: extraction of features from EEG signals and classification for detection of pathology activity. The EEG sequences were analyzed using Short-Time Fourier Transform and the classification was performed using Linear Discriminant Analysis. The accuracy of the technique was tested on three sets of EEG signals: epilepsy, healthy and Alzheimer's Disease. The classification error below 10% has been considered a success. The higher accuracy are obtained for new data of unknown classes than testing data. The methodology can be helpful in differentiation epilepsy seizure and disturbances in the EEG signal in Alzheimer's Disease.

On the integrity of functional brain networks in schizophrenia, Parkinson's disease, and advanced age: Evidence from connectivity-based single-subject classification.

PubMed

Pläschke, Rachel N; Cieslik, Edna C; Müller, Veronika I; Hoffstaedter, Felix; Plachti, Anna; Varikuti, Deepthi P; Goosses, Mareike; Latz, Anne; Caspers, Svenja; Jockwitz, Christiane; Moebus, Susanne; Gruber, Oliver; Eickhoff, Claudia R; Reetz, Kathrin; Heller, Julia; Südmeyer, Martin; Mathys, Christian; Caspers, Julian; Grefkes, Christian; Kalenscher, Tobias; Langner, Robert; Eickhoff, Simon B

2017-12-01

Previous whole-brain functional connectivity studies achieved successful classifications of patients and healthy controls but only offered limited specificity as to affected brain systems. Here, we examined whether the connectivity patterns of functional systems affected in schizophrenia (SCZ), Parkinson's disease (PD), or normal aging equally translate into high classification accuracies for these conditions. We compared classification performance between pre-defined networks for each group and, for any given network, between groups. Separate support vector machine classifications of 86 SCZ patients, 80 PD patients, and 95 older adults relative to their matched healthy/young controls, respectively, were performed on functional connectivity in 12 task-based, meta-analytically defined networks using 25 replications of a nested 10-fold cross-validation scheme. Classification performance of the various networks clearly differed between conditions, as those networks that best classified one disease were usually non-informative for the other. For SCZ, but not PD, emotion-processing, empathy, and cognitive action control networks distinguished patients most accurately from controls. For PD, but not SCZ, networks subserving autobiographical or semantic memory, motor execution, and theory-of-mind cognition yielded the best classifications. In contrast, young-old classification was excellent based on all networks and outperformed both clinical classifications. Our pattern-classification approach captured associations between clinical and developmental conditions and functional network integrity with a higher level of specificity than did previous whole-brain analyses. Taken together, our results support resting-state connectivity as a marker of functional dysregulation in specific networks known to be affected by SCZ and PD, while suggesting that aging affects network integrity in a more global way. Hum Brain Mapp 38:5845-5858, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

21 CFR 862.1420 - Isocitric dehydrogenase test system.

Code of Federal Regulations, 2013 CFR

2013-04-01

... disease such as viral hepatitis, cirrhosis, or acute inflammation of the biliary tract; pulmonary disease...), and diseases associated with pregnancy. (b) Classification. Class I (general controls). The device is...

21 CFR 862.1420 - Isocitric dehydrogenase test system.

Code of Federal Regulations, 2012 CFR

2012-04-01

... disease such as viral hepatitis, cirrhosis, or acute inflammation of the biliary tract; pulmonary disease...), and diseases associated with pregnancy. (b) Classification. Class I (general controls). The device is...

21 CFR 862.1420 - Isocitric dehydrogenase test system.

Code of Federal Regulations, 2010 CFR

2010-04-01

... disease such as viral hepatitis, cirrhosis, or acute inflammation of the biliary tract; pulmonary disease...), and diseases associated with pregnancy. (b) Classification. Class I (general controls). The device is...

Increasing CAD system efficacy for lung texture analysis using a convolutional network

NASA Astrophysics Data System (ADS)

Tarando, Sebastian Roberto; Fetita, Catalin; Faccinetto, Alex; Brillet, Pierre-Yves

2016-03-01

The infiltrative lung diseases are a class of irreversible, non-neoplastic lung pathologies requiring regular follow-up with CT imaging. Quantifying the evolution of the patient status imposes the development of automated classification tools for lung texture. For the large majority of CAD systems, such classification relies on a two-dimensional analysis of axial CT images. In a previously developed CAD system, we proposed a fully-3D approach exploiting a multi-scale morphological analysis which showed good performance in detecting diseased areas, but with a major drawback consisting of sometimes overestimating the pathological areas and mixing different type of lung patterns. This paper proposes a combination of the existing CAD system with the classification outcome provided by a convolutional network, specifically tuned-up, in order to increase the specificity of the classification and the confidence to diagnosis. The advantage of using a deep learning approach is a better regularization of the classification output (because of a deeper insight into a given pathological class over a large series of samples) where the previous system is extra-sensitive due to the multi-scale response on patient-specific, localized patterns. In a preliminary evaluation, the combined approach was tested on a 10 patient database of various lung pathologies, showing a sharp increase of true detections.

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%.

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

Bypass versus Angioplasty in Severe Ischaemia of the Leg (BASIL) trial: A description of the severity and extent of disease using the Bollinger angiogram scoring method and the TransAtlantic Inter-Society Consensus II classification.

PubMed

Bradbury, Andrew W; Adam, Donald J; Bell, Jocelyn; Forbes, John F; Fowkes, F Gerry R; Gillespie, Ian; Ruckley, Charles Vaughan; Raab, Gillian M

2010-05-01

The Bypass versus Angioplasty in Severe Ischaemia of the Leg (BASIL) trial showed in patients with severe lower limb ischemia (rest pain, tissue loss) who survive for 2 years after intervention that initial randomization to bypass surgery, compared with balloon angioplasty, was associated with an improvement in subsequent amputation-free survival and overall survival of about 6 and 7 months, respectively. The aim of this report is to describe the angiographic severity and extent of infrainguinal arterial disease in the BASIL trial cohort so that the trial outcomes can be appropriately generalized to other patient cohorts with similar anatomic (angiographic) patterns of disease. Preintervention angiograms were scored using the Bollinger method and the TransAtlantic Inter-Society Consensus (TASC) II classification system by three consultant interventional radiologists and two consultant vascular surgeons unaware of the treatment received or patient outcomes. As was to be expected from the randomization process, patients in the two trial arms were well matched in terms of angiographic severity and extent of disease as documented by Bollinger and TASC II. In patients with the least overall disease, it tended to be concentrated in the superficial femoral and popliteal arteries, which were the commonest sites of disease overall. The below knee arteries became increasingly involved as the overall severity of disease increased, but the disease in the above knee arteries did not tend to worsen. The posterior tibial artery was the most diseased crural artery, whereas the peroneal appeared relatively spared. There was less interobserver disagreement with the Bollinger method than with the TASC II classification system, which also appears inherently less sensitive to clinically important differences in infrapopliteal disease among patients with severe leg ischemia. Anatomic (angiographic) disease description in patients with severe leg ischemia requires a reproducible scoring system that is sensitive to differences in crural artery disease. The Bollinger system appears well suited for this purpose, but the TASC II classification system less so. We hope this detailed analysis will facilitate appropriate generalization of the BASIL trial data to other groups of patients affected by similar anatomic (angiographic) patterns of disease. Crown Copyright (c) 2010. Published by Mosby, Inc. All rights reserved.

Clinical aspects of autoimmune rheumatic diseases.

PubMed

Goldblatt, Fiona; O'Neill, Sean G

2013-08-31

Multisystem autoimmune rheumatic diseases are heterogeneous rare disorders associated with substantial morbidity and mortality. Efforts to create international consensus within the past decade have resulted in the publication of new classification or nomenclature criteria for several autoimmune rheumatic diseases, specifically for systemic lupus erythematosus, Sjögren's syndrome, and the systemic vasculitides. Substantial progress has been made in the formulation of new criteria in systemic sclerosis and idiopathic inflammatory myositis. Although the autoimmune rheumatic diseases share many common features and clinical presentations, differentiation between the diseases is crucial because of important distinctions in clinical course, appropriate drugs, and prognoses. We review some of the dilemmas in the diagnosis of these autoimmune rheumatic diseases, and focus on the importance of new classification criteria, clinical assessment, and interpretation of autoimmune serology. In this era of improvement of mortality rates for patients with autoimmune rheumatic diseases, we pay particular attention to the effect of leading complications, specifically cardiovascular manifestations and cancer, and we update epidemiology and prognosis. Copyright © 2013 Elsevier Ltd. All rights reserved.

The road to 11th edition of the International Classification of Diseases: trajectories of scientific consensus and contested science in the classification of intellectual disability/intellectual developmental disorders.

PubMed

Salvador-Carulla, Luis; Bertelli, Marco; Martinez-Leal, Rafael

2018-03-01

To increase the expert knowledge-base on intellectual developmental disorders (IDDs) by investigating the typology trajectories of consensus formation in the classification systems up to the 11th edition of the International Classification of Diseases (ICD-11). This expert review combines an analysis of key recent literature and the revision of the consensus formation and contestation in the expert committees contributing to the classification systems since the 1950s. Historically two main approaches have contributed to the development of this knowledge-base: a neurodevelopmental-clinical approach and a psychoeducational-social approach. These approaches show a complex interaction throughout the history of IDD and have had a diverse influence on its classification. Although in theory Diagnostic and Statistical Manual (DSM)-5 and ICD adhere to the neurodevelopmental-clinical model, the new definition in the ICD-11 follows a restrictive normality approach to intellectual quotient and to the measurement of adaptive behaviour. On the contrary DSM-5 is closer to the recommendations made by the WHO 'Working Group on Mental Retardation' for ICD-11 for an integrative approach. A cyclical pattern of consensus formation has been identified in IDD. The revision of the three major classification systems in the last decade has increased the terminological and conceptual variability and the overall scientific contestation on IDD.

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 extent of disease, and communicating the extent of disease data in a uniform manner. Methods are needed to express the overall prognosis without losing the vital anatomic content of TNM. These methods should be able to integrate multiple prognostic factors, including TNM, while permitting the TNM system to remain intact and distinct. This article discusses examples of such approaches.

New clinical practice guidelines on the classification, evaluation and management of childhood interstitial lung disease in infants: what do they mean?

PubMed

Wambach, Jennifer A; Young, Lisa R

2014-12-01

The American Thoracic Society (ATS) recently published a clinical practice guideline regarding the classification, evaluation, and management of childhood interstitial lung disease in infancy (chILD). As disease entities among infants with ILD are often distinct from forms seen in older children and adults, the guideline encourages an age-based classification system and focuses on the diagnostic approach to neonates and infants <2 years of age. The guideline reviews current evidence and recommendations for the evaluation, relevant genetic studies, and management of symptomatic infants. Here, we summarize the ATS guideline, highlight the major concepts, and discuss future strategies aimed at addressing current gaps in knowledge.

Occupational Disease Registries-Characteristics and Experiences.

PubMed

Davoodi, Somayeh; Haghighi, Khosro Sadeghniat; Kalhori, Sharareh Rostam Niakan; Hosseini, Narges Shams; Mohammadzadeh, Zeinab; Safdari, Reza

2017-06-01

Due to growth of occupational diseases and also increase of public awareness about their consequences, attention to various aspects of diseases and improve occupational health and safety has found great importance. Therefore, there is the need for appropriate information management tools such as registries in order to recognitions of diseases patterns and then making decision about prevention, early detection and treatment of them. These registries have different characteristics in various countries according to their occupational health priorities. Aim of this study is evaluate dimensions of occupational diseases registries including objectives, data sources, responsible institutions, minimum data set, classification systems and process of registration in different countries. In this study, the papers were searched using the MEDLINE (PubMed) Google scholar, Scopus, ProQuest and Google. The search was done based on keyword in English for all motor engines including "occupational disease", "work related disease", "surveillance", "reporting", "registration system" and "registry" combined with name of the countries including all subheadings. After categorizing search findings in tables, results were compared with each other. Important aspects of the registries studied in ten countries including Finland, France, United Kingdom, Australia, Czech Republic, Malaysia, United States, Singapore, Russia and Turkey. The results show that surveyed countries have statistical, treatment and prevention objectives. Data sources in almost the rest of registries were physicians and employers. The minimum data sets in most of them consist of information about patient, disease, occupation and employer. Some of countries have special occupational related classification systems for themselves and some of them apply international classification systems such as ICD-10. Finally, the process of registration system was different in countries. Because occupational diseases are often preventable, but not curable, it is necessary to all countries, to consider prevention and early detection of occupational diseases as the objectives of their registry systems. Also it is recommended that all countries reach an agreement about global characteristics of occupational disease registries. This enables country to compare their data at international levels.

Plus Disease in Retinopathy of Prematurity: Improving Diagnosis by Ranking Disease Severity and Using Quantitative Image Analysis.

PubMed

Kalpathy-Cramer, Jayashree; Campbell, J Peter; Erdogmus, Deniz; Tian, Peng; Kedarisetti, Dharanish; Moleta, Chace; Reynolds, James D; Hutcheson, Kelly; Shapiro, Michael J; Repka, Michael X; Ferrone, Philip; Drenser, Kimberly; Horowitz, Jason; Sonmez, Kemal; Swan, Ryan; Ostmo, Susan; Jonas, Karyn E; Chan, R V Paul; Chiang, Michael F

2016-11-01

To determine expert agreement on relative retinopathy of prematurity (ROP) disease severity and whether computer-based image analysis can model relative disease severity, and to propose consideration of a more continuous severity score for ROP. We developed 2 databases of clinical images of varying disease severity (100 images and 34 images) as part of the Imaging and Informatics in ROP (i-ROP) cohort study and recruited expert physician, nonexpert physician, and nonphysician graders to classify and perform pairwise comparisons on both databases. Six participating expert ROP clinician-scientists, each with a minimum of 10 years of clinical ROP experience and 5 ROP publications, and 5 image graders (3 physicians and 2 nonphysician graders) who analyzed images that were obtained during routine ROP screening in neonatal intensive care units. Images in both databases were ranked by average disease classification (classification ranking), by pairwise comparison using the Elo rating method (comparison ranking), and by correlation with the i-ROP computer-based image analysis system. Interexpert agreement (weighted κ statistic) compared with the correlation coefficient (CC) between experts on pairwise comparisons and correlation between expert rankings and computer-based image analysis modeling. There was variable interexpert agreement on diagnostic classification of disease (plus, preplus, or normal) among the 6 experts (mean weighted κ, 0.27; range, 0.06-0.63), but good correlation between experts on comparison ranking of disease severity (mean CC, 0.84; range, 0.74-0.93) on the set of 34 images. Comparison ranking provided a severity ranking that was in good agreement with ranking obtained by classification ranking (CC, 0.92). Comparison ranking on the larger dataset by both expert and nonexpert graders demonstrated good correlation (mean CC, 0.97; range, 0.95-0.98). The i-ROP system was able to model this continuous severity with good correlation (CC, 0.86). Experts diagnose plus disease on a continuum, with poor absolute agreement on classification but good relative agreement on disease severity. These results suggest that the use of pairwise rankings and a continuous severity score, such as that provided by the i-ROP system, may improve agreement on disease severity in the future. Copyright © 2016 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Comparison of severity classification in Japanese patients with antineutrophil cytoplasmic antibody-associated vasculitis in a nationwide, prospective, inception cohort study.

PubMed

Sada, Ken-Ei; Harigai, Masayoshi; Amano, Koichi; Atsumi, Tatsuya; Fujimoto, Shouichi; Yuzawa, Yukio; Takasaki, Yoshinari; Banno, Shogo; Sugihara, Takahiko; Kobayashi, Masaki; Usui, Joichi; Yamagata, Kunihiro; Homma, Sakae; Dobashi, Hiroaki; Tsuboi, Naotake; Ishizu, Akihiro; Sugiyama, Hitoshi; Okada, Yasunori; Arimura, Yoshihiro; Matsuo, Seiichi; Makino, Hirofumi

2016-09-01

To compare disease severity classification systems for six-month outcome prediction in patients with antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV). Patients with newly diagnosed AAV from 53 tertiary institutions were enrolled. Six-month remission, overall survival, and end-stage renal disease (ESRD)-free survival were evaluated. According to the European Vasculitis Study Group (EUVAS)-defined disease severity, the 321 enrolled patients were classified as follows: 14, localized; 71, early systemic; 170, generalized; and 66, severe disease. According to the rapidly progressive glomerulonephritis (RPGN) clinical grading system, the patients were divided as follows: 60, grade I; 178, grade II; 66, grade III; and 12, grade IV. According to the Five-Factor Score (FFS) 2009, 103, 109, and 109 patients had ≤1, 2, and ≥3 points, respectively. No significant difference in remission rates was found in any severity classification. The overall and ESRD-free survival rates significantly differed between grades I/II, III, and IV, regardless of renal involvement. Severe disease was a good predictor of six-month overall and ESRD-free survival. The FFS 2009 was useful to predict six-month ESRD-free survival but not overall survival. The RPGN grading system was more useful to predict six-month overall and ESRD-free survival than the EUVAS-defined severity or FFS 2009.

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

Aircrew Availability: Modeling Predictors of Duties Not Including Flying Status

DTIC Science & Technology

2017-07-25

International Classification of Diseases , Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes, were obtained from ASIMS. Participant age...diagnosis category,b no. (%): Diseases of the respiratory system 104,637 (26.83) DoD specific: education or counseling 48,117 (12.34... Diseases of the digestive system 31,177 (7.99) Diseases of the nervous system and sense organs 30,625 (7.85) Symptoms; signs, ill-defined

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.

Mining disease fingerprints from within genetic pathways.

PubMed

Nabhan, Ahmed Ragab; Sarkar, Indra Neil

2012-01-01

Mining biological networks can be an effective means to uncover system level knowledge out of micro level associations, such as encapsulated in genetic pathways. Analysis of human disease genetic pathways can lead to the identification of major mechanisms that may underlie disorders at an abstract functional level. The focus of this study was to develop an approach for structural pattern analysis and classification of genetic pathways of diseases. A probabilistic model was developed to capture characteristic components ('fingerprints') of functionally annotated pathways. A probability estimation procedure of this model searched for fingerprints in each disease pathway while improving probability estimates of model parameters. The approach was evaluated on data from the Kyoto Encyclopedia of Genes and Genomes (consisting of 56 pathways across seven disease categories). Based on the achieved average classification accuracy of up to ~77%, the findings suggest that these fingerprints may be used for classification and discovery of genetic pathways.

Mining Disease Fingerprints From Within Genetic Pathways

PubMed Central

Nabhan, Ahmed Ragab; Sarkar, Indra Neil

2012-01-01

Mining biological networks can be an effective means to uncover system level knowledge out of micro level associations, such as encapsulated in genetic pathways. Analysis of human disease genetic pathways can lead to the identification of major mechanisms that may underlie disorders at an abstract functional level. The focus of this study was to develop an approach for structural pattern analysis and classification of genetic pathways of diseases. A probabilistic model was developed to capture characteristic components (‘fingerprints’) of functionally annotated pathways. A probability estimation procedure of this model searched for fingerprints in each disease pathway while improving probability estimates of model parameters. The approach was evaluated on data from the Kyoto Encyclopedia of Genes and Genomes (consisting of 56 pathways across seven disease categories). Based on the achieved average classification accuracy of up to ∼77%, the findings suggest that these fingerprints may be used for classification and discovery of genetic pathways. PMID:23304411

Designing a training tool for imaging mental models

NASA Technical Reports Server (NTRS)

Dede, Christopher J.; Jayaram, Geetha

1990-01-01

The training process can be conceptualized as the student acquiring an evolutionary sequence of classification-problem solving mental models. For example a physician learns (1) classification systems for patient symptoms, diagnostic procedures, diseases, and therapeutic interventions and (2) interrelationships among these classifications (e.g., how to use diagnostic procedures to collect data about a patient's symptoms in order to identify the disease so that therapeutic measures can be taken. This project developed functional specifications for a computer-based tool, Mental Link, that allows the evaluative imaging of such mental models. The fundamental design approach underlying this representational medium is traversal of virtual cognition space. Typically intangible cognitive entities and links among them are visible as a three-dimensional web that represents a knowledge structure. The tool has a high degree of flexibility and customizability to allow extension to other types of uses, such a front-end to an intelligent tutoring system, knowledge base, hypermedia system, or semantic network.

An integrated healthcare system for personalized chronic disease care in home-hospital environments.

PubMed

Jeong, Sangjin; Youn, Chan-Hyun; Shim, Eun Bo; Kim, Moonjung; Cho, Young Min; Peng, Limei

2012-07-01

Facing the increasing demands and challenges in the area of chronic disease care, various studies on the healthcare system which can, whenever and wherever, extract and process patient data have been conducted. Chronic diseases are the long-term diseases and require the processes of the real-time monitoring, multidimensional quantitative analysis, and the classification of patients' diagnostic information. A healthcare system for chronic diseases is characterized as an at-hospital and at-home service according to a targeted environment. Both services basically aim to provide patients with accurate diagnoses of disease by monitoring a variety of physical states with a number of monitoring methods, but there are differences between home and hospital environments, and the different characteristics should be considered in order to provide more accurate diagnoses for patients, especially, patients having chronic diseases. In this paper, we propose a patient status classification method for effectively identifying and classifying chronic diseases and show the validity of the proposed method. Furthermore, we present a new healthcare system architecture that integrates the at-home and at-hospital environment and discuss the applicability of the architecture using practical target services.

Exploring the color feature power for psoriasis risk stratification and classification: A data mining paradigm.

PubMed

Shrivastava, Vimal K; Londhe, Narendra D; Sonawane, Rajendra S; Suri, Jasjit S

2015-10-01

A large percentage of dermatologist׳s decision in psoriasis disease assessment is based on color. The current computer-aided diagnosis systems for psoriasis risk stratification and classification lack the vigor of color paradigm. The paper presents an automated psoriasis computer-aided diagnosis (pCAD) system for classification of psoriasis skin images into psoriatic lesion and healthy skin, which solves the two major challenges: (i) fulfills the color feature requirements and (ii) selects the powerful dominant color features while retaining high classification accuracy. Fourteen color spaces are discovered for psoriasis disease analysis leading to 86 color features. The pCAD system is implemented in a support vector-based machine learning framework where the offline image data set is used for computing machine learning offline color machine learning parameters. These are then used for transformation of the online color features to predict the class labels for healthy vs. diseased cases. The above paradigm uses principal component analysis for color feature selection of dominant features, keeping the original color feature unaltered. Using the cross-validation protocol, the above machine learning protocol is compared against the standalone grayscale features with 60 features and against the combined grayscale and color feature set of 146. Using a fixed data size of 540 images with equal number of healthy and diseased, 10 fold cross-validation protocol, and SVM of polynomial kernel of type two, pCAD system shows an accuracy of 99.94% with sensitivity and specificity of 99.93% and 99.96%. Using a varying data size protocol, the mean classification accuracies for color, grayscale, and combined scenarios are: 92.85%, 93.83% and 93.99%, respectively. The reliability of the system in these three scenarios are: 94.42%, 97.39% and 96.00%, respectively. We conclude that pCAD system using color space alone is compatible to grayscale space or combined color and grayscale spaces. We validated our pCAD system against facial color databases and the results are consistent in accuracy and reliability. Copyright © 2015 Elsevier Ltd. All rights reserved.

Overweight and Obesity Prevalence Among School-Aged Nunavik Inuit Children According to Three Body Mass Index Classification Systems.

PubMed

Medehouenou, Thierry Comlan Marc; Ayotte, Pierre; St-Jean, Audray; Meziou, Salma; Roy, Cynthia; Muckle, Gina; Lucas, Michel

2015-07-01

Little is known about the suitability of three commonly used body mass index (BMI) classification system for Indigenous children. This study aims to estimate overweight and obesity prevalence among school-aged Nunavik Inuit children according to International Obesity Task Force (IOTF), Centers for Disease Control and Prevention (CDC), and World Health Organization (WHO) BMI classification systems, to measure agreement between those classification systems, and to investigate whether BMI status as defined by these classification systems is associated with levels of metabolic and inflammatory biomarkers. Data were collected on 290 school-aged children (aged 8-14 years; 50.7% girls) from the Nunavik Child Development Study with data collected in 2005-2010. Anthropometric parameters were measured and blood sampled. Participants were classified as normal weight, overweight, and obese according to BMI classification systems. Weighted kappa (κw) statistics assessed agreement between different BMI classification systems, and multivariate analysis of variance ascertained their relationship with metabolic and inflammatory biomarkers. The combined prevalence rate of overweight/obesity was 26.9% (with 6.6% obesity) with IOTF, 24.1% (11.0%) with CDC, and 40.4% (12.8%) with WHO classification systems. Agreement was the highest between IOTF and CDC (κw = .87) classifications, and substantial for IOTF and WHO (κw = .69) and for CDC and WHO (κw = .73). Insulin and high-sensitivity C-reactive protein plasma levels were significantly higher from normal weight to obesity, regardless of classification system. Among obese subjects, higher insulin level was observed with IOTF. Compared with other systems, IOTF classification appears to be more specific to identify overweight and obesity in Inuit children. Copyright © 2015 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Investigation of physicians' attitudes concerning the implementation of international classification systems of diseases as a precondition for evidence-based policy making.

PubMed

Tsikna, Vasiliki; Siskou, Olga; Galanis, Petros; Prezerakos, Panagiotis; Kaitelidou, Daphne

2013-01-01

This study investigated the main factors affecting physicians' attitudes toward the implementation of international classification systems of diseases. A cross-sectional study was carried out during September 2010. The sample consisted of 158 physicians older than 24 years who were working in a public hospital and a private hospital in central Greece. A questionnaire was drawn up based on the relevant literature. Results indicated that younger physicians and those who worked in the public hospital were most familiar with classification systems. Female physicians and specialists with more than 10 years of experience (since qualifying as a specialist) were not particularly familiar with these systems (58 percent and 56 percent, respectively). Both having a master's degree and attending conferences or seminars had a remarkable impact on knowledge of these systems. Almost all physicians (98 percent) holding a master's degree or a PhD believed that these systems contribute to the compilation of valid statistical data. The majority of physicians would like to use these systems in the future, as long as they are provided with the appropriate training.

Evaluation of Alzheimer's disease by analysis of MR images using Objective Dialectical Classifiers as an alternative to ADC maps.

PubMed

Dos Santos, Wellington P; de Assis, Francisco M; de Souza, Ricardo E; Dos Santos Filho, Plinio B

2008-01-01

Alzheimer's disease is the most common cause of dementia, yet hard to diagnose precisely without invasive techniques, particularly at the onset of the disease. This work approaches image analysis and classification of synthetic multispectral images composed by diffusion-weighted (DW) magnetic resonance (MR) cerebral images for the evaluation of cerebrospinal fluid area and measuring the advance of Alzheimer's disease. A clinical 1.5 T MR imaging system was used to acquire all images presented. The classification methods are based on Objective Dialectical Classifiers, a new method based on Dialectics as defined in the Philosophy of Praxis. A 2-degree polynomial network with supervised training is used to generate the ground truth image. The classification results are used to improve the usual analysis of the apparent diffusion coefficient map.

[Evaluation of the appropriateness of hospital admissions using the iso-gravity classification systems APR-DRG and Disease Staging and the Italian version of Appropriateness Evaluation Protocol (AEP)].

PubMed

D'Andrea, G; Capalbo, G; Volpe, M; Marchetti, M; Vicentini, F; Capelli, G; Cambieri, A; Cicchetti, A; Ricciardi, G; Catananti, C

2006-01-01

Our main purpose was to evaluate the organizational appropriateness of admissions made in a university hospital, by comparing two iso-gravity classification systems, APR-DRG and Disease Staging, with the Italian version of AEP (PRUO). Our analysis focused on admissions made in 2001, related to specific Diagnosis Related Groups (DRGs), which, according an Italian Law, would be considered at high risk of inappropriateness, if treated as ordinary admissions. The results obtained by using the 2 classification systems did not show statistically significant differences with respect to the total number of admissions. On the other hand, some DRGs showed statistically significant differences due to different algorithms of attribution of the severity levels used by the two systems. For almost all of the DRGs studied, the AEP-based analysis of a sample of medical records showed an higher number of inappropriate admissions in comparison with the number expected by iso-gravity classification methods. The difference is possibly due to the percentage limits of tolerability fixed by the Law for each DRG. Therefore, the authors suggest an integrated use of the two methods to evaluate organizational appropriateness of hospital admissions.

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.

Performance of the Delirium Rating Scale-Revised-98 Against Different Delirium Diagnostic Criteria in a Population With a High Prevalence of Dementia.

PubMed

Sepulveda, Esteban; Franco, José G; Trzepacz, Paula T; Gaviria, Ana M; Viñuelas, Eva; Palma, José; Ferré, Gisela; Grau, Imma; Vilella, Elisabet

2015-01-01

Delirium diagnosis in elderly is often complicated by underlying dementia. We evaluated performance of the Delirium Rating Scale-Revised-98 (DRS-R98) in patients with high dementia prevalence and also assessed concordance among past and current diagnostic criteria for delirium. Cross-sectional analysis of newly admitted patients to a skilled nursing facility over 6 months, who were rated within 24-48 hours after admission. Interview for Diagnostic and Statistical Manual of Mental Disorders, 3rd edition-R (DSM)-III-R, DSM-IV, DSM-5, and International Classification of Diseases 10th edition delirium ratings, administration of the DRS-R98, and assessment of dementia using the Informant Questionnaire on Cognitive Decline in the Elderly were independently performed by 3 researchers. Discriminant analyses (receiver operating characteristics curves) were used to study DRS-R98 accuracy against different diagnostic criteria. Hanley and McNeil test compared the area under the curve for DRS-R98's discriminant performance for all diagnostic criteria. Dementia was present in 85/125 (68.0%) subjects, and 36/125 (28.8%) met criteria for delirium by at least 1 classification system, whereas only 19/36 (52.8%) did by all. DSM-III-R diagnosed the most as delirious (27.2%), followed by DSM-5 (24.8%), DSM-IV-TR (22.4%), and International Classification of Diseases 10th edition (16%). DRS-R98 had the highest AUC when discriminating DSM-III-R delirium (92.9%), followed by DSM-IV (92.4%), DSM-5 (91%), and International Classification of Diseases 10th edition (90.5%), without statistical differences among them. The best DRS-R98 cutoff score was ≥14.5 for all diagnostic systems except International Classification of Diseases 10th edition (≥15.5). There is a low concordance across diagnostic systems for identification of delirium. The DRS-R98 performs well despite differences across classification systems perhaps because it broadly assesses phenomenology, even in this population with a high prevalence of dementia. Copyright © 2015 The Academy of Psychosomatic Medicine. Published by Elsevier Inc. All rights reserved.

Health information management: an introduction to disease classification and coding.

PubMed

Mony, Prem Kumar; Nagaraj, C

2007-01-01

Morbidity and mortality data constitute an important component of a health information system and their coding enables uniform data collation and analysis as well as meaningful comparisons between regions or countries. Strengthening the recording and reporting systems for health monitoring is a basic requirement for an efficient health information management system. Increased advocacy for and awareness of a uniform coding system together with adequate capacity building of physicians, coders and other allied health and information technology personnel would pave the way for a valid and reliable health information management system in India. The core requirements for the implementation of disease coding are: (i) support from national/institutional health administrators, (ii) widespread availability of the ICD-10 material for morbidity and mortality coding; (iii) enhanced human and financial resources; and (iv) optimal use of informatics. We describe the methodology of a disease classification and codification system as also its applications for developing and maintaining an effective health information management system for India.

Juvenile idiopathic arthritis in adulthood: fulfilment of classification criteria for adult rheumatic diseases, long-term outcomes and predictors of inactive disease, functional status and damage.

PubMed

Oliveira-Ramos, Filipa; Eusébio, Mónica; M Martins, Fernando; Mourão, Ana Filipa; Furtado, Carolina; Campanilho-Marques, Raquel; Cordeiro, Inês; Ferreira, Joana; Cerqueira, Marcos; Figueira, Ricardo; Brito, Iva; Canhão, Helena; Santos, Maria José; Melo-Gomes, José A; Fonseca, João Eurico

2016-01-01

To determine how adult juvenile idiopathic arthritis (JIA) patients fulfil classification criteria for adult rheumatic diseases, evaluate their outcomes and determine clinical predictors of inactive disease, functional status and damage. Patients with JIA registered on the Rheumatic Diseases Portuguese Register (Reuma.pt) older than 18 years and with more than 5 years of disease duration were included. Data regarding sociodemographic features, fulfilment of adult classification criteria, Health Assessment Questionnaire, Juvenile Arthritis Damage Index-articular (JADI-A) and Juvenile Arthritis Damage Index-extra-articular (JADI-E) damage index and disease activity were analysed. 426 patients were included. Most of patients with systemic JIA fulfilled criteria for Adult Still's disease. 95.6% of the patients with rheumatoid factor (RF)-positive polyarthritis and 57.1% of the patients with RF-negative polyarthritis matched criteria for rheumatoid arthritis (RA). 38.9% of the patients with extended oligoarthritis were classified as RA while 34.8% of the patients with persistent oligoarthritis were classified as spondyloarthritis. Patients with enthesitis-related arthritis fulfilled criteria for spondyloarthritis in 94.7%. Patients with psoriatic arthritis maintained this classification. Patients with inactive disease had lower disease duration, lower diagnosis delay and corticosteroids exposure. Longer disease duration was associated with higher HAQ, JADI-A and JADI-E. Higher JADI-A was also associated with biological treatment and retirement due to JIA disability and higher JADI-E with corticosteroids exposure. Younger age at disease onset was predictive of higher HAQ, JADI-A and JADI-E and decreased the chance of inactive disease. Most of the included patients fulfilled classification criteria for adult rheumatic diseases, maintain active disease and have functional impairment. Younger age at disease onset was predictive of higher disability and decreased the chance of inactive disease.

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.

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

Adverse events following cervical manipulative therapy: consensus on classification among Dutch medical specialists, manual therapists, and patients.

PubMed

Kranenburg, Hendrikus A; Lakke, Sandra E; Schmitt, Maarten A; Van der Schans, Cees P

2017-12-01

To obtain consensus-based agreement on a classification system of adverse events (AE) following cervical spinal manipulation. The classification system should be comprised of clear definitions, include patients' and clinicians' perspectives, and have an acceptable number of categories. Design : A three-round Delphi study. Participants : Thirty Dutch participants (medical specialists, manual therapists, and patients) participated in an online survey. Procedure : Participants inventoried AE and were asked about their preferences for either a three- or a four-category classification system. The identified AE were classified by two analysts following the International Classification of Functioning, Disability and Health (ICF), and the International Classification of Diseases and Related Health Problems (ICD-10). Participants were asked to classify the severity for all AE in relation to the time duration. Consensus occurred in a three-category classification system. There was strong consensus for 16 AE in all severities (no, minor, and major AE) and all three time durations [hours, days, weeks]. The 16 AE included anxiety, flushing, skin rash, fainting, dizziness, coma, altered sensation, muscle tenderness, pain, increased pain during movement, radiating pain, dislocation, fracture, transient ischemic attack, stroke, and death. Mild to strong consensus was reached for 13 AE. A consensus-based classification system of AE is established which includes patients' and clinicians' perspectives and has three categories. The classification comprises a precise description of potential AE in accordance with internationally accepted classifications. After international validation, clinicians and researchers may use this AE classification system to report AE in clinical practice and research.

Assessment of incidence of severe sepsis in Sweden using different ways of abstracting International Classification of Diseases codes: difficulties with methods and interpretation of results.

PubMed

Wilhelms, Susanne B; Huss, Fredrik R; Granath, Göran; Sjöberg, Folke

2010-06-01

To compare three International Classification of Diseases code abstraction strategies that have previously been reported to mirror severe sepsis by examining retrospective Swedish national data from 1987 to 2005 inclusive. Retrospective cohort study. Swedish hospital discharge database. All hospital admissions during the period 1987 to 2005 were extracted and these patients were screened for severe sepsis using the three International Classification of Diseases code abstraction strategies, which were adapted for the Swedish version of the International Classification of Diseases. Two code abstraction strategies included both International Classification of Diseases, Ninth Revision and International Classification of Diseases, Tenth Revision codes, whereas one included International Classification of Diseases, Tenth Revision codes alone. None. The three International Classification of Diseases code abstraction strategies identified 37,990, 27,655, and 12,512 patients, respectively, with severe sepsis. The incidence increased over the years, reaching 0.35 per 1000, 0.43 per 1000, and 0.13 per 1000 inhabitants, respectively. During the International Classification of Diseases, Ninth Revision period, we found 17,096 unique patients and of these, only 2789 patients (16%) met two of the code abstraction strategy lists and 14,307 (84%) met one list. The International Classification of Diseases, Tenth Revision period included 46,979 unique patients, of whom 8% met the criteria of all three International Classification of Diseases code abstraction strategies, 7% met two, and 84% met one only. The three different International Classification of Diseases code abstraction strategies generated three almost separate cohorts of patients with severe sepsis. Thus, the International Classification of Diseases code abstraction strategies for recording severe sepsis in use today provides an unsatisfactory way of estimating the true incidence of severe sepsis. Further studies relating International Classification of Diseases code abstraction strategies to the American College of Chest Physicians/Society of Critical Care Medicine scores are needed.

It is time to bring borderline intellectual functioning back into the main fold of classification systems

PubMed Central

Wieland, Jannelien; Zitman, Frans G.

2016-01-01

Borderline intellectual functioning is an important and frequently unrecognised comorbid condition relevant to the diagnosis and treatment of any and all psychiatric disorders. In the DSM-IV-TR, it is defined by IQ in the 71–84 range. In DSM-5, IQ boundaries are no longer part of the classification, leaving the concept without a clear definition. This modification is one of the least highlighted changes in DSM-5. In this article we describe the history of the classification of borderline intellectual functioning. We provide information about it and on the importance of placing it in the right context and in the right place in future DSM editions and other classification systems such as the International Classification of Diseases. PMID:27512590

The International Neuroblastoma Risk Group (INRG) staging system: an INRG Task Force report.

PubMed

Monclair, Tom; Brodeur, Garrett M; Ambros, Peter F; Brisse, Hervé J; Cecchetto, Giovanni; Holmes, Keith; Kaneko, Michio; London, Wendy B; Matthay, Katherine K; Nuchtern, Jed G; von Schweinitz, Dietrich; Simon, Thorsten; Cohn, Susan L; Pearson, Andrew D J

2009-01-10

The International Neuroblastoma Risk Group (INRG) classification system was developed to establish a consensus approach for pretreatment risk stratification. Because the International Neuroblastoma Staging System (INSS) is a postsurgical staging system, a new clinical staging system was required for the INRG pretreatment risk classification system. To stage patients before any treatment, the INRG Task Force, consisting of neuroblastoma experts from Australia/New Zealand, China, Europe, Japan, and North America, developed a new INRG staging system (INRGSS) based on clinical criteria and image-defined risk factors (IDRFs). To investigate the impact of IDRFs on outcome, survival analyses were performed on 661 European patients with INSS stages 1, 2, or 3 disease for whom IDRFs were known. In the INGRSS, locoregional tumors are staged L1 or L2 based on the absence or presence of one or more of 20 IDRFs, respectively. Metastatic tumors are defined as stage M, except for stage MS, in which metastases are confined to the skin, liver, and/or bone marrow in children younger than 18 months of age. Within the 661-patient cohort, IDRFs were present (ie, stage L2) in 21% of patients with stage 1, 45% of patients with stage 2, and 94% of patients with stage 3 disease. Patients with INRGSS stage L2 disease had significantly lower 5-year event-free survival than those with INRGSS stage L1 disease (78% +/- 4% v 90% +/- 3%; P = .0010). Use of the new staging (INRGSS) and risk classification (INRG) of neuroblastoma will greatly facilitate the comparison of risk-based clinical trials conducted in different regions of the world.

Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System.

PubMed

Li, Hongqiang; Yuan, Danyang; Wang, Youxi; Cui, Dianyin; Cao, Lu

2016-10-20

Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias.

Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System

PubMed Central

Li, Hongqiang; Yuan, Danyang; Wang, Youxi; Cui, Dianyin; Cao, Lu

2016-01-01

Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias. PMID:27775596

Automated radial basis function neural network based image classification system for diabetic retinopathy detection in retinal images

NASA Astrophysics Data System (ADS)

Anitha, J.; Vijila, C. Kezi Selva; Hemanth, D. Jude

2010-02-01

Diabetic retinopathy (DR) is a chronic eye disease for which early detection is highly essential to avoid any fatal results. Image processing of retinal images emerge as a feasible tool for this early diagnosis. Digital image processing techniques involve image classification which is a significant technique to detect the abnormality in the eye. Various automated classification systems have been developed in the recent years but most of them lack high classification accuracy. Artificial neural networks are the widely preferred artificial intelligence technique since it yields superior results in terms of classification accuracy. In this work, Radial Basis function (RBF) neural network based bi-level classification system is proposed to differentiate abnormal DR Images and normal retinal images. The results are analyzed in terms of classification accuracy, sensitivity and specificity. A comparative analysis is performed with the results of the probabilistic classifier namely Bayesian classifier to show the superior nature of neural classifier. Experimental results show promising results for the neural classifier in terms of the performance measures.

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 the performance of the proposed system is comparative to that of state-of-the-art classification models. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Efficacy measures associated to a plantar pressure based classification system in diabetic foot medicine.

PubMed

Deschamps, Kevin; Matricali, Giovanni Arnoldo; Desmet, Dirk; Roosen, Philip; Keijsers, Noel; Nobels, Frank; Bruyninckx, Herman; Staes, Filip

2016-09-01

The concept of 'classification' has, similar to many other diseases, been found to be fundamental in the field of diabetic medicine. In the current study, we aimed at determining efficacy measures of a recently published plantar pressure based classification system. Technical efficacy of the classification system was investigated by applying a high resolution, pixel-level analysis on the normalized plantar pressure pedobarographic fields of the original experimental dataset consisting of 97 patients with diabetes and 33 persons without diabetes. Clinical efficacy was assessed by considering the occurence of foot ulcers at the plantar aspect of the forefoot in this dataset. Classification efficacy was assessed by determining the classification recognition rate as well as its sensitivity and specificity using cross-validation subsets of the experimental dataset together with a novel cohort of 12 patients with diabetes. Pixel-level comparison of the four groups associated to the classification system highlighted distinct regional differences. Retrospective analysis showed the occurence of eleven foot ulcers in the experimental dataset since their gait analysis. Eight out of the eleven ulcers developed in a region of the foot which had the highest forces. Overall classification recognition rate exceeded 90% for all cross-validation subsets. Sensitivity and specificity of the four groups associated to the classification system exceeded respectively the 0.7 and 0.8 level in all cross-validation subsets. The results of the current study support the use of the novel plantar pressure based classification system in diabetic foot medicine. It may particularly serve in communication, diagnosis and clinical decision making. Copyright © 2016 Elsevier B.V. All rights reserved.

21 CFR 866.5550 - Immunoglobulin (light chain specific) immunological test system.

Code of Federal Regulations, 2014 CFR

2014-04-01

... macroglobulinemia (increased production of large immunoglobulins), and connective tissue diseases such as rheumatoid arthritis or systemic lupus erythematosus. (b) Classification. Class II (performance standards). ...

21 CFR 866.5550 - Immunoglobulin (light chain specific) immunological test system.

Code of Federal Regulations, 2010 CFR

2010-04-01

... macroglobulinemia (increased production of large immunoglobulins), and connective tissue diseases such as rheumatoid arthritis or systemic lupus erythematosus. (b) Classification. Class II (performance standards). ...

21 CFR 866.5550 - Immunoglobulin (light chain specific) immunological test system.

Code of Federal Regulations, 2013 CFR

2013-04-01

... macroglobulinemia (increased production of large immunoglobulins), and connective tissue diseases such as rheumatoid arthritis or systemic lupus erythematosus. (b) Classification. Class II (performance standards). ...

21 CFR 866.5550 - Immunoglobulin (light chain specific) immunological test system.

Code of Federal Regulations, 2011 CFR

2011-04-01

... macroglobulinemia (increased production of large immunoglobulins), and connective tissue diseases such as rheumatoid arthritis or systemic lupus erythematosus. (b) Classification. Class II (performance standards). ...

21 CFR 866.5550 - Immunoglobulin (light chain specific) immunological test system.

Code of Federal Regulations, 2012 CFR

2012-04-01

... macroglobulinemia (increased production of large immunoglobulins), and connective tissue diseases such as rheumatoid arthritis or systemic lupus erythematosus. (b) Classification. Class II (performance standards). ...

Addition of Histology to the Paris Classification of Pediatric Crohn Disease Alters Classification of Disease Location.

PubMed

Fernandes, Melissa A; Verstraete, Sofia G; Garnett, Elizabeth A; Heyman, Melvin B

2016-02-01

The aim of the study was to investigate the value of microscopic findings in the classification of pediatric Crohn disease (CD) by determining whether classification of disease changes significantly with inclusion of histologic findings. Sixty patients were randomly selected from a cohort of patients studied at the Pediatric Inflammatory Bowel Disease Clinic at the University of California, San Francisco Benioff Children's Hospital. Two physicians independently reviewed the electronic health records of the included patients to determine the Paris classification for each patient by adhering to present guidelines and then by including microscopic findings. Macroscopic and combined disease location classifications were discordant in 34 (56.6%), with no statistically significant differences between groups. Interobserver agreement was higher in the combined classification (κ = 0.73, 95% confidence interval 0.65-0.82) as opposed to when classification was limited to macroscopic findings (κ = 0.53, 95% confidence interval 0.40-0.58). When evaluating the proximal upper gastrointestinal tract (Paris L4a), the interobserver agreement was better in macroscopic compared with the combined classification. Disease extent classifications differed significantly when comparing isolated macroscopic findings (Paris classification) with the combined scheme that included microscopy. Further studies are needed to determine which scheme provides more accurate representation of disease extent.

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.

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.

A Complex Network Perspective on Clinical Science

PubMed Central

Hofmann, Stefan G.; Curtiss, Joshua; McNally, Richard J.

2016-01-01

Contemporary classification systems for mental disorders assume that abnormal behaviors are expressions of latent disease entities. An alternative to the latent disease model is the complex network approach. Instead of assuming that symptoms arise from an underlying disease entity, the complex network approach holds that disorders exist as systems of interrelated elements of a network. This approach also provides a framework for the understanding of therapeutic change. Depending on the structure of the network, change can occur abruptly once the network reaches a critical threshold (the tipping point). Homogeneous and highly connected networks often recover more slowly from local perturbations when the network approaches the tipping point, allowing for the possibility to predict treatment change, relapse, and recovery. In this article we discuss the complex network approach as an alternative to the latent disease model, and we discuss its implications for classification, therapy, relapse, and recovery. PMID:27694457

[Histological diagnosis and complications of celiac disease. Update according to the new S2k guidelines].

PubMed

Aust, D E; Bläker, H

2015-03-01

Celiac disease is a relatively common immunological systemic disease triggered by the protein gluten in genetically predisposed individuals. Classical symptoms like chronic diarrhea, steatorrhea, weight loss and growth retardation are nowadays relatively uncommon. Diagnostic workup includes serological tests for IgA antibodies against tissue transglutaminase 2 (anti-TG2-IgA) and total IgA and histology of duodenal biopsies. Histomorphological classification should be done according to the modified Marsh-Oberhuber classification. Diagnosis of celiac disease should be based on serological, clinical, and histological findings. The only treatment is a life-long gluten-free diet. Unchanged or recurrent symptoms under gluten-free diet may indicate refractory celiac disease. Enteropathy-associated T-cell lymphoma and adenocarcinomas of the small intestine are known complications of celiac disease.

Drug safety: Pregnancy rating classifications and controversies.

PubMed

Wilmer, Erin; Chai, Sandy; Kroumpouzos, George

2016-01-01

This contribution consolidates data on international pregnancy rating classifications, including the former US Food and Drug Administration (FDA), Swedish, and Australian classification systems, as well as the evidence-based medicine system, and discusses discrepancies among them. It reviews the new Pregnancy and Lactation Labeling Rule (PLLR) that replaced the former FDA labeling system with narrative-based labeling requirements. PLLR emphasizes on human data and highlights pregnancy exposure registry information. In this context, the review discusses important data on the safety of most medications used in the management of skin disease in pregnancy. There are also discussions of controversies relevant to the safety of certain dermatologic medications during gestation. Copyright © 2016 Elsevier Inc. All rights reserved.

Verification of the Robin and Graham classification system of hip disease in cerebral palsy using three-dimensional computed tomography.

PubMed

Gose, Shinichi; Sakai, Takashi; Shibata, Toru; Akiyama, Keisuke; Yoshikawa, Hideki; Sugamoto, Kazuomi

2011-12-01

We evaluated the validity of the Robin and Graham classification system of hip disease in cerebral palsy (CP) using three-dimensional computed tomography in young people with CP. A total of 91 hips in 91 consecutive children with bilateral spastic CP (57 males, 34 females; nine classified at Gross Motor Function Classification System level II, 42 at level III, 32 at level IV, and eight at level V; mean age 5 y 2 mo, SD 11 mo; range 2-6 y) were investigated retrospectively using anteroposterior plain radiographs and three-dimensional computed tomography (3D-CT) of the hip. The migration percentage was calculated on plain radiographs and all participants were classified into four groups according to migration percentage: grade II, migration percentage ≥ 10% but ≤ 15%, (four hips), grade III, migration percentage >15% but ≤ 30%, (20 hips); grade IV, migration percentage >30% but <100%, (63 hips); and grade V, migration percentage ≥ 100%, (four hips). The lateral opening angle and the sagittal inclination angle of the acetabulum, the neck-shaft angle, and the femoral anteversion of the femur were measured on 3D-CT. The three-dimensional quantitative evaluation indicated that there were significant differences in the lateral opening angle and the neck-shaft angle between the four groups (Kruskal-Wallis test, p ≤ 0.001). This three-dimensional evaluation supports the validation of the Robin and Graham classification system for hip disease in 2- to 7-year-olds with CP. © The Authors. Developmental Medicine & Child Neurology © 2011 Mac Keith Press.

Comparison of World Health Organization and Asia-Pacific body mass index classifications in COPD patients.

PubMed

Lim, Jeong Uk; Lee, Jae Ha; Kim, Ju Sang; Hwang, Yong Il; Kim, Tae-Hyung; Lim, Seong Yong; Yoo, Kwang Ha; Jung, Ki-Suck; Kim, Young Kyoon; Rhee, Chin Kook

2017-01-01

A low body mass index (BMI) is associated with increased mortality and low health-related quality of life in patients with COPD. The Asia-Pacific classification of BMI has a lower cutoff for overweight and obese categories compared to the World Health Organization (WHO) classification. The present study assessed patients with COPD among different BMI categories according to two BMI classification systems: WHO and Asia-Pacific. Patients with COPD aged 40 years or older from the Korean COPD Subtype Study cohort were selected for evaluation. We enrolled 1,462 patients. Medical history including age, sex, St George's Respiratory Questionnaire (SGRQ-C), the modified Medical Research Council (mMRC) dyspnea scale, and post-bronchodilator forced expiratory volume in 1 second (FEV 1 ) were evaluated. Patients were categorized into different BMI groups according to the two BMI classification systems. FEV 1 and the diffusing capacity of the lung for carbon monoxide (DLCO) percentage revealed an inverse "U"-shaped pattern as the BMI groups changed from underweight to obese when WHO cutoffs were applied. When Asia-Pacific cutoffs were applied, FEV 1 and DLCO (%) exhibited a linearly ascending relationship as the BMI increased, and the percentage of patients in the overweight and obese groups linearly decreased with increasing severity of the Global Initiative for Chronic Obstructive Lung Disease criteria. From the underweight to the overweight groups, SGRQ-C and mMRC had a decreasing relationship in both the WHO and Asia-Pacific classifications. The prevalence of comorbidities in the different BMI groups showed similar trends in both BMI classifications systems. The present study demonstrated that patients with COPD who have a high BMI have better pulmonary function and health-related quality of life and reduced dyspnea symptoms. Furthermore, the Asia-Pacific BMI classification more appropriately reflects the correlation of obesity and disease manifestation in Asian COPD patients than the WHO classification.

21 CFR 862.1615 - Pregnenolone test system.

Code of Federal Regulations, 2010 CFR

2010-04-01

...) MEDICAL DEVICES CLINICAL CHEMISTRY AND CLINICAL TOXICOLOGY DEVICES Clinical Chemistry Test Systems § 862... diseases of the adrenal cortex or the gonads. (b) Classification. Class I (general controls). The device is...

The International Neuroblastoma Risk Group (INRG) Staging System: An INRG Task Force Report

PubMed Central

Monclair, Tom; Brodeur, Garrett M.; Ambros, Peter F.; Brisse, Hervé J.; Cecchetto, Giovanni; Holmes, Keith; Kaneko, Michio; London, Wendy B.; Matthay, Katherine K.; Nuchtern, Jed G.; von Schweinitz, Dietrich; Simon, Thorsten; Cohn, Susan L.; Pearson, Andrew D.J.

2009-01-01

Purpose The International Neuroblastoma Risk Group (INRG) classification system was developed to establish a consensus approach for pretreatment risk stratification. Because the International Neuroblastoma Staging System (INSS) is a postsurgical staging system, a new clinical staging system was required for the INRG pretreatment risk classification system. Methods To stage patients before any treatment, the INRG Task Force, consisting of neuroblastoma experts from Australia/New Zealand, China, Europe, Japan, and North America, developed a new INRG staging system (INRGSS) based on clinical criteria and image-defined risk factors (IDRFs). To investigate the impact of IDRFs on outcome, survival analyses were performed on 661 European patients with INSS stages 1, 2, or 3 disease for whom IDRFs were known. Results In the INGRSS, locoregional tumors are staged L1 or L2 based on the absence or presence of one or more of 20 IDRFs, respectively. Metastatic tumors are defined as stage M, except for stage MS, in which metastases are confined to the skin, liver, and/or bone marrow in children younger than 18 months of age. Within the 661-patient cohort, IDRFs were present (ie, stage L2) in 21% of patients with stage 1, 45% of patients with stage 2, and 94% of patients with stage 3 disease. Patients with INRGSS stage L2 disease had significantly lower 5-year event-free survival than those with INRGSS stage L1 disease (78% ± 4% v 90% ± 3%; P = .0010). Conclusion Use of the new staging (INRGSS) and risk classification (INRG) of neuroblastoma will greatly facilitate the comparison of risk-based clinical trials conducted in different regions of the world. PMID:19047290

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

Genetic burden associated with varying degrees of disease severity in endometriosis

PubMed Central

Sapkota, Yadav; Attia, John; Gordon, Scott D.; Henders, Anjali K.; Holliday, Elizabeth G.; Rahmioglu, Nilufer; MacGregor, Stuart; Martin, Nicholas G.; McEvoy, Mark; Morris, Andrew P.; Scott, Rodney J.; Zondervan, Krina T.; Montgomery, Grant W.; Nyholt, Dale R.

2015-01-01

Endometriosis is primarily characterized by the presence of tissue resembling endometrium outside the uterine cavity and is usually diagnosed by laparoscopy. The most commonly used classification of disease, the revised American Fertility Society (rAFS) system to grade endometriosis into different stages based on disease severity (I to IV), has been questioned as it does not correlate well with underlying symptoms, posing issues in diagnosis and choice of treatment. Using two independent European genome-wide association (GWA) datasets and top-level classification of the endometriosis cases based on rAFS [minimal or mild (Stage A) and moderate-to-severe (Stage B) disease], we previously showed that Stage B endometriosis has greater contribution of common genetic variation to its aetiology than Stage A disease. Herein, we extend our previous analysis to four endometriosis stages [minimal (Stage I), mild (Stage II), moderate (Stage III) and severe (Stage IV) disease] based on the rAFS classification system and compared the genetic burden across stages. Our results indicate that genetic burden increases from minimal to severe endometriosis. For the minimal disease, genetic factors may contribute to a lesser extent than other disease categories. Mild and moderate endometriosis appeared genetically similar, making it difficult to tease them apart. Consistent with our previous reports, moderate and severe endometriosis showed greater genetic burden than minimal or mild disease. Overall, our results provide new insights into the genetic architecture of endometriosis and further investigation in larger samples may help to understand better the aetiology of varying degrees of endometriosis, enabling improved diagnostic and treatment modalities. PMID:25882541

Horror Autoinflammaticus: The Molecular Pathophysiology of Autoinflammatory Disease*

PubMed Central

Masters, Seth L.; Simon, Anna; Aksentijevich, Ivona; Kastner, Daniel L.

2010-01-01

The autoinflammatory diseases are characterized by seemingly unprovoked episodes of inflammation, without high-titer autoantibodies or antigen-specific T cells. The concept was proposed ten years ago with the identification of the genes underlying hereditary periodic fever syndromes. This nosology has taken root because of the dramatic advances in our knowledge of the genetic basis of both mendelian and complex autoinflammatory diseases, and with the recognition that these illnesses derive from genetic variants of the innate immune system. Herein we propose an updated classification scheme based on the molecular insights garnered over the past decade, supplanting a clinical classification that has served well but is opaque to the genetic, immunologic, and therapeutic interrelationships now before us. We define six categories of autoinflammatory disease: IL-1β activation disorders (inflammasomopathies), NF-κB activation syndromes, protein misfolding disorders, complement regulatory diseases, disturbances in cytokine signaling, and macrophage activation syndromes. A system based on molecular pathophysiology will bring greater clarity to our discourse while catalyzing new hypotheses both at the bench and at the bedside. PMID:19302049

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.

Neurological Disease in Lupus: Toward a Personalized Medicine Approach.

PubMed

McGlasson, Sarah; Wiseman, Stewart; Wardlaw, Joanna; Dhaun, Neeraj; Hunt, David P J

2018-01-01

The brain and nervous system are important targets for immune-mediated damage in systemic lupus erythematosus (SLE), resulting in a complex spectrum of neurological syndromes. Defining nervous system disease in lupus poses significant challenges. Among the difficulties to be addressed are a diversity of clinical manifestations and a lack of understanding of their mechanistic basis. However, despite these challenges, progress has been made in the identification of pathways which contribute to neurological disease in SLE. Understanding the molecular pathogenesis of neurological disease in lupus will inform both classification and approaches to clinical trials.

A Classification System to Guide Physical Therapy Management in Huntington Disease: A Case Series.

PubMed

Fritz, Nora E; Busse, Monica; Jones, Karen; Khalil, Hanan; Quinn, Lori

2017-07-01

Individuals with Huntington disease (HD), a rare neurological disease, experience impairments in mobility and cognition throughout their disease course. The Medical Research Council framework provides a schema that can be applied to the development and evaluation of complex interventions, such as those provided by physical therapists. Treatment-based classifications, based on expert consensus and available literature, are helpful in guiding physical therapy management across the stages of HD. Such classifications also contribute to the development and further evaluation of well-defined complex interventions in this highly variable and complex neurodegenerative disease. The purpose of this case series was to illustrate the use of these classifications in the management of 2 individuals with late-stage HD. Two females, 40 and 55 years of age, with late-stage HD participated in this case series. Both experienced progressive declines in ambulatory function and balance as well as falls or fear of falling. Both individuals received daily care in the home for activities of daily living. Physical therapy Treatment-Based Classifications for HD guided the interventions and outcomes. Eight weeks of in-home balance training, strength training, task-specific practice of functional activities including transfers and walking tasks, and family/carer education were provided. Both individuals demonstrated improvements that met or exceeded the established minimal detectible change values for gait speed and Timed Up and Go performance. Both also demonstrated improvements on Berg Balance Scale and Physical Performance Test performance, with 1 of the 2 individuals exceeding the established minimal detectible changes for both tests. Reductions in fall risk were evident in both cases. These cases provide proof-of-principle to support use of treatment-based classifications for physical therapy management in individuals with HD. Traditional classification of early-, mid-, and late-stage disease progression may not reflect patients' true capabilities; those with late-stage HD may be as responsive to interventions as those at an earlier disease stage.Video Abstract available for additional insights from the authors (see Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A172).

Occupational Disease Registries–Characteristics and Experiences

PubMed Central

Davoodi, Somayeh; Haghighi, Khosro Sadeghniat; Kalhori, Sharareh Rostam Niakan; Hosseini, Narges Shams; Mohammadzadeh, Zeinab; Safdari, Reza

2017-01-01

Introduction: Due to growth of occupational diseases and also increase of public awareness about their consequences, attention to various aspects of diseases and improve occupational health and safety has found great importance. Therefore, there is the need for appropriate information management tools such as registries in order to recognitions of diseases patterns and then making decision about prevention, early detection and treatment of them. These registries have different characteristics in various countries according to their occupational health priorities. Aim: Aim of this study is evaluate dimensions of occupational diseases registries including objectives, data sources, responsible institutions, minimum data set, classification systems and process of registration in different countries. Material and Methods: In this study, the papers were searched using the MEDLINE (PubMed) Google scholar, Scopus, ProQuest and Google. The search was done based on keyword in English for all motor engines including “occupational disease”, “work related disease”, “surveillance”, “reporting”, “registration system” and “registry” combined with name of the countries including all subheadings. After categorizing search findings in tables, results were compared with each other. Results: Important aspects of the registries studied in ten countries including Finland, France, United Kingdom, Australia, Czech Republic, Malaysia, United States, Singapore, Russia and Turkey. The results show that surveyed countries have statistical, treatment and prevention objectives. Data sources in almost the rest of registries were physicians and employers. The minimum data sets in most of them consist of information about patient, disease, occupation and employer. Some of countries have special occupational related classification systems for themselves and some of them apply international classification systems such as ICD-10. Finally, the process of registration system was different in countries. Conclusion: Because occupational diseases are often preventable, but not curable, it is necessary to all countries, to consider prevention and early detection of occupational diseases as the objectives of their registry systems. Also it is recommended that all countries reach an agreement about global characteristics of occupational disease registries. This enables country to compare their data at international levels. PMID:28883681

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

Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification.

PubMed

Sladojevic, Srdjan; Arsenovic, Marko; Anderla, Andras; Culibrk, Dubravko; Stefanovic, Darko

2016-01-01

The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%.

Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification

PubMed Central

Sladojevic, Srdjan; Arsenovic, Marko; Culibrk, Dubravko; Stefanovic, Darko

2016-01-01

The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%. PMID:27418923

What nephrolopathologists need to know about antiphospholipid syndrome-associated nephropathy: Is it time for formulating a classification for renal morphologic lesions?

PubMed

Mubarak, Muhammed; Nasri, Hamid

2014-01-01

Antiphospholipid syndrome (APS) is a systemic autoimmune disorder which commonly affects kidneys. Directory of Open Access Journals (DOAJ), Google Scholar, PubMed (NLM), LISTA (EBSCO) and Web of Science have been searched. There is sufficient epidemiological, clinical and histopathological evidence to show that antiphospholipid syndrome is a distinctive lesion caused by antiphospholipid antibodies in patients with different forms of antiphospholipid syndrome. It is now time to devise a classification for an accurate diagnosis and prognostication of the disease. Now that the morphological lesions of APSN are sufficiently well characterized, it is prime time to devise a classification which is of diagnostic and prognostic utility in this disease.

What nephrolopathologists need to know about antiphospholipid syndrome-associated nephropathy: Is it time for formulating a classification for renal morphologic lesions?

PubMed Central

Mubarak, Muhammed; Nasri, Hamid

2014-01-01

Context: Antiphospholipid syndrome (APS) is a systemic autoimmune disorder which commonly affects kidneys. Evidence Acquisitions: Directory of Open Access Journals (DOAJ), Google Scholar, PubMed (NLM), LISTA (EBSCO) and Web of Science have been searched. Results: There is sufficient epidemiological, clinical and histopathological evidence to show that antiphospholipid syndrome is a distinctive lesion caused by antiphospholipid antibodies in patients with different forms of antiphospholipid syndrome. It is now time to devise a classification for an accurate diagnosis and prognostication of the disease. Conclusions: Now that the morphological lesions of APSN are sufficiently well characterized, it is prime time to devise a classification which is of diagnostic and prognostic utility in this disease. PMID:24644536

Concordance of hypervascular liver nodule characterization between the organ procurement and transplant network and liver imaging reporting and data system classifications.

PubMed

Bashir, Mustafa R; Huang, Rong; Mayes, Nicholas; Marin, Daniele; Berg, Carl L; Nelson, Rendon C; Jaffe, Tracy A

2015-08-01

To determine the rate of agreement between the Organ Procurement and Transplant Network (OPTN) and Liver Imaging Reporting and Data System (LI-RADS) classifications for hypervascular liver nodules at least 1 cm in diameter, and for patient eligibility for hepatocellular/MELD (Model for Endstage Liver Disease) exception points. This retrospective study was approved by our Institutional Review Board and was compliant with the Health Insurance Portability and Accountability Act. The requirement for informed consent was waived. This study included 200 hypervascular hepatocellular nodules at least 1 cm in diameter on computed tomography (CT) or magnetic resonance imaging (MRI) examinations in 105 patients with chronic liver disease. Three radiologists blinded to clinical data independently evaluated nodule characteristics, including washout, capsule, size, and size on prior examination. Based on those characteristics, nodules were automatically classified as definite hepatocellular carcinoma (HCC) or not definite HCC using both the OPTN and LI-RADS classifications. Using these classifications and the Milan criteria, each examination was determined to be "below transplant criteria," "within transplant criteria," or "beyond transplant criteria." Agreement was assessed between readers and classification systems, using Fleiss' kappa, intraclass correlation coefficients (ICCs), and simple proportions. Interreader agreement was moderate for nodule features (κ = 0.59-0.69) and nodule classification (0.66-0.69). The two systems were in nearly complete agreement on nodule category assignment (98.7% [592/600]) and patient eligibility for transplant exemption priority (99.4% [313/315]). A few discrepancies occurred for the nodule feature of growth (1.3% [8/600]) and for nodule category assignment (1.3% [8/600]). Agreement between the OPTN and LI-RADS classifications is very strong for categorization of hypervascular liver nodules at least 1 cm in diameter, and for patient eligibility for hepatocellular/MELD exception points. Interreader variability is much higher than intersystem variability. © 2014 Wiley Periodicals, Inc.

Use of Magnetic Resonance Imaging as Well as Clinical Disease Activity in the Clinical Classification of Multiple Sclerosis and Assessment of Its Course

PubMed Central

Dhib-Jalbut, Suhayl; Dowling, Peter; Durelli, Luca; Ford, Corey; Giovannoni, Gavin; Halper, June; Harris, Colleen; Herbert, Joseph; Li, David; Lincoln, John A.; Lisak, Robert; Lublin, Fred D.; Lucchinetti, Claudia F.; Moore, Wayne; Naismith, Robert T.; Oehninger, Carlos; Simon, Jack; Sormani, Maria Pia

2012-01-01

It has recently been suggested that the Lublin-Reingold clinical classification of multiple sclerosis (MS) be modified to include the use of magnetic resonance imaging (MRI). An international consensus conference sponsored by the Consortium of Multiple Sclerosis Centers (CMSC) was held from March 5 to 7, 2010, to review the available evidence on the need for such modification of the Lublin-Reingold criteria and whether the addition of MRI or other biomarkers might lead to a better understanding of MS pathophysiology and disease course over time. The conference participants concluded that evidence of new MRI gadolinium-enhancing (Gd+) T1-weighted lesions and unequivocally new or enlarging T2-weighted lesions (subclinical activity, subclinical relapses) should be added to the clinical classification of MS in distinguishing relapsing inflammatory from progressive forms of the disease. The consensus was that these changes to the classification system would provide more rigorous definitions and categorization of MS course, leading to better insights as to the evolution and treatment of MS. PMID:24453741

Idiopathic inflammatory myopathies overlapping with systemic diseases

PubMed Central

Lepreux, Sébastien; Hainfellner, Johannes A.; Vital, Anne

2018-01-01

A muscle biopsy is currently requested to assess the diagnosis of an idiopathic inflammatory myopathy overlapping with a systemic disease. During the past few years, the classification of inflammatory myopathy subtypes has been revisited progressively on the basis of correlations between clinical phenotypes, autoantibodies and histological data. Several syndromic entities are now more clearly defined, and the aim of the present review is to clarify the contribution of muscle biopsy in a setting of idiopathic inflammatory myopathies overlapping with systemic diseases. PMID:29154752

Web-based newborn screening system for metabolic diseases: machine learning versus clinicians.

PubMed

Chen, Wei-Hsin; Hsieh, Sheau-Ling; Hsu, Kai-Ping; Chen, Han-Ping; Su, Xing-Yu; Tseng, Yi-Ju; Chien, Yin-Hsiu; Hwu, Wuh-Liang; Lai, Feipei

2013-05-23

A hospital information system (HIS) that integrates screening data and interpretation of the data is routinely requested by hospitals and parents. However, the accuracy of disease classification may be low because of the disease characteristics and the analytes used for classification. The objective of this study is to describe a system that enhanced the neonatal screening system of the Newborn Screening Center at the National Taiwan University Hospital. The system was designed and deployed according to a service-oriented architecture (SOA) framework under the Web services .NET environment. The system consists of sample collection, testing, diagnosis, evaluation, treatment, and follow-up services among collaborating hospitals. To improve the accuracy of newborn screening, machine learning and optimal feature selection mechanisms were investigated for screening newborns for inborn errors of metabolism. The framework of the Newborn Screening Hospital Information System (NSHIS) used the embedded Health Level Seven (HL7) standards for data exchanges among heterogeneous platforms integrated by Web services in the C# language. In this study, machine learning classification was used to predict phenylketonuria (PKU), hypermethioninemia, and 3-methylcrotonyl-CoA-carboxylase (3-MCC) deficiency. The classification methods used 347,312 newborn dried blood samples collected at the Center between 2006 and 2011. Of these, 220 newborns had values over the diagnostic cutoffs (positive cases) and 1557 had values that were over the screening cutoffs but did not meet the diagnostic cutoffs (suspected cases). The original 35 analytes and the manifested features were ranked based on F score, then combinations of the top 20 ranked features were selected as input features to support vector machine (SVM) classifiers to obtain optimal feature sets. These feature sets were tested using 5-fold cross-validation and optimal models were generated. The datasets collected in year 2011 were used as predicting cases. The feature selection strategies were implemented and the optimal markers for PKU, hypermethioninemia, and 3-MCC deficiency were obtained. The results of the machine learning approach were compared with the cutoff scheme. The number of the false positive cases were reduced from 21 to 2 for PKU, from 30 to 10 for hypermethioninemia, and 209 to 46 for 3-MCC deficiency. This SOA Web service-based newborn screening system can accelerate screening procedures effectively and efficiently. An SVM learning methodology for PKU, hypermethioninemia, and 3-MCC deficiency metabolic diseases classification, including optimal feature selection strategies, is presented. By adopting the results of this study, the number of suspected cases could be reduced dramatically.

Web-Based Newborn Screening System for Metabolic Diseases: Machine Learning Versus Clinicians

PubMed Central

Chen, Wei-Hsin; Hsu, Kai-Ping; Chen, Han-Ping; Su, Xing-Yu; Tseng, Yi-Ju; Chien, Yin-Hsiu; Hwu, Wuh-Liang; Lai, Feipei

2013-01-01

Background A hospital information system (HIS) that integrates screening data and interpretation of the data is routinely requested by hospitals and parents. However, the accuracy of disease classification may be low because of the disease characteristics and the analytes used for classification. Objective The objective of this study is to describe a system that enhanced the neonatal screening system of the Newborn Screening Center at the National Taiwan University Hospital. The system was designed and deployed according to a service-oriented architecture (SOA) framework under the Web services .NET environment. The system consists of sample collection, testing, diagnosis, evaluation, treatment, and follow-up services among collaborating hospitals. To improve the accuracy of newborn screening, machine learning and optimal feature selection mechanisms were investigated for screening newborns for inborn errors of metabolism. Methods The framework of the Newborn Screening Hospital Information System (NSHIS) used the embedded Health Level Seven (HL7) standards for data exchanges among heterogeneous platforms integrated by Web services in the C# language. In this study, machine learning classification was used to predict phenylketonuria (PKU), hypermethioninemia, and 3-methylcrotonyl-CoA-carboxylase (3-MCC) deficiency. The classification methods used 347,312 newborn dried blood samples collected at the Center between 2006 and 2011. Of these, 220 newborns had values over the diagnostic cutoffs (positive cases) and 1557 had values that were over the screening cutoffs but did not meet the diagnostic cutoffs (suspected cases). The original 35 analytes and the manifested features were ranked based on F score, then combinations of the top 20 ranked features were selected as input features to support vector machine (SVM) classifiers to obtain optimal feature sets. These feature sets were tested using 5-fold cross-validation and optimal models were generated. The datasets collected in year 2011 were used as predicting cases. Results The feature selection strategies were implemented and the optimal markers for PKU, hypermethioninemia, and 3-MCC deficiency were obtained. The results of the machine learning approach were compared with the cutoff scheme. The number of the false positive cases were reduced from 21 to 2 for PKU, from 30 to 10 for hypermethioninemia, and 209 to 46 for 3-MCC deficiency. Conclusions This SOA Web service–based newborn screening system can accelerate screening procedures effectively and efficiently. An SVM learning methodology for PKU, hypermethioninemia, and 3-MCC deficiency metabolic diseases classification, including optimal feature selection strategies, is presented. By adopting the results of this study, the number of suspected cases could be reduced dramatically. PMID:23702487

Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms.

PubMed

Ozcift, Akin; Gulten, Arif

2011-12-01

Improving accuracies of machine learning algorithms is vital in designing high performance computer-aided diagnosis (CADx) systems. Researches have shown that a base classifier performance might be enhanced by ensemble classification strategies. In this study, we construct rotation forest (RF) ensemble classifiers of 30 machine learning algorithms to evaluate their classification performances using Parkinson's, diabetes and heart diseases from literature. While making experiments, first the feature dimension of three datasets is reduced using correlation based feature selection (CFS) algorithm. Second, classification performances of 30 machine learning algorithms are calculated for three datasets. Third, 30 classifier ensembles are constructed based on RF algorithm to assess performances of respective classifiers with the same disease data. All the experiments are carried out with leave-one-out validation strategy and the performances of the 60 algorithms are evaluated using three metrics; classification accuracy (ACC), kappa error (KE) and area under the receiver operating characteristic (ROC) curve (AUC). Base classifiers succeeded 72.15%, 77.52% and 84.43% average accuracies for diabetes, heart and Parkinson's datasets, respectively. As for RF classifier ensembles, they produced average accuracies of 74.47%, 80.49% and 87.13% for respective diseases. RF, a newly proposed classifier ensemble algorithm, might be used to improve accuracy of miscellaneous machine learning algorithms to design advanced CADx systems. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

21 CFR 862.1460 - Leucine aminopeptidase test system.

Code of Federal Regulations, 2010 CFR

2010-04-01

... Systems § 862.1460 Leucine aminopeptidase test system. (a) Identification. A leucine aminopeptidase test system is a device intended to measure the activity of the enzyme leucine amino-peptidase in serum... diseases such as viral hepatitis and obstructive jaundice. (b) Classification. Class I (general controls...

21 CFR 862.1460 - Leucine aminopeptidase test system.

Code of Federal Regulations, 2011 CFR

2011-04-01

... Systems § 862.1460 Leucine aminopeptidase test system. (a) Identification. A leucine aminopeptidase test system is a device intended to measure the activity of the enzyme leucine amino-peptidase in serum... diseases such as viral hepatitis and obstructive jaundice. (b) Classification. Class I (general controls...

A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers

PubMed Central

Bennett, David A.; Blennow, Kaj; Carrillo, Maria C.; Feldman, Howard H.; Frisoni, Giovanni B.; Hampel, Harald; Jagust, William J.; Johnson, Keith A.; Knopman, David S.; Petersen, Ronald C.; Scheltens, Philip; Sperling, Reisa A.; Dubois, Bruno

2016-01-01

Biomarkers have become an essential component of Alzheimer disease (AD) research and because of the pervasiveness of AD pathology in the elderly, the same biomarkers are used in cognitive aging research. A number of current issues suggest that an unbiased descriptive classification scheme for these biomarkers would be useful. We propose the “A/T/N” system in which 7 major AD biomarkers are divided into 3 binary categories based on the nature of the pathophysiology that each measures. “A” refers to the value of a β-amyloid biomarker (amyloid PET or CSF Aβ42); “T,” the value of a tau biomarker (CSF phospho tau, or tau PET); and “N,” biomarkers of neurodegeneration or neuronal injury ([18F]-fluorodeoxyglucose–PET, structural MRI, or CSF total tau). Each biomarker category is rated as positive or negative. An individual score might appear as A+/T+/N−, or A+/T−/N−, etc. The A/T/N system includes the new modality tau PET. It is agnostic to the temporal ordering of mechanisms underlying AD pathogenesis. It includes all individuals in any population regardless of the mix of biomarker findings and therefore is suited to population studies of cognitive aging. It does not specify disease labels and thus is not a diagnostic classification system. It is a descriptive system for categorizing multidomain biomarker findings at the individual person level in a format that is easy to understand and use. Given the present lack of consensus among AD specialists on terminology across the clinically normal to dementia spectrum, a biomarker classification scheme will have broadest acceptance if it is independent from any one clinically defined diagnostic scheme. PMID:27371494

The integrated disease network.

PubMed

Sun, Kai; Buchan, Natalie; Larminie, Chris; Pržulj, Nataša

2014-11-01

The growing body of transcriptomic, proteomic, metabolomic and genomic data generated from disease states provides a great opportunity to improve our current understanding of the molecular mechanisms driving diseases and shared between diseases. The use of both clinical and molecular phenotypes will lead to better disease understanding and classification. In this study, we set out to gain novel insights into diseases and their relationships by utilising knowledge gained from system-level molecular data. We integrated different types of biological data including genome-wide association studies data, disease-chemical associations, biological pathways and Gene Ontology annotations into an Integrated Disease Network (IDN), a heterogeneous network where nodes are bio-entities and edges between nodes represent their associations. We also introduced a novel disease similarity measure to infer disease-disease associations from the IDN. Our predicted associations were systemically evaluated against the Medical Subject Heading classification and a statistical measure of disease co-occurrence in PubMed. The strong correlation between our predictions and co-occurrence associations indicated the ability of our approach to recover known disease associations. Furthermore, we presented a case study of Crohn's disease. We demonstrated that our approach not only identified well-established connections between Crohn's disease and other diseases, but also revealed new, interesting connections consistent with emerging literature. Our approach also enabled ready access to the knowledge supporting these new connections, making this a powerful approach for exploring connections between diseases.

Automated speech analysis applied to laryngeal disease categorization.

PubMed

Gelzinis, A; Verikas, A; Bacauskiene, M

2008-07-01

The long-term goal of the work is a decision support system for diagnostics of laryngeal diseases. Colour images of vocal folds, a voice signal, and questionnaire data are the information sources to be used in the analysis. This paper is concerned with automated analysis of a voice signal applied to screening of laryngeal diseases. The effectiveness of 11 different feature sets in classification of voice recordings of the sustained phonation of the vowel sound /a/ into a healthy and two pathological classes, diffuse and nodular, is investigated. A k-NN classifier, SVM, and a committee build using various aggregation options are used for the classification. The study was made using the mixed gender database containing 312 voice recordings. The correct classification rate of 84.6% was achieved when using an SVM committee consisting of four members. The pitch and amplitude perturbation measures, cepstral energy features, autocorrelation features as well as linear prediction cosine transform coefficients were amongst the feature sets providing the best performance. In the case of two class classification, using recordings from 79 subjects representing the pathological and 69 the healthy class, the correct classification rate of 95.5% was obtained from a five member committee. Again the pitch and amplitude perturbation measures provided the best performance.

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.

Medical diagnosis of atherosclerosis from Carotid Artery Doppler Signals using principal component analysis (PCA), k-NN based weighting pre-processing and Artificial Immune Recognition System (AIRS).

PubMed

Latifoğlu, Fatma; Polat, Kemal; Kara, Sadik; Güneş, Salih

2008-02-01

In this study, we proposed a new medical diagnosis system based on principal component analysis (PCA), k-NN based weighting pre-processing, and Artificial Immune Recognition System (AIRS) for diagnosis of atherosclerosis from Carotid Artery Doppler Signals. The suggested system consists of four stages. First, in the feature extraction stage, we have obtained the features related with atherosclerosis disease using Fast Fourier Transformation (FFT) modeling and by calculating of maximum frequency envelope of sonograms. Second, in the dimensionality reduction stage, the 61 features of atherosclerosis disease have been reduced to 4 features using PCA. Third, in the pre-processing stage, we have weighted these 4 features using different values of k in a new weighting scheme based on k-NN based weighting pre-processing. Finally, in the classification stage, AIRS classifier has been used to classify subjects as healthy or having atherosclerosis. Hundred percent of classification accuracy has been obtained by the proposed system using 10-fold cross validation. This success shows that the proposed system is a robust and effective system in diagnosis of atherosclerosis disease.

[On establishing comparative reference system for syndrome classification study from the thinking characteristics of syndrome differentiation dependent therapy].

PubMed

Liu, Ping; Hu, Yi-yang; Ni, Li-qiang

2006-05-01

To create a comparative referential system for syndrome classification study by viewing from the thinking characteristics of TCM on syndrome differentiation dependent therapy (SDDT), through analyzing the thinking process of SDDT, and the basic features of disease, syndrome and prescription, combining the basic principles of modern evidence-based medicine and feasibility of establishing integrative disease-syndrome animal model. The practice of creating a comparative referential system based on clinical efficacy of prescription was discussed around syndrome pathogenesis and its relationship with disease and prescription, which was one of the important scientific problems in TCM syndrome study. The authors hold that, it may be one of the available approaches for the present study on integration of disease with syndrome by way of insisting on the thinking pathway of stressing the characteristics of TCM and intermerging with modern scientific design; on taking the efficacy of prescription as the comparative reference system to accumulate and improve unceasingly according to the TCM method of syndrome diagnosis inferred from effect of prescription with reverse thought (i.e., to differentiate syndrome from the effect of prescription), and thus build up the syndrome diagnostic standard on the solid clinical and scientific base.

21 CFR 866.3870 - Trypanosoma spp. serological reagents.

Code of Federal Regulations, 2010 CFR

2010-04-01

... by fever, chills, headache, and vomiting. Central nervous system involvement produces typical.... Chagas disease, an acute form of trypanosomiasis in children, most seriously affects the central nervous system and heart muscle. (b) Classification. Class I (general controls). ...

21 CFR 866.3870 - Trypanosoma spp. serological reagents.

Code of Federal Regulations, 2011 CFR

2011-04-01

... by fever, chills, headache, and vomiting. Central nervous system involvement produces typical.... Chagas disease, an acute form of trypanosomiasis in children, most seriously affects the central nervous system and heart muscle. (b) Classification. Class I (general controls). ...

21 CFR 866.3870 - Trypanosoma spp. serological reagents.

Code of Federal Regulations, 2014 CFR

2014-04-01

... by fever, chills, headache, and vomiting. Central nervous system involvement produces typical.... Chagas disease, an acute form of trypanosomiasis in children, most seriously affects the central nervous system and heart muscle. (b) Classification. Class I (general controls). ...

21 CFR 866.3870 - Trypanosoma spp. serological reagents.

Code of Federal Regulations, 2012 CFR

2012-04-01

... by fever, chills, headache, and vomiting. Central nervous system involvement produces typical.... Chagas disease, an acute form of trypanosomiasis in children, most seriously affects the central nervous system and heart muscle. (b) Classification. Class I (general controls). ...

21 CFR 866.3870 - Trypanosoma spp. serological reagents.

Code of Federal Regulations, 2013 CFR

2013-04-01

... by fever, chills, headache, and vomiting. Central nervous system involvement produces typical.... Chagas disease, an acute form of trypanosomiasis in children, most seriously affects the central nervous system and heart muscle. (b) Classification. Class I (general controls). ...

Neurological Disease in Lupus: Toward a Personalized Medicine Approach

PubMed Central

McGlasson, Sarah; Wiseman, Stewart; Wardlaw, Joanna; Dhaun, Neeraj; Hunt, David P. J.

2018-01-01

The brain and nervous system are important targets for immune-mediated damage in systemic lupus erythematosus (SLE), resulting in a complex spectrum of neurological syndromes. Defining nervous system disease in lupus poses significant challenges. Among the difficulties to be addressed are a diversity of clinical manifestations and a lack of understanding of their mechanistic basis. However, despite these challenges, progress has been made in the identification of pathways which contribute to neurological disease in SLE. Understanding the molecular pathogenesis of neurological disease in lupus will inform both classification and approaches to clinical trials. PMID:29928273

Children's Interstitial and Diffuse Lung Disease. Progress and Future Horizons.

PubMed

Deterding, Robin R

2015-10-01

Children's interstitial and diffuse lung disease (chILD) is a term that encompasses a large and diverse group of rare pediatric diseases and disorders. Significant progress has been made over the last 2 decades in classification, clinical care, research, and organizational structure to enhance the care of children with these high-morbidity and -mortality diseases. New diseases have been defined clinically and genetically, classification systems developed and applied, organizations formed such as the chILD Research Network (chILDRN) and chILD Foundation, and basic and translational science expanded to focus on chILD diseases. Multidisciplinary collaborations and efforts to advance understanding and treatment of chILD have been extended worldwide. The future horizon holds great promise to expand scientific discoveries, collaborate more broadly, and bring new treatment to these children. An overview of key historical past developments, major clinical and research updates, and opportunities for the future in chILD is reviewed in this Perspective.

Molecular diagnostics of inflammatory disease: New tools and perspectives.

PubMed

Garzorz-Stark, Natalie; Lauffer, Felix

2017-08-01

This essay reviews current approaches to establish novel molecular diagnostic tools for inflammatory skin diseases. Moreover, it highlights the importance of stratifying patients according to molecular signatures and revising current outdated disease classification systems to eventually reach the goal of personalized medicine. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Genetic diversity of avian paramyxovirus type 1: Proposal for a unified nomenclature and classification system of Newcastle disease virus genotypes

USDA-ARS?s Scientific Manuscript database

Genetically diverse Newcastle disease virus (NDV) isolates circulate and cause disease in different geographic locations of the world. The differences found on the genome of distinct NDV isolates have been used to classify different isolates into genetic groups called genotypes or lineages. Both l...

Exudative pleural diseases in small animals.

PubMed

Epstein, Steven E

2014-01-01

Exudative pleural diseases are a common cause of respiratory distress and systemic illness in dogs and cats. This article addresses the pathophysiology, development, and classification of exudative pleural effusions. The most current diagnostic strategies, causes, imaging findings, and medical or surgical treatment options for select diseases are reviewed in detail. Copyright © 2014 Elsevier Inc. All rights reserved.

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.

Semantic Classification of Diseases in Discharge Summaries Using a Context-aware Rule-based Classifier

PubMed Central

Solt, Illés; Tikk, Domonkos; Gál, Viktor; Kardkovács, Zsolt T.

2009-01-01

Objective Automated and disease-specific classification of textual clinical discharge summaries is of great importance in human life science, as it helps physicians to make medical studies by providing statistically relevant data for analysis. This can be further facilitated if, at the labeling of discharge summaries, semantic labels are also extracted from text, such as whether a given disease is present, absent, questionable in a patient, or is unmentioned in the document. The authors present a classification technique that successfully solves the semantic classification task. Design The authors introduce a context-aware rule-based semantic classification technique for use on clinical discharge summaries. The classification is performed in subsequent steps. First, some misleading parts are removed from the text; then the text is partitioned into positive, negative, and uncertain context segments, then a sequence of binary classifiers is applied to assign the appropriate semantic labels. Measurement For evaluation the authors used the documents of the i2b2 Obesity Challenge and adopted its evaluation measures: F1-macro and F1-micro for measurements. Results On the two subtasks of the Obesity Challenge (textual and intuitive classification) the system performed very well, and achieved a F1-macro = 0.80 for the textual and F1-macro = 0.67 for the intuitive tasks, and obtained second place at the textual and first place at the intuitive subtasks of the challenge. Conclusions The authors show in the paper that a simple rule-based classifier can tackle the semantic classification task more successfully than machine learning techniques, if the training data are limited and some semantic labels are very sparse. PMID:19390101

New classification system for indications for endoscopic retrograde cholangiopancreatography predicts diagnoses and adverse events.

PubMed

Yuen, Nicholas; O'Shaughnessy, Pauline; Thomson, Andrew

2017-12-01

Indications for endoscopic retrograde cholangiopancreatography (ERCP) have received little attention, especially in scientific or objective terms. To review the prevailing ERCP indications in the literature, and to propose and evaluate a new ERCP indication system, which relies on more objective pre-procedure parameters. An analysis was conducted on 1758 consecutive ERCP procedures, in which contemporaneous use was made of an a-priori indication system. Indications were based on the objective pre-procedure parameters and divided into primary [cholangitis, clinical evidence of biliary leak, acute (biliary) pancreatitis, abnormal intraoperative cholangiogram (IOC), or change/removal of stent for benign/malignant disease] and secondary [combination of two or three of: pain attributable to biliary disease ('P'), imaging evidence of biliary disease ('I'), and abnormal liver function tests (LFTs) ('L')]. A secondary indication was only used if a primary indication was not present. The relationship between this newly developed classification system and ERCP findings and adverse events was examined. The indications of cholangitis and positive IOC were predictive of choledocholithiasis at ERCP (101/154 and 74/141 procedures, respectively). With respect to secondary indications, only if all three of 'P', 'I', and 'L' were present there was a statistically significant association with choledocholithiasis (χ 2 (1) = 35.3, p < .001). Adverse events were associated with an unusual indication leading to greater risk of unplanned hospitalization (χ 2 (1) = 17.0, p < .001). An a-priori-based indication system for ERCP, which relies on pre-ERCP objective parameters, provides a more useful and scientific classification system than is available currently.

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.

32 CFR 728.42 - NATO.

Code of Federal Regulations, 2010 CFR

2010-07-01

... evacuation system of the holding nation. (2) Classification of patients. Different channels for disposition... acute medical and surgical conditions, exclusive of nervous, mental, or contagious diseases or those...

21 CFR 866.5860 - Total spinal fluid immuno-logical test system.

Code of Federal Regulations, 2014 CFR

2014-04-01

... diagnosis of multiple sclerosis and other diseases of the nervous system. (b) Classification. Class I... 21 Food and Drugs 8 2014-04-01 2014-04-01 false Total spinal fluid immuno-logical test system. 866... SERVICES (CONTINUED) MEDICAL DEVICES IMMUNOLOGY AND MICROBIOLOGY DEVICES Immunological Test Systems § 866...

21 CFR 866.5860 - Total spinal fluid immuno-logical test system.

Code of Federal Regulations, 2013 CFR

2013-04-01

... diagnosis of multiple sclerosis and other diseases of the nervous system. (b) Classification. Class I... 21 Food and Drugs 8 2013-04-01 2013-04-01 false Total spinal fluid immuno-logical test system. 866... SERVICES (CONTINUED) MEDICAL DEVICES IMMUNOLOGY AND MICROBIOLOGY DEVICES Immunological Test Systems § 866...

21 CFR 866.5860 - Total spinal fluid immuno-logical test system.

Code of Federal Regulations, 2011 CFR

2011-04-01

... diagnosis of multiple sclerosis and other diseases of the nervous system. (b) Classification. Class I... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Total spinal fluid immuno-logical test system. 866... SERVICES (CONTINUED) MEDICAL DEVICES IMMUNOLOGY AND MICROBIOLOGY DEVICES Immunological Test Systems § 866...

21 CFR 866.5860 - Total spinal fluid immuno-logical test system.

Code of Federal Regulations, 2012 CFR

2012-04-01

... diagnosis of multiple sclerosis and other diseases of the nervous system. (b) Classification. Class I... 21 Food and Drugs 8 2012-04-01 2012-04-01 false Total spinal fluid immuno-logical test system. 866... SERVICES (CONTINUED) MEDICAL DEVICES IMMUNOLOGY AND MICROBIOLOGY DEVICES Immunological Test Systems § 866...

21 CFR 866.5860 - Total spinal fluid immuno-logical test system.

Code of Federal Regulations, 2010 CFR

2010-04-01

... diagnosis of multiple sclerosis and other diseases of the nervous system. (b) Classification. Class I... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Total spinal fluid immuno-logical test system. 866... SERVICES (CONTINUED) MEDICAL DEVICES IMMUNOLOGY AND MICROBIOLOGY DEVICES Immunological Test Systems § 866...

The scope and specific criteria of compensation for occupational diseases in Korea.

PubMed

Song, Jaechul; Kim, Inah; Choi, Byung-Soon

2014-06-01

The range of diseases covered by workers' compensation is constantly expanding. However, new regulations are required for the recognition of occupational diseases (ODs) because OD types evolve with changes in industrial structures and working conditions. OD criteria are usually based on medical relevance, but they vary depending on the social security system and laws of each country. In addition, the proposed range and extent of work-relatedness vary depending on the socio-economic conditions of each country. The Labor Standards Act (LSA) and the Industrial Accident Compensation Insurance Act (IACIA) of Korea employ lists based on their requirements without listing causes and diseases separately. Despite a considerable reshuffle in 2003, the basic framework has been maintained for 50 yr, and many cases do not fit into the international disease classification system. Since July 1, 2013, Korea has expanded the range of occupational accidents to include occupational cancers and has implemented revised LSA and IACIA enforcement decrees. There have been improvements to OD recognition standards with the inclusion of additional or modified criteria, a revised and improved classification scheme for risk factors and ODs, and so on.

Telangiectasia macularis eruptiva perstans: more than skin deep

PubMed Central

Watkins, Casey E.; Bokor, Winston B.; Leicht, Stuart; Youngberg, George; Krishnaswamy, Guha

2011-01-01

Systemic mastocytosis is a rare disease involving the infiltration and accumulation of active mast cells within any organ system. By far, the most common organ affected is the skin. Cutaneous manifestations of mastocytosis, including Urticaria Pigmentosa (UP), cutaneous mastocytoma or telangiectasia macularis eruptive perstans (TMEP), may indicate a more serious and potentially life-threatening underlying disease. The presence of either UP or TMEP in a patient with anaphylactic symptoms should suggest the likelihood of systemic mastocytosis, with the caveat that systemic complications are more likely to occur in patients with UP. TMEP can usually be identified by the typical morphology, but a skin biopsy is confirmative. In patients with elevated tryptase levels or those with frequent systemic manifestations, a bone marrow biopsy is essential in order to demonstrate mast cell infiltration. Further genetic testing for mutations of c-kit gene or the FIP1L1 gene may help with disease classification and/or therapeutic approaches. Rarely, TMEP has been described with malignancy, radiation therapy, and myeloproliferative disorders. A few familial cases have also been described. In this review, we discuss the clinical features, diagnosis and management of patients with TMEP. We also discuss the possible molecular pathogenesis and the role of genetics in disease classification and treatment. PMID:25386256

A proposal of criteria for the classification of systemic sclerosis.

PubMed

Nadashkevich, Oleg; Davis, Paul; Fritzler, Marvin J

2004-11-01

Sensitive and specific criteria for the classification of systemic sclerosis are required by clinicians and investigators to achieve higher quality clinical studies and approaches to therapy. A clinical study of systemic sclerosis patients in Europe and Canada led to a set of criteria that achieve high sensitivity and specificity. Both clinical and laboratory investigations of patients with systemic sclerosis, related conditions and diseases with clinical features that can be mistaken as part of the systemic sclerosis spectrum were undertaken. Laboratory investigations included the detection of autoantibodies to centromere proteins, Scl-70 (topoisomerase I), and fibrillarin (U3-RNP). Based on the investigation of 269 systemic sclerosis patients and 720 patients presenting with related and confounding conditions, the following set of criteria for the classification of systemic sclerosis was proposed: 1) autoantibodies to: centromere proteins, Scl-70 (topo I), fibrillarin; 2) bibasilar pulmonary fibrosis; 3) contractures of the digital joints or prayer sign; 4) dermal thickening proximal to the wrists; 5) calcinosis cutis; 6) Raynaud's phenomenon; 7) esophageal distal hypomotility or reflux-esophagitis; 8) sclerodactyly or non-pitting digital edema; 9) teleangiectasias. The classification of definite SSc requires at least three of the above criteria. Criteria for the classification of systemic sclerosis have been proposed. Preliminary testing has defined the sensitivity and specificity of these criteria as high as 99% and 100%, respectively. Testing and validation of the proposed criteria by other clinical centers is required.

Privacy-Preserving Patient-Centric Clinical Decision Support System on Naïve Bayesian Classification.

PubMed

Liu, Ximeng; Lu, Rongxing; Ma, Jianfeng; Chen, Le; Qin, Baodong

2016-03-01

Clinical decision support system, which uses advanced data mining techniques to help clinician make proper decisions, has received considerable attention recently. The advantages of clinical decision support system include not only improving diagnosis accuracy but also reducing diagnosis time. Specifically, with large amounts of clinical data generated everyday, naïve Bayesian classification can be utilized to excavate valuable information to improve a clinical decision support system. Although the clinical decision support system is quite promising, the flourish of the system still faces many challenges including information security and privacy concerns. In this paper, we propose a new privacy-preserving patient-centric clinical decision support system, which helps clinician complementary to diagnose the risk of patients' disease in a privacy-preserving way. In the proposed system, the past patients' historical data are stored in cloud and can be used to train the naïve Bayesian classifier without leaking any individual patient medical data, and then the trained classifier can be applied to compute the disease risk for new coming patients and also allow these patients to retrieve the top- k disease names according to their own preferences. Specifically, to protect the privacy of past patients' historical data, a new cryptographic tool called additive homomorphic proxy aggregation scheme is designed. Moreover, to leverage the leakage of naïve Bayesian classifier, we introduce a privacy-preserving top- k disease names retrieval protocol in our system. Detailed privacy analysis ensures that patient's information is private and will not be leaked out during the disease diagnosis phase. In addition, performance evaluation via extensive simulations also demonstrates that our system can efficiently calculate patient's disease risk with high accuracy in a privacy-preserving way.

Application of the International Classification of Functioning, Disability and Health system to symptoms of the Duchenne and Becker muscular dystrophies.

PubMed

Conway, Kristin M; Ciafaloni, Emma; Matthews, Dennis; Westfield, Chris; James, Kathy; Paramsothy, Pangaja; Romitti, Paul A

2018-07-01

Duchenne and Becker muscular dystrophies, collectively referred to as dystrophinopathies, are X-linked recessive diseases that affect dystrophin production resulting in compromised muscle function across multiple systems. The International Classification of Functioning, Disability and Health provides a systematic classification scheme from which body functions affected by a dystrophinopathy can be identified and used to examine functional health. The infrastructure of the Muscular Dystrophy Surveillance, Tracking, and Research Network was used to identify commonly affected body functions and link selected functions to clinical surveillance data collected through medical record abstraction. Seventy-one (24 second-, 41 third- and 7 fourth-level) body function categories were selected via clinician review and consensus. Of these, 15 of 24 retained second-level categories were linked to data elements from the Muscular Dystrophy Surveillance, Tracking, and Research Network surveillance database. Our findings support continued development of a core set of body functions from the International Classification of Functioning, Disability and Health system that are representative of disease progression in dystrophinopathies and the incorporation of these functions in standardized evaluations of functional health and implementation of individualized rehabilitation care plans. Implications for Rehabilitation Duchenne and Becker muscular dystrophies, collectively referred to as dystrophinopathies, are X-linked recessive disorders that affect the production of dystrophin resulting in compromised muscle function across multiple systems. The severity and progressive nature of dystrophinopathies can have considerable impact on a patient's participation in activities across multiple life domains. Our findings support continued development of an International Classification of Functioning, Disability and Health core set for childhood-onset dystrophinopathies. A standardized dystrophinopathy International Classification of Functioning, Disability and Health documentation form can be used as a screening tool by rehabilitation professionals and for patient goal setting when developing rehabilitation plans. Patient reports of perceived functional health should be incorporated into the rehabilitation plan and therapeutic progress monitored by a standardized form.

Prostatitis: myths and realities.

PubMed

Nickel, J C

1998-03-01

To explore the myths surrounding the enigmatic syndrome that the urologic community has labeled as prostatitis and to determine the actual realities associated with this disease. A critical evaluation of the syndrome of prostatitis based on examination of the recent world literature, undisputed scientific facts, solid hypotheses, common sense, and the author's personal opinion. The most common myths surrounding the importance, etiology, diagnosis, classification, and treatment of prostatitis are in fact merely myths. Recent research has led to a new awareness of the importance of prostatitis, new insights into its pathogenesis, improved disease classification and symptom assessment, and will ultimately lead to more rational diagnostic and treatment strategies. The introduction of a new more rational classification system, the development and validation of reliable symptom assessment instruments, new funding initiatives by granting agencies and the pharmaceutical industry, and an awakening appeal for intellectual examination of this common prostate disease by academic urologists guarantees that prostatitis will find an important place on the urologic agenda as we enter the next millennium.

21 CFR 862.1490 - Lysozyme (muramidase) test system.

Code of Federal Regulations, 2010 CFR

2010-04-01

... (CONTINUED) MEDICAL DEVICES CLINICAL CHEMISTRY AND CLINICAL TOXICOLOGY DEVICES Clinical Chemistry Test... monocytic leukemia and kidney disease. (b) Classification. Class I (general controls). The device is exempt...

21 CFR 862.1535 - Ornithine carbamyl transferase test system.

Code of Federal Regulations, 2010 CFR

2010-04-01

... and treatment of liver diseases, such as infectious hepatitis, acute cholecystitis (inflammation of the gall bladder), cirrhosis, and liver metastases. (b) Classification. Class I (general controls...

Systemic classification for a new diagnostic approach to acute abdominal pain in children.

PubMed

Kim, Ji Hoi; Kang, Hyun Sik; Han, Kyung Hee; Kim, Seung Hyo; Shin, Kyung-Sue; Lee, Mu Suk; Jeong, In Ho; Kim, Young Sil; Kang, Ki-Soo

2014-12-01

With previous methods based on only age and location, there are many difficulties in identifying the etiology of acute abdominal pain in children. We sought to develop a new systematic classification of acute abdominal pain and to give some helps to physicians encountering difficulties in diagnoses. From March 2005 to May 2010, clinical data were collected retrospectively from 442 children hospitalized due to acute abdominal pain with no apparent underlying disease. According to the final diagnoses, diseases that caused acute abdominal pain were classified into nine groups. The nine groups were group I "catastrophic surgical abdomen" (7 patients, 1.6%), group II "acute appendicitis and mesenteric lymphadenitis" (56 patients, 12.7%), group III "intestinal obstruction" (57 patients, 12.9%), group IV "viral and bacterial acute gastroenteritis" (90 patients, 20.4%), group V "peptic ulcer and gastroduodenitis" (66 patients, 14.9%), group VI "hepatobiliary and pancreatic disease" (14 patients, 3.2%), group VII "febrile viral illness and extraintestinal infection" (69 patients, 15.6%), group VIII "functional gastrointestinal disorder (acute manifestation)" (20 patients, 4.5%), and group IX "unclassified acute abdominal pain" (63 patients, 14.3%). Four patients were enrolled in two disease groups each. Patients were distributed unevenly across the nine groups of acute abdominal pain. In particular, the "unclassified abdominal pain" only group was not uncommon. Considering a systemic classification for acute abdominal pain may be helpful in the diagnostic approach in children.

Classification of diffuse lung diseases: why and how.

PubMed

Hansell, David M

2013-09-01

The understanding of complex lung diseases, notably the idiopathic interstitial pneumonias and small airways diseases, owes as much to repeated attempts over the years to classify them as to any single conceptual breakthrough. One of the many benefits of a successful classification scheme is that it allows workers, within and between disciplines, to be clear that they are discussing the same disease. This may be of particular importance in the recruitment of individuals for a clinical trial that requires a standardized and homogeneous study population. Different specialties require fundamentally different things from a classification: for epidemiologic studies, a classification that requires categorization of individuals according to histopathologic pattern is not usually practicable. Conversely, a scheme that simply divides diffuse parenchymal disease into inflammatory and noninflammatory categories is unlikely to further the understanding about the pathogenesis of disease. Thus, for some disease groupings, for example, pulmonary vasculopathies, there may be several appropriate classifications, each with its merits and demerits. There has been an interesting shift in the past few years, from the accepted primacy of histopathology as the sole basis on which the classification of parenchymal lung disease has rested, to new ways of considering how these entities relate to each other. Some inventive thinking has resulted in new classifications that undoubtedly benefit patients and clinicians in their endeavor to improve management and outcome. The challenge of understanding the logic behind current classifications and their shortcomings are explored in various examples of lung diseases.

Detecting Lung Diseases from Exhaled Aerosols: Non-Invasive Lung Diagnosis Using Fractal Analysis and SVM Classification

PubMed Central

Xi, Jinxiang; Zhao, Weizhong; Yuan, Jiayao Eddie; Kim, JongWon; Si, Xiuhua; Xu, Xiaowei

2015-01-01

Background Each lung structure exhales a unique pattern of aerosols, which can be used to detect and monitor lung diseases non-invasively. The challenges are accurately interpreting the exhaled aerosol fingerprints and quantitatively correlating them to the lung diseases. Objective and Methods In this study, we presented a paradigm of an exhaled aerosol test that addresses the above two challenges and is promising to detect the site and severity of lung diseases. This paradigm consists of two steps: image feature extraction using sub-regional fractal analysis and data classification using a support vector machine (SVM). Numerical experiments were conducted to evaluate the feasibility of the breath test in four asthmatic lung models. A high-fidelity image-CFD approach was employed to compute the exhaled aerosol patterns under different disease conditions. Findings By employing the 10-fold cross-validation method, we achieved 100% classification accuracy among four asthmatic models using an ideal 108-sample dataset and 99.1% accuracy using a more realistic 324-sample dataset. The fractal-SVM classifier has been shown to be robust, highly sensitive to structural variations, and inherently suitable for investigating aerosol-disease correlations. Conclusion For the first time, this study quantitatively linked the exhaled aerosol patterns with their underlying diseases and set the stage for the development of a computer-aided diagnostic system for non-invasive detection of obstructive respiratory diseases. PMID:26422016

Chemical-induced disease relation extraction with various linguistic features.

PubMed

Gu, Jinghang; Qian, Longhua; Zhou, Guodong

2016-01-01

Understanding the relations between chemicals and diseases is crucial in various biomedical tasks such as new drug discoveries and new therapy developments. While manually mining these relations from the biomedical literature is costly and time-consuming, such a procedure is often difficult to keep up-to-date. To address these issues, the BioCreative-V community proposed a challenging task of automatic extraction of chemical-induced disease (CID) relations in order to benefit biocuration. This article describes our work on the CID relation extraction task on the BioCreative-V tasks. We built a machine learning based system that utilized simple yet effective linguistic features to extract relations with maximum entropy models. In addition to leveraging various features, the hypernym relations between entity concepts derived from the Medical Subject Headings (MeSH)-controlled vocabulary were also employed during both training and testing stages to obtain more accurate classification models and better extraction performance, respectively. We demoted relation extraction between entities in documents to relation extraction between entity mentions. In our system, pairs of chemical and disease mentions at both intra- and inter-sentence levels were first constructed as relation instances for training and testing, then two classification models at both levels were trained from the training examples and applied to the testing examples. Finally, we merged the classification results from mention level to document level to acquire final relations between chemicals and diseases. Our system achieved promisingF-scores of 60.4% on the development dataset and 58.3% on the test dataset using gold-standard entity annotations, respectively. Database URL:https://github.com/JHnlp/BC5CIDTask. © The Author(s) 2016. Published by Oxford University Press.

Network medicine in disease analysis and therapeutics.

PubMed

Chen, B; Butte, A J

2013-12-01

Two parallel trends are occurring in drug discovery. The first is that we are moving away from a symptom-based disease classification system to a system based on molecules and molecular states. The second is that we are shifting from targeting a single molecule toward targeting multiple molecules, pathways, or networks. Network medicine is an approach to understanding disease and discovering therapeutics looking at many molecules and how they interrelate, and it may play a critical role in the adoption of both trends.

[Gastroenterology in the G-DRG-System 2004].

PubMed

Bunzemeier, H; Frühmorgen, P; Caspary, W F; Roeder, N

2003-11-01

After a year of preliminary voluntarily introduction of casemix funding in hospitals in 2003 nearly every German hospital will be confronted with lump sump payments on the basis of the G-DRG system for their inpatient care starting from January 2004. To analyse weaknesses referring to gastroenterology services within the G-DRG version 1.0 the German Association for Disorders of the Digestive System and Metabolism (DGVS) and the DRG-Research-Group from the University of Muenster conducted a DRG evaluation project. In the analysis patient data from 16 hospitals were included. As a result of the project recommendations for G-DRG adjustments were generated. Those recommendations were implemented in the advancement to G-DRG version 2004. Also the International Classification of Diseases (ICD-10) was modified to ICD-10 German Modification. The classification of procedures OPS-301 was revised. The main adjustments to the G-DRG system and both classifications will be presented in this paper.

Deep neural network convolution (NNC) for three-class classification of diffuse lung disease opacities in high-resolution CT (HRCT): consolidation, ground-glass opacity (GGO), and normal opacity

NASA Astrophysics Data System (ADS)

Hashimoto, Noriaki; Suzuki, Kenji; Liu, Junchi; Hirano, Yasushi; MacMahon, Heber; Kido, Shoji

2018-02-01

Consolidation and ground-glass opacity (GGO) are two major types of opacities associated with diffuse lung diseases. Accurate detection and classification of such opacities are crucially important in the diagnosis of lung diseases, but the process is subjective, and suffers from interobserver variability. Our study purpose was to develop a deep neural network convolution (NNC) system for distinguishing among consolidation, GGO, and normal lung tissue in high-resolution CT (HRCT). We developed ensemble of two deep NNC models, each of which was composed of neural network regression (NNR) with an input layer, a convolution layer, a fully-connected hidden layer, and a fully-connected output layer followed by a thresholding layer. The output layer of each NNC provided a map for the likelihood of being each corresponding lung opacity of interest. The two NNC models in the ensemble were connected in a class-selection layer. We trained our NNC ensemble with pairs of input 2D axial slices and "teaching" probability maps for the corresponding lung opacity, which were obtained by combining three radiologists' annotations. We randomly selected 10 and 40 slices from HRCT scans of 172 patients for each class as a training and test set, respectively. Our NNC ensemble achieved an area under the receiver-operating-characteristic (ROC) curve (AUC) of 0.981 and 0.958 in distinction of consolidation and GGO, respectively, from normal opacity, yielding a classification accuracy of 93.3% among 3 classes. Thus, our deep-NNC-based system for classifying diffuse lung diseases achieved high accuracies for classification of consolidation, GGO, and normal opacity.

Ischemic stroke classification and risk of embolism in patients with Chagas disease.

PubMed

Montanaro, Vinícius Viana Abreu; da Silva, Creuza Maria; de Viana Santos, Carla Verônica; Lima, Maria Inacia Ruas; Negrão, Edson Marcio; de Freitas, Gabriel R

2016-12-01

Ischemic stroke (IS) and Chagas disease are strongly related. Nevertheless, little attention has been paid to this association and its natural history. The current guidelines concerning the management and secondary prevention of IS are largely based on the incomplete information or extrapolation of knowledge from other stroke etiologies. We performed a retrospective study which compared stroke etiologies among a cohort of hospitalized patients with IS and Chagas disease. The Instituto de Pesquisa Evandro Chagas/Fundação Oswaldo Cruz (IPEC/FIOCRUZ) embolic score was also used to identify and evaluate the risk of embolism in this population. A total of 86 patients were included in the analysis. The mean age of the study population was 58 years, and 60 % were men. According to the Trial of Org 10172 in Acute Stroke Treatment (TOAST) Classification, 45 % of the strokes were of undetermined etiology and 45 % of cardioembolic origin, while the Stop Stroke Study/Causative Classification System (SSS/CCS) TOAST indicated that 34 % were undetermined and 50 % cardioembolic (p < 0.01); 44 % of these patients were classified as having a high embolic risk according to the IPEC/FIOCRUZ score. Among the undetermined causes, 83.3 % fulfilled the criteria for embolic stroke of undetermined source (ESUS). The SSS/CCS TOAST etiological classification system was superior to the classical TOAST criteria in identifying a cardioembolic etiology in patients with ischemic stroke and Chagas disease. The IPEC/FIOCRUZ score did not correlate with the number of patients who were determined to have cardioembolic stroke etiologies. The current guidelines for stroke prevention should be reviewed in this population.

A Just-in-Time Learning based Monitoring and Classification Method for Hyper/Hypocalcemia Diagnosis.

PubMed

Peng, Xin; Tang, Yang; He, Wangli; Du, Wenli; Qian, Feng

2017-01-20

This study focuses on the classification and pathological status monitoring of hyper/hypo-calcemia in the calcium regulatory system. By utilizing the Independent Component Analysis (ICA) mixture model, samples from healthy patients are collected, diagnosed, and subsequently classified according to their underlying behaviors, characteristics, and mechanisms. Then, a Just-in-Time Learning (JITL) has been employed in order to estimate the diseased status dynamically. In terms of JITL, for the purpose of the construction of an appropriate similarity index to identify relevant datasets, a novel similarity index based on the ICA mixture model is proposed in this paper to improve online model quality. The validity and effectiveness of the proposed approach have been demonstrated by applying it to the calcium regulatory system under various hypocalcemic and hypercalcemic diseased conditions.

Deciphering Suicide and Other Manners of Death Associated with Drug Intoxication: A Centers for Disease Control and Prevention Consultation Meeting Summary.

PubMed

Stone, Deborah M; Holland, Kristin M; Bartholow, Brad; E Logan, Joseph; LiKamWa McIntosh, Wendy; Trudeau, Aimee; Rockett, Ian R H

2017-08-01

Manner of death (MOD) classification (i.e., natural, accident, suicide, homicide, or undetermined cause) affects mortality surveillance and public health research, policy, and practice. Determination of MOD in deaths caused by drug intoxication is challenging, with marked variability across states. The Centers for Disease Control and Prevention hosted a multidisciplinary meeting to discuss drug intoxication deaths as they relate to suicide and other MOD. The meeting objectives were to identify individual-level, system-level, and place-based factors affecting MOD classification and identify potential solutions to classification barriers. Suggested strategies included improved standardization in death scene investigation, toxicology, and autopsy practice; greater accountability; and creation of job aids for investigators. Continued collaboration and coordination of activities are needed among stakeholders to affect prevention efforts.

Holoentropy enabled-decision tree for automatic classification of diabetic retinopathy using retinal fundus images.

PubMed

Mane, Vijay Mahadeo; Jadhav, D V

2017-05-24

Diabetic retinopathy (DR) is the most common diabetic eye disease. Doctors are using various test methods to detect DR. But, the availability of test methods and requirements of domain experts pose a new challenge in the automatic detection of DR. In order to fulfill this objective, a variety of algorithms has been developed in the literature. In this paper, we propose a system consisting of a novel sparking process and a holoentropy-based decision tree for automatic classification of DR images to further improve the effectiveness. The sparking process algorithm is developed for automatic segmentation of blood vessels through the estimation of optimal threshold. The holoentropy enabled decision tree is newly developed for automatic classification of retinal images into normal or abnormal using hybrid features which preserve the disease-level patterns even more than the signal level of the feature. The effectiveness of the proposed system is analyzed using standard fundus image databases DIARETDB0 and DIARETDB1 for sensitivity, specificity and accuracy. The proposed system yields sensitivity, specificity and accuracy values of 96.72%, 97.01% and 96.45%, respectively. The experimental result reveals that the proposed technique outperforms the existing algorithms.

Obsessive compulsive and related disorders: comparing DSM-5 and ICD-11.

PubMed

Marras, Anna; Fineberg, Naomi; Pallanti, Stefano

2016-08-01

Obsessive-compulsive disorder (OCD) has been recognized as mainly characterized by compulsivity rather than anxiety and, therefore, was removed from the anxiety disorders chapter and given its own in both the American Psychiatric Association (APA) Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and the Beta Draft Version of the 11th revision of the World Health Organization (WHO) International Classification of Diseases (ICD-11). This revised clustering is based on increasing evidence of common affected neurocircuits between disorders, differently from previous classification systems based on interrater agreement. In this article, we focus on the classification of obsessive-compulsive and related disorders (OCRDs), examining the differences in approach adopted by these 2 nosological systems, with particular attention to the proposed changes in the forthcoming ICD-11. At this stage, notable differences in the ICD classification are emerging from the previous revision, apparently converging toward a reformulation of OCRDs that is closer to the DSM-5.

Classification and evolution of human rhinoviruses.

PubMed

Palmenberg, Ann C; Gern, James E

2015-01-01

The historical classification of human rhinoviruses (RV) by serotyping has been replaced by a logical system of comparative sequencing. Given that strains must diverge within their capsid sequenced by a reasonable degree (>12-13 % pairwise base identities) before becoming immunologically distinct, the new nomenclature system makes allowances for the addition of new, future types, without compromising historical designations. Currently, three species, the RV-A, RV-B, and RV-C, are recognized. Of these, the RV-C, discovered in 2006, are the most unusual in terms of capsid structure, receptor use, and association with severe disease in children.

The IASLC/ITMIG thymic malignancies staging project: development of a stage classification for thymic malignancies.

PubMed

Detterbeck, Frank C; Asamura, Hisao; Crowley, John; Falkson, Conrad; Giaccone, Giuseppe; Giroux, Dori; Huang, James; Kim, Jhingook; Kondo, Kazuya; Lucchi, Marco; Marino, Mirella; Marom, Edith M; Nicholson, Andrew; Okumura, Meinoshin; Ruffini, Enrico; van Schil, Paul; Stratton, Kelly

2013-12-01

The lack of an official-stage classification system for thymic malignancies is an issue that hampers progress in this rare disease. A collaborative effort by the International Association for the Study of Lung Cancer and the International Thymic Malignancies Interest Group is underway to develop proposals for such a system. A database of more than 10,000 cases worldwide has been assembled to provide a solid basis for analysis. This report outlines the structure of the effort and the process that has been designed.

[Prognostic parameters in liver cirrhosis, varicose bleeding and sclerosing therapy. Prospective comparison of a prognostic system with the Child classification obtained by discriminant analysis].

PubMed

Sauerbruch, T; Ansari, H; Wotzka, R; Soehendra, N; Köpcke, W

1988-01-08

Prospective prognosis systems for predicting half-year death-rate after bleeding from oesophageal varices and sclerotherapy were tested on 129 patients. The receiver-operating-characteristic curves of three discriminant scores were compared with the Child-Pugh classification. It was found that the latter is still the best for prognosticating the course of the disease. A simplified discriminant score which contains as its only factors bilirubin and the Quick value does, however, give nearly as good information.

21 CFR 862.1315 - Galactose-1-phosphate uridyl transferase test system.

Code of Federal Regulations, 2010 CFR

2010-04-01

... of the enzyme galactose-1-phosphate uridyl transferase in erythrocytes (red blood cells... hereditary disease galactosemia (disorder of galactose metabolism) in infants. (b) Classification. Class II. ...

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.

Introduction to the history and current status of evidence-based korean medicine: a unique integrated system of allopathic and holistic medicine.

PubMed

Yin, Chang Shik; Ko, Seong-Gyu

2014-01-01

Objectives. Korean medicine, an integrated allopathic and traditional medicine, has developed unique characteristics and has been active in contributing to evidence-based medicine. Recent developments in Korean medicine have not been as well disseminated as traditional Chinese medicine. This introduction to recent developments in Korean medicine will draw attention to, and facilitate, the advancement of evidence-based complementary alternative medicine (CAM). Methods and Results. The history of and recent developments in Korean medicine as evidence-based medicine are explored through discussions on the development of a national standard classification of diseases and study reports, ranging from basic research to newly developed clinical therapies. A national standard classification of diseases has been developed and revised serially into an integrated classification of Western allopathic and traditional holistic medicine disease entities. Standard disease classifications offer a starting point for the reliable gathering of evidence and provide a representative example of the unique status of evidence-based Korean medicine as an integration of Western allopathic medicine and traditional holistic medicine. Conclusions. Recent developments in evidence-based Korean medicine show a unique development in evidence-based medicine, adopting both Western allopathic and holistic traditional medicine. It is expected that Korean medicine will continue to be an important contributor to evidence-based medicine, encompassing conventional and complementary approaches.

Mayo Clinic/Renal Pathology Society Consensus Report on Pathologic Classification, Diagnosis, and Reporting of GN.

PubMed

Sethi, Sanjeev; Haas, Mark; Markowitz, Glen S; D'Agati, Vivette D; Rennke, Helmut G; Jennette, J Charles; Bajema, Ingeborg M; Alpers, Charles E; Chang, Anthony; Cornell, Lynn D; Cosio, Fernando G; Fogo, Agnes B; Glassock, Richard J; Hariharan, Sundaram; Kambham, Neeraja; Lager, Donna J; Leung, Nelson; Mengel, Michael; Nath, Karl A; Roberts, Ian S; Rovin, Brad H; Seshan, Surya V; Smith, Richard J H; Walker, Patrick D; Winearls, Christopher G; Appel, Gerald B; Alexander, Mariam P; Cattran, Daniel C; Casado, Carmen Avila; Cook, H Terence; De Vriese, An S; Radhakrishnan, Jai; Racusen, Lorraine C; Ronco, Pierre; Fervenza, Fernando C

2016-05-01

Renal pathologists and nephrologists met on February 20, 2015 to establish an etiology/pathogenesis-based system for classification and diagnosis of GN, with a major aim of standardizing the kidney biopsy report of GN. On the basis of etiology/pathogenesis, GN is classified into the following five pathogenic types, each with specific disease entities: immune-complex GN, pauci-immune GN, antiglomerular basement membrane GN, monoclonal Ig GN, and C3 glomerulopathy. The pathogenesis-based classification forms the basis of the kidney biopsy report. To standardize the report, the diagnosis consists of a primary diagnosis and a secondary diagnosis. The primary diagnosis should include the disease entity/pathogenic type (if disease entity is not known) followed in order by pattern of injury (mixed patterns may be present); score/grade/class for disease entities, such as IgA nephropathy, lupus nephritis, and ANCA GN; and additional features as detailed herein. A pattern diagnosis as the sole primary diagnosis is not recommended. Secondary diagnoses should be reported separately and include coexisting lesions that do not form the primary diagnosis. Guidelines for the report format, light microscopy, immunofluorescence microscopy, electron microscopy, and ancillary studies are also provided. In summary, this consensus report emphasizes a pathogenesis-based classification of GN and provides guidelines for the standardized reporting of GN. Copyright © 2016 by the American Society of Nephrology.

Categorization of allergic disorders in the new World Health Organization International Classification of Diseases.

PubMed

Tanno, Luciana Kase; Calderon, Moises A; Goldberg, Bruce J; Akdis, Cezmi A; Papadopoulos, Nikolaos G; Demoly, Pascal

2014-01-01

Although efforts to improve the classification of hypersensitivity/allergic diseases have been made, they have not been considered a top-level category in the International Classification of Diseases (ICD)-10 and still are not in the ICD-11 beta phase linearization. ICD-10 is the most used classification system by the allergy community worldwide but it is not considered as appropriate for clinical practice. The Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) on the other hand contains a tightly integrated classification of hypersensitivity/allergic disorders based on the EAACI/WAO nomenclature and the World Health Organization (WHO) may plan to align ICD-11 with SNOMED CT so that they share a common ontological basis. With the aim of actively supporting the ongoing ICD-11 revision and the optimal practice of Allergology, we performed a careful comparison of ICD-10 and 11 beta phase linearization codes to identify gaps, areas of regression in allergy coding and possibly reach solutions, in collaboration with committees in charge of the ICD-11 revision. We have found a significant degree of misclassification of terms in the allergy-related hierarchies. This stems not only from unclear definitions of these conditions but also the use of common names that falsely imply allergy. The lack of understanding of the immune mechanisms underlying some of the conditions contributes to the difficulty in classification. More than providing data to support specific changes into the ongoing linearization, these results highlight the need for either a new chapter entitled Hypersensitivity/Allergic Disorders as in SNOMED CT or a high level structure in the Immunology chapter in order to make classification more appropriate and usable.

Alzheimer disease detection from structural MR images using FCM based weighted probabilistic neural network.

PubMed

Duraisamy, Baskar; Shanmugam, Jayanthi Venkatraman; Annamalai, Jayanthi

2018-02-19

An early intervention of Alzheimer's disease (AD) is highly essential due to the fact that this neuro degenerative disease generates major life-threatening issues, especially memory loss among patients in society. Moreover, categorizing NC (Normal Control), MCI (Mild Cognitive Impairment) and AD early in course allows the patients to experience benefits from new treatments. Therefore, it is important to construct a reliable classification technique to discriminate the patients with or without AD from the bio medical imaging modality. Hence, we developed a novel FCM based Weighted Probabilistic Neural Network (FWPNN) classification algorithm and analyzed the brain images related to structural MRI modality for better discrimination of class labels. Initially our proposed framework begins with brain image normalization stage. In this stage, ROI regions related to Hippo-Campus (HC) and Posterior Cingulate Cortex (PCC) from the brain images are extracted using Automated Anatomical Labeling (AAL) method. Subsequently, nineteen highly relevant AD related features are selected through Multiple-criterion feature selection method. At last, our novel FWPNN classification algorithm is imposed to remove suspicious samples from the training data with an end goal to enhance the classification performance. This newly developed classification algorithm combines both the goodness of supervised and unsupervised learning techniques. The experimental validation is carried out with the ADNI subset and then to the Bordex-3 city dataset. Our proposed classification approach achieves an accuracy of about 98.63%, 95.4%, 96.4% in terms of classification with AD vs NC, MCI vs NC and AD vs MCI. The experimental results suggest that the removal of noisy samples from the training data can enhance the decision generation process of the expert systems.

Refining diagnosis of Parkinson's disease with deep learning-based interpretation of dopamine transporter imaging.

PubMed

Choi, Hongyoon; Ha, Seunggyun; Im, Hyung Jun; Paek, Sun Ha; Lee, Dong Soo

2017-01-01

Dopaminergic degeneration is a pathologic hallmark of Parkinson's disease (PD), which can be assessed by dopamine transporter imaging such as FP-CIT SPECT. Until now, imaging has been routinely interpreted by human though it can show interobserver variability and result in inconsistent diagnosis. In this study, we developed a deep learning-based FP-CIT SPECT interpretation system to refine the imaging diagnosis of Parkinson's disease. This system trained by SPECT images of PD patients and normal controls shows high classification accuracy comparable with the experts' evaluation referring quantification results. Its high accuracy was validated in an independent cohort composed of patients with PD and nonparkinsonian tremor. In addition, we showed that some patients clinically diagnosed as PD who have scans without evidence of dopaminergic deficit (SWEDD), an atypical subgroup of PD, could be reclassified by our automated system. Our results suggested that the deep learning-based model could accurately interpret FP-CIT SPECT and overcome variability of human evaluation. It could help imaging diagnosis of patients with uncertain Parkinsonism and provide objective patient group classification, particularly for SWEDD, in further clinical studies.

21 CFR 862.1440 - Lactate dehydrogenase test system.

Code of Federal Regulations, 2013 CFR

2013-04-01

... hepatitis, cirrhosis, and metastatic carcinoma of the liver, cardiac diseases such as myocardial infarction, and tumors of the lung or kidneys. (b) Classification. Class II (special controls). The device is...

21 CFR 862.1440 - Lactate dehydrogenase test system.

Code of Federal Regulations, 2012 CFR

2012-04-01

... hepatitis, cirrhosis, and metastatic carcinoma of the liver, cardiac diseases such as myocardial infarction, and tumors of the lung or kidneys. (b) Classification. Class II (special controls). The device is...

2016 Classification Criteria for Macrophage Activation Syndrome Complicating Systemic Juvenile Idiopathic Arthritis: A European League Against Rheumatism/American College of Rheumatology/Paediatric Rheumatology International Trials Organisation Collaborative Initiative.

PubMed

Ravelli, Angelo; Minoia, Francesca; Davì, Sergio; Horne, AnnaCarin; Bovis, Francesca; Pistorio, Angela; Aricò, Maurizio; Avcin, Tadej; Behrens, Edward M; De Benedetti, Fabrizio; Filipovic, Lisa; Grom, Alexei A; Henter, Jan-Inge; Ilowite, Norman T; Jordan, Michael B; Khubchandani, Raju; Kitoh, Toshiyuki; Lehmberg, Kai; Lovell, Daniel J; Miettunen, Paivi; Nichols, Kim E; Ozen, Seza; Pachlopnik Schmid, Jana; Ramanan, Athimalaipet V; Russo, Ricardo; Schneider, Rayfel; Sterba, Gary; Uziel, Yosef; Wallace, Carol; Wouters, Carine; Wulffraat, Nico; Demirkaya, Erkan; Brunner, Hermine I; Martini, Alberto; Ruperto, Nicolino; Cron, Randy Q

2016-03-01

To develop criteria for the classification of macrophage activation syndrome (MAS) in patients with systemic juvenile idiopathic arthritis (JIA). A multistep process, based on a combination of expert consensus and analysis of real patient data, was conducted. A panel of 28 experts was first asked to classify 428 patient profiles as having or not having MAS, based on clinical and laboratory features at the time of disease onset. The 428 profiles comprised 161 patients with systemic JIA-associated MAS and 267 patients with a condition that could potentially be confused with MAS (active systemic JIA without evidence of MAS, or systemic infection). Next, the ability of candidate criteria to classify individual patients as having MAS or not having MAS was assessed by evaluating the agreement between the classification yielded using the criteria and the consensus classification of the experts. The final criteria were selected in a consensus conference. Experts achieved consensus on the classification of 391 of the 428 patient profiles (91.4%). A total of 982 candidate criteria were tested statistically. The 37 best-performing criteria and 8 criteria obtained from the literature were evaluated at the consensus conference. During the conference, 82% consensus among experts was reached on the final MAS classification criteria. In validation analyses, these criteria had a sensitivity of 0.73 and a specificity of 0.99. Agreement between the classification (MAS or not MAS) obtained using the criteria and the original diagnosis made by the treating physician was high (κ = 0.76). We have developed a set of classification criteria for MAS complicating systemic JIA and provided preliminary evidence of its validity. Use of these criteria will potentially improve understanding of MAS in systemic JIA and enhance efforts to discover effective therapies, by ensuring appropriate patient enrollment in studies. © 2015, American College of Rheumatology.

Current state of biology and diagnosis of clonal mast cell diseases in adults.

PubMed

Alvarez-Twose, I; Morgado, J M; Sánchez-Muñoz, L; García-Montero, A; Mollejo, M; Orfao, A; Escribano, L

2012-10-01

Mastocytosis comprises a heterogeneous group of disorders characterized by the presence of clonal mast cells (MC) in organs such as skin, bone marrow (BM), and gastrointestinal tract, among other tissues. The clonal nature of the disease can be established in most adult patients by the demonstration of activating KIT mutations in their BM MC. When highly sensitive techniques capable of identifying cells present at very low frequencies in a sample are applied, BM MC from virtually all systemic mastocytosis patients display unique immunophenotypical features, particularly the aberrant expression of CD25. By contrast, large, multifocal BM MC aggregates (the only World Health Organization major criterion for systemic mastocytosis) are absent in a significant proportion of patients fulfilling at least three minor criteria for systemic mastocytosis, particularly in subjects studied at early stages of the disease with very low MC burden. Moreover, recent molecular and immunophenotypical investigations of BM MC from patients with indolent systemic mastocytosis have revealed a close association of some biological features (e.g., multilineage involvement of hematopoiesis by the KIT mutation and an immature mast cell immunophenotype) with an increased risk for disease progression. These observations support the fact that, although the current consensus diagnostic criteria for systemic mastocytosis have been a major advance for the diagnosis and classification of the disease, rationale usage of the most sensitive diagnostic techniques available nowadays is needed to improve the diagnosis, refine the classification, and reach objective prognostic stratification of adult mastocytosis. © 2012 Blackwell Publishing Ltd.

21 CFR 866.6010 - Tumor-associated antigen immunological test system.

Code of Federal Regulations, 2014 CFR

2014-04-01

..., plasma, urine, or other body fluids. This device is intended as an aid in monitoring patients for disease progress or response to therapy or for the detection of recurrent or residual disease. (b) Classification.... 866.6010 Section 866.6010 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN...

21 CFR 866.6010 - Tumor-associated antigen immunological test system.

Code of Federal Regulations, 2013 CFR

2013-04-01

..., plasma, urine, or other body fluids. This device is intended as an aid in monitoring patients for disease progress or response to therapy or for the detection of recurrent or residual disease. (b) Classification.... 866.6010 Section 866.6010 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN...

21 CFR 866.6010 - Tumor-associated antigen immunological test system.

Code of Federal Regulations, 2012 CFR

2012-04-01

..., plasma, urine, or other body fluids. This device is intended as an aid in monitoring patients for disease progress or response to therapy or for the detection of recurrent or residual disease. (b) Classification.... 866.6010 Section 866.6010 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN...

21 CFR 866.6010 - Tumor-associated antigen immunological test system.

Code of Federal Regulations, 2011 CFR

2011-04-01

..., plasma, urine, or other body fluids. This device is intended as an aid in monitoring patients for disease progress or response to therapy or for the detection of recurrent or residual disease. (b) Classification.... 866.6010 Section 866.6010 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN...

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.

Detection of inter-patient left and right bundle branch block heartbeats in ECG using ensemble classifiers

PubMed Central

2014-01-01

Background Left bundle branch block (LBBB) and right bundle branch block (RBBB) not only mask electrocardiogram (ECG) changes that reflect diseases but also indicate important underlying pathology. The timely detection of LBBB and RBBB is critical in the treatment of cardiac diseases. Inter-patient heartbeat classification is based on independent training and testing sets to construct and evaluate a heartbeat classification system. Therefore, a heartbeat classification system with a high performance evaluation possesses a strong predictive capability for unknown data. The aim of this study was to propose a method for inter-patient classification of heartbeats to accurately detect LBBB and RBBB from the normal beat (NORM). Methods This study proposed a heartbeat classification method through a combination of three different types of classifiers: a minimum distance classifier constructed between NORM and LBBB; a weighted linear discriminant classifier between NORM and RBBB based on Bayesian decision making using posterior probabilities; and a linear support vector machine (SVM) between LBBB and RBBB. Each classifier was used with matching features to obtain better classification performance. The final types of the test heartbeats were determined using a majority voting strategy through the combination of class labels from the three classifiers. The optimal parameters for the classifiers were selected using cross-validation on the training set. The effects of different lead configurations on the classification results were assessed, and the performance of these three classifiers was compared for the detection of each pair of heartbeat types. Results The study results showed that a two-lead configuration exhibited better classification results compared with a single-lead configuration. The construction of a classifier with good performance between each pair of heartbeat types significantly improved the heartbeat classification performance. The results showed a sensitivity of 91.4% and a positive predictive value of 37.3% for LBBB and a sensitivity of 92.8% and a positive predictive value of 88.8% for RBBB. Conclusions A multi-classifier ensemble method was proposed based on inter-patient data and demonstrated a satisfactory classification performance. This approach has the potential for application in clinical practice to distinguish LBBB and RBBB from NORM of unknown patients. PMID:24903422

Detection of inter-patient left and right bundle branch block heartbeats in ECG using ensemble classifiers.

PubMed

Huang, Huifang; Liu, Jie; Zhu, Qiang; Wang, Ruiping; Hu, Guangshu

2014-06-05

Left bundle branch block (LBBB) and right bundle branch block (RBBB) not only mask electrocardiogram (ECG) changes that reflect diseases but also indicate important underlying pathology. The timely detection of LBBB and RBBB is critical in the treatment of cardiac diseases. Inter-patient heartbeat classification is based on independent training and testing sets to construct and evaluate a heartbeat classification system. Therefore, a heartbeat classification system with a high performance evaluation possesses a strong predictive capability for unknown data. The aim of this study was to propose a method for inter-patient classification of heartbeats to accurately detect LBBB and RBBB from the normal beat (NORM). This study proposed a heartbeat classification method through a combination of three different types of classifiers: a minimum distance classifier constructed between NORM and LBBB; a weighted linear discriminant classifier between NORM and RBBB based on Bayesian decision making using posterior probabilities; and a linear support vector machine (SVM) between LBBB and RBBB. Each classifier was used with matching features to obtain better classification performance. The final types of the test heartbeats were determined using a majority voting strategy through the combination of class labels from the three classifiers. The optimal parameters for the classifiers were selected using cross-validation on the training set. The effects of different lead configurations on the classification results were assessed, and the performance of these three classifiers was compared for the detection of each pair of heartbeat types. The study results showed that a two-lead configuration exhibited better classification results compared with a single-lead configuration. The construction of a classifier with good performance between each pair of heartbeat types significantly improved the heartbeat classification performance. The results showed a sensitivity of 91.4% and a positive predictive value of 37.3% for LBBB and a sensitivity of 92.8% and a positive predictive value of 88.8% for RBBB. A multi-classifier ensemble method was proposed based on inter-patient data and demonstrated a satisfactory classification performance. This approach has the potential for application in clinical practice to distinguish LBBB and RBBB from NORM of unknown patients.

Performance of the 2012 Systemic Lupus International Collaborating Clinics classification criteria versus the 1997 American College of Rheumatology classification criteria in adult and juvenile systemic lupus erythematosus. A systematic review and meta-analysis.

PubMed

Hartman, Esther A R; van Royen-Kerkhof, Annet; Jacobs, Johannes W G; Welsing, Paco M J; Fritsch-Stork, Ruth D E

2018-03-01

To evaluate the performance in classifying systemic lupus erythematosus by the 2012 Systemic Lupus International Collaborating Clinics criteria (SLICC'12), versus the revised American College of Rheumatology criteria from 1997 (ACR'97) in adult and juvenile SLE patients. A systematic literature search was conducted in PubMed and Embase for studies comparing SLICC'12 and ACR'97 with clinical diagnosis. A meta-analysis was performed to estimate the sensitivity and specificity of SLICC'12 and ACR'97. To assess classification earlier in the disease by either set, sensitivity and specificity were compared for patients with disease duration <5years. Sensitivity and specificity of individual criteria items were also assessed. In adult SLE (nine studies: 5236 patients, 1313 controls), SLICC'12 has higher sensitivity (94.6% vs. 89.6%) and similar specificity (95.5% vs. 98.1%) compared to ACR'97. For juvenile SLE (four studies: 568 patients, 339 controls), SLICC'12 demonstrates higher sensitivity (99.9% vs. 84.3%) than ACR'97, but much lower specificity (82.0% vs. 94.1%). SLICC'12 classifies juvenile SLE patients earlier in disease course. Individual items contributing to diagnostic accuracy are low complement, anti-ds DNA and acute cutaneous lupus in SLICC'12, and the immunologic and hematologic disorder in ACR'97. Based on sensitivity and specificity SLICC'12 is best for adult SLE. Following the view that higher specificity, i.e. avoidance of false positives, is preferable, ACR'97 is best for juvenile SLE even if associated with lower sensitivity. Our results on the contribution of the individual items of SLICC'12 and ACR´97 may be of value in future efforts to update classification criteria. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

A Feature-Free 30-Disease Pathological Brain Detection System by Linear Regression Classifier.

PubMed

Chen, Yi; Shao, Ying; Yan, Jie; Yuan, Ti-Fei; Qu, Yanwen; Lee, Elizabeth; Wang, Shuihua

2017-01-01

Alzheimer's disease patients are increasing rapidly every year. Scholars tend to use computer vision methods to develop automatic diagnosis system. (Background) In 2015, Gorji et al. proposed a novel method using pseudo Zernike moment. They tested four classifiers: learning vector quantization neural network, pattern recognition neural network trained by Levenberg-Marquardt, by resilient backpropagation, and by scaled conjugate gradient. This study presents an improved method by introducing a relatively new classifier-linear regression classification. Our method selects one axial slice from 3D brain image, and employed pseudo Zernike moment with maximum order of 15 to extract 256 features from each image. Finally, linear regression classification was harnessed as the classifier. The proposed approach obtains an accuracy of 97.51%, a sensitivity of 96.71%, and a specificity of 97.73%. Our method performs better than Gorji's approach and five other state-of-the-art approaches. Therefore, it can be used to detect Alzheimer's disease. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Childhood interstitial lung diseases: an 18-year retrospective analysis.

PubMed

Soares, Jennifer J; Deutsch, Gail H; Moore, Paul E; Fazili, Mohammad F; Austin, Eric D; Brown, Rebekah F; Sokolow, Andrew G; Hilmes, Melissa A; Young, Lisa R

2013-10-01

Childhood interstitial lung diseases (ILD) occur in a variety of clinical contexts. Advances in the understanding of disease pathogenesis and use of standardized terminology have facilitated increased case ascertainment. However, as all studies have been performed at specialized referral centers, the applicability of these findings to general pulmonary practice has been uncertain. The objective of this study was to determine the historical occurrence of childhood ILD to provide information reflecting general pediatric pulmonary practice patterns. Childhood ILD cases seen at Vanderbilt Children's Hospital from 1994 to 2011 were retrospectively reviewed and classified according to the current pediatric diffuse lung disease histopathologic classification system. A total of 93 cases were identified, of which 91.4% were classifiable. A total of 68.8% (64/93) of subjects underwent lung biopsy in their evaluations. The largest classification categories were disorders related to systemic disease processes (24.7%), disorders of the immunocompromised host (24.7%), and disorders more prevalent in infancy (22.6%). Eight cases of neuroendocrine cell hyperplasia of infancy (NEHI) were identified, including 5 that were previously unrecognized before this review. Our findings demonstrate the general scope of childhood ILD and that these cases present within a variety of pediatric subspecialties. Retrospective review was valuable in recognizing more recently described forms of childhood ILD. As a significant portion of cases were classifiable based on clinical, genetic, and/or radiographic criteria, we urge greater consideration to noninvasive diagnostic approaches and suggest modification to the current childhood ILD classification scheme to accommodate the increasing number of cases diagnosed without lung biopsy.

Bag-of-features approach for improvement of lung tissue classification in diffuse lung disease

NASA Astrophysics Data System (ADS)

Kato, Noriji; Fukui, Motofumi; Isozaki, Takashi

2009-02-01

Many automated techniques have been proposed to classify diffuse lung disease patterns. Most of the techniques utilize texture analysis approaches with second and higher order statistics, and show successful classification result among various lung tissue patterns. However, the approaches do not work well for the patterns with inhomogeneous texture distribution within a region of interest (ROI), such as reticular and honeycombing patterns, because the statistics can only capture averaged feature over the ROI. In this work, we have introduced the bag-of-features approach to overcome this difficulty. In the approach, texture images are represented as histograms or distributions of a few basic primitives, which are obtained by clustering local image features. The intensity descriptor and the Scale Invariant Feature Transformation (SIFT) descriptor are utilized to extract the local features, which have significant discriminatory power due to their specificity to a particular image class. In contrast, the drawback of the local features is lack of invariance under translation and rotation. We improved the invariance by sampling many local regions so that the distribution of the local features is unchanged. We evaluated the performance of our system in the classification task with 5 image classes (ground glass, reticular, honeycombing, emphysema, and normal) using 1109 ROIs from 211 patients. Our system achieved high classification accuracy of 92.8%, which is superior to that of the conventional system with the gray level co-occurrence matrix (GLCM) feature especially for inhomogeneous texture patterns.

Machine vision system for inspecting characteristics of hybrid rice seed

NASA Astrophysics Data System (ADS)

Cheng, Fang; Ying, Yibin

2004-03-01

Obtaining clear images advantaged of improving the classification accuracy involves many factors, light source, lens extender and background were discussed in this paper. The analysis of rice seed reflectance curves showed that the wavelength of light source for discrimination of the diseased seeds from normal rice seeds in the monochromic image recognition mode was about 815nm for jinyou402 and shanyou10. To determine optimizing conditions for acquiring digital images of rice seed using a computer vision system, an adjustable color machine vision system was developed. The machine vision system with 20mm to 25mm lens extender produce close-up images which made it easy to object recognition of characteristics in hybrid rice seeds. White background was proved to be better than black background for inspecting rice seeds infected by disease and using the algorithms based on shape. Experimental results indicated good classification for most of the characteristics with the machine vision system. The same algorithm yielded better results in optimizing condition for quality inspection of rice seed. Specifically, the image processing can correct for details such as fine fissure with the machine vision system.

Systemic Classification for a New Diagnostic Approach to Acute Abdominal Pain in Children

PubMed Central

Kim, Ji Hoi; Kang, Hyun Sik; Han, Kyung Hee; Kim, Seung Hyo; Shin, Kyung-Sue; Lee, Mu Suk; Jeong, In Ho; Kim, Young Sil

2014-01-01

Purpose With previous methods based on only age and location, there are many difficulties in identifying the etiology of acute abdominal pain in children. We sought to develop a new systematic classification of acute abdominal pain and to give some helps to physicians encountering difficulties in diagnoses. Methods From March 2005 to May 2010, clinical data were collected retrospectively from 442 children hospitalized due to acute abdominal pain with no apparent underlying disease. According to the final diagnoses, diseases that caused acute abdominal pain were classified into nine groups. Results The nine groups were group I "catastrophic surgical abdomen" (7 patients, 1.6%), group II "acute appendicitis and mesenteric lymphadenitis" (56 patients, 12.7%), group III "intestinal obstruction" (57 patients, 12.9%), group IV "viral and bacterial acute gastroenteritis" (90 patients, 20.4%), group V "peptic ulcer and gastroduodenitis" (66 patients, 14.9%), group VI "hepatobiliary and pancreatic disease" (14 patients, 3.2%), group VII "febrile viral illness and extraintestinal infection" (69 patients, 15.6%), group VIII "functional gastrointestinal disorder (acute manifestation)" (20 patients, 4.5%), and group IX "unclassified acute abdominal pain" (63 patients, 14.3%). Four patients were enrolled in two disease groups each. Conclusion Patients were distributed unevenly across the nine groups of acute abdominal pain. In particular, the "unclassified abdominal pain" only group was not uncommon. Considering a systemic classification for acute abdominal pain may be helpful in the diagnostic approach in children. PMID:25587522

21 CFR 866.5360 - Cohn fraction IV immuno-logical test system.

Code of Federal Regulations, 2012 CFR

2012-04-01

...-lipoprotein), malnutrition, iron deficiency anemia, red blood cell disorders, and kidney disease. (b) Classification. Class I (general controls). The device is exempt from the premarket notification procedures in... test system is a device that consists of or measures that fraction of plasma proteins, predominantly...

21 CFR 866.5360 - Cohn fraction IV immuno-logical test system.

Code of Federal Regulations, 2011 CFR

2011-04-01

...-lipoprotein), malnutrition, iron deficiency anemia, red blood cell disorders, and kidney disease. (b) Classification. Class I (general controls). The device is exempt from the premarket notification procedures in... test system is a device that consists of or measures that fraction of plasma proteins, predominantly...

21 CFR 866.5360 - Cohn fraction IV immuno-logical test system.

Code of Federal Regulations, 2014 CFR

2014-04-01

...-lipoprotein), malnutrition, iron deficiency anemia, red blood cell disorders, and kidney disease. (b) Classification. Class I (general controls). The device is exempt from the premarket notification procedures in... test system is a device that consists of or measures that fraction of plasma proteins, predominantly...

21 CFR 866.5360 - Cohn fraction IV immuno-logical test system.

Code of Federal Regulations, 2013 CFR

2013-04-01

...-lipoprotein), malnutrition, iron deficiency anemia, red blood cell disorders, and kidney disease. (b) Classification. Class I (general controls). The device is exempt from the premarket notification procedures in... test system is a device that consists of or measures that fraction of plasma proteins, predominantly...

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.

Localized primary gastrointestinal diffuse large B cell lymphoma received a surgical approach: an analysis of prognostic factors and comparison of staging systems in 101 patients from a single institution.

PubMed

Zhang, Shengting; Wang, Li; Yu, Dong; Shen, Yang; Cheng, Shu; Zhang, Li; Qian, Ying; Shen, Zhixiang; Li, Qinyu; Zhao, Weili

2015-08-15

Diffuse large B cell lymphoma (DLBCL) represents the most common histological subtype of primary gastrointestinal lymphoma and is a heterogeneous group of disease. Prognostic characterization of individual patients is an essential prerequisite for a proper risk-based therapeutic choice. Clinical and pathological prognostic factors were identified, and predictive value of four previously described prognostic systems were assessed in 101 primary gastrointestinal DLBCL (PG-DLBCL) patients with localized disease, including Ann Arbor staging with Musshoff modification, International Prognostic Index (IPI), Lugano classification, and Paris staging system. Univariate factors correlated with inferior survival time were clinical parameters [age>60 years old, multiple extranodal/gastrointestinal involvement, elevated serum lactate dehydrogenase and β2-microglobulin, and decreased serum albumin], as well as pathological parameters (invasion depth beyond serosa, involvement of regional lymph node or adjacent tissue, Ki-67 index, and Bcl-2 expression). Major independent variables of adverse outcome indicated by multivariate analysis were multiple gastrointestinal involvement. In patients unfit for Rituximab but received surgery, radical surgery significantly prolonged the survival time, comparing with alleviative surgery. Addition of Rituximab could overcome the negative prognostic effect of alleviative surgery. Among the four prognostic systems, IPI and Lugano classification clearly separated patients into different risk groups. IPI was able to further stratify the early-stage patients of Lugano classification into groups with distinct prognosis. Radical surgery might be proposed for the patients unfit for Rituximab treatment, and a combination of clinical and pathological staging systems was more helpful to predict the disease outcome of PG-DLBCL patients.

RAIRS2 a new expert system for diagnosing tuberculosis with real-world tournament selection mechanism inside artificial immune recognition system.

PubMed

Saybani, Mahmoud Reza; Shamshirband, Shahaboddin; Golzari, Shahram; Wah, Teh Ying; Saeed, Aghabozorgi; Mat Kiah, Miss Laiha; Balas, Valentina Emilia

2016-03-01

Tuberculosis is a major global health problem that has been ranked as the second leading cause of death from an infectious disease worldwide, after the human immunodeficiency virus. Diagnosis based on cultured specimens is the reference standard; however, results take weeks to obtain. Slow and insensitive diagnostic methods hampered the global control of tuberculosis, and scientists are looking for early detection strategies, which remain the foundation of tuberculosis control. Consequently, there is a need to develop an expert system that helps medical professionals to accurately diagnose the disease. The objective of this study is to diagnose tuberculosis using a machine learning method. Artificial immune recognition system (AIRS) has been used successfully for diagnosing various diseases. However, little effort has been undertaken to improve its classification accuracy. In order to increase the classification accuracy, this study introduces a new hybrid system that incorporates real tournament selection mechanism into the AIRS. This mechanism is used to control the population size of the model and to overcome the existing selection pressure. Patient epacris reports obtained from the Pasteur laboratory in northern Iran were used as the benchmark data set. The sample consisted of 175 records, from which 114 (65 %) were positive for TB, and the remaining 61 (35 %) were negative. The classification performance was measured through tenfold cross-validation, root-mean-square error, sensitivity, and specificity. With an accuracy of 100 %, RMSE of 0, sensitivity of 100 %, and specificity of 100 %, the proposed method was able to successfully classify tuberculosis cases. In addition, the proposed method is comparable with top classifiers used in this research.

Cascade of convolutional neural networks for lung texture classification: overcoming ontological overlapping

NASA Astrophysics Data System (ADS)

Tarando, Sebastian Roberto; Fetita, Catalin; Brillet, Pierre-Yves

2017-03-01

The infiltrative lung diseases are a class of irreversible, non-neoplastic lung pathologies requiring regular follow-up with CT imaging. Quantifying the evolution of the patient status imposes the development of automated classification tools for lung texture. Traditionally, such classification relies on a two-dimensional analysis of axial CT images. This paper proposes a cascade of the existing CNN based CAD system, specifically tuned-up. The advantage of using a deep learning approach is a better regularization of the classification output. In a preliminary evaluation, the combined approach was tested on a 13 patient database of various lung pathologies, showing an increase of 10% in True Positive Rate (TPR) with respect to the best suited state of the art CNN for this task.

The dawn of a new era in onco-cardiology: The Kumamoto Classification.

PubMed

Sueta, Daisuke; Tabata, Noriaki; Akasaka, Tomonori; Yamashita, Takayoshi; Ikemoto, Tomokazu; Hokimoto, Seiji

2016-10-01

The term "onco-cardiology" has been used in reference to cardiotoxicity in the treatment of malignant disease. In actual clinical situations, however, cardiovascular disease (CVD) associated with malignant disease and the concurrence of atherosclerotic disease with malignant disease are commonly observed, complicating the course of treatment. Patients with malignant disease associated with coronary artery disease often die from the cardiovascular disease, so it is essential to classify these disease states. Additionally, the prevalence of these classifications makes it easy to manage patients with malignant disease and coronary artery disease. We divided the broad field of onco-cardiology into 4 classifications based on clinical scenarios (CSs): CS1 represents the so-called paraneoplastic syndrome. CS2 represents cardiotoxicity during treatment of malignant diseases. CS3 represents the concurrence of atherosclerotic disease with malignant disease, and CS4 represents cardiovascular disease with benign tumors. This classification facilitates the management of patients with malignant disease and coronary artery disease by promoting not only the primary but also the secondary prevention of CVD. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Update and validation of the Society for Vascular Surgery wound, ischemia, and foot infection threatened limb classification system.

PubMed

Mills, Joseph L

2014-03-01

The diagnosis of critical limb ischemia, first defined in 1982, was intended to delineate a patient cohort with a threatened limb and at risk for amputation due to severe peripheral arterial disease. The influence of diabetes and its associated neuropathy on the pathogenesis-threatened limb was an excluded comorbidity, despite its known contribution to amputation risk. The Fontaine and Rutherford classifications of limb ischemia severity have also been used to predict amputation risk and the likelihood of tissue healing. The dramatic increase in the prevalence of diabetes mellitus and the expanding techniques of arterial revascularization has prompted modification of peripheral arterial disease classification schemes to improve outcomes analysis for patients with threatened limbs. The diabetic patient with foot ulceration and infection is at risk for limb loss, with abnormal arterial perfusion as only one determinant of outcome. The wound extent and severity of infection also impact the likelihood of limb loss. To better predict amputation risk, the Society for Vascular Surgery Lower Extremity Guidelines Committee developed a classification of the threatened lower extremity that reflects these important clinical considerations. Risk stratification is based on three major factors that impact amputation risk and clinical management: wound, ischemia, and foot infection. This classification scheme is relevant to the patient with critical limb ischemia because many are also diabetic. Implementation of the wound, ischemia, and foot infection classification system in critical limb ischemia patients is recommended and should assist the clinician in more meaningful analysis of outcomes for various forms of wound and arterial revascularizations procedures required in this challenging, patient population. Copyright © 2014 Elsevier Inc. All rights reserved.

The performance improvement of automatic classification among obstructive lung diseases on the basis of the features of shape analysis, in addition to texture analysis at HRCT

NASA Astrophysics Data System (ADS)

Lee, Youngjoo; Kim, Namkug; Seo, Joon Beom; Lee, JuneGoo; Kang, Suk Ho

2007-03-01

In this paper, we proposed novel shape features to improve classification performance of differentiating obstructive lung diseases, based on HRCT (High Resolution Computerized Tomography) images. The images were selected from HRCT images, obtained from 82 subjects. For each image, two experienced radiologists selected rectangular ROIs with various sizes (16x16, 32x32, and 64x64 pixels), representing each disease or normal lung parenchyma. Besides thirteen textural features, we employed additional seven shape features; cluster shape features, and Top-hat transform features. To evaluate the contribution of shape features for differentiation of obstructive lung diseases, several experiments were conducted with two different types of classifiers and various ROI sizes. For automated classification, the Bayesian classifier and support vector machine (SVM) were implemented. To assess the performance and cross-validation of the system, 5-folding method was used. In comparison to employing only textural features, adding shape features yields significant enhancement of overall sensitivity(5.9, 5.4, 4.4% in the Bayesian and 9.0, 7.3, 5.3% in the SVM), in the order of ROI size 16x16, 32x32, 64x64 pixels, respectively (t-test, p<0.01). Moreover, this enhancement was largely due to the improvement on class-specific sensitivity of mild centrilobular emphysema and bronchiolitis obliterans which are most hard to differentiate for radiologists. According to these experimental results, adding shape features to conventional texture features is much useful to improve classification performance of obstructive lung diseases in both Bayesian and SVM classifiers.

Chronic Mastitis in Egypt and Morocco: differentiating between idiopathic granulomatous mastitis and IgG4-related disease

PubMed Central

Allen, Steven G.; Soliman, Amr S.; Toy, Kathleen; Omar, Omar S.; Youssef, Tamer; Karkouri, Mehdi; Ayad, Essam; Abdel-Aziz, Azza; Hablas, Ahmed; Tahri, Ali; Oltean, Hanna N.; Kleer, Celina G.; Merajver, Sofia D.

2016-01-01

Idiopathic granulomatous mastitis (IGM) is a benign, frequently severe chronic inflammatory lesion of the breast. Its etiology remains unknown and reported cases vary in their presentation and histologic findings with an optimal treatment algorithm yet to be described owing mainly to the disease’s heterogeneity. IgG4-related disease (IgG4-RD) is a newly recognized systemic fibroinflammatory condition characterized by a dense lymphoplasmacytic infiltrate with many IgG4-positive plasma cells, storiform fibrosis, and obliterative phlebitis. Immunosuppressive therapy is considered to be an effective first-line therapy for IgG4-RD. We sought to clarify and classify chronic mastitis according to the histologic findings of IgG4-RD mastitis with respect to IGM and to develop a robust diagnostic framework to help select patients for optimal treatment strategies. Using the largest collection to date (43 cases from Egypt and Morocco), we show that despite sharing many features, IGM and IgG4-RD mastitis are separate diseases. To diagnostically separate the diseases, we created a classification schema – termed the Michigan Classification – based upon our large series of cases, the consensus statement on IgG4-RD, and the histologic description of IGM in the literature. Using our classification, we discerned 17 cases of IgG4-RD and 8 cases of IGM among the 43 chronic mastitis cases, with 18 indeterminate cases. Thus our Michigan Classification can form the basis of rational stratification of chronic mastitis patients between these two clinically and histopathologically heterogeneous diseases. PMID:27279578

Automatic classification of patients with idiopathic Parkinson's disease and progressive supranuclear palsy using diffusion MRI datasets

NASA Astrophysics Data System (ADS)

Talai, Sahand; Boelmans, Kai; Sedlacik, Jan; Forkert, Nils D.

2017-03-01

Parkinsonian syndromes encompass a spectrum of neurodegenerative diseases, which can be classified into various subtypes. The differentiation of these subtypes is typically conducted based on clinical criteria. Due to the overlap of intra-syndrome symptoms, the accurate differential diagnosis based on clinical guidelines remains a challenge with failure rates up to 25%. The aim of this study is to present an image-based classification method of patients with Parkinson's disease (PD) and patients with progressive supranuclear palsy (PSP), an atypical variant of PD. Therefore, apparent diffusion coefficient (ADC) parameter maps were calculated based on diffusion-tensor magnetic resonance imaging (MRI) datasets. Mean ADC values were determined in 82 brain regions using an atlas-based approach. The extracted mean ADC values for each patient were then used as features for classification using a linear kernel support vector machine classifier. To increase the classification accuracy, a feature selection was performed, which resulted in the top 17 attributes to be used as the final input features. A leave-one-out cross validation based on 56 PD and 21 PSP subjects revealed that the proposed method is capable of differentiating PD and PSP patients with an accuracy of 94.8%. In conclusion, the classification of PD and PSP patients based on ADC features obtained from diffusion MRI datasets is a promising new approach for the differentiation of Parkinsonian syndromes in the broader context of decision support systems.

Determinants of the Delegation of Health Care Aboard Ships with Women Assigned

DTIC Science & Technology

1989-06-07

Classification of Diseases (ICD-9) code7 by one of five research staff memners. 4 RESULTS Of the original 2,906 patient visits, the research staff was tnable...was recomputed: (1) neoplasms, (2) endocrine, nutritional and metabolic diseases , and immunity disorders, (3) diseases of the blood and blood-forming...organs, (4) diseases of the circulatory system, (5) complications o pregnancy, and (6) congenital anomalies. As shown in Figure 2, the results of this

Effect of various binning methods and ROI sizes on the accuracy of the automatic classification system for differentiation between diffuse infiltrative lung diseases on the basis of texture features at HRCT

NASA Astrophysics Data System (ADS)

Kim, Namkug; Seo, Joon Beom; Sung, Yu Sub; Park, Bum-Woo; Lee, Youngjoo; Park, Seong Hoon; Lee, Young Kyung; Kang, Suk-Ho

2008-03-01

To find optimal binning, variable binning size linear binning (LB) and non-linear binning (NLB) methods were tested. In case of small binning size (Q <= 10), NLB shows significant better accuracy than the LB. K-means NLB (Q = 26) is statistically significant better than every LB. To find optimal binning method and ROI size of the automatic classification system for differentiation between diffuse infiltrative lung diseases on the basis of textural analysis at HRCT Six-hundred circular regions of interest (ROI) with 10, 20, and 30 pixel diameter, comprising of each 100 ROIs representing six regional disease patterns (normal, NL; ground-glass opacity, GGO; reticular opacity, RO; honeycombing, HC; emphysema, EMPH; and consolidation, CONS) were marked by an experienced radiologist from HRCT images. Histogram (mean) and co-occurrence matrix (mean and SD of angular second moment, contrast, correlation, entropy, and inverse difference momentum) features were employed to test binning and ROI effects. To find optimal binning, variable binning size LB (bin size Q: 4~30, 32, 64, 128, 144, 196, 256, 384) and NLB (Q: 4~30) methods (K-means, and Fuzzy C-means clustering) were tested. For automated classification, a SVM classifier was implemented. To assess cross-validation of the system, a five-folding method was used. Each test was repeatedly performed twenty times. Overall accuracies with every combination of variable ROIs, and binning sizes were statistically compared. In case of small binning size (Q <= 10), NLB shows significant better accuracy than the LB. K-means NLB (Q = 26) is statistically significant better than every LB. In case of 30x30 ROI size and most of binning size, the K-means method showed better than other NLB and LB methods. When optimal binning and other parameters were set, overall sensitivity of the classifier was 92.85%. The sensitivity and specificity of the system for each class were as follows: NL, 95%, 97.9%; GGO, 80%, 98.9%; RO 85%, 96.9%; HC, 94.7%, 97%; EMPH, 100%, 100%; and CONS, 100%, 100%, respectively. We determined the optimal binning method and ROI size of the automatic classification system for differentiation between diffuse infiltrative lung diseases on the basis of texture features at HRCT.

Domain Adaptation for Alzheimer’s Disease Diagnostics

PubMed Central

Wachinger, Christian; Reuter, Martin

2016-01-01

With the increasing prevalence of Alzheimer’s disease, research focuses on the early computer-aided diagnosis of dementia with the goal to understand the disease process, determine risk and preserving factors, and explore preventive therapies. By now, large amounts of data from multi-site studies have been made available for developing, training, and evaluating automated classifiers. Yet, their translation to the clinic remains challenging, in part due to their limited generalizability across different datasets. In this work, we describe a compact classification approach that mitigates overfitting by regularizing the multinomial regression with the mixed ℓ1/ℓ2 norm. We combine volume, thickness, and anatomical shape features from MRI scans to characterize neuroanatomy for the three-class classification of Alzheimer’s disease, mild cognitive impairment and healthy controls. We demonstrate high classification accuracy via independent evaluation within the scope of the CADDementia challenge. We, furthermore, demonstrate that variations between source and target datasets can substantially influence classification accuracy. The main contribution of this work addresses this problem by proposing an approach for supervised domain adaptation based on instance weighting. Integration of this method into our classifier allows us to assess different strategies for domain adaptation. Our results demonstrate (i) that training on only the target training set yields better results than the naïve combination (union) of source and target training sets, and (ii) that domain adaptation with instance weighting yields the best classification results, especially if only a small training component of the target dataset is available. These insights imply that successful deployment of systems for computer-aided diagnostics to the clinic depends not only on accurate classifiers that avoid overfitting, but also on a dedicated domain adaptation strategy. PMID:27262241

Computer-aided diagnosis of pulmonary diseases using x-ray darkfield radiography

NASA Astrophysics Data System (ADS)

Einarsdóttir, Hildur; Yaroshenko, Andre; Velroyen, Astrid; Bech, Martin; Hellbach, Katharina; Auweter, Sigrid; Yildirim, Önder; Meinel, Felix G.; Eickelberg, Oliver; Reiser, Maximilian; Larsen, Rasmus; Kjær Ersbøll, Bjarne; Pfeiffer, Franz

2015-12-01

In this work we develop a computer-aided diagnosis (CAD) scheme for classification of pulmonary disease for grating-based x-ray radiography. In addition to conventional transmission radiography, the grating-based technique provides a dark-field imaging modality, which utilizes the scattering properties of the x-rays. This modality has shown great potential for diagnosing early stage emphysema and fibrosis in mouse lungs in vivo. The CAD scheme is developed to assist radiologists and other medical experts to develop new diagnostic methods when evaluating grating-based images. The scheme consists of three stages: (i) automatic lung segmentation; (ii) feature extraction from lung shape and dark-field image intensities; (iii) classification between healthy, emphysema and fibrosis lungs. A study of 102 mice was conducted with 34 healthy, 52 emphysema and 16 fibrosis subjects. Each image was manually annotated to build an experimental dataset. System performance was assessed by: (i) determining the quality of the segmentations; (ii) validating emphysema and fibrosis recognition by a linear support vector machine using leave-one-out cross-validation. In terms of segmentation quality, we obtained an overlap percentage (Ω) 92.63  ±  3.65%, Dice Similarity Coefficient (DSC) 89.74  ±  8.84% and Jaccard Similarity Coefficient 82.39  ±  12.62%. For classification, the accuracy, sensitivity and specificity of diseased lung recognition was 100%. Classification between emphysema and fibrosis resulted in an accuracy of 93%, whilst the sensitivity was 94% and specificity 88%. In addition to the automatic classification of lungs, deviation maps created by the CAD scheme provide a visual aid for medical experts to further assess the severity of pulmonary disease in the lung, and highlights regions affected.

White Paper: Movement System Diagnoses in Neurologic Physical Therapy.

PubMed

Hedman, Lois D; Quinn, Lori; Gill-Body, Kathleen; Brown, David A; Quiben, Myla; Riley, Nora; Scheets, Patricia L

2018-04-01

The APTA recently established a vision for physical therapists to transform society by optimizing movement to promote health and wellness, mitigate impairments, and prevent disability. An important element of this vision entails the integration of the movement system into the profession, and necessitates the development of movement system diagnoses by physical therapists. At this point in time, the profession as a whole has not agreed upon diagnostic classifications or guidelines to assist in developing movement system diagnoses that will consistently capture an individual's movement problems. We propose that, going forward, diagnostic classifications of movement system problems need to be developed, tested, and validated. The Academy of Neurologic Physical Therapy's Movement System Task Force was convened to address these issues with respect to management of movement system problems in patients with neurologic conditions. The purpose of this article is to report on the work and recommendations of the Task Force. The Task Force identified 4 essential elements necessary to develop and implement movement system diagnoses for patients with primarily neurologic involvement from existing movement system classifications. The Task Force considered the potential impact of using movement system diagnoses on clinical practice, education and, research. Recommendations were developed and provided recommendations for potential next steps to broaden this discussion and foster the development of movement system diagnostic classifications. The Task Force proposes that diagnostic classifications of movement system problems need to be developed, tested, and validated with the long-range goal to reach consensus on and adoption of a movement system diagnostic framework for clients with neurologic injury or disease states.Video Abstract available for more insights from the authors (see Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A198).

Emerging insights into the molecular and cellular basis of glioblastoma

PubMed Central

Dunn, Gavin P.; Rinne, Mikael L.; Wykosky, Jill; Genovese, Giannicola; Quayle, Steven N.; Dunn, Ian F.; Agarwalla, Pankaj K.; Chheda, Milan G.; Campos, Benito; Wang, Alan; Brennan, Cameron; Ligon, Keith L.; Furnari, Frank; Cavenee, Webster K.; Depinho, Ronald A.; Chin, Lynda; Hahn, William C.

2012-01-01

Glioblastoma is both the most common and lethal primary malignant brain tumor. Extensive multiplatform genomic characterization has provided a higher-resolution picture of the molecular alterations underlying this disease. These studies provide the emerging view that “glioblastoma” represents several histologically similar yet molecularly heterogeneous diseases, which influences taxonomic classification systems, prognosis, and therapeutic decisions. PMID:22508724

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.

Systems biology coupled with label-free high-throughput detection as a novel approach for diagnosis of chronic obstructive pulmonary disease

PubMed Central

Richens, Joanna L; Urbanowicz, Richard A; Lunt, Elizabeth AM; Metcalf, Rebecca; Corne, Jonathan; Fairclough, Lucy; O'Shea, Paul

2009-01-01

Chronic obstructive pulmonary disease (COPD) is a treatable and preventable disease state, characterised by progressive airflow limitation that is not fully reversible. Although COPD is primarily a disease of the lungs there is now an appreciation that many of the manifestations of disease are outside the lung, leading to the notion that COPD is a systemic disease. Currently, diagnosis of COPD relies on largely descriptive measures to enable classification, such as symptoms and lung function. Here the limitations of existing diagnostic strategies of COPD are discussed and systems biology approaches to diagnosis that build upon current molecular knowledge of the disease are described. These approaches rely on new 'label-free' sensing technologies, such as high-throughput surface plasmon resonance (SPR), that we also describe. PMID:19386108

Food Classification Systems Based on Food Processing: Significance and Implications for Policies and Actions: A Systematic Literature Review and Assessment.

PubMed

Moubarac, Jean-Claude; Parra, Diana C; Cannon, Geoffrey; Monteiro, Carlos A

2014-06-01

This paper is the first to make a systematic review and assessment of the literature that attempts methodically to incorporate food processing into classification of diets. The review identified 1276 papers, of which 110 were screened and 21 studied, derived from five classification systems. This paper analyses and assesses the five systems, one of which has been devised and developed by a research team that includes co-authors of this paper. The quality of the five systems is assessed and scored according to how specific, coherent, clear, comprehensive and workable they are. Their relevance to food, nutrition and health, and their use in various settings, is described. The paper shows that the significance of industrial food processing in shaping global food systems and supplies and thus dietary patterns worldwide, and its role in the pandemic of overweight and obesity, remains overlooked and underestimated. Once food processing is systematically incorporated into food classifications, they will be more useful in assessing and monitoring dietary patterns. Food classification systems that emphasize industrial food processing, and that define and distinguish relevant different types of processing, will improve understanding of how to prevent and control overweight, obesity and related chronic non-communicable diseases, and also malnutrition. They will also be a firmer basis for rational policies and effective actions designed to protect and improve public health at all levels from global to local.

Development and content validity testing of a comprehensive classification of diagnoses for pediatric nurse practitioners.

PubMed

Burns, C

1991-01-01

Pediatric nurse practitioners (PNPs) need an integrated, comprehensive classification that includes nursing, disease, and developmental diagnoses to effectively describe their practice. No such classification exists. Further, methodologic studies to help evaluate the content validity of any nursing taxonomy are unavailable. A conceptual framework was derived. Then 178 diagnoses from the North American Nursing Diagnosis Association (NANDA) 1986 list, selected diagnoses from the International Classification of Diseases, the Diagnostic and Statistical Manual, Third Revision, and others were selected. This framework identified and listed, with definitions, three domains of diagnoses: Developmental Problems, Diseases, and Daily Living Problems. The diagnoses were ranked using a 4-point scale (4 = highly related to 1 = not related) and were placed into the three domains. The rating scale was assigned by a panel of eight expert pediatric nurses. Diagnoses that were assigned to the Daily Living Problems domain were then sorted into the 11 Functional Health patterns described by Gordon (1987). Reliability was measured using proportions of agreement and Kappas. Content validity of the groups created was measured using indices of content validity and average congruency percentages. The experts used a new method to sort the diagnoses in a new way that decreased overlaps among the domains. The Developmental and Disease domains were judged reliable and valid. The Daily Living domain of nursing diagnoses showed marginally acceptable validity with acceptable reliability. Six Functional Health Patterns were judged reliable and valid, mixed results were determined for four categories, and the Coping/Stress Tolerance category was judged reliable but not valid using either test. There were considerable differences between the panel's, Gordon's (1987), and NANDA's clustering of NANDA diagnoses. This study defines the diagnostic practice of nurses from a holistic, patient-centered perspective. It is the first study to use quantitative methods to test a diagnostic classification system for nursing. The classification model could also be adapted for other nurse specialties.

Infant Mortality: Development of a Proposed Update to the Dollfus Classification of Infant Deaths

PubMed Central

Dove, Melanie S.; Minnal, Archana; Damesyn, Mark; Curtis, Michael P.

2015-01-01

Objective Identifying infant deaths with common underlying causes and potential intervention points is critical to infant mortality surveillance and the development of prevention strategies. We constructed an International Classification of Diseases 10th Revision (ICD-10) parallel to the Dollfus cause-of-death classification scheme first published in 1990, which organized infant deaths by etiology and their amenability to prevention efforts. Methods Infant death records for 1996, dual-coded to the ICD Ninth Revision (ICD-9) and ICD-10, were obtained from the CDC public-use multiple-cause-of-death file on comparability between ICD-9 and ICD-10. We used the underlying cause of death to group 27,821 infant deaths into the nine categories of the ICD-9-based update to Dollfus' original coding scheme, published by Sowards in 1999. Comparability ratios were computed to measure concordance between ICD versions. Results The Dollfus classification system updated with ICD-10 codes had limited agreement with the 1999 modified classification system. Although prematurity, congenital malformations, Sudden Infant Death Syndrome, and obstetric conditions were the first through fourth most common causes of infant death under both systems, most comparability ratios were significantly different from one system to the other. Conclusion The Dollfus classification system can be adapted for use with ICD-10 codes to create a comprehensive, etiology-based profile of infant deaths. The potential benefits of using Dollfus logic to guide perinatal mortality reduction strategies, particularly to maternal and child health programs and other initiatives focused on improving infant health, warrant further examination of this method's use in perinatal mortality surveillance. PMID:26556935

Pediatric complex chronic conditions classification system version 2: updated for ICD-10 and complex medical technology dependence and transplantation

PubMed Central

2014-01-01

Background The pediatric complex chronic conditions (CCC) classification system, developed in 2000, requires revision to accommodate the International Classification of Disease 10th Revision (ICD-10). To update the CCC classification system, we incorporated ICD-9 diagnostic codes that had been either omitted or incorrectly specified in the original system, and then translated between ICD-9 and ICD-10 using General Equivalence Mappings (GEMs). We further reviewed all codes in the ICD-9 and ICD-10 systems to include both diagnostic and procedural codes indicative of technology dependence or organ transplantation. We applied the provisional CCC version 2 (v2) system to death certificate information and 2 databases of health utilization, reviewed the resulting CCC classifications, and corrected any misclassifications. Finally, we evaluated performance of the CCC v2 system by assessing: 1) the stability of the system between ICD-9 and ICD-10 codes using data which included both ICD-9 codes and ICD-10 codes; 2) the year-to-year stability before and after ICD-10 implementation; and 3) the proportions of patients classified as having a CCC in both the v1 and v2 systems. Results The CCC v2 classification system consists of diagnostic and procedural codes that incorporate a new neonatal CCC category as well as domains of complexity arising from technology dependence or organ transplantation. CCC v2 demonstrated close comparability between ICD-9 and ICD-10 and did not detect significant discontinuity in temporal trends of death in the United States. Compared to the original system, CCC v2 resulted in a 1.0% absolute (10% relative) increase in the number of patients identified as having a CCC in national hospitalization dataset, and a 0.4% absolute (24% relative) increase in a national emergency department dataset. Conclusions The updated CCC v2 system is comprehensive and multidimensional, and provides a necessary update to accommodate widespread implementation of ICD-10. PMID:25102958

Usefulness and applicability of the revised dengue case classification by disease: multi-centre study in 18 countries

PubMed Central

2011-01-01

Background In view of the long term discussion on the appropriateness of the dengue classification into dengue fever (DF), dengue haemorrhagic fever (DHF) and dengue shock syndrome (DSS), the World Health Organization (WHO) has outlined in its new global dengue guidelines a revised classification into levels of severity: dengue fever with an intermediary group of "dengue fever with warning sings", and severe dengue. The objective of this paper was to compare the two classification systems regarding applicability in clinical practice and surveillance, as well as user-friendliness and acceptance by health staff. Methods A mix of quantitative (prospective and retrospective review of medical charts by expert reviewers, formal staff interviews), semi-quantitative (open questions in staff interviews) and qualitative methods (focus group discussions) were used in 18 countries. Quality control of data collected was undertaken by external monitors. Results The applicability of the DF/DHF/DSS classification was limited, even when strict DHF criteria were not applied (13.7% of dengue cases could not be classified using the DF/DHF/DSS classification by experienced reviewers, compared to only 1.6% with the revised classification). The fact that some severe dengue cases could not be classified in the DF/DHF/DSS system was of particular concern. Both acceptance and perceived user-friendliness of the revised system were high, particularly in relation to triage and case management. The applicability of the revised classification to retrospective data sets (of importance for dengue surveillance) was also favourable. However, the need for training, dissemination and further research on the warning signs was highlighted. Conclusions The revised dengue classification has a high potential for facilitating dengue case management and surveillance. PMID:21510901

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

International Classification of Impairments, Disabilities, and Handicaps: A Manual of Classification Relating to the Consequences of Disease.

ERIC Educational Resources Information Center

World Health Organization, Geneva (Switzerland).

The manual contains three classifications (impairments, disabilities, and handicaps), each relating to a different plane of experience consequent upon disease. Section 1 attempts to clarify the nature of health related experiences by addressing reponse to acute and chronic illness; the unifying framework for classification (principle events in the…

A new classification scheme for periodontal and peri-implant diseases and conditions - Introduction and key changes from the 1999 classification.

PubMed

G Caton, Jack; Armitage, Gary; Berglundh, Tord; Chapple, Iain L C; Jepsen, Søren; S Kornman, Kenneth; L Mealey, Brian; Papapanou, Panos N; Sanz, Mariano; S Tonetti, Maurizio

2018-06-01

A classification scheme for periodontal and peri-implant diseases and conditions is necessary for clinicians to properly diagnose and treat patients as well as for scientists to investigate etiology, pathogenesis, natural history, and treatment of the diseases and conditions. This paper summarizes the proceedings of the World Workshop on the Classification of Periodontal and Peri-implant Diseases and Conditions. The workshop was co-sponsored by the American Academy of Periodontology (AAP) and the European Federation of Periodontology (EFP) and included expert participants from all over the world. Planning for the conference, which was held in Chicago on November 9 to 11, 2017, began in early 2015. An organizing committee from the AAP and EFP commissioned 19 review papers and four consensus reports covering relevant areas in periodontology and implant dentistry. The authors were charged with updating the 1999 classification of periodontal diseases and conditions and developing a similar scheme for peri-implant diseases and conditions. Reviewers and workgroups were also asked to establish pertinent case definitions and to provide diagnostic criteria to aid clinicians in the use of the new classification. All findings and recommendations of the workshop were agreed to by consensus. This introductory paper presents an overview for the new classification of periodontal and peri-implant diseases and conditions, along with a condensed scheme for each of four workgroup sections, but readers are directed to the pertinent consensus reports and review papers for a thorough discussion of the rationale, criteria, and interpretation of the proposed classification. Changes to the 1999 classification are highlighted and discussed. Although the intent of the workshop was to base classification on the strongest available scientific evidence, lower level evidence and expert opinion were inevitably used whenever sufficient research data were unavailable. The scope of this workshop was to align and update the classification scheme to the current understanding of periodontal and peri-implant diseases and conditions. This introductory overview presents the schematic tables for the new classification of periodontal and peri-implant diseases and conditions and briefly highlights changes made to the 1999 classification. It cannot present the wealth of information included in the reviews, case definition papers, and consensus reports that has guided the development of the new classification, and reference to the consensus and case definition papers is necessary to provide a thorough understanding of its use for either case management or scientific investigation. Therefore, it is strongly recommended that the reader use this overview as an introduction to these subjects. Accessing this publication online will allow the reader to use the links in this overview and the tables to view the source papers (Table ). © 2018 American Academy of Periodontology and European Federation of Periodontology.

A new classification scheme for periodontal and peri-implant diseases and conditions - Introduction and key changes from the 1999 classification.

PubMed

G Caton, Jack; Armitage, Gary; Berglundh, Tord; Chapple, Iain L C; Jepsen, Søren; S Kornman, Kenneth; L Mealey, Brian; Papapanou, Panos N; Sanz, Mariano; S Tonetti, Maurizio

2018-06-01

A classification scheme for periodontal and peri-implant diseases and conditions is necessary for clinicians to properly diagnose and treat patients as well as for scientists to investigate etiology, pathogenesis, natural history, and treatment of the diseases and conditions. This paper summarizes the proceedings of the World Workshop on the Classification of Periodontal and Peri-implant Diseases and Conditions. The workshop was co-sponsored by the American Academy of Periodontology (AAP) and the European Federation of Periodontology (EFP) and included expert participants from all over the world. Planning for the conference, which was held in Chicago on November 9 to 11, 2017, began in early 2015. An organizing committee from the AAP and EFP commissioned 19 review papers and four consensus reports covering relevant areas in periodontology and implant dentistry. The authors were charged with updating the 1999 classification of periodontal diseases and conditions and developing a similar scheme for peri-implant diseases and conditions. Reviewers and workgroups were also asked to establish pertinent case definitions and to provide diagnostic criteria to aid clinicians in the use of the new classification. All findings and recommendations of the workshop were agreed to by consensus. This introductory paper presents an overview for the new classification of periodontal and peri-implant diseases and conditions, along with a condensed scheme for each of four workgroup sections, but readers are directed to the pertinent consensus reports and review papers for a thorough discussion of the rationale, criteria, and interpretation of the proposed classification. Changes to the 1999 classification are highlighted and discussed. Although the intent of the workshop was to base classification on the strongest available scientific evidence, lower level evidence and expert opinion were inevitably used whenever sufficient research data were unavailable. The scope of this workshop was to align and update the classification scheme to the current understanding of periodontal and peri-implant diseases and conditions. This introductory overview presents the schematic tables for the new classification of periodontal and peri-implant diseases and conditions and briefly highlights changes made to the 1999 classification. It cannot present the wealth of information included in the reviews, case definition papers, and consensus reports that has guided the development of the new classification, and reference to the consensus and case definition papers is necessary to provide a thorough understanding of its use for either case management or scientific investigation. Therefore, it is strongly recommended that the reader use this overview as an introduction to these subjects. Accessing this publication online will allow the reader to use the links in this overview and the tables to view the source papers (Table 1). © 2018 American Academy of Periodontology and European Federation of Periodontology.

Classification systems for lower extremity amputation prediction in subjects with active diabetic foot ulcer: a systematic review and meta-analysis.

PubMed

Monteiro-Soares, M; Martins-Mendes, D; Vaz-Carneiro, A; Sampaio, S; Dinis-Ribeiro, M

2014-10-01

We systematically review the available systems used to classify diabetic foot ulcers in order to synthesize their methodological qualitative issues and accuracy to predict lower extremity amputation, as this may represent a critical point in these patients' care. Two investigators searched, in EBSCO, ISI, PubMed and SCOPUS databases, and independently selected studies published until May 2013 and reporting prognostic accuracy and/or reliability of specific systems for patients with diabetic foot ulcer in order to predict lower extremity amputation. We included 25 studies reporting a prevalence of lower extremity amputation between 6% and 78%. Eight different diabetic foot ulcer descriptions and seven prognostic stratification classification systems were addressed with a variable (1-9) number of factors included, specially peripheral arterial disease (n = 12) or infection at the ulcer site (n = 10) or ulcer depth (n = 10). The Meggitt-Wagner, S(AD)SAD and Texas University Classification systems were the most extensively validated, whereas ten classifications were derived or validated only once. Reliability was reported in a single study, and accuracy measures were reported in five studies with another eight allowing their calculation. Pooled accuracy ranged from 0.65 (for gangrene) to 0.74 (for infection). There are numerous classification systems for diabetic foot ulcer outcome prediction, but only few studies evaluated their reliability or external validity. Studies rarely validated several systems simultaneously and only a few reported accuracy measures. Further studies assessing reliability and accuracy of the available systems and their composing variables are needed. Copyright © 2014 John Wiley & Sons, Ltd.

Inter-observer reliability of radiographic classifications and measurements in the assessment of Perthes' disease.

PubMed

Wiig, Ola; Terjesen, Terje; Svenningsen, Svein

2002-10-01

We evaluated the inter-observer agreement of radiographic methods when evaluating patients with Perthes' disease. The radiographs were assessed at the time of diagnosis and at the 1-year follow-up by local orthopaedic surgeons (O) and 2 experienced pediatric orthopedic surgeons (TT and SS). The Catterall, Salter-Thompson, and Herring lateral pillar classifications were compared, and the femoral head coverage (FHC), center-edge angle (CE-angle), and articulo-trochanteric distance (ATD) were measured in the affected and normal hips. On the primary evaluation, the lateral pillar and Salter-Thompson classifications had a higher level of agreement among the observers than the Catterall classification, but none of the classifications showed good agreement (weighted kappa values between O and SS 0.56, 0.54, 0.49, respectively). Combining Catterall groups 1 and 2 into one group, and groups 3 and 4 into another resulted in better agreement (kappa 0.55) than with the original 4-group system. The agreement was also better (kappa 0.62-0.70) between experienced than between less experienced examiners for all classifications. The femoral head coverage was a more reliable and accurate measure than the CE-angle for quantifying the acetabular covering of the femoral head, as indicated by higher intraclass correlation coefficients (ICC) and smaller inter-observer differences. The ATD showed good agreement in all comparisons and had low interobserver differences. We conclude that all classifications of femoral head involvement are adequate in clinical work if the radiographic assessment is done by experienced examiners. When they are less experienced examiners, a 2-group classification or the lateral pillar classification is more reliable. For evaluation of containment of the femoral head, FHC is more appropriate than the CE-angle.

An ensemble learning system for a 4-way classification of Alzheimer's disease and mild cognitive impairment.

PubMed

Yao, Dongren; Calhoun, Vince D; Fu, Zening; Du, Yuhui; Sui, Jing

2018-05-15

Discriminating Alzheimer's disease (AD) from its prodromal form, mild cognitive impairment (MCI), is a significant clinical problem that may facilitate early diagnosis and intervention, in which a more challenging issue is to classify MCI subtypes, i.e., those who eventually convert to AD (cMCI) versus those who do not (MCI). To solve this difficult 4-way classification problem (AD, MCI, cMCI and healthy controls), a competition was hosted by Kaggle to invite the scientific community to apply their machine learning approaches on pre-processed sets of T1-weighted magnetic resonance images (MRI) data and the demographic information from the international Alzheimer's disease neuroimaging initiative (ADNI) database. This paper summarizes our competition results. We first proposed a hierarchical process by turning the 4-way classification into five binary classification problems. A new feature selection technology based on relative importance was also proposed, aiming to identify a more informative and concise subset from 426 sMRI morphometric and 3 demographic features, to ensure each binary classifier to achieve its highest accuracy. As a result, about 2% of the original features were selected to build a new feature space, which can achieve the final four-way classification with a 54.38% accuracy on testing data through hierarchical grouping, higher than several alternative methods in comparison. More importantly, the selected discriminative features such as hippocampal volume, parahippocampal surface area, and medial orbitofrontal thickness, etc. as well as the MMSE score, are reasonable and consistent with those reported in AD/MCI deficits. In summary, the proposed method provides a new framework for multi-way classification using hierarchical grouping and precise feature selection. Copyright © 2018 Elsevier B.V. All rights reserved.

Clinical Characteristics and Associated Systemic Diseases in Patients With Esophageal "Absent Contractility"-A Clinical Algorithm.

PubMed

Laique, Sobia; Singh, Tavankit; Dornblaser, David; Gadre, Abhishek; Rangan, Vikram; Fass, Ronnie; Kirby, Donald; Chatterjee, Soumya; Gabbard, Scott

2018-01-19

This study was carried out to assess the clinical characteristics and associated systemic diseases seen in patients diagnosed with absent contractility as per the Chicago Classification version 3.0, allowing us to propose a diagnostic algorithm for their etiologic testing. The Chicago Classification version 3.0 has redefined major and minor esophageal motility disorders using high-resolution esophageal manometry. There is a dearth of publications based on research on absent contractility, which historically has been associated with myopathic processes such as systemic sclerosis (SSc). We conducted a retrospective, multicenter study. Data of patients diagnosed with absent contractility were pooled from Cleveland Clinic, Cleveland, OH (January 2006 to July 2016) and Metrohealth Medical Center, Cleveland, OH (July 2014 to July 2016) and included: age, gender, associated medical conditions, surgical history, medications, and specific antibody testing. A total of 207 patients, including 57 male individuals and 150 female individuals, with mean age of 56.1 and 60.0 years, respectively, were included. Disease distribution was as follows: SSc (diffuse or limited cutaneous) 132, overlap syndromes 7, systemic lupus erythematosus17, Sjögren syndrome 4, polymyositis 3, and dermatomyositis 3. Various other etiologies including gastroesophageal reflux disease, postradiation esophagitis, neuromuscular disorders, and surgical complications were seen in the remaining cohort. Most practitioners use the term "absent contractility" interchangeably with "scleroderma esophagus"; however, only 63% of patients with absent contractility had SSc. Overall, 20% had another systemic autoimmune rheumatologic disease and 16% had a nonrheumatologic etiology for absent contractility. Therefore, alternate diagnosis must be sought in these patients. We propose an algorithm for their etiologic evaluation.

Free-Text Disease Classification

DTIC Science & Technology

2011-09-01

NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS FREE-TEXT DISEASE CLASSIFICATION by Craig Maxey September 2011 Thesis Advisor: Lyn R. Whitaker...2104-10-31 Free-Text Disease Classification Craig Maxey Naval Postgraduate School Monterey, CA 93943 Department of the Navy Approved for public...POSTGRADUATE SCHOOL September 2011 Author: Craig Maxey Approved by: Lyn R. Whitaker Thesis Advisor Samuel E. Buttrey Second Reader Robert F. Dell Chair

Classification of Airflow Limitation Based on z-Score Underestimates Mortality in Patients with Chronic Obstructive Pulmonary Disease.

PubMed

Tejero, Elena; Prats, Eva; Casitas, Raquel; Galera, Raúl; Pardo, Paloma; Gavilán, Adelaida; Martínez-Cerón, Elisabet; Cubillos-Zapata, Carolina; Del Peso, Luis; García-Río, Francisco

2017-08-01

Global Lung Function Initiative recommends reporting lung function measures as z-score, and a classification of airflow limitation (AL) based on this parameter has recently been proposed. To evaluate the prognostic capacity of the AL classifications based on z-score or percentage predicted of FEV 1 in patients with chronic obstructive pulmonary disease (COPD). A cohort of 2,614 patients with COPD recruited outside the hospital setting was examined after a mean (± SD) of 57 ± 13 months of follow-up, totaling 10,322 person-years. All-cause mortality was analyzed, evaluating the predictive capacity of several AL staging systems. Based on Global Initiative for Chronic Obstructive Lung Disease guidelines, 461 patients (17.6%) had mild, 1,452 (55.5%) moderate, 590 (22.6%) severe, and 111 (4.2%) very severe AL. According to z-score classification, 66.3% of patients remained with the same severity, whereas 23.7% worsened and 10.0% improved. Unlike other staging systems, patients with severe AL according to z-score had higher mortality than those with very severe AL (increase of risk by 5.2 and 3.9 times compared with mild AL, respectively). The predictive capacity for 5-year survival was slightly higher for FEV 1 expressed as percentage of predicted than as z-score (area under the curve: 0.714-0.760 vs. 0.649-0.708, respectively). A severity-dependent relationship between AL grades by z-score and mortality was only detected in patients younger than age 60 years. In patients with COPD, the AL classification based on z-score predicts worse mortality than those based on percentage of predicted. It is possible that the z-score underestimates AL severity in patients older than 60 years of age with severe functional impairment.

Optic Nerve Lymphoma. Report of Two Cases and Review of the Literature

PubMed Central

Kim, Jennifer L.; Mendoza, Pia; Rashid, Alia; Hayek, Brent; Grossniklaus, Hans E.

2014-01-01

Lymphoma may involve the optic nerve as isolated optic nerve lymphoma or in association with CNS or systemic lymphoma. We present two biopsy-proven non-Hodgkin lymphomas of the optic nerve and compare our findings with previously reported cases. We discuss the mechanism of metastasis, classification of optic nerve involvement, clinical features, radiologic findings, optic nerve biopsy indications and techniques, histologic features, and treatments. We propose a classification system of optic nerve lymphoma: isolated optic nerve involvement, optic nerve involvement with CNS disease, optic nerve involvement with systemic disease, and optic nerve involvement with primary intraocular lymphoma. Although it is an uncommon cause of infiltrative optic neuropathy, optic nerve metastasis should be considered in patients with a history of lymphoma. The recommended approach to a patient with presumed optic nerve lymphoma includes neuroimaging, and cerebrospinal fluid evaluation as part of the initial work-up, then judicious use of optic nerve biopsy, depending on the clinical situation. PMID:25595061

A hospital-wide clinical findings dictionary based on an extension of the International Classification of Diseases (ICD).

PubMed

Bréant, C; Borst, F; Campi, D; Griesser, V; Momjian, S

1999-01-01

The use of a controlled vocabulary set in a hospital-wide clinical information system is of crucial importance for many departmental database systems to communicate and exchange information. In the absence of an internationally recognized clinical controlled vocabulary set, a new extension of the International statistical Classification of Diseases (ICD) is proposed. It expands the scope of the standard ICD beyond diagnosis and procedures to clinical terminology. In addition, the common Clinical Findings Dictionary (CFD) further records the definition of clinical entities. The construction of the vocabulary set and the CFD is incremental and manual. Tools have been implemented to facilitate the tasks of defining/maintaining/publishing dictionary versions. The design of database applications in the integrated clinical information system is driven by the CFD which is part of the Medical Questionnaire Designer tool. Several integrated clinical database applications in the field of diabetes and neuro-surgery have been developed at the HUG.

A hospital-wide clinical findings dictionary based on an extension of the International Classification of Diseases (ICD).

PubMed Central

Bréant, C.; Borst, F.; Campi, D.; Griesser, V.; Momjian, S.

1999-01-01

The use of a controlled vocabulary set in a hospital-wide clinical information system is of crucial importance for many departmental database systems to communicate and exchange information. In the absence of an internationally recognized clinical controlled vocabulary set, a new extension of the International statistical Classification of Diseases (ICD) is proposed. It expands the scope of the standard ICD beyond diagnosis and procedures to clinical terminology. In addition, the common Clinical Findings Dictionary (CFD) further records the definition of clinical entities. The construction of the vocabulary set and the CFD is incremental and manual. Tools have been implemented to facilitate the tasks of defining/maintaining/publishing dictionary versions. The design of database applications in the integrated clinical information system is driven by the CFD which is part of the Medical Questionnaire Designer tool. Several integrated clinical database applications in the field of diabetes and neuro-surgery have been developed at the HUG. Images Figure 1 PMID:10566451

The applicability of new TNM classification for humanpapilloma virus-related oropharyngeal cancer in the 8th edition of the AJCC/UICC TNM staging system in Japan: A single-centre study.

PubMed

Sano, Daisuke; Yabuki, Kenichiro; Arai, Yasuhiro; Tanabe, Teruhiko; Chiba, Yoshihiro; Nishimura, Goshi; Takahashi, Hideaki; Yamanaka, Shoji; Oridate, Nobuhiko

2018-06-01

The purpose of this study is to validate the applicability of new TNM classification for human papillomavirus (HPV)-related oropharyngeal cancer (OPC) in the 8th edition of the American Joint Committee on Cancer (AJCC)/Union for International Cancer Control (UICC) TNM staging system in Japan. A total of 91 OPC patients treated with radiation-based therapy between November 2001 and July 2015 were analyzed retrospectively in this study. HPV infection status was evaluated using tumor p16 expression. 40 OPC patients (44.0%) had HPV-positive disease in this study. The distribution of disease stage of HPV-positive OPC patients dramatically changed from the 7th edition to the 8th edition of AJCC/UICC TNM classification. However, neither the 8th edition nor the 7th edition of the AJCC/UICC TNM staging system could adequately predict outcomes of HPV-positive OPC patients in our patient series. On the other hand, our multivariate analysis indicated that matted nodes and age ≥63 were independent prognostic factors for progression-free survival. In addition, HPV-positive OPC patients with stage I without matted nodes showed significantly better overall and progression-free survival compared with those with stage I with matted nodes and stages II and III in the 8th edition of the AJCC/UICC TNM staging system (P=0.008, and P=0.043, respectively). Our results suggested that matted nodes of HPV-positive OPC patients might be additionally examined to apply the 8th edition of AJCC/UICC TNM classification for more adequate predicting outcomes of HPV-positive OPC patients. Copyright © 2017 Elsevier B.V. All rights reserved.

Reviews.

ERIC Educational Resources Information Center

Repak, Arthur J.; And Others

1988-01-01

Computer software, audiovisuals, and books are reviewed. Includes topics on interfacing, ionic equilibrium, space, the classification system, Acquired Immune Disease Syndrome, evolution, human body processes, energy, pesticides, teaching school, cells, and geological aspects. Availability, price, and a description of each are provided. (RT)

The biobank for the molecular classification of kidney disease: research translation and precision medicine in nephrology.

PubMed

Muruve, Daniel A; Mann, Michelle C; Chapman, Kevin; Wong, Josee F; Ravani, Pietro; Page, Stacey A; Benediktsson, Hallgrimur

2017-07-26

Advances in technology and the ability to interrogate disease pathogenesis using systems biology approaches are exploding. As exemplified by the substantial progress in the personalized diagnosis and treatment of cancer, the application of systems biology to enable precision medicine in other disciplines such as Nephrology is well underway. Infrastructure that permits the integration of clinical data, patient biospecimens and advanced technologies is required for institutions to contribute to, and benefit from research in molecular disease classification and to devise specific and patient-oriented treatments. We describe the establishment of the Biobank for the Molecular Classification of Kidney Disease (BMCKD) at the University of Calgary, Alberta, Canada. The BMCKD consists of a fully equipped wet laboratory, an information technology infrastructure, and a formal operational, ethical and legal framework for banking human biospecimens and storing clinical data. The BMCKD first consolidated a large retrospective cohort of kidney biopsy specimens to create a population-based renal pathology database and tissue inventory of glomerular and other kidney diseases. The BMCKD will continue to prospectively bank all kidney biopsies performed in Southern Alberta. The BMCKD is equipped to perform molecular, clinical and epidemiologic studies in renal pathology. The BMCKD also developed formal biobanking procedures for human specimens such as blood, urine and nucleic acids collected for basic and clinical research studies or for advanced diagnostic technologies in clinical care. The BMCKD is guided by standard operating procedures, an ethics framework and legal agreements with stakeholders that include researchers, data custodians and patients. The design and structure of the BMCKD permits its inclusion in a wide variety of research and clinical activities. The BMCKD is a core multidisciplinary facility that will bridge basic and clinical research and integrate precision medicine into renal pathology and nephrology.

Text mining electronic hospital records to automatically classify admissions against disease: Measuring the impact of linking data sources.

PubMed

Kocbek, Simon; Cavedon, Lawrence; Martinez, David; Bain, Christopher; Manus, Chris Mac; Haffari, Gholamreza; Zukerman, Ingrid; Verspoor, Karin

2016-12-01

Text and data mining play an important role in obtaining insights from Health and Hospital Information Systems. This paper presents a text mining system for detecting admissions marked as positive for several diseases: Lung Cancer, Breast Cancer, Colon Cancer, Secondary Malignant Neoplasm of Respiratory and Digestive Organs, Multiple Myeloma and Malignant Plasma Cell Neoplasms, Pneumonia, and Pulmonary Embolism. We specifically examine the effect of linking multiple data sources on text classification performance. Support Vector Machine classifiers are built for eight data source combinations, and evaluated using the metrics of Precision, Recall and F-Score. Sub-sampling techniques are used to address unbalanced datasets of medical records. We use radiology reports as an initial data source and add other sources, such as pathology reports and patient and hospital admission data, in order to assess the research question regarding the impact of the value of multiple data sources. Statistical significance is measured using the Wilcoxon signed-rank test. A second set of experiments explores aspects of the system in greater depth, focusing on Lung Cancer. We explore the impact of feature selection; analyse the learning curve; examine the effect of restricting admissions to only those containing reports from all data sources; and examine the impact of reducing the sub-sampling. These experiments provide better understanding of how to best apply text classification in the context of imbalanced data of variable completeness. Radiology questions plus patient and hospital admission data contribute valuable information for detecting most of the diseases, significantly improving performance when added to radiology reports alone or to the combination of radiology and pathology reports. Overall, linking data sources significantly improved classification performance for all the diseases examined. However, there is no single approach that suits all scenarios; the choice of the most effective combination of data sources depends on the specific disease to be classified. Copyright © 2016 Elsevier Inc. All rights reserved.

A Q-backpropagated time delay neural network for diagnosing severity of gait disturbances in Parkinson's disease.

PubMed

Nancy Jane, Y; Khanna Nehemiah, H; Arputharaj, Kannan

2016-04-01

Parkinson's disease (PD) is a movement disorder that affects the patient's nervous system and health-care applications mostly uses wearable sensors to collect these data. Since these sensors generate time stamped data, analyzing gait disturbances in PD becomes challenging task. The objective of this paper is to develop an effective clinical decision-making system (CDMS) that aids the physician in diagnosing the severity of gait disturbances in PD affected patients. This paper presents a Q-backpropagated time delay neural network (Q-BTDNN) classifier that builds a temporal classification model, which performs the task of classification and prediction in CDMS. The proposed Q-learning induced backpropagation (Q-BP) training algorithm trains the Q-BTDNN by generating a reinforced error signal. The network's weights are adjusted through backpropagating the generated error signal. For experimentation, the proposed work uses a PD gait database, which contains gait measures collected through wearable sensors from three different PD research studies. The experimental result proves the efficiency of Q-BP in terms of its improved classification accuracy of 91.49%, 92.19% and 90.91% with three datasets accordingly compared to other neural network training algorithms. Copyright © 2016 Elsevier Inc. All rights reserved.

Musculoskeletal manifestations of systemic lupus erythmatosus.

PubMed

Mahmoud, Khaled; Zayat, Ahmed; Vital, Edward M

2017-09-01

Imaging studies suggest potential changes to the classification and assessment of inflammatory musculoskeletal lupus. This is important because of the burden of disease but the potential for new targeted therapies. Using our current classification and treatment, musculoskeletal symptoms continue to impact significantly on quality of life and work disability. Ultrasound and MRI studies suggested that new approaches to the diagnosis, classification, and evaluation of these symptoms are needed. Many patients with pain but no synovitis have ultrasound-proven joint and tendon inflammation but would not qualify for clinical trials or score highly on disease activity instruments. MRI studies show that erosions are more common than previously thought and may have a different pathogenesis than RA. Immunology studies suggest differences from other autoimmune synovitis, with a complex role for type I interferons. A wide range of biologic therapies appear more consistently effective for arthritis than some other manifestations. Changes to the selection of patients for therapy and stratification using musculoskeletal imaging may offer new approaches to clinical trials and the routine care of systemic lupus erythematosus patients with inflammatory musculoskeletal symptoms. Outcomes may thereby be improved using existing therapies. There are significant knowledge gaps that must be addressed to achieve these potential improved outcomes.

Complex regional pain syndrome (CRPS) type I: historical perspective and critical issues.

PubMed

Iolascon, Giovanni; de Sire, Alessandro; Moretti, Antimo; Gimigliano, Francesca

2015-01-01

The history of algodystrophy is controversial and its denomination has changed significantly over time. Silas Weir Mitchell described several cases of causalgia due to gunshot wounds that occurred during the American Civil War, increasing knowledge about this clinical condition. A later key milestone in the history of CRPS is tied to the name of Paul Sudeck that, using X-ray examinations, described findings of bone atrophy following a traumatic event or infection of the upper limb. The most widely accepted pathogenic hypothesis, proposed by Rene Leriche, supported a key role of the sympathetic nervous system in the onset of the typical clinical picture of the disease, which was thus defined as "reflex sympathetic dystrophy". In the 50s John J. Bonica proposed a staging of CRPS. In a consensus conference held in Budapest in 2003, it was proposed a new classification system that included the presence of at least two clinical signs included in the four categories and at least three symptoms in its four categories. There have been other classification systems proposed for the diagnosis of CRPS, such as Veldman diagnostic criteria based on the presence of at least 4 signs and symptoms of the disease associated with a worsening of the same following the use of the limb and their location in the same area distal to the one that suffered the injury. On the other hand, the Atkins diagnostic criteria are much more objective than those proposed by IASP and are specifically applicable to an orthopaedic context. However, current classification systems and related criteria proposed to make a diagnosis of CRPS, do not include instrumental evaluations and imaging, but rely solely on clinical findings. This approach does not allow an optimal disease staging especially in orthopaedics.

Complex regional pain syndrome (CRPS) type I: historical perspective and critical issues

PubMed Central

Iolascon, Giovanni; de Sire, Alessandro; Moretti, Antimo; Gimigliano, Francesca

2015-01-01

Summary The history of algodystrophy is controversial and its denomination has changed significantly over time. Silas Weir Mitchell described several cases of causalgia due to gunshot wounds that occurred during the American Civil War, increasing knowledge about this clinical condition. A later key milestone in the history of CRPS is tied to the name of Paul Sudeck that, using X-ray examinations, described findings of bone atrophy following a traumatic event or infection of the upper limb. The most widely accepted pathogenic hypothesis, proposed by Rene Leriche, supported a key role of the sympathetic nervous system in the onset of the typical clinical picture of the disease, which was thus defined as “reflex sympathetic dystrophy”. In the 50s John J. Bonica proposed a staging of CRPS. In a consensus conference held in Budapest in 2003, it was proposed a new classification system that included the presence of at least two clinical signs included in the four categories and at least three symptoms in its four categories. There have been other classification systems proposed for the diagnosis of CRPS, such as Veldman diagnostic criteria based on the presence of at least 4 signs and symptoms of the disease associated with a worsening of the same following the use of the limb and their location in the same area distal to the one that suffered the injury. On the other hand, the Atkins diagnostic criteria are much more objective than those proposed by IASP and are specifically applicable to an orthopaedic context. However, current classification systems and related criteria proposed to make a diagnosis of CRPS, do not include instrumental evaluations and imaging, but rely solely on clinical findings. This approach does not allow an optimal disease staging especially in orthopaedics. PMID:27134625

SMASH-U: a proposal for etiologic classification of intracerebral hemorrhage.

PubMed

Meretoja, Atte; Strbian, Daniel; Putaala, Jukka; Curtze, Sami; Haapaniemi, Elena; Mustanoja, Satu; Sairanen, Tiina; Satopää, Jarno; Silvennoinen, Heli; Niemelä, Mika; Kaste, Markku; Tatlisumak, Turgut

2012-10-01

The purpose of this study was to provide a simple and practical clinical classification for the etiology of intracerebral hemorrhage (ICH). We performed a retrospective chart review of consecutive patients with ICH treated at the Helsinki University Central Hospital, January 2005 to March 2010 (n=1013). We classified ICH etiology by predefined criteria as structural vascular lesions (S), medication (M), amyloid angiopathy (A), systemic disease (S), hypertension (H), or undetermined (U). Clinical and radiological features and mortality by SMASH-U (Structural lesion, Medication, Amyloid angiopathy, Systemic/other disease, Hypertension, Undetermined) etiology were analyzed. Structural lesions, namely cavernomas and arteriovenous malformations, caused 5% of the ICH, anticoagulation 14%, and systemic disease 5% (23 liver cirrhosis, 8 thrombocytopenia, and 17 various rare conditions). Amyloid angiopathy (20%) and hypertensive angiopathy (35%) were common, but etiology remained undetermined in 21%. Interrater agreement in classifying cases was high (κ, 0.89; 95% CI, 0.82-0.96). Patients with structural lesions had the smallest hemorrhages (median volume, 2.8 mL) and best prognosis (3-month mortality 4%), whereas anticoagulation-related ICHs were largest (13.4 mL) and most often fatal (54%). Overall, median ICH survival was 5½ years, varying strongly by etiology (P<0.001). After adjustment for baseline characteristics, patients with structural lesions had the lowest 3-month mortality rates (OR, 0.06; 95% CI, 0.01-0.37) and those with anticoagulation (OR, 1.9; 1.0-3.6) or other systemic cause (OR, 4.0; 1.6-10.1) the highest. In our patients, performing the SMASH-U classification was feasible and interrater agreement excellent. A plausible etiology was determined in most patients but remained elusive in one in 5. In this series, SMASH-U based etiology was strongly associated with survival.

A computer-based information system for epilepsy and electroencephalography.

PubMed

Finnerup, N B; Fuglsang-Frederiksen, A; Røssel, P; Jennum, P

1999-08-01

This paper describes a standardised computer-based information system for electroencephalography (EEG) focusing on epilepsy. The system was developed using a prototyping approach. It is based on international recommendations for EEG examination, interpretation and terminology, international guidelines for epidemiological studies on epilepsy and classification of epileptic seizures and syndromes and international classification of diseases. It is divided into: (1) clinical information and epilepsy relevant data; and (2) EEG data, which is hierarchically structured including description and interpretation of EEG. Data is coded but is supplemented with unrestricted text. The resulting patient database can be integrated with other clinical databases and with the patient record system and may facilitate clinical and epidemiological research and development of standards and guidelines for EEG description and interpretation. The system is currently used for teleconsultation between Gentofte and Lisbon.

A clinico-pathological study of lupus nephritis based on the International Society of Nephrology-Renal Pathology Society 2003 classification system.

PubMed

Satish, Suchitha; Deka, Pallavi; Shetty, Manjunath Sanjeev

2017-01-01

Lupus nephritis (LN) is a major complication of systemic lupus erythematosus (SLE). Renal involvement is a major determinant of the prognosis of SLE. The histological classification of LN is a key factor in determining the renal survival of patients with LN. Prompt recognition and treatment of renal disease are important, as early response to therapy is correlated with better outcome and renal biopsy plays an important role in achieving this. The objective of this study was to correlate the clinical and laboratory findings with histopathological classes of LN as per the 2003 International Society of Nephrology-Renal Pathology Society (ISN/RPS) classification system. Fifty-six patients with SLE, undergoing a renal biopsy for renal dysfunction were studied. The comparison of data from multiple groups was made by Pearson's Chi-square test and between two groups by independent samples t -test. The values of P < 0.05 were considered statistically significant. Of the 56 cases studied, 51 (91.1%) were females. The most common presenting symptoms were edema, arthralgia, and hypertension. Class IV (55.4%) was the most common class. Thirty-nine (69.6%) cases showed full house immunostaining. Hypertension, hematuria, proteinuria, and tubulo-interstitial disease showed a significant correlation ( P < 0.05) with ISN/RPS classification, 2003. Assessment and management of patients with suspected LN are greatly facilitated through information obtained by renal biopsy. Since renal morphology may predict long-term prognosis, and no clinical or laboratory feature uniformly predicts prognosis, it is important to study the constellation of features in LN for better patient management.

Classification of Rhinoentomophthoromycosis into Atypical, Early, Intermediate, and Late Disease: A Proposal

PubMed Central

Blumentrath, Christian G.; Grobusch, Martin P.; Matsiégui, Pierre-Blaise; Pahlke, Friedrich; Zoleko-Manego, Rella; Nzenze-Aféne, Solange; Mabicka, Barthélemy; Sanguinetti, Maurizio; Kremsner, Peter G.; Schaumburg, Frieder

2015-01-01

Background Rhinoentomophthoromycosis, or rhino-facial conidiobolomycosis, is a rare, grossly disfiguring disease due to an infection with entomophthoralean fungi. We report a case of rhinoentomophthoromycosis from Gabon and suggest a staging system, which provides information on the prognosis and duration of antifungal therapy. Methods We present a case of rhinoentomophthoromycosis including the histopathology, mycology, and course of disease. For the suggested staging system, all cases on confirmed rhinoentomophthoromycosis published in the literature without language restriction were eligible. Exclusion criteria were missing data on (i) duration of disease before correct diagnosis, (ii) outcome, and (iii) confirmation of entomophthoralean fungus infection by histopathology and/or mycology. We classified cases into atypical (orbital cellulitis, severe pain, fever, dissemination), early, intermediate, and late disease based on the duration of symptoms before diagnosis. The outcome was evaluated for each stage of disease. Findings The literature search of the Medpilot database was conducted on January 13, 2014, (updated on January 18, 2015). The search yielded 8,333 results including 198 cases from 117 papers; of these, 145 met our inclusion criteria and were included in the final analysis. Median duration of treatment was 4, 3, 4, and 5 months in atypical, early, intermediate, and late disease, respectively. Cure rates were clearly associated with stage of disease and were 57%, 100%, 82%, and 43% in atypical, early, intermediate, and late disease, respectively. Conclusion We suggest a clinical staging system that underlines the benefit of early case detection and may guide the duration of antifungal treatment. The scientific value of this classification is its capacity to structure and harmonize the clinical and research approach towards rhinoentomophthoromycosis. PMID:26426120

New Diagnostic and Therapeutic Approaches to Eradicating Recurrent Breast Cancer

DTIC Science & Technology

2015-09-01

of metastatic disease when they are first diagnosed, yet many patients later return to the clinic with cancer that has spread throughout the body. It...treated before they experience disease relapse. 15. SUBJECT TERMS Breast cancer, metastasis, dissemination, recurrence, therapeutic resistance, systemic...instigation, microenvironment, bone marrow cells, canine , mouse models 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF

Invertebrate Iridoviruses: A Glance over the Last Decade

PubMed Central

Özcan, Orhan; Ilter-Akulke, Ayca Zeynep; Scully, Erin D.; Özgen, Arzu

2018-01-01

Members of the family Iridoviridae (iridovirids) are large dsDNA viruses that infect both invertebrate and vertebrate ectotherms and whose symptoms range in severity from minor reductions in host fitness to systemic disease and large-scale mortality. Several characteristics have been useful for classifying iridoviruses; however, novel strains are continuously being discovered and, in many cases, reliable classification has been challenging. Further impeding classification, invertebrate iridoviruses (IIVs) can occasionally infect vertebrates; thus, host range is often not a useful criterion for classification. In this review, we discuss the current classification of iridovirids, focusing on genomic and structural features that distinguish vertebrate and invertebrate iridovirids and viral factors linked to host interactions in IIV6 (Invertebrate iridescent virus 6). In addition, we show for the first time how complete genome sequences of viral isolates can be leveraged to improve classification of new iridovirid isolates and resolve ambiguous relations. Improved classification of the iridoviruses may facilitate the identification of genus-specific virulence factors linked with diverse host phenotypes and host interactions. PMID:29601483

Invertebrate Iridoviruses: A Glance over the Last Decade.

PubMed

İnce, İkbal Agah; Özcan, Orhan; Ilter-Akulke, Ayca Zeynep; Scully, Erin D; Özgen, Arzu

2018-03-30

Members of the family Iridoviridae (iridovirids) are large dsDNA viruses that infect both invertebrate and vertebrate ectotherms and whose symptoms range in severity from minor reductions in host fitness to systemic disease and large-scale mortality. Several characteristics have been useful for classifying iridoviruses; however, novel strains are continuously being discovered and, in many cases, reliable classification has been challenging. Further impeding classification, invertebrate iridoviruses (IIVs) can occasionally infect vertebrates; thus, host range is often not a useful criterion for classification. In this review, we discuss the current classification of iridovirids, focusing on genomic and structural features that distinguish vertebrate and invertebrate iridovirids and viral factors linked to host interactions in IIV6 (Invertebrate iridescent virus 6). In addition, we show for the first time how complete genome sequences of viral isolates can be leveraged to improve classification of new iridovirid isolates and resolve ambiguous relations. Improved classification of the iridoviruses may facilitate the identification of genus-specific virulence factors linked with diverse host phenotypes and host interactions.

2016 Classification Criteria for Macrophage Activation Syndrome Complicating Systemic Juvenile Idiopathic Arthritis: A European League Against Rheumatism/American College of Rheumatology/Paediatric Rheumatology International Trials Organisation Collaborative Initiative.

PubMed

Ravelli, Angelo; Minoia, Francesca; Davì, Sergio; Horne, AnnaCarin; Bovis, Francesca; Pistorio, Angela; Aricò, Maurizio; Avcin, Tadej; Behrens, Edward M; De Benedetti, Fabrizio; Filipovic, Lisa; Grom, Alexei A; Henter, Jan-Inge; Ilowite, Norman T; Jordan, Michael B; Khubchandani, Raju; Kitoh, Toshiyuki; Lehmberg, Kai; Lovell, Daniel J; Miettunen, Paivi; Nichols, Kim E; Ozen, Seza; Pachlopnik Schmid, Jana; Ramanan, Athimalaipet V; Russo, Ricardo; Schneider, Rayfel; Sterba, Gary; Uziel, Yosef; Wallace, Carol; Wouters, Carine; Wulffraat, Nico; Demirkaya, Erkan; Brunner, Hermine I; Martini, Alberto; Ruperto, Nicolino; Cron, Randy Q

2016-03-01

To develop criteria for the classification of macrophage activation syndrome (MAS) in patients with systemic juvenile idiopathic arthritis (JIA). A multistep process, based on a combination of expert consensus and analysis of real patient data, was conducted. A panel of 28 experts was first asked to classify 428 patient profiles as having or not having MAS, based on clinical and laboratory features at the time of disease onset. The 428 profiles comprised 161 patients with systemic JIA-associated MAS and 267 patients with a condition that could potentially be confused with MAS (active systemic JIA without evidence of MAS, or systemic infection). Next, the ability of candidate criteria to classify individual patients as having MAS or not having MAS was assessed by evaluating the agreement between the classification yielded using the criteria and the consensus classification of the experts. The final criteria were selected in a consensus conference. Experts achieved consensus on the classification of 391 of the 428 patient profiles (91.4%). A total of 982 candidate criteria were tested statistically. The 37 best-performing criteria and 8 criteria obtained from the literature were evaluated at the consensus conference. During the conference, 82% consensus among experts was reached on the final MAS classification criteria. In validation analyses, these criteria had a sensitivity of 0.73 and a specificity of 0.99. Agreement between the classification (MAS or not MAS) obtained using the criteria and the original diagnosis made by the treating physician was high (κ=0.76). We have developed a set of classification criteria for MAS complicating systemic JIA and provided preliminary evidence of its validity. Use of these criteria will potentially improve understanding of MAS in systemic JIA and enhance efforts to discover effective therapies, by ensuring appropriate patient enrollment in studies. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

New tools for classification and monitoring of autoimmune diseases

PubMed Central

Maecker, Holden T.; Lindstrom, Tamsin M.; Robinson, William H.; Utz, Paul J.; Hale, Matthew; Boyd, Scott D.; Shen-Orr, Shai S.; Fathman, C. Garrison

2012-01-01

Rheumatologists see patients with a range of autoimmune diseases. Phenotyping these diseases for diagnosis, prognosis and selection of therapies is an ever increasing problem. Advances in multiplexed assay technology at the gene, protein, and cellular level have enabled the identification of `actionable biomarkers'; that is, biological metrics that can inform clinical practice. Not only will such biomarkers yield insight into the development, remission, and exacerbation of a disease, they will undoubtedly improve diagnostic sensitivity and accuracy of classification, and ultimately guide treatment. This Review provides an introduction to these powerful technologies that could promote the identification of actionable biomarkers, including mass cytometry, protein arrays, and immunoglobulin and T-cell receptor high-throughput sequencing. In our opinion, these technologies should become part of routine clinical practice for the management of autoimmune diseases. The use of analytical tools to deconvolve the data obtained from use of these technologies is also presented here. These analyses are revealing a more comprehensive and interconnected view of the immune system than ever before and should have an important role in directing future treatment approaches for autoimmune diseases. PMID:22647780

Primary Immunodeficiency Diseases: An Update on the Classification from the International Union of Immunological Societies Expert Committee for Primary Immunodeficiency

PubMed Central

Al-Herz, Waleed; Bousfiha, Aziz; Casanova, Jean-Laurent; Chapel, Helen; Conley, Mary Ellen; Cunningham-Rundles, Charlotte; Etzioni, Amos; Fischer, Alain; Franco, Jose Luis; Geha, Raif S.; Hammarström, Lennart; Nonoyama, Shigeaki; Notarangelo, Luigi Daniele; Ochs, Hans Dieter; Puck, Jennifer M.; Roifman, Chaim M.; Seger, Reinhard; Tang, Mimi L. K.

2011-01-01

We report the updated classification of primary immunodeficiency diseases, compiled by the ad hoc Expert Committee of the International Union of Immunological Societies. As compared to the previous edition, more than 15 novel disease entities have been added in the updated version. For each disorders, the key clinical and laboratory features are provided. This updated classification is meant to help in the diagnostic approach to patients with these diseases. PMID:22566844

Effect of maternal age and cardiac disease severity on outcome of pregnancy in women with congenital heart disease.

PubMed

Furenäs, Eva; Eriksson, Peter; Wennerholm, Ulla-Britt; Dellborg, Mikael

2017-09-15

There is an increasing prevalence of women with congenital heart defects reaching childbearing age. In western countries women tend to give birth at a higher age compared to some decades ago. We evaluated the CARdiac disease in PREGnancy (CARPREG) and modified World Health Organization (mWHO) risk classifications for cardiac complications during pregnancies in women with congenital heart defects and analyzed the impact of age on risk of obstetric and fetal outcome. A single-center observational study of cardiac, obstetric, and neonatal complications with data from cardiac and obstetric records of pregnancies in women with congenital heart disease. Outcomes of 496 pregnancies in 232 women, including induced abortion, miscarriage, stillbirth, and live birth were analyzed regarding complications, maternal age, mode of delivery, and two risk classifications: CARPREG and mWHO. There were 28 induced abortions, 59 fetal loss, 409 deliveries with 412 neonates. Cardiac (14%), obstetric (14%), and neonatal (15%) complications were noted, including one maternal death and five stillbirths. The rate of cesarean section was 19%. Age above 35years was of borderline importance for cardiac complications (p=0.054) and was not a significant additional risk factor for obstetric or neonatal complications. Both risk classifications had moderate clinical utility, with area under the curve (AUC) 0.71 for CARPREG and 0.65 for mWHO on cardiac complications. Pregnancy complications in women with congenital heart disease are common but severe complications are rare. Advanced maternal age does not seem to affect complication rate. Existing risk classification systems are insufficient in predicting complications. Copyright © 2017 Elsevier B.V. All rights reserved.

Three Diagnostic Systems for Autism: DSM-III, DSM-III-R, and ICD-10.

ERIC Educational Resources Information Center

Volkmar, Fred R.; And Others

1992-01-01

This paper compared clinicians' diagnosis and DSM-III (Diagnostic and Statistical Manual), DSM-III-R (Revised), and ICD-10 (International Classification of Diseases) diagnoses of 52 individuals with autism and 62 nonautistic, developmentally disordered individuals. The DSM-III-R system overdiagnosed the presence of autism, and ICD-10 closely…

[Evaluation of the course of chronic obstructive lung diseases according to the classifications of the European Respiratory Society and the Global Initiative on Chronic Obstructive Lung Disease].

PubMed

Nefedov, V B; Shergina, E A; Popova, L A

2006-01-01

In 91 patients with chronic obstructive lung disease (COLD), the severity of this disease according to the Classifications of the European Respiratory Society (ERS) and the Global Initiative on Chronic Obstructive Lung Disease (GOLD) was compared with that of pulmonary dysfunction according to the data of a comprehensive study, involving the determination of bronchial patency, lung volumes, capacities, and gas-exchange function. This follows that the ERS and GOLD classifications are to be positively appraised as they provide an eligible group of patients for clinical practice in terms of the severity of pulmonary dysfunction and that of COLD. However, the concomitant clinical use of both classifications cannot be regarded as justifiable due to that there are differences in the number of detectable grades (stages) of COLD and borderline (COLD differentiating grades (stages) values of EFV1). In this connection, both classifications have approximately equally significant merits and shortcomings and it is practically impossible to give preference to one of them as the best one. The optimal way out of the established situation is to develop a new (improved) classification of the severity of COLD on the bases of these two existing classifications.

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.

Prototype learning and dissociable categorization systems in Alzheimer's disease.

PubMed

Heindel, William C; Festa, Elena K; Ott, Brian R; Landy, Kelly M; Salmon, David P

2013-08-01

Recent neuroimaging studies suggest that prototype learning may be mediated by at least two dissociable memory systems depending on the mode of acquisition, with A/Not-A prototype learning dependent upon a perceptual representation system located within posterior visual cortex and A/B prototype learning dependent upon a declarative memory system associated with medial temporal and frontal regions. The degree to which patients with Alzheimer's disease (AD) can acquire new categorical information may therefore critically depend upon the mode of acquisition. The present study examined A/Not-A and A/B prototype learning in AD patients using procedures that allowed direct comparison of learning across tasks. Despite impaired explicit recall of category features in all tasks, patients showed differential patterns of category acquisition across tasks. First, AD patients demonstrated impaired prototype induction along with intact exemplar classification under incidental A/Not-A conditions, suggesting that the loss of functional connectivity within visual cortical areas disrupted the integration processes supporting prototype induction within the perceptual representation system. Second, AD patients demonstrated intact prototype induction but impaired exemplar classification during A/B learning under observational conditions, suggesting that this form of prototype learning is dependent on a declarative memory system that is disrupted in AD. Third, the surprisingly intact classification of both prototypes and exemplars during A/B learning under trial-and-error feedback conditions suggests that AD patients shifted control from their deficient declarative memory system to a feedback-dependent procedural memory system when training conditions allowed. Taken together, these findings serve to not only increase our understanding of category learning in AD, but to also provide new insights into the ways in which different memory systems interact to support the acquisition of categorical knowledge. Copyright © 2013 Elsevier Ltd. All rights reserved.

Prototype Learning and Dissociable Categorization Systems in Alzheimer’s Disease

PubMed Central

Heindel, William C.; Festa, Elena K.; Ott, Brian R.; Landy, Kelly M.; Salmon, David P.

2015-01-01

Recent neuroimaging studies suggest that prototype learning may be mediated by at least two dissociable memory systems depending on the mode of acquisition, with A/Not-A prototype learning dependent upon a perceptual representation system located within posterior visual cortex and A/B prototype learning dependent upon a declarative memory system associated with medial temporal and frontal regions. The degree to which patients with Alzheimer’s disease (AD) can acquire new categorical information may therefore critically depend upon the mode of acquisition. The present study examined A/Not-A and A/B prototype learning in AD patients using procedures that allowed direct comparison of learning across tasks. Despite impaired explicit recall of category features in all tasks, patients showed differential patterns of category acquisition across tasks. First, AD patients demonstrated impaired prototype induction along with intact exemplar classification under incidental A/Not-A conditions, suggesting that the loss of functional connectivity within visual cortical areas disrupted the integration processes supporting prototype induction within the perceptual representation system. Second, AD patients demonstrated intact prototype induction but impaired exemplar classification during A/B learning under observational conditions, suggesting that this form of prototype learning is dependent on a declarative memory system that is disrupted in AD. Third, the surprisingly intact classification of both prototypes and exemplars during A/B learning under trial-and-error feedback conditions suggests that AD patients shifted control from their deficient declarative memory system to a feedback-dependent procedural memory system when training conditions allowed. Taken together, these findings serve to not only increase our understanding of category learning in AD, but to also provide new insights into the ways in which different memory systems interact to support the acquisition of categorical knowledge. PMID:23751172

Cancer classification in the genomic era: five contemporary problems.

PubMed

Song, Qingxuan; Merajver, Sofia D; Li, Jun Z

2015-10-19

Classification is an everyday instinct as well as a full-fledged scientific discipline. Throughout the history of medicine, disease classification is central to how we develop knowledge, make diagnosis, and assign treatment. Here, we discuss the classification of cancer and the process of categorizing cancer subtypes based on their observed clinical and biological features. Traditionally, cancer nomenclature is primarily based on organ location, e.g., "lung cancer" designates a tumor originating in lung structures. Within each organ-specific major type, finer subgroups can be defined based on patient age, cell type, histological grades, and sometimes molecular markers, e.g., hormonal receptor status in breast cancer or microsatellite instability in colorectal cancer. In the past 15+ years, high-throughput technologies have generated rich new data regarding somatic variations in DNA, RNA, protein, or epigenomic features for many cancers. These data, collected for increasingly large tumor cohorts, have provided not only new insights into the biological diversity of human cancers but also exciting opportunities to discover previously unrecognized cancer subtypes. Meanwhile, the unprecedented volume and complexity of these data pose significant challenges for biostatisticians, cancer biologists, and clinicians alike. Here, we review five related issues that represent contemporary problems in cancer taxonomy and interpretation. (1) How many cancer subtypes are there? (2) How can we evaluate the robustness of a new classification system? (3) How are classification systems affected by intratumor heterogeneity and tumor evolution? (4) How should we interpret cancer subtypes? (5) Can multiple classification systems co-exist? While related issues have existed for a long time, we will focus on those aspects that have been magnified by the recent influx of complex multi-omics data. Exploration of these problems is essential for data-driven refinement of cancer classification and the successful application of these concepts in precision medicine.

Primary central nervous system B-cell lymphoma in a young dog

PubMed Central

Kim, Na-Hyun; Ciesielski, Thomas; Kim, Jung H.; Yhee, Ji-Young; Im, Keum-Soon; Nam, Hae-Mi; Kim, Il-Hwan; Kim, Jong-Hyuk; Sur, Jung-Hyang

2012-01-01

This report describes a primary central nervous system B-cell lymphoma in a 3-year-old intact female Maltese dog. Canine primary central nervous system lymphomas constitute about 4% of all intracranial primary neoplasms, but comprehensive histopathologic classifications have rarely been carried out. This is the first report of this disease in a young adult dog. PMID:23115372

Unravelling the myotonic dystrophy type 1 clinical spectrum: A systematic registry-based study with implications for disease classification.

PubMed

De Antonio, M; Dogan, C; Hamroun, D; Mati, M; Zerrouki, S; Eymard, B; Katsahian, S; Bassez, G

2016-10-01

The broad clinical spectrum of myotonic dystrophy type 1 (DM1) creates particular challenges for both medical care and design of clinical trials. Clinical onset spans a continuum from birth to late adulthood, with symptoms that are highly variable in both severity and nature of the affected organ systems. In the literature, this complex phenotype is divided into three grades (mild, classic, and severe) and four or five main clinical categories (congenital, infantile/juvenile, adult-onset and late-onset forms), according to symptom severity and age of onset, respectively. However, these classifications are still under discussion with no consensus thus far. While some specific clinical features have been primarily reported in some forms of the disease, there are no clear distinctions. As a consequence, no modifications in the management of healthcare or the design of clinical studies have been proposed based on the clinical form of DM1. The present study has used the DM-Scope registry to assess, in a large cohort of DM1 patients, the robustness of a classification divided into five clinical forms. Our main aim was to describe the disease spectrum and investigate features of each clinical form. The five subtypes were compared by distribution of CTG expansion size, and the occurrence and onset of the main symptoms of DM1. Analyses validated the relevance of a five-grade model for DM1 classification. Patients were classified as: congenital (n=93, 4.5%); infantile (n=303, 14.8%); juvenile (n=628, 30.7%); adult (n=694, 34.0%); and late-onset (n=326, 15.9%). Our data show that the assumption of a continuum from congenital to the late-onset form is valid, and also highlights disease features specific to individual clinical forms of DM1 in terms of symptom occurrence and chronology throughout the disease course. These results support the use of the five-grade model for disease classification, and the distinct clinical profiles suggest that age of onset and clinical form may be key criteria in the design of clinical trials when considering DM1 health management and research. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

[Systemic lupus erythematosus masking the acquired immunodeficiency syndrome. A report on four cases].

PubMed

Kotyla, Przemysław; Kucharz, Eugeniusz J

2012-01-01

Systemic lupus erythematosus (SLE) is a systemic inflammatory disease of connective tissue with an unknown etiology and a rich clinical picture with involvement of multiple organs. Given the rich symptomatology, application of the current classification criteria is associated with a significant risk of attributing symptoms of other pathologies to lupus and/or other connective tissue disease. Inherited and acquired immune deficiencies may sometimes demonstrate a lupus-like clinical symptomatology. In this work we reviewed 4 of cases referred to the Department of Internal Diseases and Rheumatology of the Silesian Medical University in Katowice with suspected or confirmed systemic lupus erythematosus. A positive anti-HIV antibody test led to the diagnosis of the acquired immunodeficiency syndrome (AIDS). Due to the close similarity of the clinical picture and the presence of antinuclear antibodies in both diseases, the authors postulate that the anti-HIV antibody test should be done routinely in patients with connective tissue diseases.

Is there a relationship between periodontal disease and smoking after adjusting for job classification in Japanese employed males?

PubMed

Morita, Ichizo; Sheiham, Aubrey; Nakagaki, Haruo; Yoshii, Saori; Mizuno, Kinichiro; Sabbah, Wael

2011-01-01

The objective of this study is to examine whether the well-known association between periodontal disease and smoking persists after adjusting for job classification. A sample of 16,110 employed Japanese males aged 20-69 years was included in the study. Periodontal examinations were conducted using the Community Periodontal Index. The association between periodontal disease and smoking status was examined using logistic regression adjusting for age, diabetes and job classification. Job classification was based on criteria of the Japanese Ministry of Health, Labour and Welfare. There are nine major job groups: (1) Professional (professionals, specialists), (2) Managers, (3) Office workers (computer operators, clerks, secretaries), (4) Skilled worker (factory workers, construction workers), (5) Salesperson (shop assistants), (6) Service occupations (superintendents, cleaners or car park attendants), (7) Security (guards), (8) Farmers and fishermen, (9) Transport and telecommunication workers (truck drivers). Current and former smokers were more likely to have periodontal disease than non-smokers. Adjusting for job classification attenuated the association between smoking and periodontal disease but did not eliminate the association. The odds ratios for the association between smoking and Community Periodontal Index score 3 or 4 attenuated from 2.25 to 2.04 and from 2.62 to 2.52 for individuals aged 20 to 39 and 40 to 69 years, respectively. The effect of job classification on the association between periodontal disease and smoking was higher among younger participants aged 20 to 39 years. Smoking persisted as an important determinant of periodontal disease after adjusting for job classification in Japanese employed males.

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

Universal etiology, multifactorial diseases and the constitutive model of disease classification.

PubMed

Fuller, Jonathan

2018-02-01

Infectious diseases are often said to have a universal etiology, while chronic and noncommunicable diseases are said to be multifactorial in their etiology. It has been argued that the universal etiology of an infectious disease results from its classification using a monocausal disease model. In this article, I will reconstruct the monocausal model and argue that modern 'multifactorial diseases' are not monocausal by definition. 'Multifactorial diseases' are instead defined according to a constitutive disease model. On closer analysis, infectious diseases are also defined using the constitutive model rather than the monocausal model. As a result, our classification models alone cannot explain why infectious diseases have a universal etiology while chronic and noncommunicable diseases lack one. The explanation is instead provided by the Nineteenth Century germ theorists. Copyright © 2017 Elsevier Ltd. All rights reserved.

New workflow for classification of genetic variants' pathogenicity applied to hereditary recurrent fevers by the International Study Group for Systemic Autoinflammatory Diseases (INSAID).

PubMed

Van Gijn, Marielle E; Ceccherini, Isabella; Shinar, Yael; Carbo, Ellen C; Slofstra, Mariska; Arostegui, Juan I; Sarrabay, Guillaume; Rowczenio, Dorota; Omoyımnı, Ebun; Balci-Peynircioglu, Banu; Hoffman, Hal M; Milhavet, Florian; Swertz, Morris A; Touitou, Isabelle

2018-03-29

Hereditary recurrent fevers (HRFs) are rare inflammatory diseases sharing similar clinical symptoms and effectively treated with anti-inflammatory biological drugs. Accurate diagnosis of HRF relies heavily on genetic testing. This study aimed to obtain an experts' consensus on the clinical significance of gene variants in four well-known HRF genes: MEFV , TNFRSF1A , NLRP3 and MVK . We configured a MOLGENIS web platform to share and analyse pathogenicity classifications of the variants and to manage a consensus-based classification process. Four experts in HRF genetics submitted independent classifications of 858 variants. Classifications were driven to consensus by recruiting four more expert opinions and by targeting discordant classifications in five iterative rounds. Consensus classification was reached for 804/858 variants (94%). None of the unsolved variants (6%) remained with opposite classifications (eg, pathogenic vs benign). New mutational hotspots were found in all genes. We noted a lower pathogenic variant load and a higher fraction of variants with unknown or unsolved clinical significance in the MEFV gene. Applying a consensus-driven process on the pathogenicity assessment of experts yielded rapid classification of almost all variants of four HRF genes. The high-throughput database will profoundly assist clinicians and geneticists in the diagnosis of HRFs. The configured MOLGENIS platform and consensus evolution protocol are usable for assembly of other variant pathogenicity databases. The MOLGENIS software is available for reuse at http://github.com/molgenis/molgenis; the specific HRF configuration is available at http://molgenis.org/said/. The HRF pathogenicity classifications will be published on the INFEVERS database at https://fmf.igh.cnrs.fr/ISSAID/infevers/. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

A hybrid computational approach for efficient Alzheimer's disease classification based on heterogeneous data.

PubMed

Ding, Xuemei; Bucholc, Magda; Wang, Haiying; Glass, David H; Wang, Hui; Clarke, Dave H; Bjourson, Anthony John; Dowey, Le Roy C; O'Kane, Maurice; Prasad, Girijesh; Maguire, Liam; Wong-Lin, KongFatt

2018-06-27

There is currently a lack of an efficient, objective and systemic approach towards the classification of Alzheimer's disease (AD), due to its complex etiology and pathogenesis. As AD is inherently dynamic, it is also not clear how the relationships among AD indicators vary over time. To address these issues, we propose a hybrid computational approach for AD classification and evaluate it on the heterogeneous longitudinal AIBL dataset. Specifically, using clinical dementia rating as an index of AD severity, the most important indicators (mini-mental state examination, logical memory recall, grey matter and cerebrospinal volumes from MRI and active voxels from PiB-PET brain scans, ApoE, and age) can be automatically identified from parallel data mining algorithms. In this work, Bayesian network modelling across different time points is used to identify and visualize time-varying relationships among the significant features, and importantly, in an efficient way using only coarse-grained data. Crucially, our approach suggests key data features and their appropriate combinations that are relevant for AD severity classification with high accuracy. Overall, our study provides insights into AD developments and demonstrates the potential of our approach in supporting efficient AD diagnosis.

Serum C-reactive protein level in COPD patients stratified according to GOLD 2011 grading classification

PubMed Central

Lin, Yi-Hua; Wang, Wan-Yu; Hu, Su-Xian; Shi, Yong-Hong

2016-01-01

Background and Objective: The Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2011 grading classification has been used to evaluate the severity of patients with chronic obstructive pulmonary disease (COPD). However, little is known about the relationship between the systemic inflammation and this classification. We aimed to study the relationship between serum CRP and the components of the GOLD 2011 grading classification. Methods: C-reactive protein (CRP) levels were measured in 391 clinically stable COPD patients and in 50 controls from June 2, 2015 to October 31, 2015 in the First Affiliated Hospital of Xiamen University. The association between CRP levels and the components of the GOLD 2011 grading classification were assessed. Results: Correlation was found with the following variables: GOLD 2011 group (0.240), age (0.227), pack year (0.136), forced expiratory volume in one second % predicted (FEV1%; -0.267), forced vital capacity % predicted (-0.210), number of acute exacerbations in the past year (0.265), number of hospitalized exacerbations in the past year (0.165), British medical Research Council dyspnoea scale (0.121), COPD assessment test score (CAT, 0.233). Using multivariate analysis, FEV1% and CAT score manifested the strongest negative association with CRP levels. Conclusions: CRP levels differ in COPD patients among groups A-D based on GOLD 2011 grading classification. CRP levels are associated with several important clinical variables, of which FEV1% and CAT score manifested the strongest negative correlation. PMID:28083044

Gender and cultural issues in psychiatric nosological classification systems.

PubMed

van de Water, Tanya; Suliman, Sharain; Seedat, Soraya

2016-08-01

Much has changed since the two dominant mental health nosological systems, the International Classification of Diseases (ICD) and the Diagnostic and Statistical Manual of Mental Disorders (DSM), were first published in 1900 and 1952, respectively. Despite numerous modifications to stay up to date with scientific and cultural changes (eg, exclusion of homosexuality as a disorder) and to improve the cultural sensitivity of psychiatric diagnoses, the ICD and DSM have only recently renewed attempts at harmonization. Previous nosological iterations demonstrate the oscillation in the importance placed on the biological focus, highlighting the tension between a gender- and culture-free nosology (solely biological) and a contextually relevant understanding of mental illness. In light of the release of the DSM 5, future nosological systems, such as the ICD 11, scheduled for release in 2017, and the Research Development Criteria (RDoC), can learn from history and apply critiques. This article aims to critically consider gender and culture in previous editions of the ICD and DSM to inform forthcoming classifications.

The functional therapeutic chemical classification system.

PubMed

Croset, Samuel; Overington, John P; Rebholz-Schuhmann, Dietrich

2014-03-15

Drug repositioning is the discovery of new indications for compounds that have already been approved and used in a clinical setting. Recently, some computational approaches have been suggested to unveil new opportunities in a systematic fashion, by taking into consideration gene expression signatures or chemical features for instance. We present here a novel method based on knowledge integration using semantic technologies, to capture the functional role of approved chemical compounds. In order to computationally generate repositioning hypotheses, we used the Web Ontology Language to formally define the semantics of over 20 000 terms with axioms to correctly denote various modes of action (MoA). Based on an integration of public data, we have automatically assigned over a thousand of approved drugs into these MoA categories. The resulting new resource is called the Functional Therapeutic Chemical Classification System and was further evaluated against the content of the traditional Anatomical Therapeutic Chemical Classification System. We illustrate how the new classification can be used to generate drug repurposing hypotheses, using Alzheimers disease as a use-case. https://www.ebi.ac.uk/chembl/ftc; https://github.com/loopasam/ftc. croset@ebi.ac.uk Supplementary data are available at Bioinformatics online.

Acute pancreatitis.

PubMed

Talukdar, Rupjyoti; Vege, Santhi S

2015-09-01

To summarize recent data on classification systems, cause, risk factors, severity prediction, nutrition, and drug treatment of acute pancreatitis. Comparison of the Revised Atlanta Classification and Determinant Based Classification has shown heterogeneous results. Simvastatin has a protective effect against acute pancreatitis. Young black male, alcohol, smoldering symptoms, and subsequent diagnosis of chronic pancreatitis are risk factors associated with readmissions after acute pancreatitis. A reliable clinical or laboratory marker or a scoring system to predict severity is lacking. The PYTHON trial has shown that oral feeding with on demand nasoenteric tube feeding after 72 h is as good as nasoenteric tube feeding within 24 h in preventing infections in predicted severe acute pancreatitis. Male sex, multiple organ failure, extent of pancreatic necrosis, and heterogeneous collection are factors associated with failure of percutaneous drainage of pancreatic collections. The newly proposed classification systems of acute pancreatitis need to be evaluated more critically. New biomarkers are needed for severity prediction. Further well designed studies are required to assess the type of enteral nutritional formulations for acute pancreatitis. The optimal minimally invasive method or combination to debride the necrotic collections is evolving. There is a great need for a drug to treat the disease early on to prevent morbidity and mortality.

Assessment of Intraobserver and Interobserver Agreement of a New Classification System for Retrograde Periimplantitis.

PubMed

Shah, Rucha; Thomas, Raison; Kumar, Tarun; Mehta, Dhoom Singh

2016-12-01

Retrograde periimplantitis (RPI) is the inflammatory disease that affects the apical part of an osseointegrated implant while the coronal portion of the implant sustains a normal bone-to-implant interface. The aim of the current study was to assess the intraexaminer and interexaminer reliability of a proposed new classification system for RPI. After thorough electronic literature search, 56 intraoral periapical radiographs (IOPA) of implants with RPI were collected and were classified by 2 independent reviewers as per the new classification system into one of the 3-mild, moderate, and advanced-classes based on the amount of bone loss from the apex of the implant to the most coronal part as a percentage of the total implant length. The IOPAs were assessed twice by the same examiners and both were blinded to each other's observations. The intraobserver agreement ranged from 0.85 to 0.91, which falls under the category of almost perfect agreement. The interexaminer agreement was found to be 0.83, also considered as almost perfect agreement. The proposed classification shows good intraexaminer and interexaminer reliability and can be used for treatment planning and prognosis in cases of RPI.

The Abnormal vs. Normal ECG Classification Based on Key Features and Statistical Learning

NASA Astrophysics Data System (ADS)

Dong, Jun; Tong, Jia-Fei; Liu, Xia

As cardiovascular diseases appear frequently in modern society, the medicine and health system should be adjusted to meet the new requirements. Chinese government has planned to establish basic community medical insurance system (BCMIS) before 2020, where remote medical service is one of core issues. Therefore, we have developed the "remote network hospital system" which includes data server and diagnosis terminal by the aid of wireless detector to sample ECG. To improve the efficiency of ECG processing, in this paper, abnormal vs. normal ECG classification approach based on key features and statistical learning is presented, and the results are analyzed. Large amount of normal ECG could be filtered by computer automatically and abnormal ECG is left to be diagnosed specially by physicians.

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, diagnosis and treatment of congenital vascular anomalies, radiology plays an integral part in patient management.. · Sadick M, Müller-Wille R, Wildgruber M et al. Vascular Anomalies (Part I): Classification and Diagnostics of Vascular Anomalies. Fortschr Röntgenstr 2018; DOI: 10.1055/a-0620-8925. © Georg Thieme Verlag KG Stuttgart · New York.

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 in the future application of the widely used QOL-AD in economic evaluations. Future valuation studies will enable this tool to be used to calculate quality adjusted life years to evaluate treatments and interventions for people diagnosed with mild to moderate dementia.

IMPROVING THE AGE-RELATED MACULAR DEGENERATION CONSTRUCT: A New Classification System.

PubMed

Spaide, Richard F

2018-05-01

Previous models of disease in age-related macular degeneration (AMD) were incomplete in that they did not encompass subretinal drusenoid deposits (pseudodrusen), subtypes of neovascularization, and polypoidal choroidal vasculopathy. In addition, Type 3 neovascularization starts in the retina and may not necessarily involve the choroid. As such, the term choroidal neovascularization is not appropriate for these eyes. The new aspects in the AMD construct are to include specific lipoprotein extracellular accumulations, namely drusen and subretinal drusenoid deposits, as early AMD. The deposition of specific types of deposit seems to be highly correlated with choroidal thickness and topographical location in the macula. Late AMD includes macular neovascularization or atrophy. The particular type of extracellular deposit is predictive of the future course of the patient. For example, eyes with subretinal drusenoid deposits have a propensity to develop outer retinal atrophy, complete outer retinal and retinal pigment epithelial atrophy, or Type 3 neovascularization as specific forms of late AMD. Given Type 3 neovascularization may never involve the choroid, the term macular neovascularization is suggested for the entire spectrum of neovascular disease in AMD. In contrast to older classification systems, the proposed system encompasses the relevant presentations of disease and more precisely predicts the future course of the patient. In doing so, the concept was developed that there may be genetic risk alleles, which are not necessarily the same alleles that influence disease expression.

The Consensus Molecular Subtypes of Colorectal Cancer

PubMed Central

Guinney, Justin; Dienstmann, Rodrigo; Wang, Xin; de Reyniès, Aurélien; Schlicker, Andreas; Soneson, Charlotte; Marisa, Laetitia; Roepman, Paul; Nyamundanda, Gift; Angelino, Paolo; Bot, Brian M.; Morris, Jeffrey S.; Simon, Iris M.; Gerster, Sarah; Fessler, Evelyn; de Sousa e Melo, Felipe; Missiaglia, Edoardo; Ramay, Hena; Barras, David; Homicsko, Krisztian; Maru, Dipen; Manyam, Ganiraju C.; Broom, Bradley; Boige, Valerie; Perez-Villamil, Beatriz; Laderas, Ted; Salazar, Ramon; Gray, Joe W.; Hanahan, Douglas; Tabernero, Josep; Bernards, Rene; Friend, Stephen H.; Laurent-Puig, Pierre; Medema, Jan Paul; Sadanandam, Anguraj; Wessels, Lodewyk; Delorenzi, Mauro; Kopetz, Scott; Vermeulen, Louis; Tejpar, Sabine

2015-01-01

Colorectal cancer (CRC) is a frequently lethal disease with heterogeneous outcomes and drug responses. To resolve inconsistencies among the reported gene expression–based CRC classifications and facilitate clinical translation, we formed an international consortium dedicated to large-scale data sharing and analytics across expert groups. We show marked interconnectivity between six independent classification systems coalescing into four consensus molecular subtypes (CMS) with distinguishing features: CMS1 (MSI Immune, 14%), hypermutated, microsatellite unstable, strong immune activation; CMS2 (Canonical, 37%), epithelial, chromosomally unstable, marked WNT and MYC signaling activation; CMS3 (Metabolic, 13%), epithelial, evident metabolic dysregulation; and CMS4 (Mesenchymal, 23%), prominent transforming growth factor β activation, stromal invasion, and angiogenesis. Samples with mixed features (13%) possibly represent a transition phenotype or intra-tumoral heterogeneity. We consider the CMS groups the most robust classification system currently available for CRC – with clear biological interpretability – and the basis for future clinical stratification and subtype–based targeted interventions. PMID:26457759

The Australian experience in dental classification.

PubMed

Mahoney, Greg

2008-01-01

The Australian Defence Health Service uses a disease-risk management strategy to achieve two goals: first, to identify Australian Defence Force (ADF) members who are at high risk of developing an adverse health event, and second, to deliver intervention strategies efficiently so that maximum benefits for health within the ADF are achieved with the least cost. The present dental classification system utilized by the ADF, while an excellent dental triage tool, has been found not to be predictive of an ADF member having an adverse dental event in the following 12-month period. Clearly, there is a need for further research to establish a predictive risk-based dental classification system. This risk assessment must be sensitive enough to accurately estimate the probability that an ADF member will experience dental pain, dysfunction, or other adverse dental events within a forthcoming period, typically 12 months. Furthermore, there needs to be better epidemiological data collected in the field to assist in the research.

Systems Biology of Glucocorticoids in Muscle Disease

DTIC Science & Technology

2010-10-01

Introduction Duchenne muscular dystrophy (DMD) is the most common and incurable muscular dystrophy of childhood. Muscle regeneration fails with...SUBJECT TERMS Duchenne Muscular dystrophy , Glucocorticoids, Systems biology, Drug mechanism 16. SECURITY CLASSIFICATION OF: U 17. LIMITATION...better targeted and more effective therapies for Duchenne muscular dystrophy dynamically. This MDA grant proposal is led by Dr. Eric Hoffman, and it

The Integrative Studies of Genetic and Environmental Factors in Systemic Sclerosis

DTIC Science & Technology

2008-05-01

15. SUBJECT TERMS Scleroderma (SSc), fibroblasts, fibrosis, silica, environmental particles, susceptibility. 16. SECURITY CLASSIFICATION OF...factors in a viable system - human fibroblasts. Fibroblasts with a scleroderma (SSc) susceptible genetic background may be more vulnerable to...for understanding environmental contributions to fibrosing diseases such as scleroderma (SSc). Third, in the studies of specific biological

Periodontitis: Consensus report of workgroup 2 of the 2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions.

PubMed

Papapanou, Panos N; Sanz, Mariano; Buduneli, Nurcan; Dietrich, Thomas; Feres, Magda; Fine, Daniel H; Flemmig, Thomas F; Garcia, Raul; Giannobile, William V; Graziani, Filippo; Greenwell, Henry; Herrera, David; Kao, Richard T; Kebschull, Moritz; Kinane, Denis F; Kirkwood, Keith L; Kocher, Thomas; Kornman, Kenneth S; Kumar, Purnima S; Loos, Bruno G; Machtei, Eli; Meng, Huanxin; Mombelli, Andrea; Needleman, Ian; Offenbacher, Steven; Seymour, Gregory J; Teles, Ricardo; Tonetti, Maurizio S

2018-06-01

A new periodontitis classification scheme has been adopted, in which forms of the disease previously recognized as "chronic" or "aggressive" are now grouped under a single category ("periodontitis") and are further characterized based on a multi-dimensional staging and grading system. Staging is largely dependent upon the severity of disease at presentation as well as on the complexity of disease management, while grading provides supplemental information about biological features of the disease including a history-based analysis of the rate of periodontitis progression; assessment of the risk for further progression; analysis of possible poor outcomes of treatment; and assessment of the risk that the disease or its treatment may negatively affect the general health of the patient. Necrotizing periodontal diseases, whose characteristic clinical phenotype includes typical features (papilla necrosis, bleeding, and pain) and are associated with host immune response impairments, remain a distinct periodontitis category. Endodontic-periodontal lesions, defined by a pathological communication between the pulpal and periodontal tissues at a given tooth, occur in either an acute or a chronic form, and are classified according to signs and symptoms that have direct impact on their prognosis and treatment. Periodontal abscesses are defined as acute lesions characterized by localized accumulation of pus within the gingival wall of the periodontal pocket/sulcus, rapid tissue destruction and are associated with risk for systemic dissemination. © 2018 American Academy of Periodontology and European Federation of Periodontology.

Using ontology-based annotation to profile disease research

PubMed Central

Coulet, Adrien; LePendu, Paea; Shah, Nigam H

2012-01-01

Background Profiling the allocation and trend of research activity is of interest to funding agencies, administrators, and researchers. However, the lack of a common classification system hinders the comprehensive and systematic profiling of research activities. This study introduces ontology-based annotation as a method to overcome this difficulty. Analyzing over a decade of funding data and publication data, the trends of disease research are profiled across topics, across institutions, and over time. Results This study introduces and explores the notions of research sponsorship and allocation and shows that leaders of research activity can be identified within specific disease areas of interest, such as those with high mortality or high sponsorship. The funding profiles of disease topics readily cluster themselves in agreement with the ontology hierarchy and closely mirror the funding agency priorities. Finally, four temporal trends are identified among research topics. Conclusions This work utilizes disease ontology (DO)-based annotation to profile effectively the landscape of biomedical research activity. By using DO in this manner a use-case driven mechanism is also proposed to evaluate the utility of classification hierarchies. PMID:22494789

Associations between labial and whole salivary flow rates, systemic diseases and medications in a sample of older people.

PubMed

Smidt, Dorte; Torpet, Lis Andersen; Nauntofte, Birgitte; Heegaard, Karen Margrethe; Pedersen, Anne Marie Lynge

2010-10-01

To investigate the associations between age, gender, systemic diseases, medications and labial and whole salivary flow rates in older people. Unstimulated labial (LS) and unstimulated (UWS) and chewing-stimulated (SWS) whole salivary flow rates were measured in 389 randomly selected community-dwelling Danish women and 279 men aged 65-97 years. Systemic diseases, medications (coded according to the Anatomical Therapeutic Chemical (ATC) Classification System), tobacco and alcohol consumption were registered. The number of diseases and medications was higher and UWS lower in the older age groups. On average, women were slightly older, had more diseases, higher medication intake and lower UWS, SWS and LS than men. High number of diseases and medications was associated with low UWS, SWS and LS. In the healthy (14%) and nonmedicated (19%) participants, flow rates were not associated with age and gender, apart from SWS being lower in nonmedicated women. Low UWS were associated with psychiatric and respiratory disorders, type 2 diabetes and intake of psycholeptics, psychoanaleptics (especially SRRIs), respiratory agents, oral antidiabetics (particularly sulfonylureas), magnesium-hydroxide, cardiac agents, quinine, thiazides, calcium channel blockers, statins, urinary antispasmodics, glucosamine, NSAIDs, opioids and ophthalmologicals. SWS were lower in participants with ophthalmological disorders using ophthalmologicals (especially antiglaucoma agents and miotics), but also in those taking antidepressants, cardiac agents (mostly digitalis glycosides) and calcium channel blockers. Cardiovascular diseases and intake of anti-thrombotics (mainly low dose aspirins), calcium channel blockers and oral antidiabetics were associated with low LS. In older people, low salivary flow rates are associated with specific and high number of diseases and medications, but neither with age and gender per se nor with tobacco and alcohol consumption. Low UWS are associated with more diseases and medications than SWS and LS, which were primarily associated with cardiovascular diseases and medications including preventive agents such as low-dose aspirins and statins. New insights into medications and their association with salivary gland function were achieved using the ATC classification system. © 2010 John Wiley & Sons A/S.

A new computer aided diagnosis system for evaluation of chronic liver disease with ultrasound shear wave elastography imaging.

PubMed

Gatos, Ilias; Tsantis, Stavros; Spiliopoulos, Stavros; Karnabatidis, Dimitris; Theotokas, Ioannis; Zoumpoulis, Pavlos; Loupas, Thanasis; Hazle, John D; Kagadis, George C

2016-03-01

Classify chronic liver disease (CLD) from ultrasound shear-wave elastography (SWE) imaging by means of a computer aided diagnosis (CAD) system. The proposed algorithm employs an inverse mapping technique (red-green-blue to stiffness) to quantify 85 SWE images (54 healthy and 31 with CLD). Texture analysis is then applied involving the automatic calculation of 330 first and second order textural features from every transformed stiffness value map to determine functional features that characterize liver elasticity and describe liver condition for all available stages. Consequently, a stepwise regression analysis feature selection procedure is utilized toward a reduced feature subset that is fed into the support vector machines (SVMs) classification algorithm in the design of the CAD system. With regard to the mapping procedure accuracy, the stiffness map values had an average difference of 0.01 ± 0.001 kPa compared to the quantification results derived from the color-box provided by the built-in software of the ultrasound system. Highest classification accuracy from the SVM model was 87.0% with sensitivity and specificity values of 83.3% and 89.1%, respectively. Receiver operating characteristic curves analysis gave an area under the curve value of 0.85 with [0.77-0.89] confidence interval. The proposed CAD system employing color to stiffness mapping and classification algorithms offered superior results, comparing the already published clinical studies. It could prove to be of value to physicians improving the diagnostic accuracy of CLD and can be employed as a second opinion tool for avoiding unnecessary invasive procedures.

Nomenclature for congenital and paediatric cardiac disease: the International Paediatric and Congenital Cardiac Code (IPCCC) and the Eleventh Iteration of the International Classification of Diseases (ICD-11).

PubMed

Franklin, Rodney C G; Béland, Marie J; Colan, Steven D; Walters, Henry L; Aiello, Vera D; Anderson, Robert H; Bailliard, Frédérique; Boris, Jeffrey R; Cohen, Meryl S; Gaynor, J William; Guleserian, Kristine J; Houyel, Lucile; Jacobs, Marshall L; Juraszek, Amy L; Krogmann, Otto N; Kurosawa, Hiromi; Lopez, Leo; Maruszewski, Bohdan J; St Louis, James D; Seslar, Stephen P; Srivastava, Shubhika; Stellin, Giovanni; Tchervenkov, Christo I; Weinberg, Paul M; Jacobs, Jeffrey P

2017-12-01

An internationally approved and globally used classification scheme for the diagnosis of CHD has long been sought. The International Paediatric and Congenital Cardiac Code (IPCCC), which was produced and has been maintained by the International Society for Nomenclature of Paediatric and Congenital Heart Disease (the International Nomenclature Society), is used widely, but has spawned many "short list" versions that differ in content depending on the user. Thus, efforts to have a uniform identification of patients with CHD using a single up-to-date and coordinated nomenclature system continue to be thwarted, even if a common nomenclature has been used as a basis for composing various "short lists". In an attempt to solve this problem, the International Nomenclature Society has linked its efforts with those of the World Health Organization to obtain a globally accepted nomenclature tree for CHD within the 11th iteration of the International Classification of Diseases (ICD-11). The International Nomenclature Society has submitted a hierarchical nomenclature tree for CHD to the World Health Organization that is expected to serve increasingly as the "short list" for all communities interested in coding for congenital cardiology. This article reviews the history of the International Classification of Diseases and of the IPCCC, and outlines the process used in developing the ICD-11 congenital cardiac disease diagnostic list and the definitions for each term on the list. An overview of the content of the congenital heart anomaly section of the Foundation Component of ICD-11, published herein in its entirety, is also included. Future plans for the International Nomenclature Society include linking again with the World Health Organization to tackle procedural nomenclature as it relates to cardiac malformations. By doing so, the Society will continue its role in standardising nomenclature for CHD across the globe, thereby promoting research and better outcomes for fetuses, children, and adults with congenital heart anomalies.

Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer’s disease

PubMed Central

Gray, Katherine R.; Wolz, Robin; Heckemann, Rolf A.; Aljabar, Paul; Hammers, Alexander; Rueckert, Daniel

2012-01-01

Imaging biomarkers for Alzheimer’s disease are desirable for improved diagnosis and monitoring, as well as drug discovery. Automated image-based classification of individual patients could provide valuable diagnostic support for clinicians, when considered alongside cognitive assessment scores. We investigate the value of combining cross-sectional and longitudinal multi-region FDG-PET information for classification, using clinical and imaging data from the Alzheimer’s Disease Neuroimaging Initiative. Whole-brain segmentations into 83 anatomically defined regions were automatically generated for baseline and 12-month FDG-PET images. Regional signal intensities were extracted at each timepoint, as well as changes in signal intensity over the follow-up period. Features were provided to a support vector machine classifier. By combining 12-month signal intensities and changes over 12 months, we achieve significantly increased classification performance compared with using any of the three feature sets independently. Based on this combined feature set, we report classification accuracies of 88% between patients with Alzheimer’s disease and elderly healthy controls, and 65% between patients with stable mild cognitive impairment and those who subsequently progressed to Alzheimer’s disease. We demonstrate that information extracted from serial FDG-PET through regional analysis can be used to achieve state-of-the-art classification of diagnostic groups in a realistic multi-centre setting. This finding may be usefully applied in the diagnosis of Alzheimer’s disease, predicting disease course in individuals with mild cognitive impairment, and in the selection of participants for clinical trials. PMID:22236449

A System to Automatically Classify and Name Any Individual Genome-Sequenced Organism Independently of Current Biological Classification and Nomenclature

PubMed Central

Song, Yuhyun; Leman, Scotland; Monteil, Caroline L.; Heath, Lenwood S.; Vinatzer, Boris A.

2014-01-01

A broadly accepted and stable biological classification system is a prerequisite for biological sciences. It provides the means to describe and communicate about life without ambiguity. Current biological classification and nomenclature use the species as the basic unit and require lengthy and laborious species descriptions before newly discovered organisms can be assigned to a species and be named. The current system is thus inadequate to classify and name the immense genetic diversity within species that is now being revealed by genome sequencing on a daily basis. To address this lack of a general intra-species classification and naming system adequate for today’s speed of discovery of new diversity, we propose a classification and naming system that is exclusively based on genome similarity and that is suitable for automatic assignment of codes to any genome-sequenced organism without requiring any phenotypic or phylogenetic analysis. We provide examples demonstrating that genome similarity-based codes largely align with current taxonomic groups at many different levels in bacteria, animals, humans, plants, and viruses. Importantly, the proposed approach is only slightly affected by the order of code assignment and can thus provide codes that reflect similarity between organisms and that do not need to be revised upon discovery of new diversity. We envision genome similarity-based codes to complement current biological nomenclature and to provide a universal means to communicate unambiguously about any genome-sequenced organism in fields as diverse as biodiversity research, infectious disease control, human and microbial forensics, animal breed and plant cultivar certification, and human ancestry research. PMID:24586551

Developing a science of clinical utility in diagnostic classification systems field study strategies for ICD-11 mental and behavioral disorders.

PubMed

Keeley, Jared W; Reed, Geoffrey M; Roberts, Michael C; Evans, Spencer C; Medina-Mora, María Elena; Robles, Rebeca; Rebello, Tahilia; Sharan, Pratap; Gureje, Oye; First, Michael B; Andrews, Howard F; Ayuso-Mateos, José Luís; Gaebel, Wolfgang; Zielasek, Juergen; Saxena, Shekhar

2016-01-01

The World Health Organization (WHO) Department of Mental Health and Substance Abuse has developed a systematic program of field studies to evaluate and improve the clinical utility of the proposed diagnostic guidelines for mental and behavioral disorders in the Eleventh Revision of the International Classification of Diseases and Related Health Problems (ICD-11). The clinical utility of a diagnostic classification is critical to its function as the interface between health encounters and health information, and to making the ICD-11 be a more effective tool for helping the WHO's 194 member countries, including the United States, reduce the global disease burden of mental disorders. This article describes the WHO's efforts to develop a science of clinical utility in regard to one of the two major classification systems for mental disorders. We present the rationale and methodologies for an integrated and complementary set of field study strategies, including large international surveys, formative field studies of the structure of clinicians' conceptualization of mental disorders, case-controlled field studies using experimental methodologies to evaluate the impact of proposed changes to the diagnostic guidelines on clinicians' diagnostic decision making, and ecological implementation field studies of clinical utility in the global settings in which the guidelines will ultimately be implemented. The results of these studies have already been used in making decisions about the structure and content of ICD-11. If clinical utility is indeed among the highest aims of diagnostic systems for mental disorders, as their developers routinely claim, future revision efforts should continue to build on these efforts. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Dysregulation of Innate and Adaptive Serum Mediators Precedes Systemic Lupus Erythematosus Classification and Improves Prognostic Accuracy of Autoantibodies

PubMed Central

Guthridge, Joel M.; Bean, Krista M.; Fife, Dustin A.; Chen, Hua; Slight-Webb, Samantha R.; Keith, Michael P.; Harley, John B.; James, Judith A.

2016-01-01

Systemic lupus erythematosus (SLE) is a complex autoimmune disease with a poorly understood preclinical stage of immune dysregulation and symptom accrual. Accumulation of antinuclear autoantibody (ANA) specificities is a hallmark of impending clinical disease. Yet, many ANA-positive individuals remain healthy, suggesting that additional immune dysregulation underlies SLE pathogenesis. Indeed, we have recently demonstrated that interferon (IFN) pathways are dysregulated in preclinical SLE. To determine if other forms of immune dysregulation contribute to preclinical SLE pathogenesis, we measured SLE-associated autoantibodies and soluble mediators in samples from 84 individuals collected prior to SLE classification (average timespan = 5.98 years), compared to unaffected, healthy control samples matched by race, gender, age (± 5 years), and time of sample procurement. We found that multiple soluble mediators, including interleukin (IL)-5, IL-6, and IFN-γ, were significantly elevated in cases compared to controls more than 3.5 years pre-classification, prior to or concurrent with autoantibody positivity. Additional mediators, including innate cytokines, IFN-associated chemokines, and soluble tumor necrosis factor (TNF) superfamily mediators increased longitudinally in cases approaching SLE classification, but not in controls. In particular, levels of B lymphocyte stimulator (BLyS) and a proliferation-inducing ligand (APRIL) were comparable in cases and controls until less than 10 months pre-classification. Over the entire pre-classification period, random forest models incorporating ANA and anti-Ro/SSA positivity with levels of IL-5, IL-6, and the IFN-γ-induced chemokine, MIG, distinguished future SLE patients with 92% (± 1.8%) accuracy, compared to 78% accuracy utilizing ANA positivity alone. These data suggest that immune dysregulation involving multiple pathways contributes to SLE pathogenesis. Importantly, distinct immunological profiles are predictive for individuals who will develop clinical SLE and may be useful for delineating early pathogenesis, discovering therapeutic targets, and designing prevention trials. PMID:27338520

Colorectal Cancer Classification and Cell Heterogeneity: A Systems Oncology Approach

PubMed Central

Blanco-Calvo, Moisés; Concha, Ángel; Figueroa, Angélica; Garrido, Federico; Valladares-Ayerbes, Manuel

2015-01-01

Colorectal cancer is a heterogeneous disease that manifests through diverse clinical scenarios. During many years, our knowledge about the variability of colorectal tumors was limited to the histopathological analysis from which generic classifications associated with different clinical expectations are derived. However, currently we are beginning to understand that under the intense pathological and clinical variability of these tumors there underlies strong genetic and biological heterogeneity. Thus, with the increasing available information of inter-tumor and intra-tumor heterogeneity, the classical pathological approach is being displaced in favor of novel molecular classifications. In the present article, we summarize the most relevant proposals of molecular classifications obtained from the analysis of colorectal tumors using powerful high throughput techniques and devices. We also discuss the role that cancer systems biology may play in the integration and interpretation of the high amount of data generated and the challenges to be addressed in the future development of precision oncology. In addition, we review the current state of implementation of these novel tools in the pathological laboratory and in clinical practice. PMID:26084042

Clashing Diagnostic Approaches: DSM-ICD versus RDoC

PubMed Central

Lilienfeld, Scott O.; Treadway, Michael T.

2016-01-01

Since at least the middle of the past century, one overarching model of psychiatric classification, namely, that of the Diagnostic and Statistical Manual of Mental Disorders and International Classification of Diseases (DSM-ICD), has reigned supreme. This DSM-ICD approach embraces an Aristotelian view of mental disorders as largely discrete entities that are characterized by distinctive signs, symptoms, and natural histories. Over the past several years, however, a competing vision, namely, the Research Domain Criteria (RDoC) initiative launched by the National Institute of Mental Health, has emerged in response to accumulating anomalies within the DSM-ICD system. In contrast to DSM-ICD, RDoC embraces a Galilean view of psychopathology as the product of dysfunctions in neural circuitry. RDoC appears to be a valuable endeavor that holds out the long-term promise of an alternative system of mental illness classification. We delineate three sets of pressing challenges – conceptual, methodological, and logistical/pragmatic – that must be addressed for RDoC to realize its scientific potential, and conclude with a call for further research, including investigation of a rapprochement between Aristotelian and Galilean approaches to psychiatric classification. PMID:26845519

MedlinePlus Connect

MedlinePlus

... code requests: Problems/Diagnoses • ICD-9-CM (International Classification of Disease, 9 th edition, Clinical Modification) • ICD-10-CM (International Classification of Disease, 10 th edition, Clinical Modification) • SNOMED ...

An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease

PubMed Central

Schmitter, Daniel; Roche, Alexis; Maréchal, Bénédicte; Ribes, Delphine; Abdulkadir, Ahmed; Bach-Cuadra, Meritxell; Daducci, Alessandro; Granziera, Cristina; Klöppel, Stefan; Maeder, Philippe; Meuli, Reto; Krueger, Gunnar

2014-01-01

Voxel-based morphometry from conventional T1-weighted images has proved effective to quantify Alzheimer's disease (AD) related brain atrophy and to enable fairly accurate automated classification of AD patients, mild cognitive impaired patients (MCI) and elderly controls. Little is known, however, about the classification power of volume-based morphometry, where features of interest consist of a few brain structure volumes (e.g. hippocampi, lobes, ventricles) as opposed to hundreds of thousands of voxel-wise gray matter concentrations. In this work, we experimentally evaluate two distinct volume-based morphometry algorithms (FreeSurfer and an in-house algorithm called MorphoBox) for automatic disease classification on a standardized data set from the Alzheimer's Disease Neuroimaging Initiative. Results indicate that both algorithms achieve classification accuracy comparable to the conventional whole-brain voxel-based morphometry pipeline using SPM for AD vs elderly controls and MCI vs controls, and higher accuracy for classification of AD vs MCI and early vs late AD converters, thereby demonstrating the potential of volume-based morphometry to assist diagnosis of mild cognitive impairment and Alzheimer's disease. PMID:25429357

Juvenile nasopharyngeal angiofibroma staging: An overview.

PubMed

Alshaikh, Nada Ali; Eleftheriadou, Anna

2015-06-01

Staging of tumors is very important in treatment and surgical decision making, as well as in predicting disease recurrence and prognosis. This review focuses on the different available classifications of juvenile nasopharyngeal angiofibroma (JNA) and their impact on the evaluation, management, and prognosis of JNA. The literature was reviewed, and publications on JNA staging were examined. Our MEDLINE search of the entire English-language literature found no review article on the current available staging systems for JNA. In this article, we review the common JNA classification systems that have been published, and we discuss some of their advantages and disadvantages. The most commonly used staging systems for JNA are the Radkowski and the Andrews-Fisch staging systems. However, some newer staging systems that are based on advances in technology and surgical approaches-the Onerci, INCan, and UPMC systems-have shown promising utility, and they will probably gain popularity in the future.

Association of Krouse Classification for Sinonasal Inverted Papilloma With Recurrence: A Systematic Review and Meta-analysis.

PubMed

Lisan, Quentin; Moya-Plana, Antoine; Bonfils, Pierre

2017-11-01

The risk factors for the recurrence of sinonasal inverted papilloma are still unclear. To investigate the potential association between the Krouse classification and the recurrence rates of sinonasal inverted papilloma. The EMBASE and MEDLINE databases were searched for the period January 1, 1964, through September 30, 2016, using the following search strategy: (paranasal sinuses [Medical Subject Headings (MeSH) terms] OR sinonasal [all fields]) AND (inverted papilloma [MeSH terms] OR (inverted [all fields] AND papilloma [all fields]). The inclusion criteria were (1) studies including sinonasal inverted papilloma only and no other forms of papillomas, such as oncocytic papilloma; (2) minimum follow-up of 1 year after the surgery; and (3) clear report of cases (recurrence) and controls according to the Krouse classification system or deducible from the full-text article. Literature search was performed by 2 reviewers. Of the 625 articles retrieved in the literature, 97 full-text articles were reviewed. Observational cohort studies or randomized controlled trials were included, and the following variables were extracted from full-text articles: authors of the study, publication year, follow-up data, and number of cases (recurrence) and controls (no recurrence) in each of the 4 stages of the Krouse classification system. The Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines were followed. Odds ratios (ORs) and 95% CIs were estimated, and data of included studies were pooled using a random-effects model. The main outcome was recurrence after surgical removal of sinonasal inverted papilloma according to each stage of the Krouse classification system. Thirteen studies comprising 1787 patients were analyzed. A significant increased risk of recurrence (51%) was highlighted for Krouse stage T3 disease when compared with stage T2 (pooled OR, 1.51; 95% CI, 1.09-2.09). No significant difference in risk of recurrence was found between Krouse stages T1 and T2 disease (pooled OR, 1.14; 95% CI, 0.63-2.04) or between stages T3 and T4 (pooled OR, 1.27; 95% CI, 0.72-2.26). Inverted papillomas classified as stage T3 according to the Krouse classification system presented a 51% higher likelihood of recurrence. Head and neck surgeons must be aware of this higher likelihood of recurrence when planning and performing surgery for sinonasal inverted papilloma.

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.

An automatic graph-based approach for artery/vein classification in retinal images.

PubMed

Dashtbozorg, Behdad; Mendonça, Ana Maria; Campilho, Aurélio

2014-03-01

The classification of retinal vessels into artery/vein (A/V) is an important phase for automating the detection of vascular changes, and for the calculation of characteristic signs associated with several systemic diseases such as diabetes, hypertension, and other cardiovascular conditions. This paper presents an automatic approach for A/V classification based on the analysis of a graph extracted from the retinal vasculature. The proposed method classifies the entire vascular tree deciding on the type of each intersection point (graph nodes) and assigning one of two labels to each vessel segment (graph links). Final classification of a vessel segment as A/V is performed through the combination of the graph-based labeling results with a set of intensity features. The results of this proposed method are compared with manual labeling for three public databases. Accuracy values of 88.3%, 87.4%, and 89.8% are obtained for the images of the INSPIRE-AVR, DRIVE, and VICAVR databases, respectively. These results demonstrate that our method outperforms recent approaches for A/V classification.

The 7th lung cancer TNM classification and staging system: Review of the changes and implications.

PubMed

Mirsadraee, Saeed; Oswal, Dilip; Alizadeh, Yalda; Caulo, Andrea; van Beek, Edwin

2012-04-28

Lung cancer is the most common cause of death from cancer in males, accounting for more than 1.4 million deaths in 2008. It is a growing concern in China, Asia and Africa as well. Accurate staging of the disease is an important part of the management as it provides estimation of patient's prognosis and identifies treatment sterategies. It also helps to build a database for future staging projects. A major revision of lung cancer staging has been announced with effect from January 2010. The new classification is based on a larger surgical and non-surgical cohort of patients, and thus more accurate in terms of outcome prediction compared to the previous classification. There are several original papers regarding this new classification which give comprehensive description of the methodology, the changes in the staging and the statistical analysis. This overview is a simplified description of the changes in the new classification and their potential impact on patients' treatment and prognosis.

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 applied by gynecologic pathologists. Further refinement of criteria should allow use of this powerful classification system to delineate which cervical adenocarcinomas can be safely treated conservatively. PMID:27255163

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.

HEp-2 cell image classification method based on very deep convolutional networks with small datasets

NASA Astrophysics Data System (ADS)

Lu, Mengchi; Gao, Long; Guo, Xifeng; Liu, Qiang; Yin, Jianping

2017-07-01

Human Epithelial-2 (HEp-2) cell images staining patterns classification have been widely used to identify autoimmune diseases by the anti-Nuclear antibodies (ANA) test in the Indirect Immunofluorescence (IIF) protocol. Because manual test is time consuming, subjective and labor intensive, image-based Computer Aided Diagnosis (CAD) systems for HEp-2 cell classification are developing. However, methods proposed recently are mostly manual features extraction with low accuracy. Besides, the scale of available benchmark datasets is small, which does not exactly suitable for using deep learning methods. This issue will influence the accuracy of cell classification directly even after data augmentation. To address these issues, this paper presents a high accuracy automatic HEp-2 cell classification method with small datasets, by utilizing very deep convolutional networks (VGGNet). Specifically, the proposed method consists of three main phases, namely image preprocessing, feature extraction and classification. Moreover, an improved VGGNet is presented to address the challenges of small-scale datasets. Experimental results over two benchmark datasets demonstrate that the proposed method achieves superior performance in terms of accuracy compared with existing methods.

[The use of the orthostatic test for determining the work capacity of convalescents].

PubMed

Reshetiuk, A L; Volkova, P S; Zemskaia, L I; Gdal', V A

1990-06-01

The course of responses to the orthostatic test is analyzed in convalescents with a history of different diseases. Diagnostic factors were singled out characteristic of different groups of diseases including values of the cardiovascular, neuromuscular systems of the body. On the basis of obtained data a decimal classification of the working capacity of convalescents was worked out by their reaction to orthostatic effects.

Plus disease in retinopathy of prematurity: a continuous spectrum of vascular abnormality as basis of diagnostic variability

PubMed Central

Campbell, J. Peter; Kalpathy-Cramer, Jayashree; Erdogmus, Deniz; Tian, Peng; Kedarisetti, Dharanish; Moleta, Chace; Reynolds, James D.; Hutcheson, Kelly; Shapiro, Michael J.; Repka, Michael X.; Ferrone, Philip; Drenser, Kimberly; Horowitz, Jason; Sonmez, Kemal; Swan, Ryan; Ostmo, Susan; Jonas, Karyn E.; Chan, R.V. Paul; Chiang, Michael F.

2016-01-01

Objective To identify patterns of inter-expert discrepancy in plus disease diagnosis in retinopathy of prematurity (ROP). Design We developed two datasets of clinical images of varying disease severity (100 images and 34 images) as part of the Imaging and Informatics in ROP study, and determined a consensus reference standard diagnosis (RSD) for each image, based on 3 independent image graders and the clinical exam. We recruited 8 expert ROP clinicians to classify these images and compared the distribution of classifications between experts and the RSD. Subjects, Participants, and/or Controls Images obtained during routine ROP screening in neonatal intensive care units. 8 participating experts with >10 years of clinical ROP experience and >5 peer-reviewed ROP publications. Methods, Intervention, or Testing Expert classification of images of plus disease in ROP. Main Outcome Measures Inter-expert agreement (weighted kappa statistic), and agreement and bias on ordinal classification between experts (ANOVA) and the RSD (percent agreement). Results There was variable inter-expert agreement on diagnostic classifications between the 8 experts and the RSD (weighted kappa 0 – 0.75, mean 0.30). RSD agreement ranged from 80 – 94% agreement for the dataset of 100 images, and 29 – 79% for the dataset of 34 images. However, when images were ranked in order of disease severity (by average expert classification), the pattern of expert classification revealed a consistent systematic bias for each expert consistent with unique cut points for the diagnosis of plus disease and pre-plus disease. The two-way ANOVA model suggested a highly significant effect of both image and user on the average score (P<0.05, adjusted R2=0.82 for dataset A, and P< 0.05 and adjusted R2 =0.6615 for dataset B). Conclusions and Relevance There is wide variability in the classification of plus disease by ROP experts, which occurs because experts have different “cut-points” for the amounts of vascular abnormality required for presence of plus and pre-plus disease. This has important implications for research, teaching and patient care for ROP, and suggests that a continuous ROP plus disease severity score may more accurately reflect the behavior of expert ROP clinicians, and may better standardize classification in the future. PMID:27591053

Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection.

PubMed

Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa; Al-Garadi, Mohammed Ali

2017-01-01

Widespread implementation of electronic databases has improved the accessibility of plaintext clinical information for supplementary use. Numerous machine learning techniques, such as supervised machine learning approaches or ontology-based approaches, have been employed to obtain useful information from plaintext clinical data. This study proposes an automatic multi-class classification system to predict accident-related causes of death from plaintext autopsy reports through expert-driven feature selection with supervised automatic text classification decision models. Accident-related autopsy reports were obtained from one of the largest hospital in Kuala Lumpur. These reports belong to nine different accident-related causes of death. Master feature vector was prepared by extracting features from the collected autopsy reports by using unigram with lexical categorization. This master feature vector was used to detect cause of death [according to internal classification of disease version 10 (ICD-10) classification system] through five automated feature selection schemes, proposed expert-driven approach, five subset sizes of features, and five machine learning classifiers. Model performance was evaluated using precisionM, recallM, F-measureM, accuracy, and area under ROC curve. Four baselines were used to compare the results with the proposed system. Random forest and J48 decision models parameterized using expert-driven feature selection yielded the highest evaluation measure approaching (85% to 90%) for most metrics by using a feature subset size of 30. The proposed system also showed approximately 14% to 16% improvement in the overall accuracy compared with the existing techniques and four baselines. The proposed system is feasible and practical to use for automatic classification of ICD-10-related cause of death from autopsy reports. The proposed system assists pathologists to accurately and rapidly determine underlying cause of death based on autopsy findings. Furthermore, the proposed expert-driven feature selection approach and the findings are generally applicable to other kinds of plaintext clinical reports.

Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection

PubMed Central

Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa; Al-Garadi, Mohammed Ali

2017-01-01

Objectives Widespread implementation of electronic databases has improved the accessibility of plaintext clinical information for supplementary use. Numerous machine learning techniques, such as supervised machine learning approaches or ontology-based approaches, have been employed to obtain useful information from plaintext clinical data. This study proposes an automatic multi-class classification system to predict accident-related causes of death from plaintext autopsy reports through expert-driven feature selection with supervised automatic text classification decision models. Methods Accident-related autopsy reports were obtained from one of the largest hospital in Kuala Lumpur. These reports belong to nine different accident-related causes of death. Master feature vector was prepared by extracting features from the collected autopsy reports by using unigram with lexical categorization. This master feature vector was used to detect cause of death [according to internal classification of disease version 10 (ICD-10) classification system] through five automated feature selection schemes, proposed expert-driven approach, five subset sizes of features, and five machine learning classifiers. Model performance was evaluated using precisionM, recallM, F-measureM, accuracy, and area under ROC curve. Four baselines were used to compare the results with the proposed system. Results Random forest and J48 decision models parameterized using expert-driven feature selection yielded the highest evaluation measure approaching (85% to 90%) for most metrics by using a feature subset size of 30. The proposed system also showed approximately 14% to 16% improvement in the overall accuracy compared with the existing techniques and four baselines. Conclusion The proposed system is feasible and practical to use for automatic classification of ICD-10-related cause of death from autopsy reports. The proposed system assists pathologists to accurately and rapidly determine underlying cause of death based on autopsy findings. Furthermore, the proposed expert-driven feature selection approach and the findings are generally applicable to other kinds of plaintext clinical reports. PMID:28166263

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

[International multidisciplinary classification of acute pancreatitis severity: the 2013 Spanish edition].

PubMed

Maraví-Poma, E; Patchen Dellinger, E; Forsmark, C E; Layer, P; Lévy, P; Shimosegawa, T; Siriwardena, A K; Uomo, G; Whitcomb, D C; Windsor, J A; Petrov, M S

2014-05-01

To develop a new classification of acute pancreatitis severity on the basis of a sound conceptual framework, comprehensive review of the published evidence, and worldwide consultation. The Atlanta definitions of acute pancreatitis severity are ingrained in the lexicon of specialist in pancreatic diseases, but are suboptimal because these definitions are based on the empiric description of events not associated with severity. A personal invitation to contribute to the development of a new classification of acute pancreatitis severity was sent to all surgeons, gastroenterologists, internists, intensivists and radiologists currently active in the field of clinical acute pancreatitis. The invitation was not limited to members of certain associations or residents of certain countries. A global web-based survey was conducted, and a dedicated international symposium was organized to bring contributors from different disciplines together and discuss the concept and definitions. The new classification of severity is based on the actual local and systemic determinants of severity, rather than on the description of events that are non-causally associated with severity. The local determinant relates to whether there is (peri) pancreatic necrosis or not, and if present, whether it is sterile or infected. The systemic determinant relates to whether there is organ failure or not, and if present, whether it is transient or persistent. The presence of one determinant can modify the effect of another, whereby the presence of both infected (peri) pancreatic necrosis and persistent organ failure has a greater impact upon severity than either determinant alone. The derivation of a classification based on the above principles results in four categories of severity: mild, moderate, severe, and critical. This classification is the result of a consultative process among specialists in pancreatic diseases from 49 countries spanning North America, South America, Europe, Asia, Oceania and Africa. It provides a set of concise up to date definitions of all the main entities pertinent to classifying the severity of acute pancreatitis in clinical practice and research. This ensures that the determinant-based classification can be used in a uniform manner throughout the world. Copyright © 2013 Elsevier España, S.L. and SEMICYUC. All rights reserved.

Comparative study of classification algorithms for immunosignaturing data

PubMed Central

2012-01-01

Background High-throughput technologies such as DNA, RNA, protein, antibody and peptide microarrays are often used to examine differences across drug treatments, diseases, transgenic animals, and others. Typically one trains a classification system by gathering large amounts of probe-level data, selecting informative features, and classifies test samples using a small number of features. As new microarrays are invented, classification systems that worked well for other array types may not be ideal. Expression microarrays, arguably one of the most prevalent array types, have been used for years to help develop classification algorithms. Many biological assumptions are built into classifiers that were designed for these types of data. One of the more problematic is the assumption of independence, both at the probe level and again at the biological level. Probes for RNA transcripts are designed to bind single transcripts. At the biological level, many genes have dependencies across transcriptional pathways where co-regulation of transcriptional units may make many genes appear as being completely dependent. Thus, algorithms that perform well for gene expression data may not be suitable when other technologies with different binding characteristics exist. The immunosignaturing microarray is based on complex mixtures of antibodies binding to arrays of random sequence peptides. It relies on many-to-many binding of antibodies to the random sequence peptides. Each peptide can bind multiple antibodies and each antibody can bind multiple peptides. This technology has been shown to be highly reproducible and appears promising for diagnosing a variety of disease states. However, it is not clear what is the optimal classification algorithm for analyzing this new type of data. Results We characterized several classification algorithms to analyze immunosignaturing data. We selected several datasets that range from easy to difficult to classify, from simple monoclonal binding to complex binding patterns in asthma patients. We then classified the biological samples using 17 different classification algorithms. Using a wide variety of assessment criteria, we found ‘Naïve Bayes’ far more useful than other widely used methods due to its simplicity, robustness, speed and accuracy. Conclusions ‘Naïve Bayes’ algorithm appears to accommodate the complex patterns hidden within multilayered immunosignaturing microarray data due to its fundamental mathematical properties. PMID:22720696

ID-Viewer: a visual analytics architecture for infectious diseases surveillance and response management in Pakistan.

PubMed

Ali, M A; Ahsan, Z; Amin, M; Latif, S; Ayyaz, A; Ayyaz, M N

2016-05-01

Globally, disease surveillance systems are playing a significant role in outbreak detection and response management of Infectious Diseases (IDs). However, in developing countries like Pakistan, epidemic outbreaks are difficult to detect due to scarcity of public health data and absence of automated surveillance systems. Our research is intended to formulate an integrated service-oriented visual analytics architecture for ID surveillance, identify key constituents and set up a baseline for easy reproducibility of such systems in the future. This research focuses on development of ID-Viewer, which is a visual analytics decision support system for ID surveillance. It is a blend of intelligent approaches to make use of real-time streaming data from Emergency Departments (EDs) for early outbreak detection, health care resource allocation and epidemic response management. We have developed a robust service-oriented visual analytics architecture for ID surveillance, which provides automated mechanisms for ID data acquisition, outbreak detection and epidemic response management. Classification of chief-complaints is accomplished using dynamic classification module, which employs neural networks and fuzzy-logic to categorize syndromes. Standard routines by Center for Disease Control (CDC), i.e. c1-c3 (c1-mild, c2-medium and c3-ultra), and spatial scan statistics are employed for detection of temporal and spatio-temporal disease outbreaks respectively. Prediction of imminent disease threats is accomplished using support vector regression for early warnings and response planning. Geographical visual analytics displays are developed that allow interactive visualization of syndromic clusters, monitoring disease spread patterns, and identification of spatio-temporal risk zones. We analysed performance of surveillance framework using ID data for year 2011-2015. Dynamic syndromic classifier is able to classify chief-complaints to appropriate syndromes with high classification accuracy. Outbreak detection methods are able to detect the ID outbreaks in start of epidemic time zones. Prediction model is able to forecast dengue trend for 20 weeks ahead with nominal normalized root mean square error of 0.29. Interactive geo-spatiotemporal displays, i.e. heat-maps, and choropleth are shown in respective sections. The proposed framework will set a standard and provide necessary details for future implementation of such a system for resource-constrained regions. It will improve early outbreak detection attributable to natural and man-made biological threats, monitor spatio-temporal epidemic trends and provide assurance that an outbreak has, or has not occurred. Advanced analytics features will be beneficial in timely organization/formulation of health management policies, disease control activities and efficient health care resource allocation. Copyright © 2016 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

ICD-11 and DSM-5 personality trait domains capture categorical personality disorders: Finding a common ground.

PubMed

Bach, Bo; Sellbom, Martin; Skjernov, Mathias; Simonsen, Erik

2018-05-01

The five personality disorder trait domains in the proposed International Classification of Diseases, 11th edition and the Diagnostic and Statistical Manual of Mental Disorders, 5th edition are comparable in terms of Negative Affectivity, Detachment, Antagonism/Dissociality and Disinhibition. However, the International Classification of Diseases, 11th edition model includes a separate domain of Anankastia, whereas the Diagnostic and Statistical Manual of Mental Disorders, 5th edition model includes an additional domain of Psychoticism. This study examined associations of International Classification of Diseases, 11th edition and Diagnostic and Statistical Manual of Mental Disorders, 5th edition trait domains, simultaneously, with categorical personality disorders. Psychiatric outpatients ( N = 226) were administered the Structured Clinical Interview for DSM-IV Axis II Personality Disorders Interview and the Personality Inventory for DSM-5. International Classification of Diseases, 11th edition and Diagnostic and Statistical Manual of Mental Disorders, 5th edition trait domain scores were obtained using pertinent scoring algorithms for the Personality Inventory for DSM-5. Associations between categorical personality disorders and trait domains were examined using correlation and multiple regression analyses. Both the International Classification of Diseases, 11th edition and the Diagnostic and Statistical Manual of Mental Disorders, 5th edition domain models showed relevant continuity with categorical personality disorders and captured a substantial amount of their information. As expected, the International Classification of Diseases, 11th edition model was superior in capturing obsessive-compulsive personality disorder, whereas the Diagnostic and Statistical Manual of Mental Disorders, 5th edition model was superior in capturing schizotypal personality disorder. These preliminary findings suggest that little information is 'lost' in a transition to trait domain models and potentially adds to narrowing the gap between Diagnostic and Statistical Manual of Mental Disorders, 5th edition and the proposed International Classification of Diseases, 11th edition model. Accordingly, the International Classification of Diseases, 11th edition and Diagnostic and Statistical Manual of Mental Disorders, 5th edition domain models may be used to delineate one another as well as features of familiar categorical personality disorder types. A preliminary category-to-domain 'cross walk' is provided in the article.

Diagnosis and Management of Systemic Sclerosis: A Practical Approach.

PubMed

Lee, Jason J; Pope, Janet E

2016-02-01

Systemic sclerosis is a devastating multisystem rheumatologic condition that is characterized by autoimmunity, tissue fibrosis, obliterative vasculopathy and inflammation. Clinical presentation and course of the condition vary greatly, which complicates both diagnosis and corresponding treatment. In this regard, recent advances in disease understanding, both clinically and biochemically, have led to newer classification criteria for systemic sclerosis that are more inclusive than ever before. Still, significant disease modifying therapies do not yet exist for most patients. Therefore, organ-based management strategies are employed and research has been directed within this paradigm focusing on either the most debilitating symptoms, such as Raynaud's phenomenon, digital ulcers and cutaneous sclerosis, or life-threatening organ involvement such as interstitial lung disease and pulmonary arterial hypertension. The current trends in systemic sclerosis diagnosis, evidence-based treatment recommendations and potential future directions in systemic sclerosis treatment are discussed.

Clinical epidemiology of ulcerative colitis in Arabs based on the Montréal classification.

PubMed

Alharbi, Othman R; Azzam, Nahla A; Almalki, Ahmed S; Almadi, Majid A; Alswat, Khalid A; Sadaf, Nazia; Aljebreen, Abdulrahman M

2014-12-14

To determine the clinical, epidemiological and phenotypic characteristics of ulcerative colitis (UC) in Saudi Arabia by studying the largest cohort of Arab UC patients. Data from UC patients attending gastroenterology clinics in four tertiary care centers in three cities between September 2009 and September 2013 were entered into a validated web-based registry, inflammatory bowel disease information system (IBDIS). The IBDIS database covers numerous aspects of inflammatory bowel disease. Patient characteristics, disease phenotype and behavior, age at diagnosis, course of the disease, and extraintestinal manifestations were recorded. Among 394 UC patients, males comprised 51.0% and females 49.0%. According to the Montréal classification of age, the major chunk of our patients belonged to the A2 category for age of diagnosis at 17-40 years (68.4%), while 24.2% belonged to the A3 category for age of diagnosis at > 40 years. According to the same classification, a majority of patients had extensive UC (42.7%), 35.3% had left-sided colitis and 29.2% had only proctitis. Moreover, 51.3% were in remission, 16.6% had mild UC, 23.4% had moderate UC and 8.6% had severe UC. Frequent relapse occurred in 17.4% patients, infrequent relapse in 77% and 4.8% had chronic disease. A majority (85.2%) of patients was steroid responsive. With regard to extraintestinal manifestations, arthritis was present in 16.4%, osteopenia in 31.4%, osteoporosis in 17.1% and cutaneous involvement in 7.0%. The majority of UC cases were young people (17-40 years), with a male preponderance. While the disease course was found to be similar to that reported in Western countries, more similarities were found with Asian countries with regards to the extent of the disease and response to steroid therapy.

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

PubMed Central

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

2014-01-01

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

MERRF Classification: Implications for Diagnosis and Clinical Trials.

PubMed

Finsterer, Josef; Zarrouk-Mahjoub, Sinda; Shoffner, John M

2018-03-01

Given the etiologic heterogeneity of disease classification using clinical phenomenology, we employed contemporary criteria to classify variants associated with myoclonic epilepsy with ragged-red fibers (MERRF) syndrome and to assess the strength of evidence of gene-disease associations. Standardized approaches are used to clarify the definition of MERRF, which is essential for patient diagnosis, patient classification, and clinical trial design. Systematic literature and database search with application of standardized assessment of gene-disease relationships using modified Smith criteria and of variants reported to be associated with MERRF using modified Yarham criteria. Review of available evidence supports a gene-disease association for two MT-tRNAs and for POLG. Using modified Smith criteria, definitive evidence of a MERRF gene-disease association is identified for MT-TK. Strong gene-disease evidence is present for MT-TL1 and POLG. Functional assays that directly associate variants with oxidative phosphorylation impairment were critical to mtDNA variant classification. In silico analysis was of limited utility to the assessment of individual MT-tRNA variants. With the use of contemporary classification criteria, several mtDNA variants previously reported as pathogenic or possibly pathogenic are reclassified as neutral variants. MERRF is primarily an MT-TK disease, with pathogenic variants in this gene accounting for ~90% of MERRF patients. Although MERRF is phenotypically and genotypically heterogeneous, myoclonic epilepsy is the clinical feature that distinguishes MERRF from other categories of mitochondrial disorders. Given its low frequency in mitochondrial disorders, myoclonic epilepsy is not explained simply by an impairment of cellular energetics. Although MERRF phenocopies can occur in other genes, additional data are needed to establish a MERRF disease-gene association. This approach to MERRF emphasizes standardized classification rather than clinical phenomenology, thus improving patient diagnosis and clinical trial design. Copyright © 2017 Elsevier Inc. All rights reserved.

Using the International Classification of Functioning, Disability and Health Children and Youth version in education systems: a new approach to eligibility.

PubMed

Hollenweger, Judith; Moretti, Marta

2012-02-01

In developed countries, establishing eligibility for persons with disabilities is a requirement for accessing specialized services or benefits. The underlying conceptualizations of disability are often problematic because they concentrate on deficits but try to promote social participation and focus on dependence while trying to strengthen independence. In addition, such conceptualizations are unable to respond to the rights-based approach of the UN Convention on the Rights of Persons with Disabilities. The International Classification of Functioning, Disability and Health Version for Children and Youth provides a model and classification that allows relating disease- or impairment-specific information to participation in the life domains relevant for a specific policy area. Establishing eligibility in education systems needs to be compatible with the principles of inclusive education, participation, and social justice. In addition, the overall goals of education and individualized goals for a specific child with disabilities need to be taken into account. Using the International Classification of Functioning, Disability and Health Version for Children and Youth as a model and classification, the different factors influencing eligibility-related decisions (impairments, activity/participation, environment, personal factors) can be made transparent to provide the basis for a decision-making process to which parents and the child actively contribute.

Constructing a classification of hypersensitivity/allergic diseases for ICD-11 by crowdsourcing the allergist community.

PubMed

Tanno, L K; Calderon, M A; Goldberg, B J; Gayraud, J; Bircher, A J; Casale, T; Li, J; Sanchez-Borges, M; Rosenwasser, L J; Pawankar, R; Papadopoulos, N G; Demoly, P

2015-06-01

The global allergy community strongly believes that the 11th revision of the International Classification of Diseases (ICD-11) offers a unique opportunity to improve the classification and coding of hypersensitivity/allergic diseases via inclusion of a specific chapter dedicated to this disease area to facilitate epidemiological studies, as well as to evaluate the true size of the allergy epidemic. In this context, an international collaboration has decided to revise the classification of hypersensitivity/allergic diseases and to validate it for ICD-11 by crowdsourcing the allergist community. After careful comparison between ICD-10 and 11 beta phase linearization codes, we identified gaps and trade-offs allowing us to construct a classification proposal, which was sent to the European Academy of Allergy and Clinical Immunology (EAACI) sections, interest groups, executive committee as well as the World Allergy Organization (WAO), and American Academy of Allergy Asthma and Immunology (AAAAI) leaderships. The crowdsourcing process produced comments from 50 of 171 members contacted by e-mail. The classification proposal has also been discussed at face-to-face meetings with experts of EAACI sections and interest groups and presented in a number of business meetings during the 2014 EAACI annual congress in Copenhagen. As a result, a high-level complex structure of classification for hypersensitivity/allergic diseases has been constructed. The model proposed has been presented to the WHO groups in charge of the ICD revision. The international collaboration of allergy experts appreciates bilateral discussion and aims to get endorsement of their proposals for the final ICD-11. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

The role of Brazilian National Health Information Systems in assessing the impact of Zika virus outbreak.

PubMed

Pavão, Ana Luiza Braz; Barcellos, Christovam; Pedroso, Marcel; Boccolini, Cristiano; Romero, Dália

2017-01-01

The Zika virus (ZIKV) epidemic has become a public health emergency following its association with severe neurological complications. We aim to discuss how the Brazilian National Health Information Systems can help to assess the impact of the ZIKV epidemic on health outcomes potentially related to ZIKV. Health outcomes potentially related to ZIKV infection were described based on a literature review of published studies on ZIKV infection outcomes and on recent protocols developed and published by the Brazilian Ministry of Health for different stages of the life cycle. These outcomes were correlated with the International Classification of Diseases 10th Revision (ICD-10) classification system, as this is the diagnostic classification registered in the Health Information System. A suggested list of 50 clinical manifestations, dispersed into 4 ICD chapters, and their information sources was created to help monitor the ZIKV epidemics and trends. Correlation of these selected ICD-10 codes and the HIS, as well as, a review of the potentialities and limitations of health information systems were performed. The potential of the Health Information System and its underutilization by stakeholders and researchers have been a barrier in diagnosing and reporting ZIKV infection and its complications. The ZIKV outbreak is still a challenge for health practice and the Brazilian Health Information System.

A language of health in action: Read Codes, classifications and groupings.

PubMed Central

Stuart-Buttle, C. D.; Read, J. D.; Sanderson, H. F.; Sutton, Y. M.

1996-01-01

A cornerstone of the Information Management and Technology Strategy of the National Health Service's (NHS) Executive is fully operational, person-based clinical information systems, from which flow all of the data needed for direct and indirect care of patients by healthcare providers, and local and national management of the NHS. The currency of these data flows are firstly Read-coded clinical terms, secondly the classifications, the International, Classification of Disease and Health Related Problems, 10th Revision (ICD-10) and The Office of Population Censuses and Surveys Classification of Surgical Operations and Procedures, 4th Revision (OPCS-4), and thirdly Healthcare Resource Groups and Health Benefit Groups, all of which together are called the "language of health", an essential element of the electronic clinical record. This paper briefly describes the three main constituents of the language, and how, together with person-based, fully operational clinical information systems, it enables more effective and efficient healthcare delivery. It also describes how the remaining projects of the IM&T Strategy complete the key components necessary to provide the systems that will enable the flow of person-based data, collected once at the point of care and shared amongst all legitimate users via the electronic patient record. PMID:8947631

Posterior Circulation Moyamoya Disease versus Primitive Vertebral-Basilar Artery System Moyamoya Disease: New Classification of Moyamoya Disease from the Perspective of Embryology.

PubMed

Tan, Cunxin; Duan, Ran; Ye, Xun; Zhang, Dong; Wang, Rong

2016-12-01

Moyamoya disease (MMD) is a chronic cerebrovascular disorder with little known etiology. We aim to propose a new classification system for MMD from the perspective of embryology. MMD patients' digital subtraction angiograms were retrospectively analyzed. Every angiogram was analyzed to find the abnormal vessels and from which part of the posterior cerebral artery (PCA) the lesions begin. In 262 MMD cases, 32 pediatric patients had PCA involvement, of which 17 were male and 15 were female; 68 adults had PCA involvement, of which 33 were male and 35 were female. The initially affected part of the PCA was compared between sexes and between pediatric and adult patients, and the findings are not statistically significant (P = 0.233, P = 0.855, P = 0.343, respectively). However, of the 100 cases with PCA involvement, only 4 had the lesions begin from the first part of the PCA, and all of the 4 cases had the basilar artery lesions. All the other 96 cases had the lesions begin from the second part of the PCA or from the posterior communication artery, which is derived from the caudal ramus of the primitive intracarotid artery, leaving the first part of the PCA and basilar artery excluded from affection. MMD should be classified into primitive intracarotid artery system-involved type and primitive vertebral basilar artery system-involved type. The reason that the vertebral basilar artery is so rarely involved in MMD might be because of its late development in the brain. Copyright © 2016 Elsevier Inc. All rights reserved.

Combining anatomical, diffusion, and resting state functional magnetic resonance imaging for individual classification of mild and moderate Alzheimer's disease.

PubMed

Schouten, Tijn M; Koini, Marisa; de Vos, Frank; Seiler, Stephan; van der Grond, Jeroen; Lechner, Anita; Hafkemeijer, Anne; Möller, Christiane; Schmidt, Reinhold; de Rooij, Mark; Rombouts, Serge A R B

2016-01-01

Magnetic resonance imaging (MRI) is sensitive to structural and functional changes in the brain caused by Alzheimer's disease (AD), and can therefore be used to help in diagnosing the disease. Improving classification of AD patients based on MRI scans might help to identify AD earlier in the disease's progress, which may be key in developing treatments for AD. In this study we used an elastic net classifier based on several measures derived from the MRI scans of mild to moderate AD patients (N = 77) from the prospective registry on dementia study and controls (N = 173) from the Austrian Stroke Prevention Family Study. We based our classification on measures from anatomical MRI, diffusion weighted MRI and resting state functional MRI. Our unimodal classification performance ranged from an area under the curve (AUC) of 0.760 (full correlations between functional networks) to 0.909 (grey matter density). When combining measures from multiple modalities in a stepwise manner, the classification performance improved to an AUC of 0.952. This optimal combination consisted of grey matter density, white matter density, fractional anisotropy, mean diffusivity, and sparse partial correlations between functional networks. Classification performance for mild AD as well as moderate AD also improved when using this multimodal combination. We conclude that different MRI modalities provide complementary information for classifying AD. Moreover, combining multiple modalities can substantially improve classification performance over unimodal classification.

Inter-observer agreement for Crohn's disease sub-phenotypes using the Montreal Classification: How good are we? A multi-centre Australasian study.

PubMed

Krishnaprasad, Krupa; Andrews, Jane M; Lawrance, Ian C; Florin, Timothy; Gearry, Richard B; Leong, Rupert W L; Mahy, Gillian; Bampton, Peter; Prosser, Ruth; Leach, Peta; Chitti, Laurie; Cock, Charles; Grafton, Rachel; Croft, Anthony R; Cooke, Sharon; Doecke, James D; Radford-Smith, Graham L

2012-04-01

Crohn's disease (CD) exhibits significant clinical heterogeneity. Classification systems attempt to describe this; however, their utility and reliability depends on inter-observer agreement (IOA). We therefore sought to evaluate IOA using the Montreal Classification (MC). De-identified clinical records of 35 CD patients from 6 Australian IBD centres were presented to 13 expert practitioners from 8 Australia and New Zealand Inflammatory Bowel Disease Consortium (ANZIBDC) centres. Practitioners classified the cases using MC and forwarded data for central blinded analysis. IOA on smoking and medications was also tested. Kappa statistics, with pre-specified outcomes of κ>0.8 excellent; 0.61-0.8 good; 0.41-0.6 moderate and ≤0.4 poor, were used. 97% of study cases had colonoscopy reports, however, only 31% had undergone a complete set of diagnostic investigations (colonoscopy, histology, SB imaging). At diagnosis, IOA was excellent for age, κ=0.84; good for disease location, κ=0.73; only moderate for upper GI disease (κ=0.57) and disease behaviour, κ=0.54; and good for the presence of perianal disease, κ=0.6. At last follow-up, IOA was good for location, κ=0.68; only moderate for upper GI disease (κ=0.43) and disease behaviour, κ=0.46; but excellent for the presence/absence of perianal disease, κ=0.88. IOA for immunosuppressant use ever and presence of stricture were both good (κ=0.79 and 0.64 respectively). IOA using MC is generally good; however some areas are less consistent than others. Omissions and inaccuracies reduce the value of clinical data when comparing cohorts across different centres, and may impair the ability to translate genetic discoveries into clinical practice. Crown Copyright © 2011. Published by Elsevier B.V. All rights reserved.

Plus Disease in Retinopathy of Prematurity: A Continuous Spectrum of Vascular Abnormality as a Basis of Diagnostic Variability.

PubMed

Campbell, J Peter; Kalpathy-Cramer, Jayashree; Erdogmus, Deniz; Tian, Peng; Kedarisetti, Dharanish; Moleta, Chace; Reynolds, James D; Hutcheson, Kelly; Shapiro, Michael J; Repka, Michael X; Ferrone, Philip; Drenser, Kimberly; Horowitz, Jason; Sonmez, Kemal; Swan, Ryan; Ostmo, Susan; Jonas, Karyn E; Chan, R V Paul; Chiang, Michael F

2016-11-01

To identify patterns of interexpert discrepancy in plus disease diagnosis in retinopathy of prematurity (ROP). We developed 2 datasets of clinical images as part of the Imaging and Informatics in ROP study and determined a consensus reference standard diagnosis (RSD) for each image based on 3 independent image graders and the clinical examination results. We recruited 8 expert ROP clinicians to classify these images and compared the distribution of classifications between experts and the RSD. Eight participating experts with more than 10 years of clinical ROP experience and more than 5 peer-reviewed ROP publications who analyzed images obtained during routine ROP screening in neonatal intensive care units. Expert classification of images of plus disease in ROP. Interexpert agreement (weighted κ statistic) and agreement and bias on ordinal classification between experts (analysis of variance [ANOVA]) and the RSD (percent agreement). There was variable interexpert agreement on diagnostic classifications between the 8 experts and the RSD (weighted κ, 0-0.75; mean, 0.30). The RSD agreement ranged from 80% to 94% for the dataset of 100 images and from 29% to 79% for the dataset of 34 images. However, when images were ranked in order of disease severity (by average expert classification), the pattern of expert classification revealed a consistent systematic bias for each expert consistent with unique cut points for the diagnosis of plus disease and preplus disease. The 2-way ANOVA model suggested a highly significant effect of both image and user on the average score (dataset A: P < 0.05 and adjusted R 2  = 0.82; and dataset B: P < 0.05 and adjusted R 2  = 0.6615). There is wide variability in the classification of plus disease by ROP experts, which occurs because experts have different cut points for the amounts of vascular abnormality required for presence of plus and preplus disease. This has important implications for research, teaching, and patient care for ROP and suggests that a continuous ROP plus disease severity score may reflect more accurately the behavior of expert ROP clinicians and may better standardize classification in the future. Copyright © 2016 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Candida albicans Pathogenesis: Fitting within the Host-Microbe Damage Response Framework

PubMed Central

Kong, Eric F.; Tsui, Christina; Nguyen, M. Hong; Clancy, Cornelius J.; Fidel, Paul L.; Noverr, Mairi

2016-01-01

Historically, the nature and extent of host damage by a microbe were considered highly dependent on virulence attributes of the microbe. However, it has become clear that disease is a complex outcome which can arise because of pathogen-mediated damage, host-mediated damage, or both, with active participation from the host microbiota. This awareness led to the formulation of the damage response framework (DRF), a revolutionary concept that defined microbial virulence as a function of host immunity. The DRF outlines six classifications of host damage outcomes based on the microbe and the strength of the immune response. In this review, we revisit this concept from the perspective of Candida albicans, a microbial pathogen uniquely adapted to its human host. This fungus commonly colonizes various anatomical sites without causing notable damage. However, depending on environmental conditions, a diverse array of diseases may occur, ranging from mucosal to invasive systemic infections resulting in microbe-mediated and/or host-mediated damage. Remarkably, C. albicans infections can fit into all six DRF classifications, depending on the anatomical site and associated host immune response. Here, we highlight some of these diverse and site-specific diseases and how they fit the DRF classifications, and we describe the animal models available to uncover pathogenic mechanisms and related host immune responses. PMID:27430274

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.

Associations between oral and ocular dryness, labial and whole salivary flow rates, systemic diseases and medications in a sample of older people.

PubMed

Smidt, D; Torpet, L A; Nauntofte, B; Heegaard, K M; Pedersen, A M L

2011-06-01

To investigate the associations between age, gender, systemic diseases, medications, labial and whole salivary flow rates and oral and ocular dryness in older people. Symptoms of oral and ocular dryness, systemic diseases, medications (coded according to the Anatomical therapeutic chemical (ATC) classification system), tobacco and alcohol consumption were registered, and unstimulated labial (LS) and unstimulated (UWS) and chewing-stimulated (SWS) whole salivary flow rates were measured in 668 randomly selected community-dwelling elderly aged 65-95. Presence of oral (12%) and ocular (11%) dryness was positively correlated. Oral dryness was associated with low UWS, SWS and LS, and ocular dryness with low UWS and SWS. Oral and ocular dryness was related to female gender, but not to age. Only four persons in the healthy and nonmedicated subgroups reported oral and ocular dryness. The numbers of diseases and medications were higher in the older age groups and associated with oral and ocular dryness, low UWS, SWS and LS. On average, women were slightly older, reported more oral and ocular dryness and had lower UWS, SWS, LS and higher numbers of diseases and medications. High prevalence and odds ratios for oral dryness were associated with metabolic, respiratory and neurological diseases and intake of thyroid hormones, respiratory agents (primarily glucocorticoids), psycholeptics and/or psychoanaleptics, antineoplastics, proton pump inhibitors, antidiabetics, loop diuretics, antispasmodics, quinine and bisphosphonates. Ocular dryness was especially associated with neurological diseases and intake of psycholeptics and/or psychoanaleptics. Intake of magnesium hydroxide, antithrombotics, cardiac agents, thiazides, beta-blockers, calcium channel blockers, ACE inhibitors/angiotensin II antagonists, statins, glucosamine, paracetamol/opioids, ophthalmologicals and certain combination therapies was related to oral and ocular dryness. In older people, oral and ocular dryness are associated with low salivary flow rates, specific as well as high number of diseases and medications, but neither with age and gender per se nor with tobacco and alcohol consumption. New detailed information concerning associations between medications and oral and ocular dryness has been obtained using the ATC classification system. © 2010 John Wiley & Sons A/S.

Challenges in congenital syphilis surveillance: how are congenital syphilis investigations classified?

PubMed

Introcaso, Camille E; Gruber, DeAnn; Bradley, Heather; Peterman, Thomas A; Ewell, Joy; Wendell, Debbie; Foxhood, Joseph; Su, John R; Weinstock, Hillard S; Markowitz, Lauri E

2013-09-01

Congenital syphilis is a serious, preventable, and nationally notifiable disease. Despite the existence of a surveillance case definition, congenital syphilis is sometimes classified differently using an algorithm on the Centers for Disease Control and Prevention's case reporting form. We reviewed Louisiana's congenital syphilis electronic reporting system for investigations of infants born from January 2010 to October 2011, abstracted data required for classification, and applied the surveillance definition and the algorithm. We calculated the sensitivities and specificities of the algorithm and Louisiana's classification using the surveillance definition as the surveillance gold standard. Among 349 congenital syphilis investigations, the surveillance definition identified 62 cases. The algorithm had a sensitivity of 91.9% and a specificity of 64.1%. Louisiana's classification had a sensitivity of 50% and a specificity of 91.3% compared with the surveillance definition. The differences between the algorithm and the surveillance definition led to misclassification of congenital syphilis cases. The algorithm should match the surveillance definition. Other state and local health departments should assure that their reported cases meet the surveillance definition.

The Society for Vascular Surgery Lower Extremity Threatened Limb Classification System: risk stratification based on wound, ischemia, and foot infection (WIfI).

PubMed

Mills, Joseph L; Conte, Michael S; Armstrong, David G; Pomposelli, Frank B; Schanzer, Andres; Sidawy, Anton N; Andros, George

2014-01-01

Critical limb ischemia, first defined in 1982, was intended to delineate a subgroup of patients with a threatened lower extremity primarily because of chronic ischemia. It was the intent of the original authors that patients with diabetes be excluded or analyzed separately. The Fontaine and Rutherford Systems have been used to classify risk of amputation and likelihood of benefit from revascularization by subcategorizing patients into two groups: ischemic rest pain and tissue loss. Due to demographic shifts over the last 40 years, especially a dramatic rise in the incidence of diabetes mellitus and rapidly expanding techniques of revascularization, it has become increasingly difficult to perform meaningful outcomes analysis for patients with threatened limbs using these existing classification systems. Particularly in patients with diabetes, limb threat is part of a broad disease spectrum. Perfusion is only one determinant of outcome; wound extent and the presence and severity of infection also greatly impact the threat to a limb. Therefore, the Society for Vascular Surgery Lower Extremity Guidelines Committee undertook the task of creating a new classification of the threatened lower extremity that reflects these important considerations. We term this new framework, the Society for Vascular Surgery Lower Extremity Threatened Limb Classification System. Risk stratification is based on three major factors that impact amputation risk and clinical management: Wound, Ischemia, and foot Infection (WIfI). The implementation of this classification system is intended to permit more meaningful analysis of outcomes for various forms of therapy in this challenging, but heterogeneous population. Copyright © 2014 Society for Vascular Surgery. Published by Mosby, Inc. All rights reserved.

Diagnosing tuberculosis with a novel support vector machine-based artificial immune recognition system.

PubMed

Saybani, Mahmoud Reza; Shamshirband, Shahaboddin; Golzari Hormozi, Shahram; Wah, Teh Ying; Aghabozorgi, Saeed; Pourhoseingholi, Mohamad Amin; Olariu, Teodora

2015-04-01

Tuberculosis (TB) is a major global health problem, which has been ranked as the second leading cause of death from an infectious disease worldwide. Diagnosis based on cultured specimens is the reference standard, however results take weeks to process. Scientists are looking for early detection strategies, which remain the cornerstone of tuberculosis control. Consequently there is a need to develop an expert system that helps medical professionals to accurately and quickly diagnose the disease. Artificial Immune Recognition System (AIRS) has been used successfully for diagnosing various diseases. However, little effort has been undertaken to improve its classification accuracy. In order to increase the classification accuracy of AIRS, this study introduces a new hybrid system that incorporates a support vector machine into AIRS for diagnosing tuberculosis. Patient epacris reports obtained from the Pasteur laboratory of Iran were used as the benchmark data set, with the sample size of 175 (114 positive samples for TB and 60 samples in the negative group). The strategy of this study was to ensure representativeness, thus it was important to have an adequate number of instances for both TB and non-TB cases. The classification performance was measured through 10-fold cross-validation, Root Mean Squared Error (RMSE), sensitivity and specificity, Youden's Index, and Area Under the Curve (AUC). Statistical analysis was done using the Waikato Environment for Knowledge Analysis (WEKA), a machine learning program for windows. With an accuracy of 100%, sensitivity of 100%, specificity of 100%, Youden's Index of 1, Area Under the Curve of 1, and RMSE of 0, the proposed method was able to successfully classify tuberculosis patients. There have been many researches that aimed at diagnosing tuberculosis faster and more accurately. Our results described a model for diagnosing tuberculosis with 100% sensitivity and 100% specificity. This model can be used as an additional tool for experts in medicine to diagnose TBC more accurately and quickly.

Looking for Alzheimer's Disease morphometric signatures using machine learning techniques.

PubMed

Donnelly-Kehoe, Patricio Andres; Pascariello, Guido Orlando; Gómez, Juan Carlos

2018-05-15

We present our results in the International challenge for automated prediction of MCI from MRI data. We evaluate the performance of MRI-based neuromorphometrics features (nMF) in the classification of Healthy Controls (HC), Mild Cognitive Impairment (MCI), converters MCI (cMCI) and Alzheimer's Disease (AD) patients. We propose to segregate participants in three groups according to Mini Mental State Examination score (MMSEs), searching for the main nMF in each group. Then we use them to develop a Multi Classifier System (MCS). We compare the MCS against a single classifier scheme using both MMSEs+nMF and nMF only. We repeat this comparison using three state-of-the-art classification algorithms. The MCS showed the best performance on both Accuracy and Area Under the Receiver Operating Curve (AUC) in comparison with single classifiers. The multiclass AUC for the MCS classification on Test Dataset were 0.83 for HC, 0.76 for cMCI, 0.65 for MCI and 0.95 for AD. Furthermore, MCS's optimum accuracy on Neurodegenerative Disease (ND) detection (AD+cMCI vs MCI+HC) was 81.0% (AUC=0.88), while the single classifiers got 71.3% (AUC=0.86) and 63.1% (AUC=0.79) for MMSEs+nMF and only nMF respectively. The proposed MCS showed a better performance than using all nMF into a single state-of-the-art classifier. These findings suggest that using cognitive scoring, e.g. MMSEs, in the design of a Multi Classifier System improves performance by allowing a better selection of MRI-based features. Copyright © 2017 Elsevier B.V. All rights reserved.

Using classification models for the generation of disease-specific medications from biomedical literature and clinical data repository.

PubMed

Wang, Liqin; Haug, Peter J; Del Fiol, Guilherme

2017-05-01

Mining disease-specific associations from existing knowledge resources can be useful for building disease-specific ontologies and supporting knowledge-based applications. Many association mining techniques have been exploited. However, the challenge remains when those extracted associations contained much noise. It is unreliable to determine the relevance of the association by simply setting up arbitrary cut-off points on multiple scores of relevance; and it would be expensive to ask human experts to manually review a large number of associations. We propose that machine-learning-based classification can be used to separate the signal from the noise, and to provide a feasible approach to create and maintain disease-specific vocabularies. We initially focused on disease-medication associations for the purpose of simplicity. For a disease of interest, we extracted potentially treatment-related drug concepts from biomedical literature citations and from a local clinical data repository. Each concept was associated with multiple measures of relevance (i.e., features) such as frequency of occurrence. For the machine purpose of learning, we formed nine datasets for three diseases with each disease having two single-source datasets and one from the combination of previous two datasets. All the datasets were labeled using existing reference standards. Thereafter, we conducted two experiments: (1) to test if adding features from the clinical data repository would improve the performance of classification achieved using features from the biomedical literature only, and (2) to determine if classifier(s) trained with known medication-disease data sets would be generalizable to new disease(s). Simple logistic regression and LogitBoost were two classifiers identified as the preferred models separately for the biomedical-literature datasets and combined datasets. The performance of the classification using combined features provided significant improvement beyond that using biomedical-literature features alone (p-value<0.001). The performance of the classifier built from known diseases to predict associated concepts for new diseases showed no significant difference from the performance of the classifier built and tested using the new disease's dataset. It is feasible to use classification approaches to automatically predict the relevance of a concept to a disease of interest. It is useful to combine features from disparate sources for the task of classification. Classifiers built from known diseases were generalizable to new diseases. Copyright © 2017 Elsevier Inc. All rights reserved.

Medical and surgical management of esophageal and gastric motor dysfunction.

PubMed

Awad, R A

2012-09-01

he occurrence of esophageal and gastric motor dysfunctions happens, when the software of the esophagus and the stomach is injured. This is really a program previously established in the enteric nervous system as a constituent of the newly called neurogastroenterology. The enteric nervous system is composed of small aggregations of nerve cells, enteric ganglia, the neural connections between these ganglia, and nerve fibers that supply effectors tissues, including the muscle of the gut wall. The wide range of enteric neuropathies that includes esophageal achalasia and gastroparesis highlights the importance of the enteric nervous system. A classification of functional gastrointestinal disorders based on symptoms has received attention. However, a classification based solely in symptoms and consensus may lack an integral approach of disease. As an alternative to the Rome classification, an international working team in Bangkok presented a classification of motility disorders as a physiology-based diagnosis. Besides, the Chicago Classification of esophageal motility was developed to facilitate the interpretation of clinical high-resolution esophageal pressure topography studies. This review covers exclusively the medical and surgical management of the esophageal and gastric motor dysfunction using evidence from well-designed studies. Motor control of the esophagus and the stomach, motor esophageal and gastric alterations, treatment failure, side effects of PPIs, overlap of gastrointestinal symptoms, predictors of treatment, burden of GERD medical management, data related to conservative treatment vs. antireflux surgery, and postsurgical esophagus and gastric motor dysfunction are also taken into account.

An Ensemble Multilabel Classification for Disease Risk Prediction

PubMed Central

Liu, Wei; Zhao, Hongling; Zhang, Chaoyang

2017-01-01

It is important to identify and prevent disease risk as early as possible through regular physical examinations. We formulate the disease risk prediction into a multilabel classification problem. A novel Ensemble Label Power-set Pruned datasets Joint Decomposition (ELPPJD) method is proposed in this work. First, we transform the multilabel classification into a multiclass classification. Then, we propose the pruned datasets and joint decomposition methods to deal with the imbalance learning problem. Two strategies size balanced (SB) and label similarity (LS) are designed to decompose the training dataset. In the experiments, the dataset is from the real physical examination records. We contrast the performance of the ELPPJD method with two different decomposition strategies. Moreover, the comparison between ELPPJD and the classic multilabel classification methods RAkEL and HOMER is carried out. The experimental results show that the ELPPJD method with label similarity strategy has outstanding performance. PMID:29065647

Optimization and simplification of the Allergic and Hypersensitivity conditions classification for the ICD-11.

PubMed

Tanno, L K; Calderon, M A; Demoly, P

2016-05-01

Since 2013, an international collaboration of Allergy Academies, including first the World Allergy Organization (WAO), the American Academy of Allergy Asthma and Immunology (AAAAI), and the European Academy of Allergy and Clinical Immunology (EAACI), and then the American College of Allergy, Asthma and Immunology (ACAAI), the Latin American Society of Allergy, Asthma and Immunology (SLAAI), and the Asia Pacific Association of Allergy, Asthma and Clinical Immunology (APAAACI), has spent tremendous efforts to have a better and updated classification of allergic and hypersensitivity conditions in the forthcoming International Classification of Diseases (ICD)-11 version by providing evidences and promoting actions for the need for changes. The latest action was the implementation of a classification proposal of hypersensitivity/allergic diseases built by crowdsourcing the Allergy Academy leaderships. Following bilateral discussions with the representatives of the ICD-11 revision, a face-to-face meeting was held at the United Nations Office in Geneva and a simplification process of the hypersensitivity/allergic disorders classification was carried out to better fit the ICD structure. We are here presenting the end result of what we consider to be a model of good collaboration between the World Health Organization and a specialty. We strongly believe that the outcomes of all past and future actions will impact positively the recognition of the allergy specialty as well as the quality improvement of healthcare system for allergic and hypersensitivity conditions worldwide. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Etiological classifications of transient ischemic attacks: subtype classification by TOAST, CCS and ASCO--a pilot study.

PubMed

Amort, Margareth; Fluri, Felix; Weisskopf, Florian; Gensicke, Henrik; Bonati, Leo H; Lyrer, Philippe A; Engelter, Stefan T

2012-01-01

In patients with transient ischemic attacks (TIA), etiological classification systems are not well studied. The Trial of ORG 10172 in Acute Stroke Treatment (TOAST), the Causative Classification System (CCS), and the Atherosclerosis Small Vessel Disease Cardiac Source Other Cause (ASCO) classification may be useful to determine the underlying etiology. We aimed at testing the feasibility of each of the 3 systems. Furthermore, we studied and compared their prognostic usefulness. In a single-center TIA registry prospectively ascertained over 2 years, we applied 3 etiological classification systems. We compared the distribution of underlying etiologies, the rates of patients with determined versus undetermined etiology, and studied whether etiological subtyping distinguished TIA patients with versus without subsequent stroke or TIA within 3 months. The 3 systems were applicable in all 248 patients. A determined etiology with the highest level of causality was assigned similarly often with TOAST (35.9%), CCS (34.3%), and ASCO (38.7%). However, the frequency of undetermined causes differed significantly between the classification systems and was lowest for ASCO (TOAST: 46.4%; CCS: 37.5%; ASCO: 18.5%; p < 0.001). In TOAST, CCS, and ASCO, cardioembolism (19.4/14.5/18.5%) was the most common etiology, followed by atherosclerosis (11.7/12.9/14.5%). At 3 months, 33 patients (13.3%, 95% confidence interval 9.3-18.2%) had recurrent cerebral ischemic events. These were strokes in 13 patients (5.2%; 95% confidence interval 2.8-8.8%) and TIAs in 20 patients (8.1%, 95% confidence interval 5.0-12.2%). Patients with a determined etiology (high level of causality) had higher rates of subsequent strokes than those without a determined etiology [TOAST: 6.7% (95% confidence interval 2.5-14.1%) vs. 4.4% (95% confidence interval 1.8-8.9%); CSS: 9.3% (95% confidence interval 4.1-17.5%) vs. 3.1% (95% confidence interval 1.0-7.1%); ASCO: 9.4% (95% confidence interval 4.4-17.1%) vs. 2.6% (95% confidence interval 0.7-6.6%)]. However, this difference was only significant in the ASCO classification (p = 0.036). Using ASCO, there was neither an increase in risk of subsequent stroke among patients with incomplete diagnostic workup (at least one subtype scored 9) compared with patients with adequate workup (no subtype scored 9), nor among patients with multiple causes compared with patients with a single cause. In TIA patients, all etiological classification systems provided a similar distribution of underlying etiologies. The increase in stroke risk in TIA patients with determined versus undetermined etiology was most evident using the ASCO classification. Copyright © 2012 S. Karger AG, Basel.

Multi-label classification of chronically ill patients with bag of words and supervised dimensionality reduction algorithms.

PubMed

Bromuri, Stefano; Zufferey, Damien; Hennebert, Jean; Schumacher, Michael

2014-10-01

This research is motivated by the issue of classifying illnesses of chronically ill patients for decision support in clinical settings. Our main objective is to propose multi-label classification of multivariate time series contained in medical records of chronically ill patients, by means of quantization methods, such as bag of words (BoW), and multi-label classification algorithms. Our second objective is to compare supervised dimensionality reduction techniques to state-of-the-art multi-label classification algorithms. The hypothesis is that kernel methods and locality preserving projections make such algorithms good candidates to study multi-label medical time series. We combine BoW and supervised dimensionality reduction algorithms to perform multi-label classification on health records of chronically ill patients. The considered algorithms are compared with state-of-the-art multi-label classifiers in two real world datasets. Portavita dataset contains 525 diabetes type 2 (DT2) patients, with co-morbidities of DT2 such as hypertension, dyslipidemia, and microvascular or macrovascular issues. MIMIC II dataset contains 2635 patients affected by thyroid disease, diabetes mellitus, lipoid metabolism disease, fluid electrolyte disease, hypertensive disease, thrombosis, hypotension, chronic obstructive pulmonary disease (COPD), liver disease and kidney disease. The algorithms are evaluated using multi-label evaluation metrics such as hamming loss, one error, coverage, ranking loss, and average precision. Non-linear dimensionality reduction approaches behave well on medical time series quantized using the BoW algorithm, with results comparable to state-of-the-art multi-label classification algorithms. Chaining the projected features has a positive impact on the performance of the algorithm with respect to pure binary relevance approaches. The evaluation highlights the feasibility of representing medical health records using the BoW for multi-label classification tasks. The study also highlights that dimensionality reduction algorithms based on kernel methods, locality preserving projections or both are good candidates to deal with multi-label classification tasks in medical time series with many missing values and high label density. Copyright © 2014 Elsevier Inc. All rights reserved.

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

A Reduced Set of Features for Chronic Kidney Disease Prediction

PubMed Central

Misir, Rajesh; Mitra, Malay; Samanta, Ranjit Kumar

2017-01-01

Chronic kidney disease (CKD) is one of the life-threatening diseases. Early detection and proper management are solicited for augmenting survivability. As per the UCI data set, there are 24 attributes for predicting CKD or non-CKD. At least there are 16 attributes need pathological investigations involving more resources, money, time, and uncertainties. The objective of this work is to explore whether we can predict CKD or non-CKD with reasonable accuracy using less number of features. An intelligent system development approach has been used in this study. We attempted one important feature selection technique to discover reduced features that explain the data set much better. Two intelligent binary classification techniques have been adopted for the validity of the reduced feature set. Performances were evaluated in terms of four important classification evaluation parameters. As suggested from our results, we may more concentrate on those reduced features for identifying CKD and thereby reduces uncertainty, saves time, and reduces costs. PMID:28706750

A clinicopathological approach to the diagnosis of dementia

PubMed Central

Elahi, Fanny M.; Miller, Bruce L.

2018-01-01

The most definitive classification systems for dementia are based on the underlying pathology which, in turn, is categorized largely according to the observed accumulation of abnormal protein aggregates in neurons and glia. These aggregates perturb molecular processes, cellular functions and, ultimately, cell survival, with ensuing disruption of large-scale neural networks subserving cognitive, behavioural and sensorimotor functions. The functional domains affected and the evolution of deficits in these domains over time serve as footprints that the clinician can trace back with various levels of certainty to the underlying neuropathology. The process of phenotyping and syndromic classification has substantially improved over decades of careful clinicopathological correlation, and through the discovery of in vivo biomarkers of disease. Here, we present an overview of the salient features of the most common dementia subtypes — Alzheimer disease, vascular dementia, frontotemporal dementia and related syndromes, Lewy body dementias, and prion diseases — with an emphasis on neuropathology, relevant epidemiology, risk factors, and signature signs and symptoms. PMID:28708131

Classification of Chronic Obstructive Pulmonary Disease (COPD) according to the new Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2017: Comparison with GOLD 2011.

PubMed

Marçôa, Raquel; Rodrigues, Daniela Marta; Dias, Margarida; Ladeira, Inês; Vaz, Ana Paula; Lima, Ricardo; Guimarães, Miguel

2018-02-01

Chronic Obstructive Pulmonary Disease (COPD) is a major cause of morbidity and mortality worldwide. The Global Initiative for Chronic Obstructive Lung Disease (GOLD) project has been working to improve awareness, prevention and management of this disease. The aim of this study is to evaluate how COPD patients are reclassified by the 2017 GOLD system (versus GOLD 2011), to calculate the level of agreement between these two classifications in allocation to categories and to compare the performance of each classification to predict future exacerbations. Two-hundred COPD patients (>40 years, post bronchodilator forced expiratory volume in one second/forced vital capacity<0.7) followed in pulmonology consultation were recruited into this prospective multicentric study. Approximately half of the patients classified as GOLD D [2011] changed to GOLD B [2017]. The extent of agreement between GOLD 2011 and GOLD 2017 was moderate (Cohen's Kappa = 0.511; p < 0.001) and the ability to predict exacerbations was similar (69.7% and 67.6%, respectively). GOLD B [2017] exacerbated 17% more than GOLD B [2011] and had a lower percent predicted post bronchodilator forced expiratory volume in one second (FEV1). GOLD B [2017] turned to be the predominant category, more heterogeneous and with a higher risk of exacerbation versus GOLD B [2011]. Physicians should be cautious in assessing the GOLD B [2017] patients. The assessment of patients should always be personalized. More studies are needed to evaluate the impact of the 2017 reclassification in predicting outcomes such as future exacerbations and mortality.

Sexual orientation and gender identity: review of concepts, controversies and their relation to psychopathology classification systems

PubMed Central

Moleiro, Carla; Pinto, Nuno

2015-01-01

Numerous controversies and debates have taken place throughout the history of psychopathology (and its main classification systems) with regards to sexual orientation and gender identity. These are still reflected on present reformulations of gender dysphoria in both the Diagnostic and Statistical Manual and the International Classification of Diseases, and in more or less subtle micro-aggressions experienced by lesbian, gay, bisexual and trans patients in mental health care. The present paper critically reviews this history and current controversies. It reveals that this deeply complex field contributes (i) to the reflection on the very concept of mental illness; (ii) to the focus on subjective distress and person-centered experience of psychopathology; and (iii) to the recognition of stigma and discrimination as significant intervening variables. Finally, it argues that sexual orientation and gender identity have been viewed, in the history of the field of psychopathology, between two poles: gender transgression and gender variance/fluidity. PMID:26483748

A Real-Time Cardiac Arrhythmia Classification System with Wearable Sensor Networks

PubMed Central

Hu, Sheng; Wei, Hongxing; Chen, Youdong; Tan, Jindong

2012-01-01

Long term continuous monitoring of electrocardiogram (ECG) in a free living environment provides valuable information for prevention on the heart attack and other high risk diseases. This paper presents the design of a real-time wearable ECG monitoring system with associated cardiac arrhythmia classification algorithms. One of the striking advantages is that ECG analog front-end and on-node digital processing are designed to remove most of the noise and bias. In addition, the wearable sensor node is able to monitor the patient's ECG and motion signal in an unobstructive way. To realize the real-time medical analysis, the ECG is digitalized and transmitted to a smart phone via Bluetooth. On the smart phone, the ECG waveform is visualized and a novel layered hidden Markov model is seamlessly integrated to classify multiple cardiac arrhythmias in real time. Experimental results demonstrate that the clean and reliable ECG waveform can be captured in multiple stressed conditions and the real-time classification on cardiac arrhythmia is competent to other workbenches. PMID:23112746

Sexual orientation and gender identity: review of concepts, controversies and their relation to psychopathology classification systems.

PubMed

Moleiro, Carla; Pinto, Nuno

2015-01-01

Numerous controversies and debates have taken place throughout the history of psychopathology (and its main classification systems) with regards to sexual orientation and gender identity. These are still reflected on present reformulations of gender dysphoria in both the Diagnostic and Statistical Manual and the International Classification of Diseases, and in more or less subtle micro-aggressions experienced by lesbian, gay, bisexual and trans patients in mental health care. The present paper critically reviews this history and current controversies. It reveals that this deeply complex field contributes (i) to the reflection on the very concept of mental illness; (ii) to the focus on subjective distress and person-centered experience of psychopathology; and (iii) to the recognition of stigma and discrimination as significant intervening variables. Finally, it argues that sexual orientation and gender identity have been viewed, in the history of the field of psychopathology, between two poles: gender transgression and gender variance/fluidity.

Update on Mastocytosis (Part 2): Categories, Prognosis, and Treatment.

PubMed

Azaña, J M; Torrelo, A; Matito, A

2016-01-01

Mastocytosis is a term used to describe a heterogeneous group of disorders characterized by clonal proliferation of mast cells in different organs. The organ most often affected is the skin. The World Health Organization classifies cutaneous mastocytosis into mastocytoma, maculopapular cutaneous mastocytosis, and diffuse mastocytosis. The systemic variants in this classification are as follows: indolent systemic mastocytosis (SM), aggressive SM, SM with an associated clonal hematological non-mast cell lineage disease, mast cell leukemia, mast cell sarcoma, and extracutaneous mastocytoma. The two latest systemic variants are rare. Although the course of disease is unpredictable in children, lesions generally resolve by early adulthood. In adults, however, the disease tends to persist. The goal of treatment should be to control clinical manifestations caused by the release of mast cell mediators and, in more aggressive forms of the disease, to reduce mast cell burden. Copyright © 2015 Elsevier España, S.L.U. and AEDV. All rights reserved.

Maximum-likelihood techniques for joint segmentation-classification of multispectral chromosome images.

PubMed

Schwartzkopf, Wade C; Bovik, Alan C; Evans, Brian L

2005-12-01

Traditional chromosome imaging has been limited to grayscale images, but recently a 5-fluorophore combinatorial labeling technique (M-FISH) was developed wherein each class of chromosomes binds with a different combination of fluorophores. This results in a multispectral image, where each class of chromosomes has distinct spectral components. In this paper, we develop new methods for automatic chromosome identification by exploiting the multispectral information in M-FISH chromosome images and by jointly performing chromosome segmentation and classification. We (1) develop a maximum-likelihood hypothesis test that uses multispectral information, together with conventional criteria, to select the best segmentation possibility; (2) use this likelihood function to combine chromosome segmentation and classification into a robust chromosome identification system; and (3) show that the proposed likelihood function can also be used as a reliable indicator of errors in segmentation, errors in classification, and chromosome anomalies, which can be indicators of radiation damage, cancer, and a wide variety of inherited diseases. We show that the proposed multispectral joint segmentation-classification method outperforms past grayscale segmentation methods when decomposing touching chromosomes. We also show that it outperforms past M-FISH classification techniques that do not use segmentation information.

Imaging of juvenile idiopathic arthritis. Part I: Clinical classifications and radiographs

PubMed Central

Matuszewska, Genowefa; Gietka, Piotr; Płaza, Mateusz; Walentowska-Janowicz, Marta

2016-01-01

Juvenile idiopathic arthritis is the most common autoimmune systemic disease of the connective tissue affecting individuals at the developmental age. Radiography is the primary modality employed in the diagnostic imaging in order to identify changes typical of this disease entity and rule out other bone-related pathologies, such as neoplasms, posttraumatic changes, developmental defects and other forms of arthritis. The standard procedure involves the performance of comparative joint radiographs in two planes. Radiographic changes in juvenile idiopathic arthritis are detected in later stages of the disease. Bone structures are assessed in the first place. Radiographs can also indirectly indicate the presence of soft tissue inflammation (i.e. in joint cavities, sheaths and bursae) based on swelling and increased density of the soft tissue as well as dislocation of fat folds. Signs of articular cartilage defects are also seen in radiographs indirectly – based on joint space width changes. The first part of the publication presents the classification of juvenile idiopathic arthritis and discusses its radiographic images. The authors list the affected joints as well as explain the spectrum and specificity of radiographic signs resulting from inflammatory changes overlapping with those caused by the maturation of the skeletal system. Moreover, certain dilemmas associated with the monitoring of the disease are reviewed. The second part of the publication will explain issues associated with ultrasonography and magnetic resonance imaging, which are more and more commonly applied in juvenile idiopathic arthritis for early detection of pathological features as well as the disease complications. PMID:27679726

Functional classification of pulmonary hypertension in children: Report from the PVRI pediatric taskforce, Panama 2011.

PubMed

Lammers, Astrid E; Adatia, Ian; Cerro, Maria Jesus Del; Diaz, Gabriel; Freudenthal, Alexandra Heath; Freudenthal, Franz; Harikrishnan, S; Ivy, Dunbar; Lopes, Antonio A; Raj, J Usha; Sandoval, Julio; Stenmark, Kurt; Haworth, Sheila G

2011-08-02

The members of the Pediatric Task Force of the Pulmonary Vascular Research Institute (PVRI) were aware of the need to develop a functional classification of pulmonary hypertension in children. The proposed classification follows the same pattern and uses the same criteria as the Dana Point pulmonary hypertension specific classification for adults. Modifications were necessary for children, since age, physical growth and maturation influences the way in which the functional effects of a disease are expressed. It is essential to encapsulate a child's clinical status, to make it possible to review progress with time as he/she grows up, as consistently and as objectively as possible. Particularly in younger children we sought to include objective indicators such as thriving, need for supplemental feeds and the record of school or nursery attendance. This helps monitor the clinical course of events and response to treatment over the years. It also facilitates the development of treatment algorithms for children. We present a consensus paper on a functional classification system for children with pulmonary hypertension, discussed at the Annual Meeting of the PVRI in Panama City, February 2011.

Application of support vector machine method for the analysis of absorption spectra of exhaled air of patients with broncho-pulmonary diseases

NASA Astrophysics Data System (ADS)

Bukreeva, Ekaterina B.; Bulanova, Anna A.; Kistenev, Yury V.; Kuzmin, Dmitry A.; Tuzikov, Sergei A.; Yumov, Evgeny L.

2014-11-01

The results of the joint use of laser photoacoustic spectroscopy and chemometrics methods in gas analysis of exhaled air of patients with respiratory diseases (chronic obstructive pulmonary disease, pneumonia and lung cancer) are presented. The absorption spectra of exhaled breath of all volunteers were measured, the classification methods of the scans of the absorption spectra were applied, the sensitivity/specificity of the classification results were determined. It were obtained a result of nosological in pairs classification for all investigated volunteers, indices of sensitivity and specificity.

Enhancing image classification models with multi-modal biomarkers

NASA Astrophysics Data System (ADS)

Caban, Jesus J.; Liao, David; Yao, Jianhua; Mollura, Daniel J.; Gochuico, Bernadette; Yoo, Terry

2011-03-01

Currently, most computer-aided diagnosis (CAD) systems rely on image analysis and statistical models to diagnose, quantify, and monitor the progression of a particular disease. In general, CAD systems have proven to be effective at providing quantitative measurements and assisting physicians during the decision-making process. As the need for more flexible and effective CADs continues to grow, questions about how to enhance their accuracy have surged. In this paper, we show how statistical image models can be augmented with multi-modal physiological values to create more robust, stable, and accurate CAD systems. In particular, this paper demonstrates how highly correlated blood and EKG features can be treated as biomarkers and used to enhance image classification models designed to automatically score subjects with pulmonary fibrosis. In our results, a 3-5% improvement was observed when comparing the accuracy of CADs that use multi-modal biomarkers with those that only used image features. Our results show that lab values such as Erythrocyte Sedimentation Rate and Fibrinogen, as well as EKG measurements such as QRS and I:40, are statistically significant and can provide valuable insights about the severity of the pulmonary fibrosis disease.

The proportion of uncoded diagnoses in computerized health insurance claims in Japan in May 2010 according to ICD-10 disease categories.

PubMed

Tanihara, Shinichi

2014-01-01

Uncoded diagnoses in computerized health insurance claims are excluded from statistical summaries of health-related risks and other factors. The effects of these uncoded diagnoses, coded according to ICD-10 disease categories, have not been investigated to date in Japan. I obtained all computerized health insurance claims (outpatient medical care, inpatient medical care, and diagnosis procedure-combination per-diem payment system [DPC/PDPS] claims) submitted to the National Health Insurance Organization of Kumamoto Prefecture in May 2010. These were classified according to the disease categories of the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10). I used accompanying text documentation related to the uncoded diagnoses to classify these diagnoses. Using these classifications, I calculated the proportion of uncoded diagnoses by ICD-10 category. The number of analyzed diagnoses was 3,804,246, with uncoded diagnoses accounting for 9.6% of the total. The proportion of uncoded diagnoses in claims for outpatient medical care, inpatient medical care, and DPC/PDPS were 9.3%, 10.9%, and 14.2%, respectively. Among the diagnoses, Congenital malformations, deformations, and chromosomal abnormalities had the highest proportion of uncoded diagnoses (19.3%), and Diseases of the respiratory system had the lowest proportion of uncoded diagnoses (4.7%). The proportion of uncoded diagnoses differed by the type of health insurance claim and disease category. These findings indicate that Japanese health statistics computed using computerized health insurance claims might be biased by the exclusion of uncoded diagnoses.

Development and validation of an administrative case definition for inflammatory bowel diseases

PubMed Central

Rezaie, Ali; Quan, Hude; Fedorak, Richard N; Panaccione, Remo; Hilsden, Robert J

2012-01-01

BACKGROUND: A population-based database of inflammatory bowel disease (IBD) patients is invaluable to explore and monitor the epidemiology and outcome of the disease. In this context, an accurate and validated population-based case definition for IBD becomes critical for researchers and health care providers. METHODS: IBD and non-IBD individuals were identified through an endoscopy database in a western Canadian health region (Calgary Health Region, Calgary, Alberta). Subsequently, using a novel algorithm, a series of case definitions were developed to capture IBD cases in the administrative databases. In the second stage of the study, the criteria were validated in the Capital Health Region (Edmonton, Alberta). RESULTS: A total of 150 IBD case definitions were developed using 1399 IBD patients and 15,439 controls in the development phase. In the validation phase, 318,382 endoscopic procedures were searched and 5201 IBD patients were identified. After consideration of sensitivity, specificity and temporal stability of each validated case definition, a diagnosis of IBD was assigned to individuals who experienced at least two hospitalizations or had four physician claims, or two medical contacts in the Ambulatory Care Classification System database with an IBD diagnostic code within a two-year period (specificity 99.8%; sensitivity 83.4%; positive predictive value 97.4%; negative predictive value 98.5%). An alternative case definition was developed for regions without access to the Ambulatory Care Classification System database. A novel scoring system was developed that detected Crohn disease and ulcerative colitis patients with a specificity of >99% and a sensitivity of 99.1% and 86.3%, respectively. CONCLUSION: Through a robust methodology, a reproducible set of criteria to capture IBD patients through administrative databases was developed. The methodology may be used to develop similar administrative definitions for chronic diseases. PMID:23061064

A low-cost mobile adaptive tracking system for chronic pulmonary patients in home environment.

PubMed

Işik, Ali Hakan; Güler, Inan; Sener, Melahat Uzel

2013-01-01

The main objective of this study is presenting a real-time mobile adaptive tracking system for patients diagnosed with diseases such as asthma or chronic obstructive pulmonary disease and application results at home. The main role of the system is to support and track chronic pulmonary patients in real time who are comfortable in their home environment. It is not intended to replace the doctor, regular treatment, and diagnosis. In this study, the Java 2 micro edition-based system is integrated with portable spirometry, smartphone, extensible markup language-based Web services, Web server, and Web pages for visualizing pulmonary function test results. The Bluetooth(®) (Bluetooth SIG, Kirkland, WA) virtual serial port protocol is used to obtain the test results from spirometry. General packet radio service, wireless local area network, or third-generation-based wireless networks are used to send the test results from a smartphone to the remote database. The system provides real-time classification of test results with the back propagation artificial neural network algorithm on a mobile smartphone. It also provides the generation of appropriate short message service-based notification and sending of all data to the Web server. In this study, the test results of 486 patients, obtained from Atatürk Chest Diseases and Thoracic Surgery Training and Research Hospital in Ankara, Turkey, are used as the training and test set in the algorithm. The algorithm has 98.7% accuracy, 97.83% specificity, 97.63% sensitivity, and 0.946 correlation values. The results show that the system is cheap (900 Euros) and reliable. The developed real-time system provides improvement in classification accuracy and facilitates tracking of chronic pulmonary patients.

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.

Systemic Lupus Erythematosus: Primary Care Approach to Diagnosis and Management.

PubMed

Lam, Nguyet-Cam Vu; Ghetu, Maria V; Bieniek, Marzena L

2016-08-15

Systemic lupus erythematosus is an autoimmune disease that affects many systems, including the skin, musculoskeletal, renal, neuropsychiatric, hematologic, cardiovascular, pulmonary, and reproductive systems. Family physicians should be familiar with the manifestations of lupus to aid in early diagnosis, monitoring patients with mild disease, recognizing warning signs that require referral to a rheumatologist, and helping to monitor disease activity and treatment in patients with moderate to severe disease. The American College of Rheumatology has 11 classification criteria for lupus. If a patient meets at least four criteria, lupus can be diagnosed with 95% specificity and 85% sensitivity. All patients with lupus should receive education, counseling, and support. Hydroxychloroquine is the cornerstone of treatment because it reduces disease flares and other constitutional symptoms. Low-dose glucocorticoids can be used to treat most manifestations of lupus. The use of immunosuppressive and cytotoxic agents depends on the body systems affected. Patients with mild disease that does not involve major organ systems can be monitored by their family physician. Patients with increased disease activity, complications, or adverse effects from treatment should be referred to a rheumatologist. To optimize treatment, it is important that a rheumatologist coordinate closely with the patient's family physician to improve chronic care as well as preventive health services.

Autoantibodies Associated With Connective Tissue Diseases: What Meaning for Clinicians?

PubMed Central

Didier, Kevin; Bolko, Loïs; Giusti, Delphine; Toquet, Segolene; Robbins, Ailsa; Antonicelli, Frank; Servettaz, Amelie

2018-01-01

Connective tissue diseases (CTDs) such as systemic lupus erythematosus, systemic sclerosis, myositis, Sjögren’s syndrome, and rheumatoid arthritis are systemic diseases which are often associated with a challenge in diagnosis. Autoantibodies (AAbs) can be detected in these diseases and help clinicians in their diagnosis. Actually, pathophysiology of these diseases is associated with the presence of antinuclear antibodies. In the last decades, many new antibodies were discovered, but their implication in pathogenesis of CTDs remains unclear. Furthermore, the classification of these AAbs is nowadays misused, as their targets can be localized outside of the nuclear compartment. Interestingly, in most cases, each antibody is associated with a specific phenotype in CTDs and therefore help in better defining either the disease subtypes or diseases activity and outcome. Because of recent progresses in their detection and in the comprehension of their pathogenesis implication in CTD-associated antibodies, clinicians should pay attention to the presence of these different AAbs to improve patient’s management. In this review, we propose to focus on the different phenotypes and features associated with each autoantibody used in clinical practice in those CTDs. PMID:29632529

Dental History Predictors of Caries Related Dental Emergencies.

DTIC Science & Technology

1981-11-01

10+) 50% of those with U- lesions would be selected and only 4% of those without disease would be selected. The accuracy of such a system as well as...sufficient sensitivity, specificity, and diagnostic power to be useful as predictive tools. Dental health classification systems are typically only...predicted with some reliability given the intimacy of the relationship and the relatively long duration of the pre-emergency state. The incidence of

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.

Computer-Aided Classification of Visual Ventilation Patterns in Patients with Chronic Obstructive Pulmonary Disease at Two-Phase Xenon-Enhanced CT

PubMed Central

Yoon, Soon Ho; Jung, Julip; Hong, Helen; Park, Eun Ah; Lee, Chang Hyun; Lee, Youkyung; Jin, Kwang Nam; Choo, Ji Yung; Lee, Nyoung Keun

2014-01-01

Objective To evaluate the technical feasibility, performance, and interobserver agreement of a computer-aided classification (CAC) system for regional ventilation at two-phase xenon-enhanced CT in patients with chronic obstructive pulmonary disease (COPD). Materials and Methods Thirty-eight patients with COPD underwent two-phase xenon ventilation CT with resulting wash-in (WI) and wash-out (WO) xenon images. The regional ventilation in structural abnormalities was visually categorized into four patterns by consensus of two experienced radiologists who compared the xenon attenuation of structural abnormalities with that of adjacent normal parenchyma in the WI and WO images, and it served as the reference. Two series of image datasets of structural abnormalities were randomly extracted for optimization and validation. The proportion of agreement on a per-lesion basis and receiver operating characteristics on a per-pixel basis between CAC and reference were analyzed for optimization. Thereafter, six readers independently categorized the regional ventilation in structural abnormalities in the validation set without and with a CAC map. Interobserver agreement was also compared between assessments without and with CAC maps using multirater κ statistics. Results Computer-aided classification maps were successfully generated in 31 patients (81.5%). The proportion of agreement and the average area under the curve of optimized CAC maps were 94% (75/80) and 0.994, respectively. Multirater κ value was improved from moderate (κ = 0.59; 95% confidence interval [CI], 0.56-0.62) at the initial assessment to excellent (κ = 0.82; 95% CI, 0.79-0.85) with the CAC map. Conclusion Our proposed CAC system demonstrated the potential for regional ventilation pattern analysis and enhanced interobserver agreement on visual classification of regional ventilation. PMID:24843245

Computer-aided classification of visual ventilation patterns in patients with chronic obstructive pulmonary disease at two-phase xenon-enhanced CT.

PubMed

Yoon, Soon Ho; Goo, Jin Mo; Jung, Julip; Hong, Helen; Park, Eun Ah; Lee, Chang Hyun; Lee, Youkyung; Jin, Kwang Nam; Choo, Ji Yung; Lee, Nyoung Keun

2014-01-01

To evaluate the technical feasibility, performance, and interobserver agreement of a computer-aided classification (CAC) system for regional ventilation at two-phase xenon-enhanced CT in patients with chronic obstructive pulmonary disease (COPD). Thirty-eight patients with COPD underwent two-phase xenon ventilation CT with resulting wash-in (WI) and wash-out (WO) xenon images. The regional ventilation in structural abnormalities was visually categorized into four patterns by consensus of two experienced radiologists who compared the xenon attenuation of structural abnormalities with that of adjacent normal parenchyma in the WI and WO images, and it served as the reference. Two series of image datasets of structural abnormalities were randomly extracted for optimization and validation. The proportion of agreement on a per-lesion basis and receiver operating characteristics on a per-pixel basis between CAC and reference were analyzed for optimization. Thereafter, six readers independently categorized the regional ventilation in structural abnormalities in the validation set without and with a CAC map. Interobserver agreement was also compared between assessments without and with CAC maps using multirater κ statistics. Computer-aided classification maps were successfully generated in 31 patients (81.5%). The proportion of agreement and the average area under the curve of optimized CAC maps were 94% (75/80) and 0.994, respectively. Multirater κ value was improved from moderate (κ = 0.59; 95% confidence interval [CI], 0.56-0.62) at the initial assessment to excellent (κ = 0.82; 95% CI, 0.79-0.85) with the CAC map. Our proposed CAC system demonstrated the potential for regional ventilation pattern analysis and enhanced interobserver agreement on visual classification of regional ventilation.

Fractal measures of video-recorded trajectories can classify motor subtypes in Parkinson's Disease

NASA Astrophysics Data System (ADS)

Figueiredo, Thiago C.; Vivas, Jamile; Peña, Norberto; Miranda, José G. V.

2016-11-01

Parkinson's Disease is one of the most prevalent neurodegenerative diseases in the world and affects millions of individuals worldwide. The clinical criteria for classification of motor subtypes in Parkinson's Disease are subjective and may be misleading when symptoms are not clearly identifiable. A video recording protocol was used to measure hand tremor of 14 individuals with Parkinson's Disease and 7 healthy subjects. A method for motor subtype classification was proposed based on the spectral distribution of the movement and compared with the existing clinical criteria. Box-counting dimension and Hurst Exponent calculated from the trajectories were used as the relevant measures for the statistical tests. The classification based on the power-spectrum is shown to be well suited to separate patients with and without tremor from healthy subjects and could provide clinicians with a tool to aid in the diagnosis of patients in an early stage of the disease.

[Severity classification of chronic obstructive pulmonary disease based on deep learning].

PubMed

Ying, Jun; Yang, Ceyuan; Li, Quanzheng; Xue, Wanguo; Li, Tanshi; Cao, Wenzhe

2017-12-01

In this paper, a deep learning method has been raised to build an automatic classification algorithm of severity of chronic obstructive pulmonary disease. Large sample clinical data as input feature were analyzed for their weights in classification. Through feature selection, model training, parameter optimization and model testing, a classification prediction model based on deep belief network was built to predict severity classification criteria raised by the Global Initiative for Chronic Obstructive Lung Disease (GOLD). We get accuracy over 90% in prediction for two different standardized versions of severity criteria raised in 2007 and 2011 respectively. Moreover, we also got the contribution ranking of different input features through analyzing the model coefficient matrix and confirmed that there was a certain degree of agreement between the more contributive input features and the clinical diagnostic knowledge. The validity of the deep belief network model was proved by this result. This study provides an effective solution for the application of deep learning method in automatic diagnostic decision making.

Gastric precancerous diseases classification using CNN with a concise model.

PubMed

Zhang, Xu; Hu, Weiling; Chen, Fei; Liu, Jiquan; Yang, Yuanhang; Wang, Liangjing; Duan, Huilong; Si, Jianmin

2017-01-01

Gastric precancerous diseases (GPD) may deteriorate into early gastric cancer if misdiagnosed, so it is important to help doctors recognize GPD accurately and quickly. In this paper, we realize the classification of 3-class GPD, namely, polyp, erosion, and ulcer using convolutional neural networks (CNN) with a concise model called the Gastric Precancerous Disease Network (GPDNet). GPDNet introduces fire modules from SqueezeNet to reduce the model size and parameters about 10 times while improving speed for quick classification. To maintain classification accuracy with fewer parameters, we propose an innovative method called iterative reinforced learning (IRL). After training GPDNet from scratch, we apply IRL to fine-tune the parameters whose values are close to 0, and then we take the modified model as a pretrained model for the next training. The result shows that IRL can improve the accuracy about 9% after 6 iterations. The final classification accuracy of our GPDNet was 88.90%, which is promising for clinical GPD recognition.

A Machine Learning Ensemble Classifier for Early Prediction of Diabetic Retinopathy.

PubMed

S K, Somasundaram; P, Alli

2017-11-09

The main complication of diabetes is Diabetic retinopathy (DR), retinal vascular disease and it leads to the blindness. Regular screening for early DR disease detection is considered as an intensive labor and resource oriented task. Therefore, automatic detection of DR diseases is performed only by using the computational technique is the great solution. An automatic method is more reliable to determine the presence of an abnormality in Fundus images (FI) but, the classification process is poorly performed. Recently, few research works have been designed for analyzing texture discrimination capacity in FI to distinguish the healthy images. However, the feature extraction (FE) process was not performed well, due to the high dimensionality. Therefore, to identify retinal features for DR disease diagnosis and early detection using Machine Learning and Ensemble Classification method, called, Machine Learning Bagging Ensemble Classifier (ML-BEC) is designed. The ML-BEC method comprises of two stages. The first stage in ML-BEC method comprises extraction of the candidate objects from Retinal Images (RI). The candidate objects or the features for DR disease diagnosis include blood vessels, optic nerve, neural tissue, neuroretinal rim, optic disc size, thickness and variance. These features are initially extracted by applying Machine Learning technique called, t-distributed Stochastic Neighbor Embedding (t-SNE). Besides, t-SNE generates a probability distribution across high-dimensional images where the images are separated into similar and dissimilar pairs. Then, t-SNE describes a similar probability distribution across the points in the low-dimensional map. This lessens the Kullback-Leibler divergence among two distributions regarding the locations of the points on the map. The second stage comprises of application of ensemble classifiers to the extracted features for providing accurate analysis of digital FI using machine learning. In this stage, an automatic detection of DR screening system using Bagging Ensemble Classifier (BEC) is investigated. With the help of voting the process in ML-BEC, bagging minimizes the error due to variance of the base classifier. With the publicly available retinal image databases, our classifier is trained with 25% of RI. Results show that the ensemble classifier can achieve better classification accuracy (CA) than single classification models. Empirical experiments suggest that the machine learning-based ensemble classifier is efficient for further reducing DR classification time (CT).

21 CFR 866.5785 - Anti-Saccharomyces cerevisiae (S. cerevisiae) antibody (ASCA) test systems.

Code of Federal Regulations, 2011 CFR

2011-04-01

... an in vitro diagnostic device that consists of the reagents used to measure, by immunochemical techniques, antibodies to S. cerevisiae (baker's or brewer's yeast) in human serum or plasma. Detection of S. cerevisiae antibodies may aid in the diagnosis of Crohn's disease. (b) Classification. Class II (special...

Identification of ICD Codes Suggestive of Child Maltreatment

ERIC Educational Resources Information Center

Schnitzer, Patricia G.; Slusher, Paula L.; Kruse, Robin L.; Tarleton, Molly M.

2011-01-01

Objective: In order to be reimbursed for the care they provide, hospitals in the United States are required to use a standard system to code all discharge diagnoses: the International Classification of Disease, 9th Revision, Clinical Modification (ICD-9). Although ICD-9 codes specific for child maltreatment exist, they do not identify all…

Issues of diagnostic review in brain tumor studies: from the Brain Tumor Epidemiology Consortium.

PubMed

Davis, Faith G; Malmer, Beatrice S; Aldape, Ken; Barnholtz-Sloan, Jill S; Bondy, Melissa L; Brännström, Thomas; Bruner, Janet M; Burger, Peter C; Collins, V Peter; Inskip, Peter D; Kruchko, Carol; McCarthy, Bridget J; McLendon, Roger E; Sadetzki, Siegal; Tihan, Tarik; Wrensch, Margaret R; Buffler, Patricia A

2008-03-01

Epidemiologists routinely conduct centralized single pathology reviews to minimize interobserver diagnostic variability, but this practice does not facilitate the combination of studies across geographic regions and institutions where diagnostic practices differ. A meeting of neuropathologists and epidemiologists focused on brain tumor classification issues in the context of protocol needs for consortial studies (http://epi.grants.cancer.gov/btec/). It resulted in recommendations relevant to brain tumors and possibly other rare disease studies. Two categories of brain tumors have enough general agreement over time, across regions, and between individual pathologists that one can consider using existing diagnostic data without further review: glioblastomas and meningiomas (as long as uniform guidelines such as those provided by the WHO are used). Prospective studies of these tumors benefit from collection of pathology reports, at a minimum recording the pathology department and classification system used in the diagnosis. Other brain tumors, such as oligodendroglioma, are less distinct and require careful histopathologic review for consistent classification across study centers. Epidemiologic study protocols must consider the study specific aims, diagnostic changes that have taken place over time, and other issues unique to the type(s) of tumor being studied. As diagnostic changes are being made rapidly, there are no readily available answers on disease classification issues. It is essential that epidemiologists and neuropathologists collaborate to develop appropriate study designs and protocols for specific hypothesis and populations.

[Validation of the VISA-A-G questionnaire for German-speaking patients suffering from Haglund's disease].

PubMed

Lohrer, H; Nauck, T

2010-06-01

The VISA-A questionnaire is currently the only valid, reliable, and disease specific patient administered questionnaire for research in Achilles tendinopathy. To perform multinational and multilingual investigations this instrument was already adapted to several languages. According to the "guidelines for the process of cross-cultural adaptation of self-report measures" we already translated and validated the VISA-A questionnaire for patients with Achilles tendinopathy. To cross-culturally adapt and validate the VISA-A Questionnaire for German-speaking patients suffering from Haglund's disease. The VISA-A-G questionnaire was tested for reliability, validity, and internal consistency in 39 Haglund's disease patients and 79 asymptomatic persons. For concurrent validity the VISA-A-G was compared with the Curwin and Stanish tendon grading system and with the Percy and Conochie classification system for the effect of pain on athletic performance. VISA-A-G results in Haglund's disease were additionally compared with VISA-A-G results obtained from Achilles tendinopathy patients and with VISA-A results presented in the international literature. ICC for the VISA-A-G questionnaire in conservatively treated Haglund's disease patients was 0.96. In asymptomatic students and joggers ICC was 0.97 and 0.60. When correlated with the grading system of Curwin and Stanish and with the Percy and Conochie classification rho was -0.95 and 0.94, respectively. Internal consistency (Cronbach's alpha) for the total VISA-A-G scores of the patients was calculated to be 0.87. Compared with VISA-A-G results obtained from Achilles tendinopathy patients there was no relevant difference discernible. Compared with VISA-A results presented in the original publication no difference was found statistically for students, healthy people, conservative, and preoperative patients, respectively. This study confirms that the VISA-A-G is a valid and reliable measure for German-speaking patients suffering from Haglund's disease. Georg Thieme Verlag KG Stuttgart, New York.

New Casemix Classification as an Alternative Method for Budget Allocation in Thai Oral Healthcare Service: A Pilot Study

PubMed Central

Wisaijohn, Thunthita; Pimkhaokham, Atiphan; Lapying, Phenkhae; Itthichaisri, Chumpot; Pannarunothai, Supasit; Igarashi, Isao; Kawabuchi, Koichi

2010-01-01

This study aimed to develop a new casemix classification system as an alternative method for the budget allocation of oral healthcare service (OHCS). Initially, the International Statistical of Diseases and Related Health Problem, 10th revision, Thai Modification (ICD-10-TM) related to OHCS was used for developing the software “Grouper”. This model was designed to allow the translation of dental procedures into eight-digit codes. Multiple regression analysis was used to analyze the relationship between the factors used for developing the model and the resource consumption. Furthermore, the coefficient of variance, reduction in variance, and relative weight (RW) were applied to test the validity. The results demonstrated that 1,624 OHCS classifications, according to the diagnoses and the procedures performed, showed high homogeneity within groups and heterogeneity between groups. Moreover, the RW of the OHCS could be used to predict and control the production costs. In conclusion, this new OHCS casemix classification has a potential use in a global decision making. PMID:20936134

New casemix classification as an alternative method for budget allocation in thai oral healthcare service: a pilot study.

PubMed

Wisaijohn, Thunthita; Pimkhaokham, Atiphan; Lapying, Phenkhae; Itthichaisri, Chumpot; Pannarunothai, Supasit; Igarashi, Isao; Kawabuchi, Koichi

2010-01-01

This study aimed to develop a new casemix classification system as an alternative method for the budget allocation of oral healthcare service (OHCS). Initially, the International Statistical of Diseases and Related Health Problem, 10th revision, Thai Modification (ICD-10-TM) related to OHCS was used for developing the software "Grouper". This model was designed to allow the translation of dental procedures into eight-digit codes. Multiple regression analysis was used to analyze the relationship between the factors used for developing the model and the resource consumption. Furthermore, the coefficient of variance, reduction in variance, and relative weight (RW) were applied to test the validity. The results demonstrated that 1,624 OHCS classifications, according to the diagnoses and the procedures performed, showed high homogeneity within groups and heterogeneity between groups. Moreover, the RW of the OHCS could be used to predict and control the production costs. In conclusion, this new OHCS casemix classification has a potential use in a global decision making.

Sensitivity and Specificity of Cardiac Tissue Discrimination Using Fiber-Optics Confocal Microscopy.

PubMed

Huang, Chao; Sachse, Frank B; Hitchcock, Robert W; Kaza, Aditya K

2016-01-01

Disturbances of the cardiac conduction system constitute a major risk after surgical repair of complex cases of congenital heart disease. Intraoperative identification of the conduction system may reduce the incidence of these disturbances. We previously developed an approach to identify cardiac tissue types using fiber-optics confocal microscopy and extracellular fluorophores. Here, we applied this approach to investigate sensitivity and specificity of human and automated classification in discriminating images of atrial working myocardium and specialized tissue of the conduction system. Two-dimensional image sequences from atrial working myocardium and nodal tissue of isolated perfused rodent hearts were acquired using a fiber-optics confocal microscope (Leica FCM1000). We compared two methods for local application of extracellular fluorophores: topical via pipette and with a dye carrier. Eight blinded examiners evaluated 162 randomly selected images of atrial working myocardium (n = 81) and nodal tissue (n = 81). In addition, we evaluated the images using automated classification. Blinded examiners achieved a sensitivity and specificity of 99.2 ± 0.3% and 98.0 ± 0.7%, respectively, with the dye carrier method of dye application. Sensitivity and specificity was similar for dye application via a pipette (99.2 ± 0.3% and 94.0 ± 2.4%, respectively). Sensitivity and specificity for automated methods of tissue discrimination were similarly high. Human and automated classification achieved high sensitivity and specificity in discriminating atrial working myocardium and nodal tissue. We suggest that our findings facilitate clinical translation of fiber-optics confocal microscopy as an intraoperative imaging modality to reduce the incidence of conduction disturbances during surgical correction of congenital heart disease.

Stratifying the risks of oral anticoagulation in patients with liver disease.

PubMed

Efird, Lydia M; Mishkin, Daniel S; Berlowitz, Dan R; Ash, Arlene S; Hylek, Elaine M; Ozonoff, Al; Reisman, Joel I; Zhao, Shibei; Jasuja, Guneet K; Rose, Adam J

2014-05-01

Chronic liver disease presents a relative contraindication to warfarin therapy, but some patients with liver disease nevertheless require long-term anticoagulation. The goal is to identify which patients with liver disease might safely receive warfarin. Among 102 134 patients who received warfarin from the Veterans Affairs from 2007 to 2008, International Classification of Diseases-Ninth Revision codes identified 1763 patients with chronic liver disease. Specific diagnoses and laboratory values (albumin, aspartate aminotransferase, alanine aminotransferase, creatinine, and cholesterol) were examined to identify risk of adverse outcomes, while controlling for available bleeding risk factors. Outcomes included percent time in therapeutic range, a measure of anticoagulation control, and major hemorrhagic events, by International Classification of Diseases-Ninth Revision codes. Patients with liver disease had lower mean time in therapeutic range (53.5%) when compared with patients without (61.7%; P<0.001) and more hemorrhages (hazard ratio, 2.02; P<0.001). Among patients with liver disease, serum albumin and creatinine levels were the strongest predictors of both outcomes. We created a 4-point score system: patients received 1 point each for albumin (2.5-3.49 g/dL) or creatinine (1.01-1.99 mg/dL), and 2 points each for albumin (<2.5 g/dL) or creatinine (≥2 mg/dL). This score predicted both anticoagulation control and hemorrhage. When compared with patients without liver disease, those with a score of zero had modestly lower time in therapeutic range (56.7%) and no increase in hemorrhages (hazard ratio, 1.16; P=0.59), whereas those with the worst score (4) had poor control (29.4%) and high hazard of hemorrhage (hazard ratio, 8.53; P<0.001). Patients with liver disease receiving warfarin have poorer anticoagulation control and more hemorrhages. A simple 4-point scoring system using albumin and creatinine identifies those at risk for poor outcomes. © 2014 American Heart Association, Inc.

Systemic lupus erythematosus: an update.

PubMed

Golder, Vera; Hoi, Alberta

2017-03-20

Systemic lupus erythematosus (SLE) is a chronic multisystem autoimmune disease predominantly affecting women of childbearing age. New classification criteria for SLE have greater sensitivity and therefore improve the diagnostic certainty for some patients, especially those who may previously have been labelled as having undifferentiated symptoms. Uncontrolled disease activity leads to irreversible end-organ damage, which in turn increases the risk of premature death; early and sustained control of disease activity can usually be achieved by conventional immunosuppressant therapy. The development of biological therapy lags behind that for other rheumatic diseases, with belimumab being the only targeted therapy approved by the Therapeutic Goods Administration. "Treat-to-target" concepts are changing trial design and clinical practice, with evidence-based definition of response criteria in the form of remission and low disease activity now on the horizon. While new therapies are awaited, research should also focus on optimising the use of current therapy and improving the quality of care of patients with SLE.

Neurofibromatosis of the head and neck: classification and surgical management.

PubMed

Latham, Kerry; Buchanan, Edward P; Suver, Daniel; Gruss, Joseph S

2015-03-01

Neurofibromatosis is common and presents with variable penetrance and manifestations in one in 2500 to one in 3000 live births. The management of these patients is often multidisciplinary because of the complexity of the disease. Plastic surgeons are frequently involved in the surgical management of patients with head and neck involvement. A 20-year retrospective review of patients treated surgically for head and neck neurofibroma was performed. Patients were identified according to International Classification of Diseases, Ninth Revision codes for neurofibromatosis and from the senior author's database. A total of 59 patients with head and neck neurofibroma were identified. These patients were categorized into five distinct, but not exclusive, categories to assist with diagnosis and surgical management. These categories included plexiform, cranioorbital, facial, neck, and parotid/auricular neurofibromatosis. A surgical classification system and clinical characteristics of head and neck neurofibromatosis is presented to assist practitioners with diagnosis and surgical management of this complex disease. The surgical management of the cranioorbital type is discussed in detail in 24 patients. The importance and safety of facial nerve dissection and preservation using intraoperative nerve monitoring were validated in 16 dissections in 15 patients. Massive involvement of the neck extending from the skull base to the mediastinum, frequently considered inoperable, has been safely resected by the use of access osteotomies of the clavicle and sternum, muscle takedown, and brachial plexus dissection and preservation using intraoperative nerve monitoring. Therapeutic, IV.

Machine learning for the assessment of Alzheimer's disease through DTI

NASA Astrophysics Data System (ADS)

Lella, Eufemia; Amoroso, Nicola; Bellotti, Roberto; Diacono, Domenico; La Rocca, Marianna; Maggipinto, Tommaso; Monaco, Alfonso; Tangaro, Sabina

2017-09-01

Digital imaging techniques have found several medical applications in the development of computer aided detection systems, especially in neuroimaging. Recent advances in Diffusion Tensor Imaging (DTI) aim to discover biological markers for the early diagnosis of Alzheimer's disease (AD), one of the most widespread neurodegenerative disorders. We explore here how different supervised classification models provide a robust support to the diagnosis of AD patients. We use DTI measures, assessing the structural integrity of white matter (WM) fiber tracts, to reveal patterns of disrupted brain connectivity. In particular, we provide a voxel-wise measure of fractional anisotropy (FA) and mean diffusivity (MD), thus identifying the regions of the brain mostly affected by neurodegeneration, and then computing intensity features to feed supervised classification algorithms. In particular, we evaluate the accuracy of discrimination of AD patients from healthy controls (HC) with a dataset of 80 subjects (40 HC, 40 AD), from the Alzheimer's Disease Neurodegenerative Initiative (ADNI). In this study, we compare three state-of-the-art classification models: Random Forests, Naive Bayes and Support Vector Machines (SVMs). We use a repeated five-fold cross validation framework with nested feature selection to perform a fair comparison between these algorithms and evaluate the information content they provide. Results show that AD patterns are well localized within the brain, thus DTI features can support the AD diagnosis.

Epstein–Barr virus-associated lymphoproliferative disease in non-immunocompromised hosts: a status report and summary of an international meeting, 8–9 September 2008

PubMed Central

Cohen, J. I.; Kimura, H.; Nakamura, S.; Ko, Y.-H.; Jaffe, E. S.

2009-01-01

Background: Recently novel Epstein–Barr virus (EBV) lymphoproliferative diseases (LPDs) have been identified in non-immunocompromised hosts, both in Asia and Western countries. These include aggressive T-cell and NK-cell LPDs often subsumed under the heading of chronic active Epstein–Barr virus (CAEBV) infection and EBV-driven B-cell LPDs mainly affecting the elderly. Design: To better define the pathogenesis, classification, and treatment of these disorders, participants from Asia, The Americas, Europe, and Australia presented clinical and experimental data at an international meeting. Results: The term systemic EBV-positive T-cell LPD, as adopted by the WHO classification, is preferred as a pathological classification over CAEBV (the favored clinical term) for those cases that are clonal. The disease has an aggressive clinical course, but may arise in the background of CAEBV. Hydroa vacciniforme (HV) and HV-like lymphoma represent a spectrum of clonal EBV-positive T-cell LPDs, which have a more protracted clinical course; spontaneous regression may occur in adult life. Severe mosquito bite allergy is a related syndrome usually of NK cell origin. Immune senescence in the elderly is associated with both reactive and neoplastic EBV-driven LPDs, including EBV-positive diffuse large B-cell lymphomas. Conclusion: The participants proposed an international consortium to facilitate further clinical and biological studies of novel EBV-driven LPDs. PMID:19515747

Epstein-Barr virus-associated lymphoproliferative disease in non-immunocompromised hosts: a status report and summary of an international meeting, 8-9 September 2008.

PubMed

Cohen, J I; Kimura, H; Nakamura, S; Ko, Y-H; Jaffe, E S

2009-09-01

Recently novel Epstein-Barr virus (EBV) lymphoproliferative diseases (LPDs) have been identified in non-immunocompromised hosts, both in Asia and Western countries. These include aggressive T-cell and NK-cell LPDs often subsumed under the heading of chronic active Epstein-Barr virus (CAEBV) infection and EBV-driven B-cell LPDs mainly affecting the elderly. To better define the pathogenesis, classification, and treatment of these disorders, participants from Asia, The Americas, Europe, and Australia presented clinical and experimental data at an international meeting. The term systemic EBV-positive T-cell LPD, as adopted by the WHO classification, is preferred as a pathological classification over CAEBV (the favored clinical term) for those cases that are clonal. The disease has an aggressive clinical course, but may arise in the background of CAEBV. Hydroa vacciniforme (HV) and HV-like lymphoma represent a spectrum of clonal EBV-positive T-cell LPDs, which have a more protracted clinical course; spontaneous regression may occur in adult life. Severe mosquito bite allergy is a related syndrome usually of NK cell origin. Immune senescence in the elderly is associated with both reactive and neoplastic EBV-driven LPDs, including EBV-positive diffuse large B-cell lymphomas. The participants proposed an international consortium to facilitate further clinical and biological studies of novel EBV-driven LPDs.

Sauvages' paperwork: how disease classification arose from scholarly note-taking.

PubMed

Hess, Volker; Mendelsohn, Andrew

2014-01-01

What was classification as it first took modern form in the eighteenth century, and how did it relate to earlier ways of describing and ordering? We offer new answers to these questions by examining medicine rather than botany and by reconstructing practice on paper. First among disease classifications was the 'nosology' of the Montpellier physician François Boissier de Sauvages de Lacroix. Analysis of his hitherto unstudied notebooks and of the nosology's many editions (1731-1772) shows that Boissier de Sauvages broke with earlier physicians' humanistic ordering of disease while sustaining the paper practices they had used. Scientific method was scholarly method. Classification arose through an incomplete break with, and intensified practice of, a past library-based way of ordering the described world. A new empiricism of generalizations (species) arose out of an older one of particulars (observationes). This happened through the rewriting--not the replacement--of the canon of disease knowledge since antiquity and its reordering on the printed page.

Immunosignature Differentiation of Non-Infectious Meningoencephalomyelitis and Intracranial Neoplasia in Dogs.

PubMed

Lake, Bathilda B; Rossmeisl, John Henry; Cecere, Julie; Stafford, Phillip; Zimmerman, Kurt L

2018-01-01

A variety of inflammatory conditions of unknown cause (meningoencephalomyelitis of unknown etiology-MUE) and neoplastic diseases can affect the central nervous system (CNS) of dogs. MUE can mimic intracranial neoplasia both clinically, radiologically and even in some cases, histologically. Serum immunosignature protein microarray assays have been used in humans to identify CNS diseases such as Alzheimer's and neoplasia, and in dogs, to detect lymphoma and its progression. This study evaluated the effectiveness of immunosignature profiles for distinguishing between three cohorts of dogs: healthy, intracranial neoplasia, and MUE. Using the learned peptide patterns for these three cohorts, classification prediction was evaluated for the same groups using a 10-fold cross validation methodology. Accuracy for classification was 100%, as well as 100% specific and 100% sensitive. This pilot study demonstrates that immunosignature profiles may help serve as a minimally invasive tool to distinguish between MUE and intracranial neoplasia in dogs.

Update on the classification and treatment of localized scleroderma.

PubMed

Bielsa Marsol, I

2013-10-01

Morphea or localized scleroderma is a distinctive inflammatory disease that leads to sclerosis of the skin and subcutaneous tissues. It comprises a number of subtypes differentiated according to their clinical presentation and the structure of the skin and underlying tissues involved in the fibrotic process. However, classification is difficult because the boundaries between the different types of morphea are blurred and different entities frequently overlap. The main subtypes are plaque morphea, linear scleroderma, generalized morphea, and pansclerotic morphea. With certain exceptions, the disorder does not have serious systemic repercussions, but it can cause considerable morbidity. In the case of lesions affecting the head, neurological and ocular complications may occur. There is no really effective and universal treatment so it is important to make a correct assessment of the extent and severity of the disease before deciding on a treatment approach. Copyright © 2011 Elsevier España, S.L. and AEDV. All rights reserved.

Computer-aided assessment of pulmonary disease in novel swine-origin H1N1 influenza on CT

NASA Astrophysics Data System (ADS)

Yao, Jianhua; Dwyer, Andrew J.; Summers, Ronald M.; Mollura, Daniel J.

2011-03-01

The 2009 pandemic is a global outbreak of novel H1N1 influenza. Radiologic images can be used to assess the presence and severity of pulmonary infection. We develop a computer-aided assessment system to analyze the CT images from Swine-Origin Influenza A virus (S-OIV) novel H1N1 cases. The technique is based on the analysis of lung texture patterns and classification using a support vector machine (SVM). Pixel-wise tissue classification is computed from the SVM value. The method was validated on four H1N1 cases and ten normal cases. We demonstrated that the technique can detect regions of pulmonary abnormality in novel H1N1 patients and differentiate these regions from visually normal lung (area under the ROC curve is 0.993). This technique can also be applied to differentiate regions infected by different pulmonary diseases.

Empirical Wavelet Transform Based Features for Classification of Parkinson's Disease Severity.

PubMed

Oung, Qi Wei; Muthusamy, Hariharan; Basah, Shafriza Nisha; Lee, Hoileong; Vijean, Vikneswaran

2017-12-29

Parkinson's disease (PD) is a type of progressive neurodegenerative disorder that has affected a large part of the population till now. Several symptoms of PD include tremor, rigidity, slowness of movements and vocal impairments. In order to develop an effective diagnostic system, a number of algorithms were proposed mainly to distinguish healthy individuals from the ones with PD. However, most of the previous works were conducted based on a binary classification, with the early PD stage and the advanced ones being treated equally. Therefore, in this work, we propose a multiclass classification with three classes of PD severity level (mild, moderate, severe) and healthy control. The focus is to detect and classify PD using signals from wearable motion and audio sensors based on both empirical wavelet transform (EWT) and empirical wavelet packet transform (EWPT) respectively. The EWT/EWPT was applied to decompose both speech and motion data signals up to five levels. Next, several features are extracted after obtaining the instantaneous amplitudes and frequencies from the coefficients of the decomposed signals by applying the Hilbert transform. The performance of the algorithm was analysed using three classifiers - K-nearest neighbour (KNN), probabilistic neural network (PNN) and extreme learning machine (ELM). Experimental results demonstrated that our proposed approach had the ability to differentiate PD from non-PD subjects, including their severity level - with classification accuracies of more than 90% using EWT/EWPT-ELM based on signals from motion and audio sensors respectively. Additionally, classification accuracy of more than 95% was achieved when EWT/EWPT-ELM is applied to signals from integration of both signal's information.

Electronic health record use to classify patients with newly diagnosed versus preexisting type 2 diabetes: infrastructure for comparative effectiveness research and population health management.

PubMed

Kudyakov, Rustam; Bowen, James; Ewen, Edward; West, Suzanne L; Daoud, Yahya; Fleming, Neil; Masica, Andrew

2012-02-01

Use of electronic health record (EHR) content for comparative effectiveness research (CER) and population health management requires significant data configuration. A retrospective cohort study was conducted using patients with diabetes followed longitudinally (N=36,353) in the EHR deployed at outpatient practice networks of 2 health care systems. A data extraction and classification algorithm targeting identification of patients with a new diagnosis of type 2 diabetes mellitus (T2DM) was applied, with the main criterion being a minimum 30-day window between the first visit documented in the EHR and the entry of T2DM on the EHR problem list. Chart reviews (N=144) validated the performance of refining this EHR classification algorithm with external administrative data. Extraction using EHR data alone designated 3205 patients as newly diagnosed with T2DM with classification accuracy of 70.1%. Use of external administrative data on that preselected population improved classification accuracy of cases identified as new T2DM diagnosis (positive predictive value was 91.9% with that step). Laboratory and medication data did not help case classification. The final cohort using this 2-stage classification process comprised 1972 patients with a new diagnosis of T2DM. Data use from current EHR systems for CER and disease management mandates substantial tailoring. Quality between EHR clinical data generated in daily care and that required for population health research varies. As evidenced by this process for classification of newly diagnosed T2DM cases, validation of EHR data with external sources can be a valuable step.

The 2016 WHO Classification of Tumours of the Urinary System and Male Genital Organs-Part A: Renal, Penile, and Testicular Tumours.

PubMed

Moch, Holger; Cubilla, Antonio L; Humphrey, Peter A; Reuter, Victor E; Ulbright, Thomas M

2016-07-01

The fourth edition of the World Health Organization (WHO) classification of urogenital tumours (WHO "blue book"), published in 2016, contains significant revisions. These revisions were performed after consideration by a large international group of pathologists with special expertise in this area. A subgroup of these persons met at the WHO Consensus Conference in Zurich, Switzerland, in 2015 to finalize the revisions. This review summarizes the most significant differences between the newly published classification and the prior version for renal, penile, and testicular tumours. Newly recognized epithelial renal tumours are hereditary leiomyomatosis and renal cell carcinoma (RCC) syndrome-associated RCC, succinate dehydrogenase-deficient RCC, tubulocystic RCC, acquired cystic disease-associated RCC, and clear cell papillary RCC. The WHO/International Society of Urological Pathology renal tumour grading system was recommended, and the definition of renal papillary adenoma was modified. The new WHO classification of penile squamous cell carcinomas is based on the presence of human papillomavirus and defines histologic subtypes accordingly. Germ cell neoplasia in situ (GCNIS) of the testis is the WHO-recommended term for precursor lesions of invasive germ cell tumours, and testicular germ cell tumours are now separated into two fundamentally different groups: those derived from GCNIS and those unrelated to GCNIS. Spermatocytic seminoma has been designated as a spermatocytic tumour and placed within the group of non-GCNIS-related tumours in the 2016 WHO classification. The 2016 World Health Organization (WHO) classification contains new renal tumour entities. The classification of penile squamous cell carcinomas is based on the presence of human papillomavirus. Germ cell neoplasia in situ of the testis is the WHO-recommended term for precursor lesions of invasive germ cell tumours. Copyright © 2016 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Performance comparison of classifiers for differentiation among obstructive lung diseases based on features of texture analysis at HRCT

NASA Astrophysics Data System (ADS)

Lee, Youngjoo; Seo, Joon Beom; Kang, Bokyoung; Kim, Dongil; Lee, June Goo; Kim, Song Soo; Kim, Namkug; Kang, Suk Ho

2007-03-01

The performance of classification algorithms for differentiating among obstructive lung diseases based on features from texture analysis using HRCT (High Resolution Computerized Tomography) images was compared. HRCT can provide accurate information for the detection of various obstructive lung diseases, including centrilobular emphysema, panlobular emphysema and bronchiolitis obliterans. Features on HRCT images can be subtle, however, particularly in the early stages of disease, and image-based diagnosis is subject to inter-observer variation. To automate the diagnosis and improve the accuracy, we compared three types of automated classification systems, naÃve Bayesian classifier, ANN (Artificial Neural Net) and SVM (Support Vector Machine), based on their ability to differentiate among normal lung and three types of obstructive lung diseases. To assess the performance and cross-validation of these three classifiers, 5 folding methods with 5 randomly chosen groups were used. For a more robust result, each validation was repeated 100 times. SVM showed the best performance, with 86.5% overall sensitivity, significantly different from the other classifiers (one way ANOVA, p<0.01). We address the characteristics of each classifier affecting performance and the issue of which classifier is the most suitable for clinical applications, and propose an appropriate method to choose the best classifier and determine its optimal parameters for optimal disease discrimination. These results can be applied to classifiers for differentiation of other diseases.

Who Must We Target Now to Minimize Future Cardiovascular Events and Total Mortality?: Lessons From the Surveillance, Prevention and Management of Diabetes Mellitus (SUPREME-DM) Cohort Study.

PubMed

Desai, Jay R; Vazquez-Benitez, Gabriela; Xu, Zhiyuan; Schroeder, Emily B; Karter, Andrew J; Steiner, John F; Nichols, Gregory A; Reynolds, Kristi; Xu, Stanley; Newton, Katherine; Pathak, Ram D; Waitzfelder, Beth; Lafata, Jennifer Elston; Butler, Melissa G; Kirchner, H Lester; Thomas, Abraham; O'Connor, Patrick J

2015-09-01

Examining trends in cardiovascular events and mortality in US health systems can guide the design of targeted clinical and public health strategies to reduce cardiovascular events and mortality rates. We conducted an observational cohort study from 2005 to 2011 among 1.25 million diabetic subjects and 1.25 million nondiabetic subjects from 11 health systems that participate in the Surveillance, Prevention and Management of Diabetes Mellitus (SUPREME-DM) DataLink. Annual rates (per 1000 person-years) of myocardial infarction/acute coronary syndrome (International Classification of Diseases-Ninth Revision, 410.0–410.91, 411.1–411.8), stroke (International Classification of Diseases-Ninth Revision, 430–432.9, 433–434.9), heart failure (International Classification of Diseases-Ninth Revision, 428–428.9), and all-cause mortality were monitored by diabetes mellitus (DM) status, age, sex, race/ethnicity, and a prior cardiovascular history. We observed significant declines in cardiovascular events and mortality rates in subjects with and without DM. However, there was substantial variation by age, sex, race/ethnicity, and prior cardiovascular history. Mortality declined from 44.7 to 27.1 (P<0.0001) for those with DM and cardiovascular disease (CVD), from 11.2 to 10.9 (P=0.03) for those with DM only, and from 18.9 to 13.0 (P<0.0001) for those with CVD only. Yet, in the [almost equal to]85% of subjects with neither DM nor CVD, overall mortality (7.0 to 6.8; P=0.10) and stroke rates (1.6–1.6; P=0.77) did not decline and heart failure rates increased (0.9–1.15; P=0.0005). To sustain improvements in myocardial infarction, stroke, heart failure, and mortality, health systems that have successfully focused on care improvement in high-risk adults with DM or CVD must broaden their improvement strategies to target lower risk adults who have not yet developed DM or CVD.

Integrating disease management and wound care critical pathways in home care.

PubMed

Barr, J E

1999-10-01

This article discusses the need for an integration of the concepts of disease management and critical pathways as a foundation of a healthcare delivery system. The steps in the process for development, implementation, and evaluation of a wound care critical pathway are reviewed and variance classifications are defined. Co-pathways and algorithms are presented as methodologies for dealing with variances. A template of a wound care critical pathway that has been developed for use in the home care setting is included.

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.

Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization

PubMed Central

Nalluri, MadhuSudana Rao; K., Kannan; M., Manisha

2017-01-01

With the widespread adoption of e-Healthcare and telemedicine applications, accurate, intelligent disease diagnosis systems have been profoundly coveted. In recent years, numerous individual machine learning-based classifiers have been proposed and tested, and the fact that a single classifier cannot effectively classify and diagnose all diseases has been almost accorded with. This has seen a number of recent research attempts to arrive at a consensus using ensemble classification techniques. In this paper, a hybrid system is proposed to diagnose ailments using optimizing individual classifier parameters for two classifier techniques, namely, support vector machine (SVM) and multilayer perceptron (MLP) technique. We employ three recent evolutionary algorithms to optimize the parameters of the classifiers above, leading to six alternative hybrid disease diagnosis systems, also referred to as hybrid intelligent systems (HISs). Multiple objectives, namely, prediction accuracy, sensitivity, and specificity, have been considered to assess the efficacy of the proposed hybrid systems with existing ones. The proposed model is evaluated on 11 benchmark datasets, and the obtained results demonstrate that our proposed hybrid diagnosis systems perform better in terms of disease prediction accuracy, sensitivity, and specificity. Pertinent statistical tests were carried out to substantiate the efficacy of the obtained results. PMID:29065626

[Aetiological classification of ischaemic strokes: comparison of the new A-S-C-O classification and the classification by the Spanish Society of Neurology's Cerebrovascular Disease Study Group].

PubMed

Sobrino García, P; García Pastor, A; García Arratibel, A; Vicente Peracho, G; Rodriguez Cruz, P M; Pérez Sánchez, J R; Díaz Otero, F; Vázquez Alén, P; Villanueva Osorio, J A; Gil Núñez, A

2013-09-01

The A-S-C-O classification may be better than other methods for classifying ischaemic stroke by aetiology. Our aims are to describe A-S-C-O phenotype distribution (A: atherosclerosis, S: small vessel disease, C: cardiac source, O: other causes; 1: potential cause, 2: causality uncertain, 3: unlikely to be a direct cause although disease is present) and compare them to the Spanish Society of Neurology's Cerebrovascular Disease Study Group (GEECV/SEN) classification. We will also find the degree of concordance between these classification methods and determine whether using the A-S-C-O classification delivers a smaller percentage of strokes of undetermined cause. We analysed those patients with ischaemic stroke admitted to our stroke unit in 2010 with strokes that were classified according to GEECV/SEN and A-S-C-O criteria. The study included 496 patients. The percentages of strokes caused by atherosclerosis and small vessel disease according to GEECV/SEN criteria were higher than the percentages for potential atherosclerotic stroke (A1) (14.1 vs. 11.9%; P=.16) and potential small vessel stroke (S1) (14.3 vs. 3%; P<.001). Cardioembolic stroke (C1) was more frequent (22.2 vs. 31%; P<.001). No differences between unusual cause of stroke and other potential causes (O1) were observed. Some degree of atherosclerosis was present in 53.5% of patients (A1, A2, or A3); 65.5% showed markers of small vessel disease (S1, S2, or S3), and 74.9% showed signs of cardioembolism (C1, C2, or C3). Fewer patients in the group without scores of 1 or 2 for any of the A-S-C-O phenotypes were identified as having a stroke of undetermined cause (46.6 vs. 29.2%; P<.001). The agreement between the 2 classifications ranged from κ<0.2 (small vessel and S1) to κ>0.8 (unusual causes and O1). Our results show that GEECV/SEN and A-S-C-O classifications are neither fully comparable nor consistent. Using the A-S-C-O classification provided additional information on co-morbidities and delivered a smaller percentage of strokes classified as having an undetermined cause. Copyright © 2012 Sociedad Española de Neurología. Published by Elsevier Espana. All rights reserved.

Classification algorithm of lung lobe for lung disease cases based on multislice CT images

NASA Astrophysics Data System (ADS)

Matsuhiro, M.; Kawata, Y.; Niki, N.; Nakano, Y.; Mishima, M.; Ohmatsu, H.; Tsuchida, T.; Eguchi, K.; Kaneko, M.; Moriyama, N.

2011-03-01

With the development of multi-slice CT technology, to obtain an accurate 3D image of lung field in a short time is possible. To support that, a lot of image processing methods need to be developed. In clinical setting for diagnosis of lung cancer, it is important to study and analyse lung structure. Therefore, classification of lung lobe provides useful information for lung cancer analysis. In this report, we describe algorithm which classify lungs into lung lobes for lung disease cases from multi-slice CT images. The classification algorithm of lung lobes is efficiently carried out using information of lung blood vessel, bronchus, and interlobar fissure. Applying the classification algorithms to multi-slice CT images of 20 normal cases and 5 lung disease cases, we demonstrate the usefulness of the proposed algorithms.

Ultrahigh field MRI in clinical neuroimmunology: a potential contribution to improved diagnostics and personalised disease management.

PubMed

Sinnecker, Tim; Kuchling, Joseph; Dusek, Petr; Dörr, Jan; Niendorf, Thoralf; Paul, Friedemann; Wuerfel, Jens

2015-01-01

Conventional magnetic resonance imaging (MRI) at 1.5 Tesla (T) is limited by modest spatial resolution and signal-to-noise ratio (SNR), impeding the identification and classification of inflammatory central nervous system changes in current clinical practice. Gaining from enhanced susceptibility effects and improved SNR, ultrahigh field MRI at 7 T depicts inflammatory brain lesions in great detail. This review summarises recent reports on 7 T MRI in neuroinflammatory diseases and addresses the question as to whether ultrahigh field MRI may eventually improve clinical decision-making and personalised disease management.

Histopathological features of coeliac disease in a sample of Sudanese patients.

PubMed

Mokhtar, M A N; Mekki, S O; Mudawi, H M Y; Sulaiman, S H; Tahir, M A; Tigani, M A; Omer, I A; Yousif, B M; Fragalla, I A; Mohammed, Z; Dafaalla, M

2016-12-01

Coeliac disease can occur at any age but is more common in children. Its diagnosis requires correlation between clinical presentations, serological results, endoscopic findings and histopathological classification using the modified Marsh grading system. This study of coeliac disease with biopsies received in the department of histopathology at Soba University Hospital, and Fedail Hospital aimed to gain insight into the demographic profile, clinical presentations and histopathological classification of patients with coeliac disease. This was a descriptive study carried out at Soba University Hospital and Fedail Hospital during the period from January 2010-December 2013. Haematoxylin & Eosin and CD3-stained slides of small intestinal biopsies of coeliac disease patients were reviewed for various histological features (1) intraepithelial lymphocytes (IEL) count per 100 enterocytes, (2) crypt hyperplasia and (3) degree of villous atrophy. Based on the histopathological findings, the cases were categorized according to the modified Marsh classification. Demographic and clinical data were obtained from the patient request forms. The data were analyzed using Statistical Package for Social Sciences Software (SPSS). The study included 60 patients. Their age ranged from 2 to 70 years with a mean of 19.5 years (±15.7 SD). The most common age group was below 10 years old (41.6%). Male and female are equally affected. The most common clinical presentation was chronic diarrhoea (55.0%), followed by iron deficiency anemia (41.7%). The degree of villous atrophy ranged from complete atrophy (45.0%), marked atrophy (38.3%) to mild atrophy (16.6%). Marsh grade IIIC was the most common grade. The younger age-groups had a higher prevalence of iron deficiency anaemia and higher Marsh grade.

Clinical classification of age-related macular degeneration.

PubMed

Ferris, Frederick L; Wilkinson, C P; Bird, Alan; Chakravarthy, Usha; Chew, Emily; Csaky, Karl; Sadda, SriniVas R

2013-04-01

To develop a clinical classification system for age-related macular degeneration (AMD). Evidence-based investigation, using a modified Delphi process. Twenty-six AMD experts, 1 neuro-ophthalmologist, 2 committee chairmen, and 1 methodologist. Each committee member completed an online assessment of statements summarizing current AMD classification criteria, indicating agreement or disagreement with each statement on a 9-step scale. The group met, reviewed the survey results, discussed the important components of a clinical classification system, and defined new data analyses needed to refine a classification system. After the meeting, additional data analyses from large studies were provided to the committee to provide risk estimates related to the presence of various AMD lesions. Delphi review of the 9-item set of statements resulting from the meeting. Consensus was achieved in generating a basic clinical classification system based on fundus lesions assessed within 2 disc diameters of the fovea in persons older than 55 years. The committee agreed that a single term, age-related macular degeneration, should be used for the disease. Persons with no visible drusen or pigmentary abnormalities should be considered to have no signs of AMD. Persons with small drusen (<63 μm), also termed drupelets, should be considered to have normal aging changes with no clinically relevant increased risk of late AMD developing. Persons with medium drusen (≥ 63-<125 μm), but without pigmentary abnormalities thought to be related to AMD, should be considered to have early AMD. Persons with large drusen or with pigmentary abnormalities associated with at least medium drusen should be considered to have intermediate AMD. Persons with lesions associated with neovascular AMD or geographic atrophy should be considered to have late AMD. Five-year risks of progressing to late AMD are estimated to increase approximately 100 fold, ranging from a 0.5% 5-year risk for normal aging changes to a 50% risk for the highest intermediate AMD risk group. The proposed basic clinical classification scale seems to be of value in predicting the risk of late AMD. Incorporating consistent nomenclature into the practice patterns of all eye care providers may improve communication and patient care. Copyright © 2013 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Application of Weka environment to determine factors that stand behind non-alcoholic fatty liver disease (NAFLD)

NASA Astrophysics Data System (ADS)

Plutecki, Michal M.; Wierzbicka, Aldona; Socha, Piotr; Mulawka, Jan J.

2009-06-01

The paper describes an innovative approach to discover new knowledge in non-alcoholic fatty liver disease (NAFLD). In order to determine the factors that may cause the disease a number of classification and attribute selection algorithms have been applied. Only those with the best classification results were chosen. Several interesting facts associated with this unclear disease have been discovered. All data mining computations were made in Weka environment.

Disease scoring systems for oral lichen planus; a critical appraisal

PubMed Central

Wang, Jing

2015-01-01

The aim of the present study has been to critically review 22 disease scoring systems (DSSs) on oral lichen planus (OLP) that have been reported in the literature during the past decades. Although the presently available DSSs may all have some merit, particularly for research purposes, the diversity of both the objective and subjective parameters used in these systems and the lack of acceptance of one of these systems for uniform use, there is a need for an international, authorized consensus meeting on this subject. Because of the natural course of OLP characterized by remissions and exacerbations and also due to the varying distribution pattern and the varying clinical types, e.g. reticular and erosive, the relevance of a DSS based on morphologic parameters is somewhat questionable. Instead, one may consider to only look for a quality of life scoring system adapted for use in OLP patients. Key words:Oral lichen planus, disease scoring system, classification. PMID:25681372

Sequential segmental classification of feline congenital heart disease.

PubMed

Scansen, Brian A; Schneider, Matthias; Bonagura, John D

2015-12-01

Feline congenital heart disease is less commonly encountered in veterinary medicine than acquired feline heart diseases such as cardiomyopathy. Understanding the wide spectrum of congenital cardiovascular disease demands a familiarity with a variety of lesions, occurring both in isolation and in combination, along with an appreciation of complex nomenclature and variable classification schemes. This review begins with an overview of congenital heart disease in the cat, including proposed etiologies and prevalence, examination approaches, and principles of therapy. Specific congenital defects are presented and organized by a sequential segmental classification with respect to their morphologic lesions. Highlights of diagnosis, treatment options, and prognosis are offered. It is hoped that this review will provide a framework for approaching congenital heart disease in the cat, and more broadly in other animal species based on the sequential segmental approach, which represents an adaptation of the common methodology used in children and adults with congenital heart disease. Copyright © 2015 Elsevier B.V. All rights reserved.

Classification of Parkinsonian syndromes from FDG-PET brain data using decision trees with SSM/PCA features.

PubMed

Mudali, D; Teune, L K; Renken, R J; Leenders, K L; Roerdink, J B T M

2015-01-01

Medical imaging techniques like fluorodeoxyglucose positron emission tomography (FDG-PET) have been used to aid in the differential diagnosis of neurodegenerative brain diseases. In this study, the objective is to classify FDG-PET brain scans of subjects with Parkinsonian syndromes (Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy) compared to healthy controls. The scaled subprofile model/principal component analysis (SSM/PCA) method was applied to FDG-PET brain image data to obtain covariance patterns and corresponding subject scores. The latter were used as features for supervised classification by the C4.5 decision tree method. Leave-one-out cross validation was applied to determine classifier performance. We carried out a comparison with other types of classifiers. The big advantage of decision tree classification is that the results are easy to understand by humans. A visual representation of decision trees strongly supports the interpretation process, which is very important in the context of medical diagnosis. Further improvements are suggested based on enlarging the number of the training data, enhancing the decision tree method by bagging, and adding additional features based on (f)MRI data.

Cerebrospinal fluid circulation and hydrocephalus.

PubMed

Leinonen, Ville; Vanninen, Ritva; Rauramaa, Tuomas

2017-01-01

Hydrocephalus (HC) is classically defined as dynamic imbalance between the production and absorption of cerebrospinal fluid (CSF) leading to enlarged ventricles. Potential causative factors include various brain disorders like tumors causing obstruction of CSF flow within the ventricular system or the subarachnoid space. Classification of HC is based on the site of CSF flow obstruction guiding optimal treatment, with endoscopic third ventriculostomy in intraventricular obstruction and CSF shunt in communicating HC. Another clinically relevant classification is acute and chronic; the most frequent chronic form is idiopathic normal-pressure hydrocephalus (iNPH). The reported incidence of HC varies according to the study population and classification used. The incidence of congenital HC is approximately 0.4-0.6/1,000 newborns and the annual incidence of iNPH varies from 0.5/100,000 to 5.5/100,000. Radiologically, ventricular dilatation may be nonspecific, and differentiation of iNPH from other neurodegenerative diseases may be ambiguous. There are no known specific microscopic findings of HC but a systematic neuropathologic examination is needed to detect comorbid diseases and possible etiologic factors of HC. Depending on the etiology of HC, there are several nonspecific signs potentially to be seen. Copyright © 2017 Elsevier B.V. All rights reserved.

Automatic Classification of Specific Melanocytic Lesions Using Artificial Intelligence

PubMed Central

Jaworek-Korjakowska, Joanna; Kłeczek, Paweł

2016-01-01

Background. Given its propensity to metastasize, and lack of effective therapies for most patients with advanced disease, early detection of melanoma is a clinical imperative. Different computer-aided diagnosis (CAD) systems have been proposed to increase the specificity and sensitivity of melanoma detection. Although such computer programs are developed for different diagnostic algorithms, to the best of our knowledge, a system to classify different melanocytic lesions has not been proposed yet. Method. In this research we present a new approach to the classification of melanocytic lesions. This work is focused not only on categorization of skin lesions as benign or malignant but also on specifying the exact type of a skin lesion including melanoma, Clark nevus, Spitz/Reed nevus, and blue nevus. The proposed automatic algorithm contains the following steps: image enhancement, lesion segmentation, feature extraction, and selection as well as classification. Results. The algorithm has been tested on 300 dermoscopic images and achieved accuracy of 92% indicating that the proposed approach classified most of the melanocytic lesions correctly. Conclusions. A proposed system can not only help to precisely diagnose the type of the skin mole but also decrease the amount of biopsies and reduce the morbidity related to skin lesion excision. PMID:26885520

Automatic Classification of Specific Melanocytic Lesions Using Artificial Intelligence.

PubMed

Jaworek-Korjakowska, Joanna; Kłeczek, Paweł

2016-01-01

Given its propensity to metastasize, and lack of effective therapies for most patients with advanced disease, early detection of melanoma is a clinical imperative. Different computer-aided diagnosis (CAD) systems have been proposed to increase the specificity and sensitivity of melanoma detection. Although such computer programs are developed for different diagnostic algorithms, to the best of our knowledge, a system to classify different melanocytic lesions has not been proposed yet. In this research we present a new approach to the classification of melanocytic lesions. This work is focused not only on categorization of skin lesions as benign or malignant but also on specifying the exact type of a skin lesion including melanoma, Clark nevus, Spitz/Reed nevus, and blue nevus. The proposed automatic algorithm contains the following steps: image enhancement, lesion segmentation, feature extraction, and selection as well as classification. The algorithm has been tested on 300 dermoscopic images and achieved accuracy of 92% indicating that the proposed approach classified most of the melanocytic lesions correctly. A proposed system can not only help to precisely diagnose the type of the skin mole but also decrease the amount of biopsies and reduce the morbidity related to skin lesion excision.

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.

Serum peptide expression and treatment responses in patients with advanced non-small-cell lung cancer

PubMed Central

An, Juan; Tang, Chuan-Hao; Wang, Na; Liu, Yi; Lv, Jin; Xu, Bin; Li, Xiao-Yan; Guo, Wan-Feng; Gao, Hong-Jun; He, Kun; Liu, Xiao-Qing

2018-01-01

Epidermal growth factor receptor (EGFR) mutation is an important predictor for response to personalized treatments of patients with advanced non-small-cell lung cancer (NSCLC). However its usage is limited due to the difficult of obtaining tissue specimens. A novel prediction system using matrix assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has been reported to be a perspective tool in European countries to identify patients who are likely to benefit from EGFR tyrosine kinase inhibitor (TKI) treatment. In the present study, MALDI-TOF MS was used on pretreatment serum samples of patients with advanced non-small-cell lung cancer to discriminate the spectra between disease control and disease progression groups in one cohort of Chinese patients. The candidate features for classification were subsequently validated in a blinded fashion in another set of patients. The correlation between plasma EGFR mutation status and the intensities of representative spectra for classification was evaluated. A total of 103 patients that were treated with EGFR-TKIs were included. It was determined that 8 polypeptides peaks were significant different between the disease control and disease progression group. A total of 6 polypeptides were established in the classification algorithm. The sensitivity of the algorithm to predict treatment responses was 76.2% (16/21) and the specificity was 81.8% (18/22). The accuracy rate of the algorithm was 79.1% (34/43). A total of 3 polypeptides were significantly correlated with EGFR mutations (P=0.04, P=0.03 and P=0.04, respectively). The present study confirmed that MALDI-TOF MS analysis can be used to predict responses to EGFR-TKI treatment of the Asian population where the EGFR mutation status differs from the European population. Furthermore, the expression intensities of the three polypeptides in the classification model were associated with EGFR mutation. PMID:29844828

A new coding system for metabolic disorders demonstrates gaps in the international disease classifications ICD-10 and SNOMED-CT, which can be barriers to genotype-phenotype data sharing.

PubMed

Sollie, Annet; Sijmons, Rolf H; Lindhout, Dick; van der Ploeg, Ans T; Rubio Gozalbo, M Estela; Smit, G Peter A; Verheijen, Frans; Waterham, Hans R; van Weely, Sonja; Wijburg, Frits A; Wijburg, Rudolph; Visser, Gepke

2013-07-01

Data sharing is essential for a better understanding of genetic disorders. Good phenotype coding plays a key role in this process. Unfortunately, the two most widely used coding systems in medicine, ICD-10 and SNOMED-CT, lack information necessary for the detailed classification and annotation of rare and genetic disorders. This prevents the optimal registration of such patients in databases and thus data-sharing efforts. To improve care and to facilitate research for patients with metabolic disorders, we developed a new coding system for metabolic diseases with a dedicated group of clinical specialists. Next, we compared the resulting codes with those in ICD and SNOMED-CT. No matches were found in 76% of cases in ICD-10 and in 54% in SNOMED-CT. We conclude that there are sizable gaps in the SNOMED-CT and ICD coding systems for metabolic disorders. There may be similar gaps for other classes of rare and genetic disorders. We have demonstrated that expert groups can help in addressing such coding issues. Our coding system has been made available to the ICD and SNOMED-CT organizations as well as to the Orphanet and HPO organizations for further public application and updates will be published online (www.ddrmd.nl and www.cineas.org). © 2013 WILEY PERIODICALS, INC.

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

A detailed comparison of analysis processes for MCC-IMS data in disease classification—Automated methods can replace manual peak annotations

PubMed Central

Horsch, Salome; Kopczynski, Dominik; Kuthe, Elias; Baumbach, Jörg Ingo; Rahmann, Sven

2017-01-01

Motivation Disease classification from molecular measurements typically requires an analysis pipeline from raw noisy measurements to final classification results. Multi capillary column—ion mobility spectrometry (MCC-IMS) is a promising technology for the detection of volatile organic compounds in the air of exhaled breath. From raw measurements, the peak regions representing the compounds have to be identified, quantified, and clustered across different experiments. Currently, several steps of this analysis process require manual intervention of human experts. Our goal is to identify a fully automatic pipeline that yields competitive disease classification results compared to an established but subjective and tedious semi-manual process. Method We combine a large number of modern methods for peak detection, peak clustering, and multivariate classification into analysis pipelines for raw MCC-IMS data. We evaluate all combinations on three different real datasets in an unbiased cross-validation setting. We determine which specific algorithmic combinations lead to high AUC values in disease classifications across the different medical application scenarios. Results The best fully automated analysis process achieves even better classification results than the established manual process. The best algorithms for the three analysis steps are (i) SGLTR (Savitzky-Golay Laplace-operator filter thresholding regions) and LM (Local Maxima) for automated peak identification, (ii) EM clustering (Expectation Maximization) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) for the clustering step and (iii) RF (Random Forest) for multivariate classification. Thus, automated methods can replace the manual steps in the analysis process to enable an unbiased high throughput use of the technology. PMID:28910313

Proposal of a new staging system for intrahepatic cholangiocarcinoma: Analysis of surgical patients from a nationwide survey of the Liver Cancer Study Group of Japan.

PubMed

Sakamoto, Yoshihiro; Kokudo, Norihiro; Matsuyama, Yutaka; Sakamoto, Michiie; Izumi, Namiki; Kadoya, Masumi; Kaneko, Shuichi; Ku, Yonson; Kudo, Masatoshi; Takayama, Tadatoshi; Nakashima, Osamu

2016-01-01

In the current American Joint Committee on Cancer/International Union Against Cancer staging system (seventh edition) for intrahepatic cholangiocarcinoma (ICC), tumor size was excluded, and periductal invasion was added as a new tumor classification-defining factor. The objective of the current report was to propose a new staging system for ICC that would be better for stratifying the survival of patients based on data from the nationwide Liver Cancer Study Group of Japan database. Of 756 patients who underwent surgical resection for ICC between 2000 and 2005, multivariate analyses of the clinicopathologic factors of 419 patients who had complete data sets were performed to elucidate relevant factors for inclusion in a new tumor classification and staging system. Overall survival data were best stratified using a cutoff value of 2 cm using a minimal P value approach to discriminate patient survival. The 5-year survival rate of 15 patients who had ICC measuring ≤ 2 cm in greatest dimension without lymph node metastasis or vascular invasion was 100%, and this cohort was defined as T1. Multivariate analysis of prognostic factors for 267 patients with lymph node-negative and metastasis-negative (N0M0) disease indicated that the number of tumors, the presence arterial invasion, and the presence major biliary invasion were independent and significant prognostic factors. The proposed new system, which included tumor number, tumor size, arterial invasion, and major biliary invasion for tumor classification, provided good stratification of overall patient survival according to disease stage. Macroscopic periductal invasion was associated with major biliary invasion and an inferior prognosis. The proposed new staging system, which includes a tumor cutoff size of 2 cm and major biliary invasion, may be useful for assigning patients to surgery. © 2015 The Authors. Cancer published by Wiley Periodicals, Inc. on behalf of American Cancer Society.

Creating a Canonical Scientific and Technical Information Classification System for NCSTRL+

NASA Technical Reports Server (NTRS)

Tiffany, Melissa E.; Nelson, Michael L.

1998-01-01

The purpose of this paper is to describe the new subject classification system for the NCSTRL+ project. NCSTRL+ is a canonical digital library (DL) based on the Networked Computer Science Technical Report Library (NCSTRL). The current NCSTRL+ classification system uses the NASA Scientific and Technical (STI) subject classifications, which has a bias towards the aerospace, aeronautics, and engineering disciplines. Examination of other scientific and technical information classification systems showed similar discipline-centric weaknesses. Traditional, library-oriented classification systems represented all disciplines, but were too generalized to serve the needs of a scientific and technically oriented digital library. Lack of a suitable existing classification system led to the creation of a lightweight, balanced, general classification system that allows the mapping of more specialized classification schemes into the new framework. We have developed the following classification system to give equal weight to all STI disciplines, while being compact and lightweight.

Resilience in women with autoimmune rheumatic diseases.

PubMed

Rojas, Manuel; Rodriguez, Yhojan; Pacheco, Yovana; Zapata, Elizabeth; Monsalve, Diana M; Mantilla, Rubén D; Rodríguez-Jimenez, Monica; Ramírez-Santana, Carolina; Molano-González, Nicolás; Anaya, Juan-Manuel

2017-12-28

To evaluate the relationship between resilience and clinical outcomes in patients with autoimmune rheumatic diseases. Focus groups, individual interviews, and chart reviews were done to collect data on 188 women with autoimmune rheumatic diseases, namely rheumatoid arthritis (n=51), systemic lupus erythematosus (n=70), systemic sclerosis (n=35), and Sjögren's syndrome (n=32). Demographic, clinical, and laboratory variables were assessed including disease activity by patient reported outcomes. Resilience was evaluated by using the Brief Resilience Scale. Bivariate, multiple linear regression, and classification and regression trees were used to analyse data. Resilience was influenced by age, duration of disease, and socioeconomic status. Lower resilience scores were observed in younger patients (<48years) with systemic lupus erythematosus, rheumatoid arthritis, and systemic sclerosis who had low socioeconomic status, whereas older patients (>50years) had higher resilience scores regardless of socioeconomic status. There was no influence of disease activity on resilience. A particular behaviour was observed in systemic sclerosis in which patients with high socioeconomic status and regular physical activity had higher resilience scores. Resilience in patients with autoimmune rheumatic diseases is a continuum process influenced by age and socioeconomic status. The ways in which these variables along with exercise influence resilience deserve further investigation. Copyright © 2017 Société française de rhumatologie. Published by Elsevier SAS. All rights reserved.

Survival Analysis of Papillary Thyroid Carcinoma in Relation to Stage and Recurrence Risk: A 20-Year Experience in Pakistan.

PubMed

Hassan, Aamna; Razi, Mairah; Riaz, Saima; Khalid, Madeeha; Nawaz, M Khalid; Syed, Aamir Ali; Bashir, Humayun

2016-08-01

The aim of this study was to evaluate the overall and progression-free survival of papillary thyroid carcinoma (PTC), comparing the American Thyroid Association (ATA) guideline for risk of recurrence with the TNM staging system with dynamic assessment at 2 years. This study is a retrospective analysis of 689 PTC patients over a 20-year period at a single center. Disease-free survival based on the TNM staging and ATA recurrence risk was calculated using Kaplan-Meier curves. Dynamic response assessment during the first 2 years was compared for both systems. Survival was calculated based on age, baseline resectability, and postthyroidectomy serum tumor marker levels. Six hundred eighty-nine (72.2%) of the total thyroid cancer patients had PTC. Four hundred sixty-nine patients were females, and 220 patients were males. The age range was 6 to 87 years. Five hundred thirty-five patients were resectable, and 56 patients were unresectable. One hundred fifty-one patients were excluded due to insufficient information on recurrence risk. By ATA categorization, 39% had low risk, no disease-related mortality; 44% had intermediate risk, 3 died; and 17% had high risk, 32 died. The 5-year disease-free survival was 54%, 26%, and 5% in low-, intermediate-, and high-risk groups, respectively. The log-rank test showed a significant difference in the percent survival (P < 0.01). TNM stage wise, in terms of survival, 1.3% in stage I, 2.2% in stage II, 0% in stage III, and 37.5% in stage IV died. The 20-year disease-free survival showed the following: stage I, 43%; stage II, 28%; stage III, 18%; and stage IV, 2%. There is significant difference in survival rate (P < 0.01). Both ATA risk classification and TNM staging were significant predictors of disease-free survival. On bivariate analysis, ATA classification (hazards ratio, 2.1; 95% confidence interval, 1.64-2.67; P = 0.001) was better predictive of overall survival versus TNM classification (hazards ratio, 1.3; 95% confidence interval, 1.11-1.43; P = 0.063). The ATA risk stratification and continuous reassessment during the first 2 years predicts disease-free survival better than the TNM staging. Age older than 45 years, unresectable disease, and elevated postthyroidectomy thyroglobulin levels dictate a poorer prognosis.

Neurodevelopmental Disorders (ASD and ADHD): DSM-5, ICD-10, and ICD-11.

PubMed

Doernberg, Ellen; Hollander, Eric

2016-08-01

Neurodevelopmental disorders, specifically autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) have undergone considerable diagnostic evolution in the past decade. In the United States, the current system in place is the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), whereas worldwide, the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) serves as a general medical system. This review will examine the differences in neurodevelopmental disorders between these two systems. First, we will review the important revisions made from the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) to the DSM-5, with respect to ASD and ADHD. Next, we will cover the similarities and differences between ASD and ADHD classification in the DSM-5 and the ICD-10, and how these differences may have an effect on neurodevelopmental disorder diagnostics and classification. By examining the changes made for the DSM-5 in 2013, and critiquing the current ICD-10 system, we can help to anticipate and advise on the upcoming ICD-11, due to come online in 2017. Overall, this review serves to highlight the importance of progress towards complementary diagnostic classification systems, keeping in mind the difference in tradition and purpose of the DSM and the ICD, and that these systems are dynamic and changing as more is learned about neurodevelopmental disorders and their underlying etiology. Finally this review will discuss alternative diagnostic approaches, such as the Research Domain Criteria (RDoC) initiative, which links symptom domains to underlying biological and neurological mechanisms. The incorporation of new diagnostic directions could have a great effect on treatment development and insurance coverage for neurodevelopmental disorders worldwide.

Novel Noninvasive Brain Disease Detection System Using a Facial Image Sensor

PubMed Central

Shu, Ting; Zhang, Bob; Tang, Yuan Yan

2017-01-01

Brain disease including any conditions or disabilities that affect the brain is fast becoming a leading cause of death. The traditional diagnostic methods of brain disease are time-consuming, inconvenient and non-patient friendly. As more and more individuals undergo examinations to determine if they suffer from any form of brain disease, developing noninvasive, efficient, and patient friendly detection systems will be beneficial. Therefore, in this paper, we propose a novel noninvasive brain disease detection system based on the analysis of facial colors. The system consists of four components. A facial image is first captured through a specialized sensor, where four facial key blocks are next located automatically from the various facial regions. Color features are extracted from each block to form a feature vector for classification via the Probabilistic Collaborative based Classifier. To thoroughly test the system and its performance, seven facial key block combinations were experimented. The best result was achieved using the second facial key block, where it showed that the Probabilistic Collaborative based Classifier is the most suitable. The overall performance of the proposed system achieves an accuracy −95%, a sensitivity −94.33%, a specificity −95.67%, and an average processing time (for one sample) of <1 min at brain disease detection. PMID:29292716

Overweight and obesity prevalence among Cree youth of Eeyou Istchee according to three body mass index classification systems.

PubMed

St-Jean, Audray; Meziou, Salma; Ayotte, Pierre; Lucas, Michel

2017-11-22

Little is known about the suitability of three commonly used body mass index (BMI) classification systems for Indigenous youth. We estimated overweight and obesity prevalence among Cree youth of Eeyou Istchee according to three BMI classification systems, assessed the level of agreement between them, and evaluated their accuracy through body fat and cardiometabolic risk factors. Data on 288 youth (aged 8-17 years) were collected. Overweight and obesity prevalence were estimated with Centers for Disease Control and Prevention (CDC), International Obesity Task Force (IOTF) and World Health Organization (WHO) criteria. Agreement was measured with weighted kappa (κw). Associations with body fat and cardiometabolic risk factors were evaluated by analysis of variance. Obesity prevalence was 42.7% with IOTF, 47.2% with CDC, and 49.3% with WHO criteria. Agreement was almost perfect between IOTF and CDC (κw = 0.93), IOTF and WHO (κw = 0.91), and WHO and CDC (κw = 0.94). Means of body fat and cardiometabolic risk factors were significantly higher (P trend  < 0.001) from normal weight to obesity, regardless of the system used. Youth considered overweight by IOTF but obese by CDC or WHO exhibited less severe clinical obesity. IOTF seems to be more accurate in identifying obesity in Cree youth.

Classification of holter registers by dynamic clustering using multi-dimensional particle swarm optimization.

PubMed

Kiranyaz, Serkan; Ince, Turker; Pulkkinen, Jenni; Gabbouj, Moncef

2010-01-01

In this paper, we address dynamic clustering in high dimensional data or feature spaces as an optimization problem where multi-dimensional particle swarm optimization (MD PSO) is used to find out the true number of clusters, while fractional global best formation (FGBF) is applied to avoid local optima. Based on these techniques we then present a novel and personalized long-term ECG classification system, which addresses the problem of labeling the beats within a long-term ECG signal, known as Holter register, recorded from an individual patient. Due to the massive amount of ECG beats in a Holter register, visual inspection is quite difficult and cumbersome, if not impossible. Therefore the proposed system helps professionals to quickly and accurately diagnose any latent heart disease by examining only the representative beats (the so called master key-beats) each of which is representing a cluster of homogeneous (similar) beats. We tested the system on a benchmark database where the beats of each Holter register have been manually labeled by cardiologists. The selection of the right master key-beats is the key factor for achieving a highly accurate classification and the proposed systematic approach produced results that were consistent with the manual labels with 99.5% average accuracy, which basically shows the efficiency of the system.

Disability and Profiles of Functioning of Patients with Parkinson's Disease Described with ICF Classification

ERIC Educational Resources Information Center

Raggi, Alberto; Leonardi, Matilde; Ajovalasit, Daniela; Carella, Francesco; Soliveri, Paola; Albanese, Alberto; Romito, Luigi

2011-01-01

The objective of this study was to describe the functional profiles of patients with Parkinson's disease (PD), and the relationships between impairment in body functions, limitations in activities, and environmental factors, using the World Health Organization's International Classification of Functioning, Disability, and Health (ICF). Patients…

Systemic Mastocytosis with Smoldering Multiple Myeloma: Report of a Case

PubMed Central

Garcia, Gwenalyn; Ying, Liu; Hurford, Matthew; Odaimi, Marcel

2016-01-01

Systemic mastocytosis (SM) is a disease characterized by a clonal infiltration of mast cells affecting various tissues of the body. It is grouped into six different subtypes according to the World Health Organization classification. It is called indolent systemic mastocytosis (ISM) when there is no evidence of end organ dysfunction, while the presence of end organ dysfunction defines aggressive systemic mastocytosis (ASM). When SM coexists with a clonal hematological disorder, it is classified as systemic mastocytosis with associated clonal hematological nonmast cell lineage disease (SM-AHNMD). Over 80% of SM-AHNMD cases involve disorders of the myeloid cell lines. To our knowledge, there are only 8 reported cases to date of SM associated with a plasma cell disorder. We report a patient with ISM who was found to have concomitant smoldering multiple myeloma. His disease later progressed to ASM. We discuss this rare association between SM and a plasma cell disorder, and potential common pathophysiologic mechanisms linking the two disorders will be reviewed. We also discuss prognostic factors in SM as well as the management options considered during the evolution of the patient's disease. PMID:27293930

Predictive analysis effectiveness in determining the epidemic disease infected area

NASA Astrophysics Data System (ADS)

Ibrahim, Najihah; Akhir, Nur Shazwani Md.; Hassan, Fadratul Hafinaz

2017-10-01

Epidemic disease outbreak had caused nowadays community to raise their great concern over the infectious disease controlling, preventing and handling methods to diminish the disease dissemination percentage and infected area. Backpropagation method was used for the counter measure and prediction analysis of the epidemic disease. The predictive analysis based on the backpropagation method can be determine via machine learning process that promotes the artificial intelligent in pattern recognition, statistics and features selection. This computational learning process will be integrated with data mining by measuring the score output as the classifier to the given set of input features through classification technique. The classification technique is the features selection of the disease dissemination factors that likely have strong interconnection between each other in causing infectious disease outbreaks. The predictive analysis of epidemic disease in determining the infected area was introduced in this preliminary study by using the backpropagation method in observation of other's findings. This study will classify the epidemic disease dissemination factors as the features for weight adjustment on the prediction of epidemic disease outbreaks. Through this preliminary study, the predictive analysis is proven to be effective method in determining the epidemic disease infected area by minimizing the error value through the features classification.

High resolution esophageal manometry--the switch from "intuitive" visual interpretation to Chicago classification.

PubMed

Srinivas, M; Balakumaran, T A; Palaniappan, S; Srinivasan, Vijaya; Batcha, M; Venkataraman, Jayanthi

2014-03-01

High resolution esophageal manometry (HREM) has been interpreted all along by visual interpretation of color plots until the recent introduction of Chicago classification which categorises HREM using objective measurements. It compares HREM diagnosis of esophageal motor disorders by visual interpretation and Chicago classification. Using software Trace 1.2v, 77 consecutive tracings diagnosed by visual interpretation were re-analyzed by Chicago classification and findings compared for concordance between the two systems of interpretation. Kappa agreement rate between the two observations was determined. There were 57 males (74 %) and cohort median age was 41 years (range: 14-83 years). Majority of the referrals were for gastroesophageal reflux disease, dysphagia and achalasia. By "intuitive" visual interpretation, the tracing were reported as normal in 45 (58.4 %), achalasia 14 (18.2 %), ineffective esophageal motility 3 (3.9 %), nutcracker esophagus 11 (14.3 %) and nonspecific motility changes 4 (5.2 %). By Chicago classification, there was 100 % agreement (Kappa 1) for achalasia (type 1: 9; type 2: 5) and ineffective esophageal motility ("failed peristalsis" on visual interpretation). Normal esophageal motility, nutcracker esophagus and nonspecific motility disorder on visual interpretation were reclassified as rapid contraction and esophagogastric junction (EGJ) outflow obstruction by Chicago classification. Chicago classification identified distinct clinical phenotypes including EGJ outflow obstruction not identified by visual interpretation. A significant number of unclassified HREM by visual interpretation were also classified by it.

Physiological sensor signals classification for healthcare using sensor data fusion and case-based reasoning.

PubMed

Begum, Shahina; Barua, Shaibal; Ahmed, Mobyen Uddin

2014-07-03

Today, clinicians often do diagnosis and classification of diseases based on information collected from several physiological sensor signals. However, sensor signal could easily be vulnerable to uncertain noises or interferences and due to large individual variations sensitivity to different physiological sensors could also vary. Therefore, multiple sensor signal fusion is valuable to provide more robust and reliable decision. This paper demonstrates a physiological sensor signal classification approach using sensor signal fusion and case-based reasoning. The proposed approach has been evaluated to classify Stressed or Relaxed individuals using sensor data fusion. Physiological sensor signals i.e., Heart Rate (HR), Finger Temperature (FT), Respiration Rate (RR), Carbon dioxide (CO2) and Oxygen Saturation (SpO2) are collected during the data collection phase. Here, sensor fusion has been done in two different ways: (i) decision-level fusion using features extracted through traditional approaches; and (ii) data-level fusion using features extracted by means of Multivariate Multiscale Entropy (MMSE). Case-Based Reasoning (CBR) is applied for the classification of the signals. The experimental result shows that the proposed system could classify Stressed or Relaxed individual 87.5% accurately compare to an expert in the domain. So, it shows promising result in the psychophysiological domain and could be possible to adapt this approach to other relevant healthcare systems.

What can we learn in drug allergy management from World Health Organization's international classifications?

PubMed

Tanno, L K; Torres, M J; Castells, M; Demoly, P

2018-05-01

Drug hypersensitivity reactions (DHRs) represent growing health problem worldwide, affecting more than 7% of the general population, and represent an important public health problem. However, knowledge in DHRs morbidity and mortality epidemiological data is still not optimal and international comparable standards remain poorly accessed. Institutional databases worldwide increasingly use the WHO International Classification of Diseases (ICD) system to classify diagnoses, health services utilization, and death data. The misclassification of disorders in the ICD system contributes to a lack of ascertainment and recognition of their importance for healthcare planning and resource allocation. It also hampers clinical practice and prevention actions. To further inform the allergy community and to ensure that the revision process is transparent as advised in the WHO ICD-11 revision agenda, we report the advances and use of the pioneering "Drug hypersensitivity" subsection of ICD-11 and implementation in the WHO International Classification of Health Interventions (ICHI). The new classification addressed to DHRs will enable the collection of more accurate epidemiological data to support quality management of patients with drug allergies and better facilitate healthcare planning and decision-making and public health measures to prevent and reduce the morbidity and mortality attributable to DHRs. © 2017 EAACI and John Wiley and Sons A/S. Published by John Wiley and Sons Ltd.

A quantitative approach to neuropsychiatry: The why and the how.

PubMed

Kas, Martien J; Penninx, Brenda; Sommer, Bernd; Serretti, Alessandro; Arango, Celso; Marston, Hugh

2017-12-12

The current nosology of neuropsychiatric disorders allows for a pragmatic approach to treatment choice, regulation and clinical research. However, without a biological rationale for these disorders, drug development has stagnated. The recently EU-funded PRISM project aims to develop a quantitative biological approach to the understanding and classification of neuropsychiatric diseases to accelerate the discovery and development of better treatments. By combining clinical data sets from major worldwide disease cohorts and by applying innovative technologies to deeply phenotype stratified patient groups, we will define a set of quantifiable biological parameters for social withdrawal and cognitive deficits common to Schizophrenia (SZ), Major Depression (MD), and Alzheimer's Disease (AD). These studies aim to provide new classification and assessment tools for social and cognitive performance across neuropsychiatric disorders, clinically relevant substrates for treatment development, and predictive, preclinical animal systems. With patients and regulatory agencies, we seek to provide clear routes for the future translation and regulatory approval for new treatments and provide solutions to the growing public health challenges of psychiatry and neurology. Copyright © 2017. Published by Elsevier Ltd.

Maize Cropping Systems Mapping Using RapidEye Observations in Agro-Ecological Landscapes in Kenya.

PubMed

Richard, Kyalo; Abdel-Rahman, Elfatih M; Subramanian, Sevgan; Nyasani, Johnson O; Thiel, Michael; Jozani, Hosein; Borgemeister, Christian; Landmann, Tobias

2017-11-03

Cropping systems information on explicit scales is an important but rarely available variable in many crops modeling routines and of utmost importance for understanding pests and disease propagation mechanisms in agro-ecological landscapes. In this study, high spatial and temporal resolution RapidEye bio-temporal data were utilized within a novel 2-step hierarchical random forest (RF) classification approach to map areas of mono- and mixed maize cropping systems. A small-scale maize farming site in Machakos County, Kenya was used as a study site. Within the study site, field data was collected during the satellite acquisition period on general land use/land cover (LULC) and the two cropping systems. Firstly, non-cropland areas were masked out from other land use/land cover using the LULC mapping result. Subsequently an optimized RF model was applied to the cropland layer to map the two cropping systems (2nd classification step). An overall accuracy of 93% was attained for the LULC classification, while the class accuracies (PA: producer's accuracy and UA: user's accuracy) for the two cropping systems were consistently above 85%. We concluded that explicit mapping of different cropping systems is feasible in complex and highly fragmented agro-ecological landscapes if high resolution and multi-temporal satellite data such as 5 m RapidEye data is employed. Further research is needed on the feasibility of using freely available 10-20 m Sentinel-2 data for wide-area assessment of cropping systems as an important variable in numerous crop productivity models.

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.

Periodontal manifestations of systemic diseases and developmental and acquired conditions: Consensus report of workgroup 3 of the 2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions.

PubMed

Jepsen, Søren; Caton, Jack G; Albandar, Jasim M; Bissada, Nabil F; Bouchard, Philippe; Cortellini, Pierpaolo; Demirel, Korkud; de Sanctis, Massimo; Ercoli, Carlo; Fan, Jingyuan; Geurs, Nicolaas C; Hughes, Francis J; Jin, Lijian; Kantarci, Alpdogan; Lalla, Evanthia; Madianos, Phoebus N; Matthews, Debora; McGuire, Michael K; Mills, Michael P; Preshaw, Philip M; Reynolds, Mark A; Sculean, Anton; Susin, Cristiano; West, Nicola X; Yamazaki, Kazuhisa

2018-06-01

A variety of systemic diseases and conditions can affect the course of periodontitis or have a negative impact on the periodontal attachment apparatus. Gingival recessions are highly prevalent and often associated with hypersensitivity, the development of caries and non-carious cervical lesions on the exposed root surface and impaired esthetics. Occlusal forces can result in injury of teeth and periodontal attachment apparatus. Several developmental or acquired conditions associated with teeth or prostheses may predispose to diseases of the periodontium. The aim of this working group was to review and update the 1999 classification with regard to these diseases and conditions, and to develop case definitions and diagnostic considerations. Discussions were informed by four reviews on 1) periodontal manifestions of systemic diseases and conditions; 2) mucogingival conditions around natural teeth; 3) traumatic occlusal forces and occlusal trauma; and 4) dental prostheses and tooth related factors. This consensus report is based on the results of these reviews and on expert opinion of the participants. Key findings included the following: 1) there are mainly rare systemic conditions (such as Papillon-Lefevre Syndrome, leucocyte adhesion deficiency, and others) with a major effect on the course of periodontitis and more common conditions (such as diabetes mellitus) with variable effects, as well as conditions affecting the periodontal apparatus independently of dental plaque biofilm-induced inflammation (such as neoplastic diseases); 2) diabetes-associated periodontitis should not be regarded as a distinct diagnosis, but diabetes should be recognized as an important modifying factor and included in a clinical diagnosis of periodontitis as a descriptor; 3) likewise, tobacco smoking - now considered a dependence to nicotine and a chronic relapsing medical disorder with major adverse effects on the periodontal supporting tissues - is an important modifier to be included in a clinical diagnosis of periodontitis as a descriptor; 4) the importance of the gingival phenotype, encompassing gingival thickness and width in the context of mucogingival conditions, is recognized and a novel classification for gingival recessions is introduced; 5) there is no evidence that traumatic occlusal forces lead to periodontal attachment loss, non-carious cervical lesions, or gingival recessions; 6) traumatic occlusal forces lead to adaptive mobility in teeth with normal support, whereas they lead to progressive mobility in teeth with reduced support, usually requiring splinting; 7) the term biologic width is replaced by supracrestal tissue attachment consisting of junctional epithelium and supracrestal connective tissue; 8) infringement of restorative margins within the supracrestal connective tissue attachment is associated with inflammation and/or loss of periodontal supporting tissue. However, it is not evident whether the negative effects on the periodontium are caused by dental plaque biofilm, trauma, toxicity of dental materials or a combination of these factors; 9) tooth anatomical factors are related to dental plaque biofilm-induced gingival inflammation and loss of periodontal supporting tissues. An updated classification of the periodontal manifestations and conditions affecting the course of periodontitis and the periodontal attachment apparatus, as well as of developmental and acquired conditions, is introduced. Case definitions and diagnostic considerations are also presented. © 2018 American Academy of Periodontology and European Federation of Periodontology.

[Ankyloblepharon filiforme adnatum].

PubMed

Haustein, M; Reschke, F; Terai, N; Lesczcynska, A; Wozniak, K; Pillunat, L E; Sommer, F

2014-02-01

The ankyloblepharon filiforme adnatum is a congenital eyelid anomaly in which the development of the eyelids is completed but the eyelids are not completely separated at birth. The abnormality can occur as an isolated anomaly, together with other eye diseases or in the context of systemic syndromes. In this case report the current classification and essential diagnostics of AFA will be reviewed.

Animated Simulation: Determining Cost Effective Nurse Staffing for an Acute Care Unit

DTIC Science & Technology

1997-06-19

Rate - Unscheduled Physician Visits Post- - Decubitus Ulcer Rate Discharge - Nosocomial Infection Rate (total) - Patient Knowledge of Disease...Condition - Nosocomial Urinary Tract Infection Rate and Care Requirements - Nosocomial Pneumonia Rate - Nosocomial Surgical Wound Infection Rate PROCESS...Nagaprasanna, 1988). A maternity unit at Bristol Hospital displayed dissatisfaction with their patient classification system. They found the patient

A web-based land cover classification system based on ontology model of different classification systems

NASA Astrophysics Data System (ADS)

Lin, Y.; Chen, X.

2016-12-01

Land cover classification systems used in remote sensing image data have been developed to meet the needs for depicting land covers in scientific investigations and policy decisions. However, accuracy assessments of a spate of data sets demonstrate that compared with the real physiognomy, each of the thematic map of specific land cover classification system contains some unavoidable flaws and unintended deviation. This work proposes a web-based land cover classification system, an integrated prototype, based on an ontology model of various classification systems, each of which is assigned the same weight in the final determination of land cover type. Ontology, a formal explication of specific concepts and relations, is employed in this prototype to build up the connections among different systems to resolve the naming conflicts. The process is initialized by measuring semantic similarity between terminologies in the systems and the search key to produce certain set of satisfied classifications, and carries on through searching the predefined relations in concepts of all classification systems to generate classification maps with user-specified land cover type highlighted, based on probability calculated by votes from data sets with different classification system adopted. The present system is verified and validated by comparing the classification results with those most common systems. Due to full consideration and meaningful expression of each classification system using ontology and the convenience that the web brings with itself, this system, as a preliminary model, proposes a flexible and extensible architecture for classification system integration and data fusion, thereby providing a strong foundation for the future work.

A support vector machine classifier reduces interscanner variation in the HRCT classification of regional disease pattern in diffuse lung disease: Comparison to a Bayesian classifier

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chang, Yongjun; Lim, Jonghyuck; Kim, Namkug

2013-05-15

Purpose: To investigate the effect of using different computed tomography (CT) scanners on the accuracy of high-resolution CT (HRCT) images in classifying regional disease patterns in patients with diffuse lung disease, support vector machine (SVM) and Bayesian classifiers were applied to multicenter data. Methods: Two experienced radiologists marked sets of 600 rectangular 20 Multiplication-Sign 20 pixel regions of interest (ROIs) on HRCT images obtained from two scanners (GE and Siemens), including 100 ROIs for each of local patterns of lungs-normal lung and five of regional pulmonary disease patterns (ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation). Each ROI was assessedmore » using 22 quantitative features belonging to one of the following descriptors: histogram, gradient, run-length, gray level co-occurrence matrix, low-attenuation area cluster, and top-hat transform. For automatic classification, a Bayesian classifier and a SVM classifier were compared under three different conditions. First, classification accuracies were estimated using data from each scanner. Next, data from the GE and Siemens scanners were used for training and testing, respectively, and vice versa. Finally, all ROI data were integrated regardless of the scanner type and were then trained and tested together. All experiments were performed based on forward feature selection and fivefold cross-validation with 20 repetitions. Results: For each scanner, better classification accuracies were achieved with the SVM classifier than the Bayesian classifier (92% and 82%, respectively, for the GE scanner; and 92% and 86%, respectively, for the Siemens scanner). The classification accuracies were 82%/72% for training with GE data and testing with Siemens data, and 79%/72% for the reverse. The use of training and test data obtained from the HRCT images of different scanners lowered the classification accuracy compared to the use of HRCT images from the same scanner. For integrated ROI data obtained from both scanners, the classification accuracies with the SVM and Bayesian classifiers were 92% and 77%, respectively. The selected features resulting from the classification process differed by scanner, with more features included for the classification of the integrated HRCT data than for the classification of the HRCT data from each scanner. For the integrated data, consisting of HRCT images of both scanners, the classification accuracy based on the SVM was statistically similar to the accuracy of the data obtained from each scanner. However, the classification accuracy of the integrated data using the Bayesian classifier was significantly lower than the classification accuracy of the ROI data of each scanner. Conclusions: The use of an integrated dataset along with a SVM classifier rather than a Bayesian classifier has benefits in terms of the classification accuracy of HRCT images acquired with more than one scanner. This finding is of relevance in studies involving large number of images, as is the case in a multicenter trial with different scanners.« less

Soluble Mediators and Clinical Features Discern Risk of Transitioning to Classified Disease in Relatives of Systemic Lupus Erythematosus Patients

PubMed Central

Munroe, Melissa E.; Young, Kendra A.; Kamen, Diane L.; Guthridge, Joel M.; Niewold, Timothy B.; Costenbader, Karen H.; Weisman, Michael H.; Ishimori, Mariko L.; Wallace, Daniel J.; Gilkeson, Gary S.; Karp, David R.; Harley, John B.; Norris, Jill M.; James, Judith A.

2016-01-01

Objective Systemic lupus erythematosus (SLE) and other autoimmune diseases cause significant morbidity. Identifying populations at risk of developing SLE is essential to curtail irreversible inflammatory damage. The objective of this study was to identify factors associated with transition to classified disease that inform SLE risk. Methods Previously identified lupus patient blood relatives with < 4 American College of Rheumatology SLE classification criteria at baseline (n=409) were enrolled in this follow-up study. Participants provided detailed family, demographic, and clinical information, including the SLE-specific portion of the Connective Tissue Disease Screening Questionnaire (SLE-CSQ). Plasma samples were tested for the presence of lupus-associated autoantibodies and 52 soluble mediators. Generalized estimating equations (GEE) were applied to identify factors anticipating disease transition. Results Forty-five relatives (11%) transitioned to classified SLE during follow-up (mean time=6.4 years). Relatives who transitioned displayed more lupus-associated autoantibody specificities and higher SLE-CSQ scores (p<0.0001) at baseline than non-transitioned relatives. Importantly, they also had elevated baseline plasma levels of inflammatory mediators, including B-lymphocyte stimulator (BLyS), stem cell factor (SCF), and interferon-associated chemokines (p≤0.02), with concurrent decreases in levels of regulatory mediators, tumor growth factor (TGF)-β and interleukin (IL)-10 (p≤0.03). GEE revealed that baseline SLE-CSQ or ACR scores and plasma levels of SCF and TGF-β (p≤0.03), but not autoantibodies, were significant and independent predictors of SLE transition. Conclusions Altered levels of soluble mediators anticipate transition to classified disease in lupus relatives. Thus, immune perturbations precede SLE classification and can help identify high-risk relatives for rheumatology referral and potential enrollment in prevention trials. PMID:27863174

Using the EC decision on case definitions for communicable diseases as a terminology source--lessons learned.

PubMed

Balkanyi, Laszlo; Heja, Gergely; Nagy, Attlia

2014-01-01

Extracting scientifically accurate terminology from an EU public health regulation is part of the knowledge engineering work at the European Centre for Disease Prevention and Control (ECDC). ECDC operates information systems at the crossroads of many areas - posing a challenge for transparency and consistency. Semantic interoperability is based on the Terminology Server (TS). TS value sets (structured vocabularies) describe shared domains as "diseases", "organisms", "public health terms", "geo-entities" "organizations" and "administrative terms" and others. We extracted information from the relevant EC Implementing Decision on case definitions for reporting communicable diseases, listing 53 notifiable infectious diseases, containing clinical, diagnostic, laboratory and epidemiological criteria. We performed a consistency check; a simplification - abstraction; we represented lab criteria in triplets: as 'y' procedural result /of 'x' organism-substance/on 'z' specimen and identified negations. The resulting new case definition value set represents the various formalized criteria, meanwhile the existing disease value set has been extended, new signs and symptoms were added. New organisms enriched the organism value set. Other new categories have been added to the public health value set, as transmission modes; substances; specimens and procedures. We identified problem areas, as (a) some classification error(s); (b) inconsistent granularity of conditions; (c) seemingly nonsense criteria, medical trivialities; (d) possible logical errors, (e) seemingly factual errors that might be phrasing errors. We think our hypothesis regarding room for possible improvements is valid: there are some open issues and a further improved legal text might lead to more precise epidemiologic data collection. It has to be noted that formal representation for automatic classification of cases was out of scope, such a task would require other formalism, as e.g. those used by rule-based decision support systems.

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.

An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification.

PubMed

Siddiqui, Muhammad Faisal; Reza, Ahmed Wasif; Kanesan, Jeevan

2015-01-01

A wide interest has been observed in the medical health care applications that interpret neuroimaging scans by machine learning systems. This research proposes an intelligent, automatic, accurate, and robust classification technique to classify the human brain magnetic resonance image (MRI) as normal or abnormal, to cater down the human error during identifying the diseases in brain MRIs. In this study, fast discrete wavelet transform (DWT), principal component analysis (PCA), and least squares support vector machine (LS-SVM) are used as basic components. Firstly, fast DWT is employed to extract the salient features of brain MRI, followed by PCA, which reduces the dimensions of the features. These reduced feature vectors also shrink the memory storage consumption by 99.5%. At last, an advanced classification technique based on LS-SVM is applied to brain MR image classification using reduced features. For improving the efficiency, LS-SVM is used with non-linear radial basis function (RBF) kernel. The proposed algorithm intelligently determines the optimized values of the hyper-parameters of the RBF kernel and also applied k-fold stratified cross validation to enhance the generalization of the system. The method was tested by 340 patients' benchmark datasets of T1-weighted and T2-weighted scans. From the analysis of experimental results and performance comparisons, it is observed that the proposed medical decision support system outperformed all other modern classifiers and achieves 100% accuracy rate (specificity/sensitivity 100%/100%). Furthermore, in terms of computation time, the proposed technique is significantly faster than the recent well-known methods, and it improves the efficiency by 71%, 3%, and 4% on feature extraction stage, feature reduction stage, and classification stage, respectively. These results indicate that the proposed well-trained machine learning system has the potential to make accurate predictions about brain abnormalities from the individual subjects, therefore, it can be used as a significant tool in clinical practice.

Non-linear dynamical classification of short time series of the rössler system in high noise regimes.

PubMed

Lainscsek, Claudia; Weyhenmeyer, Jonathan; Hernandez, Manuel E; Poizner, Howard; Sejnowski, Terrence J

2013-01-01

Time series analysis with delay differential equations (DDEs) reveals non-linear properties of the underlying dynamical system and can serve as a non-linear time-domain classification tool. Here global DDE models were used to analyze short segments of simulated time series from a known dynamical system, the Rössler system, in high noise regimes. In a companion paper, we apply the DDE model developed here to classify short segments of encephalographic (EEG) data recorded from patients with Parkinson's disease and healthy subjects. Nine simulated subjects in each of two distinct classes were generated by varying the bifurcation parameter b and keeping the other two parameters (a and c) of the Rössler system fixed. All choices of b were in the chaotic parameter range. We diluted the simulated data using white noise ranging from 10 to -30 dB signal-to-noise ratios (SNR). Structure selection was supervised by selecting the number of terms, delays, and order of non-linearity of the model DDE model that best linearly separated the two classes of data. The distances d from the linear dividing hyperplane was then used to assess the classification performance by computing the area A' under the ROC curve. The selected model was tested on untrained data using repeated random sub-sampling validation. DDEs were able to accurately distinguish the two dynamical conditions, and moreover, to quantify the changes in the dynamics. There was a significant correlation between the dynamical bifurcation parameter b of the simulated data and the classification parameter d from our analysis. This correlation still held for new simulated subjects with new dynamical parameters selected from each of the two dynamical regimes. Furthermore, the correlation was robust to added noise, being significant even when the noise was greater than the signal. We conclude that DDE models may be used as a generalizable and reliable classification tool for even small segments of noisy data.

Non-Linear Dynamical Classification of Short Time Series of the Rössler System in High Noise Regimes

PubMed Central

Lainscsek, Claudia; Weyhenmeyer, Jonathan; Hernandez, Manuel E.; Poizner, Howard; Sejnowski, Terrence J.

2013-01-01

Time series analysis with delay differential equations (DDEs) reveals non-linear properties of the underlying dynamical system and can serve as a non-linear time-domain classification tool. Here global DDE models were used to analyze short segments of simulated time series from a known dynamical system, the Rössler system, in high noise regimes. In a companion paper, we apply the DDE model developed here to classify short segments of encephalographic (EEG) data recorded from patients with Parkinson’s disease and healthy subjects. Nine simulated subjects in each of two distinct classes were generated by varying the bifurcation parameter b and keeping the other two parameters (a and c) of the Rössler system fixed. All choices of b were in the chaotic parameter range. We diluted the simulated data using white noise ranging from 10 to −30 dB signal-to-noise ratios (SNR). Structure selection was supervised by selecting the number of terms, delays, and order of non-linearity of the model DDE model that best linearly separated the two classes of data. The distances d from the linear dividing hyperplane was then used to assess the classification performance by computing the area A′ under the ROC curve. The selected model was tested on untrained data using repeated random sub-sampling validation. DDEs were able to accurately distinguish the two dynamical conditions, and moreover, to quantify the changes in the dynamics. There was a significant correlation between the dynamical bifurcation parameter b of the simulated data and the classification parameter d from our analysis. This correlation still held for new simulated subjects with new dynamical parameters selected from each of the two dynamical regimes. Furthermore, the correlation was robust to added noise, being significant even when the noise was greater than the signal. We conclude that DDE models may be used as a generalizable and reliable classification tool for even small segments of noisy data. PMID:24379798

Seeking order amidst chaos: a systematic review of classification systems for causes of stillbirth and neonatal death, 2009-2014.

PubMed

Leisher, Susannah Hopkins; Teoh, Zheyi; Reinebrant, Hanna; Allanson, Emma; Blencowe, Hannah; Erwich, Jan Jaap; Frøen, J Frederik; Gardosi, Jason; Gordijn, Sanne; Gülmezoglu, A Metin; Heazell, Alexander E P; Korteweg, Fleurisca; Lawn, Joy; McClure, Elizabeth M; Pattinson, Robert; Smith, Gordon C S; Tunçalp, Ӧzge; Wojcieszek, Aleena M; Flenady, Vicki

2016-10-05

Each year, about 5.3 million babies die in the perinatal period. Understanding of causes of death is critical for prevention, yet there is no globally acceptable classification system. Instead, many disparate systems have been developed and used. We aimed to identify all systems used or created between 2009 and 2014, with their key features, including extent of alignment with the International Classification of Diseases (ICD) and variation in features by region, to inform the World Health Organization's development of a new global approach to classifying perinatal deaths. A systematic literature review (CINAHL, EMBASE, Medline, Global Health, and PubMed) identified published and unpublished studies and national reports describing new classification systems or modifications of existing systems for causes of perinatal death, or that used or tested such systems, between 2009 and 2014. Studies reporting ICD use only were excluded. Data were independently double-extracted (except from non-English publications). Subgroup analyses explored variation by extent and region. Eighty-one systems were identified as new, modifications of existing systems, or having been used between 2009 and 2014, with an average of ten systems created/modified each year. Systems had widely varying characteristics: (i) comprehensiveness (40 systems classified both stillbirths and neonatal deaths); (ii) extent of use (systems were created in 28 countries and used in 40; 17 were created for national use; 27 were widely used); (iii) accessibility (three systems available in e-format); (iv) underlying cause of death (64 systems required a single cause of death); (v) reliability (10 systems tested for reliability, with overall Kappa scores ranging from .35-.93); and (vi) ICD alignment (17 systems used ICD codes). Regional databases were not searched, so system numbers may be underestimated. Some non-differential misclassification of systems was possible. The plethora of systems in use, and continuing system development, hamper international efforts to improve understanding of causes of death. Recognition of the features of currently used systems, combined with a better understanding of the drivers of continued system creation, may help the development of a truly effective global system.

A cloud-based system for automatic glaucoma screening.

PubMed

Fengshou Yin; Damon Wing Kee Wong; Ying Quan; Ai Ping Yow; Ngan Meng Tan; Gopalakrishnan, Kavitha; Beng Hai Lee; Yanwu Xu; Zhuo Zhang; Jun Cheng; Jiang Liu

2015-08-01

In recent years, there has been increasing interest in the use of automatic computer-based systems for the detection of eye diseases including glaucoma. However, these systems are usually standalone software with basic functions only, limiting their usage in a large scale. In this paper, we introduce an online cloud-based system for automatic glaucoma screening through the use of medical image-based pattern classification technologies. It is designed in a hybrid cloud pattern to offer both accessibility and enhanced security. Raw data including patient's medical condition and fundus image, and resultant medical reports are collected and distributed through the public cloud tier. In the private cloud tier, automatic analysis and assessment of colour retinal fundus images are performed. The ubiquitous anywhere access nature of the system through the cloud platform facilitates a more efficient and cost-effective means of glaucoma screening, allowing the disease to be detected earlier and enabling early intervention for more efficient intervention and disease management.

The revised dengue fever classification in German travelers: clinical manifestations and indicators for severe disease.

PubMed

Hoffmeister, B; Suttorp, N; Zoller, T

2015-02-01

The number of dengue cases imported to Germany has increased significantly in recent years. Among returning travelers, dengue is now a frequent cause of hospitalization. The aim of this study was to determine the proportion of patients with severe disease hospitalized in a European, non-endemic country applying the revised 2009 WHO classification system and to determine predictors of severe disease. A retrospective single-center analysis of clinical data from 56 patients, 31 (55 %) women and 25 (45 %) men, between 14 and 70 years of age treated in a tertiary care hospital between 1996 and 2010 was conducted. Thirty-nine patients (69.6 %) presented with dengue fever without warning signs, 11 (19.6 %) with warning signs and 6 (10.7 %) with signs for severe dengue fever. Two patients (4 %) developed dengue shock syndrome. Non-European descent (p = 0.001), plasma protein level <6.5 mg/dl (p = 0.001), platelets <30/nl (p = 0.017) and activated partial thromboplastin time (aPTT) >44 s (p = 0.003) were associated with severe disease. A significant proportion of patients hospitalized with symptomatic imported dengue fever in Germany have evidence of severe disease. Simple routine laboratory parameters such as complete blood count, plasma protein level and aPTT are helpful tools for identifying adult patients at risk for severe disease.

Differentiation of arterioles from venules in mouse histology images using machine learning

NASA Astrophysics Data System (ADS)

Elkerton, J. S.; Xu, Yiwen; Pickering, J. G.; Ward, Aaron D.

2016-03-01

Analysis and morphological comparison of arteriolar and venular networks are essential to our understanding of multiple diseases affecting every organ system. We have developed and evaluated the first fully automatic software system for differentiation of arterioles from venules on high-resolution digital histology images of the mouse hind limb immunostained for smooth muscle α-actin. Classifiers trained on texture and morphologic features by supervised machine learning provided excellent classification accuracy for differentiation of arterioles and venules, achieving an area under the receiver operating characteristic curve of 0.90 and balanced false-positive and false-negative rates. Feature selection was consistent across cross-validation iterations, and a small set of three features was required to achieve the reported performance, suggesting potential generalizability of the system. This system eliminates the need for laborious manual classification of the hundreds of microvessels occurring in a typical sample, and paves the way for high-throughput analysis the arteriolar and venular networks in the mouse.

Advances in human genetics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harris, H.; Hirschhorn, K.

1993-01-01

This book has five chapters covering peroxisomal diseases, X-linked immunodeficiencies, genetic mutations affecting human lipoproteins and their receptors and enzymes, genetic aspects of cancer, and Gaucher disease. The chapter on peroxisomes covers their discovery, structure, functions, disorders, etc. The chapter on X-linked immunodeficiencies discusses such diseases as agammaglobulinemia, severe combined immunodeficiency, Wiskott-Aldrich syndrome, animal models, linkage analysis, etc. Apolipoprotein formation, synthesis, gene regulation, proteins, etc. are the main focus of chapter 3. The chapter on cancer covers such topics as oncogene mapping and the molecular characterization of some recessive oncogenes. Gaucher disease is covered from its diagnosis, classification, and prevention,more » to its organ system involvement and molecular biology.« less

Risk factors contributing to a poor prognosis of papillary thyroid carcinoma: validity of UICC/AJCC TNM classification and stage grouping.

PubMed

Ito, Yasuhiro; Miyauchi, Akira; Jikuzono, Tomoo; Higashiyama, Takuya; Takamura, Yuuki; Miya, Akihiro; Kobayashi, Kaoru; Matsuzuka, Fumio; Ichihara, Kiyoshi; Kuma, Kanji

2007-04-01

In 2002, the UICC/AJCC TNM classification for papillary thyroid carcinoma was revised. In this study, we examined the validity of this classification system by investigating the predictors of disease-free survival (DFS) and cause-specific survival (CSS) in patients. We examined various clinicopathological features, including the component of the TNM classification, for 1,740 patients who underwent initial and curative surgery for papillary carcinoma between 1987 and 1995. Clinical and pathological T4a, clinical N1b in the TNM classification, and patient age were recognized as independent predictors of not only DFS, but also CSS of patients. Tumor size, male gender, and central node metastasis independently affected DFS only. There were 1,005 pathological N1b patients, but pathological N1b did not independently affect either DFS or CSS. Regarding the stage grouping, clinical stage IVA including clinical N1b more clearly affected DFS and CSS than pathological stage IVA including pathological N1b. Clinical stage grouping was more useful than pathological stage grouping for predicting the prognosis of papillary carcinoma patients possibly because pathological stage overestimates the biological characteristics of many pathological N1b tumors.

42 CFR 412.620 - Patient classification system.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 42 Public Health 2 2010-10-01 2010-10-01 false Patient classification system. 412.620 Section 412... Inpatient Rehabilitation Hospitals and Rehabilitation Units § 412.620 Patient classification system. (a) Classification methodology. (1) A patient classification system is used to classify patients in inpatient...

42 CFR 412.620 - Patient classification system.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 42 Public Health 2 2011-10-01 2011-10-01 false Patient classification system. 412.620 Section 412... Inpatient Rehabilitation Hospitals and Rehabilitation Units § 412.620 Patient classification system. (a) Classification methodology. (1) A patient classification system is used to classify patients in inpatient...

A Comprehensive Study of Retinal Vessel Classification Methods in Fundus Images

PubMed Central

Miri, Maliheh; Amini, Zahra; Rabbani, Hossein; Kafieh, Raheleh

2017-01-01

Nowadays, it is obvious that there is a relationship between changes in the retinal vessel structure and diseases such as diabetic, hypertension, stroke, and the other cardiovascular diseases in adults as well as retinopathy of prematurity in infants. Retinal fundus images provide non-invasive visualization of the retinal vessel structure. Applying image processing techniques in the study of digital color fundus photographs and analyzing their vasculature is a reliable approach for early diagnosis of the aforementioned diseases. Reduction in the arteriolar–venular ratio of retina is one of the primary signs of hypertension, diabetic, and cardiovascular diseases which can be calculated by analyzing the fundus images. To achieve a precise measuring of this parameter and meaningful diagnostic results, accurate classification of arteries and veins is necessary. Classification of vessels in fundus images faces with some challenges that make it difficult. In this paper, a comprehensive study of the proposed methods for classification of arteries and veins in fundus images is presented. Considering that these methods are evaluated on different datasets and use different evaluation criteria, it is not possible to conduct a fair comparison of their performance. Therefore, we evaluate the classification methods from modeling perspective. This analysis reveals that most of the proposed approaches have focused on statistics, and geometric models in spatial domain and transform domain models have received less attention. This could suggest the possibility of using transform models, especially data adaptive ones, for modeling of the fundus images in future classification approaches. PMID:28553578

Classification of Alzheimer's disease and prediction of mild cognitive impairment-to-Alzheimer's conversion from structural magnetic resource imaging using feature ranking and a genetic algorithm.

PubMed

Beheshti, Iman; Demirel, Hasan; Matsuda, Hiroshi

2017-04-01

We developed a novel computer-aided diagnosis (CAD) system that uses feature-ranking and a genetic algorithm to analyze structural magnetic resonance imaging data; using this system, we can predict conversion of mild cognitive impairment (MCI)-to-Alzheimer's disease (AD) at between one and three years before clinical diagnosis. The CAD system was developed in four stages. First, we used a voxel-based morphometry technique to investigate global and local gray matter (GM) atrophy in an AD group compared with healthy controls (HCs). Regions with significant GM volume reduction were segmented as volumes of interest (VOIs). Second, these VOIs were used to extract voxel values from the respective atrophy regions in AD, HC, stable MCI (sMCI) and progressive MCI (pMCI) patient groups. The voxel values were then extracted into a feature vector. Third, at the feature-selection stage, all features were ranked according to their respective t-test scores and a genetic algorithm designed to find the optimal feature subset. The Fisher criterion was used as part of the objective function in the genetic algorithm. Finally, the classification was carried out using a support vector machine (SVM) with 10-fold cross validation. We evaluated the proposed automatic CAD system by applying it to baseline values from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (160 AD, 162 HC, 65 sMCI and 71 pMCI subjects). The experimental results indicated that the proposed system is capable of distinguishing between sMCI and pMCI patients, and would be appropriate for practical use in a clinical setting. Copyright © 2017 Elsevier Ltd. All rights reserved.

Prediction of Disease Case Severity Level To Determine INA CBGs Rate

NASA Astrophysics Data System (ADS)

Puspitorini, Sukma; Kusumadewi, Sri; Rosita, Linda

2017-03-01

Indonesian Case-Based Groups (INA CBGs) is case-mix payment system using software grouper application. INA CBGs consisting of four digits code where the last digits indicating the severity level of disease cases. Severity level influence by secondary diagnosis (complications and co-morbidity) related to resource intensity level. It is medical resources used to treat a hospitalized patient. Objectives of this research is developing decision support system to predict severity level of disease cases and illustrate INA CBGs rate by using data mining decision tree classification model. Primary diagnosis (DU), first secondary diagnosis (DS 1), and second secondary diagnosis (DS 2) are attributes that used as input of severity level. The training process using C4.5 algorithm and the rules will represent in the IF-THEN form. Credibility of the system analyzed through testing process and confusion matrix present the results. Outcome of this research shows that first secondary diagnosis influence significant to form severity level predicting rules from new disease cases and INA CBGs rate illustration.

Intra- and Interobserver Reliability of Three Classification Systems for Hallux Rigidus.

PubMed

Dillard, Sarita; Schilero, Christina; Chiang, Sharon; Pham, Peter

2018-04-18

There are over ten classification systems currently used in the staging of hallux rigidus. This results in confusion and inconsistency with radiographic interpretation and treatment. The reliability of hallux rigidus classification systems has not yet been tested. The purpose of this study was to evaluate intra- and interobserver reliability using three commonly used classifications for hallux rigidus. Twenty-one plain radiograph sets were presented to ten ACFAS board-certified foot and ankle surgeons. Each physician classified each radiograph based on clinical experience and knowledge according to the Regnauld, Roukis, and Hattrup and Johnson classification systems. The two-way mixed single-measure consistency intraclass correlation was used to calculate intra- and interrater reliability. The intrarater reliability of individual sets for the Roukis and Hattrup and Johnson classification systems was "fair to good" (Roukis, 0.62±0.19; Hattrup and Johnson, 0.62±0.28), whereas the intrarater reliability of individual sets for the Regnauld system bordered between "fair to good" and "poor" (0.43±0.24). The interrater reliability of the mean classification was "excellent" for all three classification systems. Conclusions Reliable and reproducible classification systems are essential for treatment and prognostic implications in hallux rigidus. In our study, Roukis classification system had the best intrarater reliability. Although there are various classification systems for hallux rigidus, our results indicate that all three of these classification systems show reliability and reproducibility.

Systemic mastocytosis: CT and US features of abdominal manifestations.

PubMed

Avila, N A; Ling, A; Worobec, A S; Mican, J M; Metcalfe, D D

1997-02-01

To study the imaging findings in patients with systemic mastocytosis and to correlate the findings with the severity of disease on the basis of an established classification system. Pathologic findings, when available, were correlated with imaging findings. Computed tomographic (CT) and ultrasound (US) scans and corresponding pathologic findings, when available, were retrospectively reviewed in 27 patients with systemic mastocytosis. Only five (19%) of the patients in our series had normal abdominal CT and/or US examination results. Common abdominal imaging findings associated with systemic mastocytosis were hepatosplenomegaly, retroperitoneal adenopathy, periportal adenopathy, mesenteric adenopathy, thickening of the omentum and the mesentery, and ascites. Less common findings included hepatofugal portal venous flow, Budd-Chiari syndrome, cavernous transformation of the portal vein, ovarian mass, and complications such as chloroma. The findings were more common in patients with category II and those with category III disease. Abdominal findings at CT and US are common in patients with systemic mastocytosis. Although the findings in patients with systemic mastocytosis are not specific to the disease, they are useful in directing further studies for diagnostic confirmation and in estimating the extent of systemic involvement.

Subnetwork mining on functional connectivity network for classification of minimal hepatic encephalopathy.

PubMed

Zhang, Daoqiang; Tu, Liyang; Zhang, Long-Jiang; Jie, Biao; Lu, Guang-Ming

2018-06-01

Hepatic encephalopathy (HE), as a complication of cirrhosis, is a serious brain disease, which may lead to death. Accurate diagnosis of HE and its intermediate stage, i.e., minimal HE (MHE), is very important for possibly early diagnosis and treatment. Brain connectivity network, as a simple representation of brain interaction, has been widely used for the brain disease (e.g., HE and MHE) analysis. However, those studies mainly focus on finding disease-related abnormal connectivity between brain regions, although a large number of studies have indicated that some brain diseases are usually related to local structure of brain connectivity network (i.e., subnetwork), rather than solely on some single brain regions or connectivities. Also, mining such disease-related subnetwork is a challenging task because of the complexity of brain network. To address this problem, we proposed a novel frequent-subnetwork-based method to mine disease-related subnetworks for MHE classification. Specifically, we first mine frequent subnetworks from both groups, i.e., MHE patients and non-HE (NHE) patients, respectively. Then we used the graph-kernel based method to select the most discriminative subnetworks for subsequent classification. We evaluate our proposed method on a MHE dataset with 77 cirrhosis patients, including 38 MHE patients and 39 NHE patients. The results demonstrate that our proposed method can not only obtain the improved classification performance in comparison with state-of-the-art network-based methods, but also identify disease-related subnetworks which can help us better understand the pathology of the brain diseases.

Cartography of Pathway Signal Perturbations Identifies Distinct Molecular Pathomechanisms in Malignant and Chronic Lung Diseases

PubMed Central

Arakelyan, Arsen; Nersisyan, Lilit; Petrek, Martin; Löffler-Wirth, Henry; Binder, Hans

2016-01-01

Lung diseases are described by a wide variety of developmental mechanisms and clinical manifestations. Accurate classification and diagnosis of lung diseases are the bases for development of effective treatments. While extensive studies are conducted toward characterization of various lung diseases at molecular level, no systematic approach has been developed so far. Here we have applied a methodology for pathway-centered mining of high throughput gene expression data to describe a wide range of lung diseases in the light of shared and specific pathway activity profiles. We have applied an algorithm combining a Pathway Signal Flow (PSF) algorithm for estimation of pathway activity deregulation states in lung diseases and malignancies, and a Self Organizing Maps algorithm for classification and clustering of the pathway activity profiles. The analysis results allowed clearly distinguish between cancer and non-cancer lung diseases. Lung cancers were characterized by pathways implicated in cell proliferation, metabolism, while non-malignant lung diseases were characterized by deregulations in pathways involved in immune/inflammatory response and fibrotic tissue remodeling. In contrast to lung malignancies, chronic lung diseases had relatively heterogeneous pathway deregulation profiles. We identified three groups of interstitial lung diseases and showed that the development of characteristic pathological processes, such as fibrosis, can be initiated by deregulations in different signaling pathways. In conclusion, this paper describes the pathobiology of lung diseases from systems viewpoint using pathway centered high-dimensional data mining approach. Our results contribute largely to current understanding of pathological events in lung cancers and non-malignant lung diseases. Moreover, this paper provides new insight into molecular mechanisms of a number of interstitial lung diseases that have been studied to a lesser extent. PMID:27200087

Machine Learning Classification Combining Multiple Features of A Hyper-Network of fMRI Data in Alzheimer's Disease

PubMed Central

Guo, Hao; Zhang, Fan; Chen, Junjie; Xu, Yong; Xiang, Jie

2017-01-01

Exploring functional interactions among various brain regions is helpful for understanding the pathological underpinnings of neurological disorders. Brain networks provide an important representation of those functional interactions, and thus are widely applied in the diagnosis and classification of neurodegenerative diseases. Many mental disorders involve a sharp decline in cognitive ability as a major symptom, which can be caused by abnormal connectivity patterns among several brain regions. However, conventional functional connectivity networks are usually constructed based on pairwise correlations among different brain regions. This approach ignores higher-order relationships, and cannot effectively characterize the high-order interactions of many brain regions working together. Recent neuroscience research suggests that higher-order relationships between brain regions are important for brain network analysis. Hyper-networks have been proposed that can effectively represent the interactions among brain regions. However, this method extracts the local properties of brain regions as features, but ignores the global topology information, which affects the evaluation of network topology and reduces the performance of the classifier. This problem can be compensated by a subgraph feature-based method, but it is not sensitive to change in a single brain region. Considering that both of these feature extraction methods result in the loss of information, we propose a novel machine learning classification method that combines multiple features of a hyper-network based on functional magnetic resonance imaging in Alzheimer's disease. The method combines the brain region features and subgraph features, and then uses a multi-kernel SVM for classification. This retains not only the global topological information, but also the sensitivity to change in a single brain region. To certify the proposed method, 28 normal control subjects and 38 Alzheimer's disease patients were selected to participate in an experiment. The proposed method achieved satisfactory classification accuracy, with an average of 91.60%. The abnormal brain regions included the bilateral precuneus, right parahippocampal gyrus\\hippocampus, right posterior cingulate gyrus, and other regions that are known to be important in Alzheimer's disease. Machine learning classification combining multiple features of a hyper-network of functional magnetic resonance imaging data in Alzheimer's disease obtains better classification performance. PMID:29209156

Effects of gross motor function and manual function levels on performance-based ADL motor skills of children with spastic cerebral palsy.

PubMed

Park, Myoung-Ok

2017-02-01

[Purpose] The purpose of this study was to determine effects of Gross Motor Function Classification System and Manual Ability Classification System levels on performance-based motor skills of children with spastic cerebral palsy. [Subjects and Methods] Twenty-three children with cerebral palsy were included. The Assessment of Motor and Process Skills was used to evaluate performance-based motor skills in daily life. Gross motor function was assessed using Gross Motor Function Classification Systems, and manual function was measured using the Manual Ability Classification System. [Results] Motor skills in daily activities were significantly different on Gross Motor Function Classification System level and Manual Ability Classification System level. According to the results of multiple regression analysis, children categorized as Gross Motor Function Classification System level III scored lower in terms of performance based motor skills than Gross Motor Function Classification System level I children. Also, when analyzed with respect to Manual Ability Classification System level, level II was lower than level I, and level III was lower than level II in terms of performance based motor skills. [Conclusion] The results of this study indicate that performance-based motor skills differ among children categorized based on Gross Motor Function Classification System and Manual Ability Classification System levels of cerebral palsy.

Landscape Risk Factors for Lyme Disease in the Eastern Broadleaf Forest Province of the Hudson River Valley and the Effect of Explanatory Data Classification Resolution

EPA Science Inventory

This study assessed how landcover classification affects associations between landscape characteristics and Lyme disease rate. Landscape variables were derived from the National Land Cover Database (NLCD), including native classes (e.g., deciduous forest, developed low intensity)...

A Classification Scheme for Analyzing Mobile Apps Used to Prevent and Manage Disease in Late Life

PubMed Central

Wang, Aiguo; Lu, Xin; Chen, Hongtu; Li, Changqun; Levkoff, Sue

2014-01-01

Background There are several mobile apps that offer tools for disease prevention and management among older adults, and promote health behaviors that could potentially reduce or delay the onset of disease. A classification scheme that categorizes apps could be useful to both older adult app users and app developers. Objective The objective of our study was to build and evaluate the effectiveness of a classification scheme that classifies mobile apps available for older adults in the “Health & Fitness” category of the iTunes App Store. Methods We constructed a classification scheme for mobile apps according to three dimensions: (1) the Precede-Proceed Model (PPM), which classifies mobile apps in terms of predisposing, enabling, and reinforcing factors for behavior change; (2) health care process, specifically prevention versus management of disease; and (3) health conditions, including physical health and mental health. Content analysis was conducted by the research team on health and fitness apps designed specifically for older adults, as well as those applicable to older adults, released during the months of June and August 2011 and August 2012. Face validity was assessed by a different group of individuals, who were not related to the study. A reliability analysis was conducted to confirm the accuracy of the coding scheme of the sample apps in this study. Results After applying sample inclusion and exclusion criteria, a total of 119 apps were included in the study sample, of which 26/119 (21.8%) were released in June 2011, 45/119 (37.8%) in August 2011, and 48/119 (40.3%) in August 2012. Face validity was determined by interviewing 11 people, who agreed that this scheme accurately reflected the nature of this application. The entire study sample was successfully coded, demonstrating satisfactory inter-rater reliability by two independent coders (95.8% initial concordance and 100% concordance after consensus was reached). The apps included in the study sample were more likely to be used for the management of disease than prevention of disease (109/119, 91.6% vs 15/119, 12.6%). More apps contributed to physical health rather than mental health (81/119, 68.1% vs 47/119, 39.5%). Enabling apps (114/119, 95.8%) were more common than reinforcing (20/119, 16.8%) or predisposing apps (10/119, 8.4%). Conclusions The findings, including face validity and inter-rater reliability, support the integrity of the proposed classification scheme for categorizing mobile apps for older adults in the “Health and Fitness” category available in the iTunes App Store. Using the proposed classification system, older adult app users would be better positioned to identify apps appropriate for their needs, and app developers would be able to obtain the distributions of available mobile apps for health-related concerns of older adults more easily. PMID:25098687

78 FR 18252 - Prevailing Rate Systems; North American Industry Classification System Based Federal Wage System...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-03-26

...-AM78 Prevailing Rate Systems; North American Industry Classification System Based Federal Wage System... 2007 North American Industry Classification System (NAICS) codes currently used in Federal Wage System... (OPM) issued a final rule (73 FR 45853) to update the 2002 North American Industry Classification...

Classification of nervous system withdrawn and approved drugs with ToxPrint features via machine learning strategies.

PubMed

Onay, Aytun; Onay, Melih; Abul, Osman

2017-04-01

Early-phase virtual screening of candidate drug molecules plays a key role in pharmaceutical industry from data mining and machine learning to prevent adverse effects of the drugs. Computational classification methods can distinguish approved drugs from withdrawn ones. We focused on 6 data sets including maximum 110 approved and 110 withdrawn drugs for all and nervous system diseases to distinguish approved drugs from withdrawn ones. In this study, we used support vector machines (SVMs) and ensemble methods (EMs) such as boosted and bagged trees to classify drugs into approved and withdrawn categories. Also, we used CORINA Symphony program to identify Toxprint chemotypes including over 700 predefined chemotypes for determination of risk and safety assesment of candidate drug molecules. In addition, we studied nervous system withdrawn drugs to determine the key fragments with The ParMol package including gSpan algorithm. According to our results, the descriptors named as the number of total chemotypes and bond CN_amine_aliphatic_generic were more significant descriptors. The developed Medium Gaussian SVM model reached 78% prediction accuracy on test set for drug data set including all disease. Here, bagged tree and linear SVM models showed 89% of accuracies for phycholeptics and psychoanaleptics drugs. A set of discriminative fragments in nervous system withdrawn drug (NSWD) data sets was obtained. These fragments responsible for the drugs removed from market were benzene, toluene, N,N-dimethylethylamine, crotylamine, 5-methyl-2,4-heptadiene, octatriene and carbonyl group. This paper covers the development of computational classification methods to distinguish approved drugs from withdrawn ones. In addition, the results of this study indicated the identification of discriminative fragments is of significance to design a new nervous system approved drugs with interpretation of the structures of the NSWDs. Copyright © 2017 Elsevier B.V. All rights reserved.

[CLASSIFICATION OF ACUTE PANCREATITIS: CURRENT STATE OF THE ISSUE].

PubMed

Bagnenko, S F; Gol'tsov, V P; Savello, V E; Vashetko, R V

2015-01-01

The article analyzed disadvantages of "Atlanta-92" classification of acute pancreatitis and its two modifications: APCWG-2012 and IAP-2011. The school of Saint-Petersburg pancreatologists suggested the classification AP of Russian Surgical Society (2014), which represented the concept of disease staging.

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

Prevalence of systemic lupus erythematosus and associated comorbidities in Puerto Rico

PubMed Central

Molina, María J.; Mayor, Angel M.; Franco, Alejandro E.; Morell, Carlos A.; López, Miguel A.; Vilá, Luis M.

2013-01-01

Objective To determine the prevalence of systemic lupus erythematosus (SLE) and its associated comorbidities in patients from Puerto Rico using a database from a health insurance company. Methods The insurance claims submitted by physicians in 2003 to a health insurance company of Puerto Rico were examined. Of 552,733 insured people, 877 had a diagnosis of SLE (code 710.0) per the International Classification of Diseases, Ninth Revision (ICD-9). Demographic parameters and selected comorbidities were determined. The diagnosis of comorbities was ascertained using the ICD-9 code, the Current Procedural Terminology-4 (CPT-4) code (for disease specific procedures) and/or the Medi-Span Therapeutic Classification System (for disease specific pharmacologic treatment). Fisher exact test and Chi-square were used to evaluate differences between SLE patients groups. Results The mean age was 42.0 ± 13 and the female to male ratio was 12.5:1. The overall prevalence of SLE was 159 per 100,000 individuals. The prevalence for females was 277 per 100,000 women and for males it was 25 per 100,000 men. The most common comorbidities were high blood pressure (33.7%), osteopenia/osteoporosis (22.2%), hypothyroidism (19.0%), diabetes mellitus (11.6%) and hypercholesterolemia (11.6%). Overall, high blood pressure, diabetes mellitus, hypercholesterolemia, and coronary artery disease were more prevalent in SLE patients older than 54 years. Osteopenia/osteoporosis was more prevalent in women than in men. Conclusions The prevalence of SLE in Puerto Rico is very high. High blood pressure, diabetes mellitus, hypercholesterolemia, hypothyroidism and osteopenia/osteoporosis are common comorbidities in these patients. Identification and management of these comorbidities are critical for optimal medical care to this population. PMID:17762454

Prevalence of systemic lupus erythematosus and associated comorbidities in Puerto Rico.

PubMed

Molina, María J; Mayor, Angel M; Franco, Alejandro E; Morell, Carlos A; López, Miguel A; Vilá, Luis M

2007-08-01

To examine the prevalence of systemic lupus erythematosus (SLE) and its associated comorbidities in patients from Puerto Rico using a database from a health insurance company. The insurance claims submitted by physicians in 2003 to a health insurance company of Puerto Rico were examined. Of 552,733 insured people, 877 had a diagnosis of SLE (code 710.0) per the International Classification of Diseases, Ninth Revision (ICD-9). Demographic parameters and selected comorbidities were determined. The diagnosis of comorbities was ascertained using the ICD-9 code, the Current Procedural Terminology-4 code (for disease-specific procedures) and/or the Medi-Span Therapeutic Classification System (for disease-specific pharmacologic treatment). Fisher exact test and chi were used to evaluate differences between SLE patients groups. The mean age was 42.0 +/- 13.5, and the female-to-male ratio was 12.5:1. The overall prevalence of SLE was 159 per 100,000 individuals. The prevalence for females was 277 per 100,000 women and for males it was 25 per 100,000 men. The most common comorbidities were high blood pressure (33.7%), osteopenia/osteoporosis (22.2%), hypothyroidism (19.0%), diabetes mellitus (11.6%), and hypercholesterolemia (11.6%). Overall, high blood pressure, diabetes mellitus, hypercholesterolemia, and coronary artery disease were more prevalent in SLE patients older than 54 years. Osteopenia/osteoporosis was more prevalent in women than in men. The prevalence of SLE in Puerto Rico is very high. High blood pressure, diabetes mellitus and hypercholesterolemia, hypothyroidism, and osteopenia/osteoporosis are common comorbidities in these patients. Identification and management of these comorbidities are critical for optimal medical care to this population.

The Development of Combined Raman Spectroscopy-Optical Coherence Tomography and Application for Skin Cancer Diagnosis

NASA Astrophysics Data System (ADS)

Patil, Chetan

2009-11-01

Optical spectroscopy and imaging have shown promise for performing rapid, non-invasive disease detection and diagnosis in vivo. Independently, Raman Spectroscopy (RS) has demonstrated the ability to perform diagnosis of epithelial cancers such the cervix with excellent overall classification accuracy due to the inherent biochemical specificity of the technique, however relating features of tissue morphology with techniques such as Raman mapping is clinically impractical due to the weak nature of the scattering phenomena resulting in prohibitively long acquisition times. Optical Coherence Tomography (OCT), on the other hand, has demonstrated the ability to perform real-time, high-resolution, cross-sectional imaging of the microstructural characteristics of disease, but typically lacks molecularly specific information that can assist in classifying pathological lesions. We present the development of a combined Raman Spectroscopy-OCT (RS-OCT) instrument capable of compensating for the limitations of each technique individually and performing both biochemical and microstructural evaluation of tissues. We will include the design and development of benchtop RS-OCT implementations based on independent 785 nm Raman and 1310 nm time-domain OCT system backbones, as well as with a 785nm Raman / 850nm spectral-domain OCT setup employing an integrated detection arm. These systems motivated the ultimate design of a clinical RS-OCT system for application in dermatology. In order to aid in the development of our Raman spectral processing and classification methods, we conducted a simultaneous pilot study in which RS alone was used to measure basal and squamous cell carcinomas. We will present the initial results from our clinical experiences with the combined RS-OCT device, and include a discussion of spectral classification and the ultimate potential of combined RS-OCT for skin cancer diagnosis.

The usefulness of the revised classification for chronic kidney disease by the KDIGO for determining the frequency of diabetic micro- and macroangiopathies in Japanese patients with type 2 diabetes mellitus.

PubMed

Ito, Hiroyuki; Oshikiri, Koshiro; Mifune, Mizuo; Abe, Mariko; Antoku, Shinichi; Takeuchi, Yuichiro; Togane, Michiko; Yukawa, Chizuko

2012-01-01

A new classification of chronic kidney disease (CKD) was proposed by the Kidney Disease: Improving Global Outcomes (KDIGO) in 2011. The major point of revision of this classification was the introduction of a two-dimensional staging of the CKD according to the level of albuminuria in addition to the GFR level. Furthermore, the previous CKD stage 3 was subdivided into two stages (G3a and G3b). We examined the prevalence of diabetic micro- and macroangiopathies in patients with type 2 diabetes mellitus based on the new classification. A cross-sectional study was performed in 2018 patients with type 2 diabetes mellitus. All of the diabetic micro- and macroangiopathies significantly more common in the later stages of both the GFR and albuminuria. The proportion of subjects with diabetic retinopathy, neuropathy, cerebrovascular disease and coronary heart disease was significantly higher in the G3b group than in the G3a group. The brachial-ankle pulse wave velocity, which is one of the surrogate markers for atherosclerosis, was also significantly greater in the G3b group compared to the G3a group. The subdivision of the G3 stage in the revised classification proposed by the KDIGO is useful to evaluate the risk for diabetic vascular complications. Copyright © 2012 Elsevier Inc. All rights reserved.

Validation of the Japanese disease severity classification and the GAP model in Japanese patients with idiopathic pulmonary fibrosis.

PubMed

Kondoh, Shun; Chiba, Hirofumi; Nishikiori, Hirotaka; Umeda, Yasuaki; Kuronuma, Koji; Otsuka, Mitsuo; Yamada, Gen; Ohnishi, Hirofumi; Mori, Mitsuru; Kondoh, Yasuhiro; Taniguchi, Hiroyuki; Homma, Sakae; Takahashi, Hiroki

2016-09-01

The clinical course of idiopathic pulmonary fibrosis (IPF) shows great inter-individual differences. It is important to standardize the severity classification to accurately evaluate each patient׳s prognosis. In Japan, an original severity classification (the Japanese disease severity classification, JSC) is used. In the United States, the new multidimensional index and staging system (the GAP model) has been proposed. The objective of this study was to evaluate the model performance for the prediction of mortality risk of the JSC and GAP models using a large cohort of Japanese patients with IPF. This is a retrospective cohort study including 326 patients with IPF in the Hokkaido prefecture from 2003 to 2007. We obtained the survival curves of each stage of the GAP and JSC models to perform a comparison. In the GAP model, the prognostic value for mortality risk of Japanese patients was also evaluated. In the JSC, patient prognoses were roughly divided into two groups, mild cases (Stages I and II) and severe cases (Stages III and IV). In the GAP model, there was no significant difference in survival between Stages II and III, and the mortality rates in the patients classified into the GAP Stages I and II were underestimated. It is difficult to predict accurate prognosis of IPF using the JSC and the GAP models. A re-examination of the variables from the two models is required, as well as an evaluation of the prognostic value to revise the severity classification for Japanese patients with IPF. Copyright © 2016 The Japanese Respiratory Society. Published by Elsevier B.V. All rights reserved.

Integrated feature extraction and selection for neuroimage classification

NASA Astrophysics Data System (ADS)

Fan, Yong; Shen, Dinggang

2009-02-01

Feature extraction and selection are of great importance in neuroimage classification for identifying informative features and reducing feature dimensionality, which are generally implemented as two separate steps. This paper presents an integrated feature extraction and selection algorithm with two iterative steps: constrained subspace learning based feature extraction and support vector machine (SVM) based feature selection. The subspace learning based feature extraction focuses on the brain regions with higher possibility of being affected by the disease under study, while the possibility of brain regions being affected by disease is estimated by the SVM based feature selection, in conjunction with SVM classification. This algorithm can not only take into account the inter-correlation among different brain regions, but also overcome the limitation of traditional subspace learning based feature extraction methods. To achieve robust performance and optimal selection of parameters involved in feature extraction, selection, and classification, a bootstrapping strategy is used to generate multiple versions of training and testing sets for parameter optimization, according to the classification performance measured by the area under the ROC (receiver operating characteristic) curve. The integrated feature extraction and selection method is applied to a structural MR image based Alzheimer's disease (AD) study with 98 non-demented and 100 demented subjects. Cross-validation results indicate that the proposed algorithm can improve performance of the traditional subspace learning based classification.

Severity of Airflow Obstruction in Chronic Obstructive Pulmonary Disease (COPD): Proposal for a New Classification.

PubMed

Coton, Sonia; Vollmer, William M; Bateman, Eric; Marks, Guy B; Tan, Wan; Mejza, Filip; Juvekar, Sanjay; Janson, Christer; Mortimer, Kevin; P A, Mahesh; Buist, A Sonia; Burney, Peter G J

2017-10-01

Current classifications of Chronic Obstructive Pulmonary Disease (COPD) severity are complex and do not grade levels of obstruction. Obstruction is a simpler construct and independent of ethnicity. We constructed an index of obstruction severity based on the FEV 1 /FVC ratio, with cut-points dividing the Burden of Obstructive Lung Disease (BOLD) study population into four similarly sized strata to those created by the GOLD criteria that uses FEV 1 . We measured the agreement between classifications and the validity of the FEV 1 -based classification in identifying the level of obstruction as defined by the new groupings. We compared the strengths of association of each classification with quality of life (QoL), MRC dyspnoea score and the self-reported exacerbation rate. Agreement between classifications was only fair. FEV 1 -based criteria for moderate COPD identified only 79% of those with moderate obstruction and misclassified half of the participants with mild obstruction as having more severe COPD. Both scales were equally strongly associated with QoL, exertional dyspnoea and respiratory exacerbations. Severity assessed using the FEV 1 /FVC ratio is only in moderate agreement with the severity assessed using FEV 1 but is equally strongly associated with other outcomes. Severity assessed using the FEV 1 /FVC ratio is likely to be independent of ethnicity.

Why patients visit their doctors: assessing the most prevalent conditions in a defined American population.

PubMed

St Sauver, Jennifer L; Warner, David O; Yawn, Barbara P; Jacobson, Debra J; McGree, Michaela E; Pankratz, Joshua J; Melton, L Joseph; Roger, Véronique L; Ebbert, Jon O; Rocca, Walter A

2013-01-01

To describe the prevalence of nonacute conditions among patients seeking health care in a defined US population, emphasizing age, sex, and ethnic differences. The Rochester Epidemiology Project (REP) medical records linkage system was used to identify all residents of Olmsted County, Minnesota, on April 1, 2009, who had consented to review of their medical records for research (142,377 patients). We then electronically extracted all International Classification of Diseases, Ninth Revision codes noted in the records of these patients by any health care institution between January 1, 2005, and December 31, 2009. We grouped International Classification of Diseases, Ninth Revision codes into clinical classification codes and then into 47 broader disease groups associated with health-related quality of life. Age- and sex-specific prevalence was estimated by dividing the number of individuals within each group by the corresponding age- and sex-specific population. Patients within a group who had multiple codes were counted only once. We included a total of 142,377 patients, 75,512 (53%) of whom were female. Skin disorders (42.7%), osteoarthritis and joint disorders (33.6%), back problems (23.9%), disorders of lipid metabolism (22.4%), and upper respiratory tract disease (22.1%, excluding asthma) were the most prevalent disease groups in this population. Ten of the 15 most prevalent disease groups were more common in women in almost all age groups, whereas disorders of lipid metabolism, hypertension, and diabetes were more common in men. Additionally, the prevalence of 7 of the 10 most common groups increased with advancing age. Prevalence also varied across ethnic groups (whites, blacks, and Asians). Our findings suggest areas for focused research that may lead to better health care delivery and improved population health. Copyright © 2013 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.

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

Diffuse Lung Disease in Biopsied Children 2 to 18 Years of Age. Application of the chILD Classification Scheme.

PubMed

Fan, Leland L; Dishop, Megan K; Galambos, Csaba; Askin, Frederic B; White, Frances V; Langston, Claire; Liptzin, Deborah R; Kroehl, Miranda E; Deutsch, Gail H; Young, Lisa R; Kurland, Geoffrey; Hagood, James; Dell, Sharon; Trapnell, Bruce C; Deterding, Robin R

2015-10-01

Children's Interstitial and Diffuse Lung Disease (chILD) is a heterogeneous group of disorders that is challenging to categorize. In previous study, a classification scheme was successfully applied to children 0 to 2 years of age who underwent lung biopsies for chILD. This classification scheme has not been evaluated in children 2 to 18 years of age. This multicenter interdisciplinary study sought to describe the spectrum of biopsy-proven chILD in North America and to apply a previously reported classification scheme in children 2 to 18 years of age. Mortality and risk factors for mortality were also assessed. Patients 2 to 18 years of age who underwent lung biopsies for diffuse lung disease from 12 North American institutions were included. Demographic and clinical data were collected and described. The lung biopsies were reviewed by pediatric lung pathologists with expertise in diffuse lung disease and were classified by the chILD classification scheme. Logistic regression was used to determine risk factors for mortality. A total of 191 cases were included in the final analysis. Number of biopsies varied by center (5-49 biopsies; mean, 15.8) and by age (2-18 yr; mean, 10.6 yr). The most common classification category in this cohort was Disorders of the Immunocompromised Host (40.8%), and the least common was Disorders of Infancy (4.7%). Immunocompromised patients suffered the highest mortality (52.8%). Additional associations with mortality included mechanical ventilation, worse clinical status at time of biopsy, tachypnea, hemoptysis, and crackles. Pulmonary hypertension was found to be a risk factor for mortality but only in the immunocompetent patients. In patients 2 to 18 years of age who underwent lung biopsies for diffuse lung disease, there were far fewer diagnoses prevalent in infancy and more overlap with adult diagnoses. Immunocompromised patients with diffuse lung disease who underwent lung biopsies had less than 50% survival at time of last follow-up.

[Analysis on Research Projects Supported by the National Natural Science Foundation of China at the National Institute of Parasitic Diseases during 2003-2013].

PubMed

Zhou, Xiao-jun; Zheng, Bin; Yi, Feng-yun; Xiong, Yan-hong; Zhang, Min-qi

2015-04-01

The data of the National Natural Science Foundation (NSFC) projests obtained by the National Institute of Parasitic Diseases (NIPD), Chinese Center for Disease Control and Prevention (China CDC) during 2003-2013 were collected from internet-based science information system of NSFC, and NSFC search tool of Dingxiang Garden (http://nsfc.biomart.cn/). The number of funded projects, their subject classification and approved amount were analyzed, and compared with the other institutes of China CDC. Furthermore, the rationalization proposals were given in order to enhance the level of foundation management in the future.

Epidemiology and Clinical Aspects of Genetic Cardiomyopathies.

PubMed

Masarone, Daniele; Kaski, Juan Pablo; Pacileo, Giuseppe; Elliott, Perry M; Bossone, Eduardo; Day, Sharlene M; Limongelli, Giuseppe

2018-04-01

Cardiomyopathies (CMPs) are an increasingly recognized cause of heart failure and sudden death, particularly in young patients. Since their original description, major advances were achieved in the phenotype knowledge, natural history, and nosography of CMPs leading to different classification systems and therapies. However, a deeper knowledge of different causes, genotype-phenotype link, and natural history in different disease stages (preclinical, overt disease, and end-stage disease) according to a recognized standard of care (ie, international guidelines) is needed. Clinical registries can fill gaps in our knowledge regarding the uncovered issues on cause, clinical course, and management of CMPs. Copyright © 2017 Elsevier Inc. All rights reserved.

Contemporary management of pericardial effusion: practical aspects for clinical practice.

PubMed

Imazio, Massimo; Gaido, Luca; Battaglia, Alberto; Gaita, Fiorenzo

2017-03-01

A pericardial effusion (PE) is a relatively common finding in clinical practice. It may be either isolated or associated with pericarditis with or without an underlying disease. The aetiology is varied and may be either infectious (especially tuberculosis as the most common cause in developing countries) or non-infectious (cancer, systemic inflammatory diseases). The management is essentially guided by the hemodynamic effect (presence or absence of cardiac tamponade), the presence of concomitant pericarditis or underlying disease, and its size and duration. The present paper reviews the current knowledge on the aetiology, classification, diagnosis, management, therapy, and prognosis of PE in clinical practice.

Evaluation of gene expression classification studies: factors associated with classification performance.

PubMed

Novianti, Putri W; Roes, Kit C B; Eijkemans, Marinus J C

2014-01-01

Classification methods used in microarray studies for gene expression are diverse in the way they deal with the underlying complexity of the data, as well as in the technique used to build the classification model. The MAQC II study on cancer classification problems has found that performance was affected by factors such as the classification algorithm, cross validation method, number of genes, and gene selection method. In this paper, we study the hypothesis that the disease under study significantly determines which method is optimal, and that additionally sample size, class imbalance, type of medical question (diagnostic, prognostic or treatment response), and microarray platform are potentially influential. A systematic literature review was used to extract the information from 48 published articles on non-cancer microarray classification studies. The impact of the various factors on the reported classification accuracy was analyzed through random-intercept logistic regression. The type of medical question and method of cross validation dominated the explained variation in accuracy among studies, followed by disease category and microarray platform. In total, 42% of the between study variation was explained by all the study specific and problem specific factors that we studied together.

Multivariate Approach for Alzheimer's Disease Detection Using Stationary Wavelet Entropy and Predator-Prey Particle Swarm Optimization.

PubMed

Zhang, Yudong; Wang, Shuihua; Sui, Yuxiu; Yang, Ming; Liu, Bin; Cheng, Hong; Sun, Junding; Jia, Wenjuan; Phillips, Preetha; Gorriz, Juan Manuel

2017-07-17

The number of patients with Alzheimer's disease is increasing rapidly every year. Scholars often use computer vision and machine learning methods to develop an automatic diagnosis system. In this study, we developed a novel machine learning system that can make diagnoses automatically from brain magnetic resonance images. First, the brain imaging was processed, including skull stripping and spatial normalization. Second, one axial slice was selected from the volumetric image, and stationary wavelet entropy (SWE) was done to extract the texture features. Third, a single-hidden-layer neural network was used as the classifier. Finally, a predator-prey particle swarm optimization was proposed to train the weights and biases of the classifier. Our method used 4-level decomposition and yielded 13 SWE features. The classification yielded an overall accuracy of 92.73±1.03%, a sensitivity of 92.69±1.29%, and a specificity of 92.78±1.51%. The area under the curve is 0.95±0.02. Additionally, this method only cost 0.88 s to identify a subject in online stage, after its volumetric image is preprocessed. In terms of classification performance, our method performs better than 10 state-of-the-art approaches and the performance of human observers. Therefore, this proposed method is effective in the detection of Alzheimer's disease.

[Scoring systems in intensive care medicine : principles, models, application and limits].

PubMed

Fleig, V; Brenck, F; Wolff, M; Weigand, M A

2011-10-01

Scoring systems are used in all diagnostic areas of medicine. Several parameters are evaluated and rated with points according to their value in order to simplify a complex clinical situation with a score. The application ranges from the classification of disease severity through determining the number of staff for the intensive care unit (ICU) to the evaluation of new therapies under study conditions. Since the introduction of scoring systems in the 1980's a variety of different score models has been developed. The scoring systems that are employed in intensive care and are discussed in this article can be categorized into prognostic scores, expenses scores and disease-specific scores. Since the introduction of compulsory recording of two scoring systems for accounting in the German diagnosis-related groups (DRG) system, these tools have gained more importance for all intensive care physicians. Problems remain in the valid calculation of scores and interpretation of the results.

Novel nursing terminologies for the rapid response system.

PubMed

Wong, Elizabeth

2009-01-01

Nursing terminology with implications for the rapid response system (RRS) is introduced and proposed: critical incident nursing diagnosis (CIND), defined as the recognition of an acute life-threatening event that occurs as a result of disease, surgery, treatment, or medication; critical incident nursing intervention, defined as any indirect or direct care registered nurse-initiated treatment, based upon clinical judgment and knowledge that a registered nurse performs in response to a CIND; and critical incident control, defined as a response that attempts to reverse a life-threatening condition. The current literature, research studies, meta-analyses from a variety of disciplines, and personal clinical experience serve as the data sources for this article. The current nursing diagnoses, nursing interventions, and nursing outcomes listed in the North American Nursing Diagnosis Association International Classification, Nursing Interventions Classification (NIC), and Nursing Outcomes Classification (NOC), respectively, are inaccurate or inadequate for describing nursing care during life-threatening situations. The lack of such standardized nursing terminology creates a barrier that may impede critical communication and patient care during life-threatening situations when activating the RRS. The North American Nursing Diagnosis Association International Classification, NIC, and NOC are urged to refine their classifications and include CIND, critical incident nursing intervention, and critical incident control. The RRS should incorporate standardized nursing terminology to describe patient care during life-threatening situations. Refining the diagnoses, interventions, and outcomes classifications will permit nursing researchers, among others, to conduct studies on the efficacy of the proposed novel nursing terminology when providing care to patients during life-threatening situations. In addition, including the proposed novel nursing terminology in the RRS offers a means of improving care in such situations.

Understanding the use of standardized nursing terminology and classification systems in published research: A case study using the International Classification for Nursing Practice(®).

PubMed

Strudwick, Gillian; Hardiker, Nicholas R

2016-10-01

In the era of evidenced based healthcare, nursing is required to demonstrate that care provided by nurses is associated with optimal patient outcomes, and a high degree of quality and safety. The use of standardized nursing terminologies and classification systems are a way that nursing documentation can be leveraged to generate evidence related to nursing practice. Several widely-reported nursing specific terminologies and classifications systems currently exist including the Clinical Care Classification System, International Classification for Nursing Practice(®), Nursing Intervention Classification, Nursing Outcome Classification, Omaha System, Perioperative Nursing Data Set and NANDA International. However, the influence of these systems on demonstrating the value of nursing and the professions' impact on quality, safety and patient outcomes in published research is relatively unknown. This paper seeks to understand the use of standardized nursing terminology and classification systems in published research, using the International Classification for Nursing Practice(®) as a case study. A systematic review of international published empirical studies on, or using, the International Classification for Nursing Practice(®) were completed using Medline and the Cumulative Index for Nursing and Allied Health Literature. Since 2006, 38 studies have been published on the International Classification for Nursing Practice(®). The main objectives of the published studies have been to validate the appropriateness of the classification system for particular care areas or populations, further develop the classification system, or utilize it to support the generation of new nursing knowledge. To date, most studies have focused on the classification system itself, and a lesser number of studies have used the system to generate information about the outcomes of nursing practice. Based on the published literature that features the International Classification for Nursing Practice, standardized nursing terminology and classification systems appear to be well developed for various populations, settings and to harmonize with other health-related terminology systems. However, the use of the systems to generate new nursing knowledge, and to validate nursing practice is still in its infancy. There is an opportunity now to utilize the well-developed systems in their current state to further what is know about nursing practice, and how best to demonstrate improvements in patient outcomes through nursing care. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

PrionHome: a database of prions and other sequences relevant to prion phenomena.

PubMed

Harbi, Djamel; Parthiban, Marimuthu; Gendoo, Deena M A; Ehsani, Sepehr; Kumar, Manish; Schmitt-Ulms, Gerold; Sowdhamini, Ramanathan; Harrison, Paul M

2012-01-01

Prions are units of propagation of an altered state of a protein or proteins; prions can propagate from organism to organism, through cooption of other protein copies. Prions contain no necessary nucleic acids, and are important both as both pathogenic agents, and as a potential force in epigenetic phenomena. The original prions were derived from a misfolded form of the mammalian Prion Protein PrP. Infection by these prions causes neurodegenerative diseases. Other prions cause non-Mendelian inheritance in budding yeast, and sometimes act as diseases of yeast. We report the bioinformatic construction of the PrionHome, a database of >2000 prion-related sequences. The data was collated from various public and private resources and filtered for redundancy. The data was then processed according to a transparent classification system of prionogenic sequences (i.e., sequences that can make prions), prionoids (i.e., proteins that propagate like prions between individual cells), and other prion-related phenomena. There are eight PrionHome classifications for sequences. The first four classifications are derived from experimental observations: prionogenic sequences, prionoids, other prion-related phenomena, and prion interactors. The second four classifications are derived from sequence analysis: orthologs, paralogs, pseudogenes, and candidate-prionogenic sequences. Database entries list: supporting information for PrionHome classifications, prion-determinant areas (where relevant), and disordered and compositionally-biased regions. Also included are literature references for the PrionHome classifications, transcripts and genomic coordinates, and structural data (including comparative models made for the PrionHome from manually curated alignments). We provide database usage examples for both vertebrate and fungal prion contexts. Using the database data, we have performed a detailed analysis of the compositional biases in known budding-yeast prionogenic sequences, showing that the only abundant bias pattern is for asparagine bias with subsidiary serine bias. We anticipate that this database will be a useful experimental aid and reference resource. It is freely available at: http://libaio.biol.mcgill.ca/prion.

PrionHome: A Database of Prions and Other Sequences Relevant to Prion Phenomena

PubMed Central

Harbi, Djamel; Parthiban, Marimuthu; Gendoo, Deena M. A.; Ehsani, Sepehr; Kumar, Manish; Schmitt-Ulms, Gerold; Sowdhamini, Ramanathan; Harrison, Paul M.

2012-01-01

Prions are units of propagation of an altered state of a protein or proteins; prions can propagate from organism to organism, through cooption of other protein copies. Prions contain no necessary nucleic acids, and are important both as both pathogenic agents, and as a potential force in epigenetic phenomena. The original prions were derived from a misfolded form of the mammalian Prion Protein PrP. Infection by these prions causes neurodegenerative diseases. Other prions cause non-Mendelian inheritance in budding yeast, and sometimes act as diseases of yeast. We report the bioinformatic construction of the PrionHome, a database of >2000 prion-related sequences. The data was collated from various public and private resources and filtered for redundancy. The data was then processed according to a transparent classification system of prionogenic sequences (i.e., sequences that can make prions), prionoids (i.e., proteins that propagate like prions between individual cells), and other prion-related phenomena. There are eight PrionHome classifications for sequences. The first four classifications are derived from experimental observations: prionogenic sequences, prionoids, other prion-related phenomena, and prion interactors. The second four classifications are derived from sequence analysis: orthologs, paralogs, pseudogenes, and candidate-prionogenic sequences. Database entries list: supporting information for PrionHome classifications, prion-determinant areas (where relevant), and disordered and compositionally-biased regions. Also included are literature references for the PrionHome classifications, transcripts and genomic coordinates, and structural data (including comparative models made for the PrionHome from manually curated alignments). We provide database usage examples for both vertebrate and fungal prion contexts. Using the database data, we have performed a detailed analysis of the compositional biases in known budding-yeast prionogenic sequences, showing that the only abundant bias pattern is for asparagine bias with subsidiary serine bias. We anticipate that this database will be a useful experimental aid and reference resource. It is freely available at: http://libaio.biol.mcgill.ca/prion. PMID:22363733

[Medical and psychological rehabilitation of patients and disabled persons].

PubMed

Zaĭtsev, V P

2013-01-01

The paper unveils the concept of medical rehabilitation and defines its place in clinical medicine. It underlines the inextricable link and interaction of different components of a rehabilitation system. The value of the psychological aspect of rehabilitation is considered. Categories of patients and disabled persons who need psychological rehabilitation are identified; a classification of personal reactions to disease and the changes in the psychological state of patients in different periods after disease onset are given. The factors influencing the process of psychological readjustment in patients and the disabled are analyzed. The psychological rehabilitation system for patients and disabled persons is considered in detail. Data on its medical and socioeconomic efficiency are presented.

Automatic Screening and Grading of Age-Related Macular Degeneration from Texture Analysis of Fundus Images

PubMed Central

Phan, Thanh Vân; Seoud, Lama; Chakor, Hadi; Cheriet, Farida

2016-01-01

Age-related macular degeneration (AMD) is a disease which causes visual deficiency and irreversible blindness to the elderly. In this paper, an automatic classification method for AMD is proposed to perform robust and reproducible assessments in a telemedicine context. First, a study was carried out to highlight the most relevant features for AMD characterization based on texture, color, and visual context in fundus images. A support vector machine and a random forest were used to classify images according to the different AMD stages following the AREDS protocol and to evaluate the features' relevance. Experiments were conducted on a database of 279 fundus images coming from a telemedicine platform. The results demonstrate that local binary patterns in multiresolution are the most relevant for AMD classification, regardless of the classifier used. Depending on the classification task, our method achieves promising performances with areas under the ROC curve between 0.739 and 0.874 for screening and between 0.469 and 0.685 for grading. Moreover, the proposed automatic AMD classification system is robust with respect to image quality. PMID:27190636

[Generalized anxiety disorder, now and the future: a perspective to the DSM-5].

PubMed

Otsubo, Tempei

2012-01-01

Generalized, persistent, and free-floating anxiety was first described by Freud in 1894. The diagnostic term generalized anxiety disorder (GAD) was not in classification systems until the publication of the diagnostic and statistical manual for mental disorders, third edition (DSM-III) in 1980. Initially considered as a residual category to be used when no other diagnosis could be made, it is not accepted that GAD represents a distinct diagnostic category yet. Since 1980, revisions to the diagnostic criteria for GAD in the DSM-III-R, DSM-IV and DSM-5 classifications have slightly redefined this disorder. The classification is fluid. The duration criterion has increased to 6 months in DSM-IV, but decreased to 3 months in DSM-5. This article reviews the development of diagnostic criteria for defining GAD from Freud to DSM-5 and compares the DSM-5 criterion with DSM-IV and the tenth revision of the International Classification of Disease. The impact of the changes in diagnostic criteria on research into GAD, and on diagnosis, differential diagnosis, will be discussed.

Military Interoperable Digital Hospital Testbed (MIDHT) Phase II

DTIC Science & Technology

2011-07-01

personal health records has been limited, resulting in a small sample size to date. Additional providers and a new disease condition ( gestational diabetes ...Syndrome Picture Archive and Communications System User Satisfaction Gestational Diabetes 16. SECURITY CLASSIFICATION OF: 17...Consumer Informatics in the Chronic Care Model: Metabolic Syndrome and Gestational Diabetes in a Rural Setting. This arm focuses on finding innovative

Study of Morbidity Profile of a Rural Population in Tamil Nadu

PubMed Central

Ganeshkumar, P.; Katta, Ajitha

2015-01-01

Objective: To identify the reported morbidity profile of people according to age, gender and organ system affected using International Classification of Diseases (ICD) coding, in a demographically defined area in Tamil Nadu in order to identify their health care needs and to plan appropriate interventions strategies. Materials and Methods: This is a-cross sectional study using a convenience sample of 12308 persons sceened from the 41 panchayat units of the Kattankulathur block, comprising 90 villages with a population of about 2,00,890, over a period of one year. Diagnosis made were coded using ICD 10 version and data collected was analysed by appropriate statistical methods to explain the distribution of morbidity profile among the study population. Result: Out of total, 38.1% screened were males and 61.9% were females. Underfives were 5.3%, school going children 43.3%, adults 39.2% and elderly 12.3%. Majority had illness affecting respiratory system (20%), ‘symptoms and signs’ (19%), musculo-skeletal system (16.1%) and digestive system(11.9%). ‘Symptoms and signs’ classification, is a group of conditions which is of nonspecific diseases, signs, symptoms, abnormal findings and complaints, apart from the system specific conditions diagnosed properly and not elsewhere classified, More males were affeced with respiratory, digestive and illnesses with ‘symptoms and signs’ while more women were affected with musculo-skeletal problems. Only 9.7 % of patients reported with non-communicable diseases. Among them, 55 % women and 42.3 % men had osteoarthritis and 15.7 % women and 21.3 % men had cataract. About 15.8 % women and 18.1 % men had hypertension and other heart diseases while 9.7 % women and 8.4 % men had diabetes and 10.0 % men and 3.9 % women had chronic respiratory diseases. Conclusion: School going children and adults have higher levels of morbidity when compared to elderly and under five children. More females reported with illness but morbidity was found to be higher among males. The burden of illness increased with age. Acute ailments were responsible for high morbidity among children, while chronic ailments caused high morbidity among the elderly. PMID:25859470

The ICD diagnoses of fetishism and sadomasochism.

PubMed

Reiersøl, Odd; Skeid, Svein

2006-01-01

In this article we discuss psychiatric diagnoses of sexual deviation as they appear in the International Classification of Diseases (ICD-10), the internationally accepted classification and diagnostic system of the World Health Organization (WHO). Namely, we discuss the background of three diagnostic categories: Fetishism (F65.0), Fetishistic Transvestism (F65.1), and Sadomasochism (F65.5). Pertinent background issues regarding the above categories are followed by a critique of the usefulness of diagnosing these phenomena today. Specifically, we argue that Fetishism, Fetishistic Transvestism, and Sadomasochism, also labeled Paraphilia or perversion, should not be considered illnesses. Finally, we present the efforts of an initiative known as ReviseF65, which was established in 1997, to abolish these diagnoses.

Has Kahlbaum syndrome disappeared or is it underdiagnosed? Reexamining the nosology of catatonia.

PubMed

Rao, Naren P; Kasal, Vishal; Mutalik, Narayan R; Behere, Rishikesh V; Venkatasubramanian, Ganesan; Varambally, Shivarama; Gangadhar, Bangalore N

2012-03-01

In contemporary psychiatric classification such as the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, and International Classification of Diseases, 10th Revision, catatonia is classified as a subtype of schizophrenia and not as an independent disorder. However, catatonia does not seem to obey nosological boundaries and is seen with both affective and nonaffective psychoses. We conducted a chart review of patients to examine the nosological status of catatonia. Our data suggest that catatonia is a syndrome of varied manifestation possibly related to both affective and nonaffective psychoses with a subgroup independent of both. Further prospective studies examining the natural course are needed, which could have significant implications on future classificatory systems.

An Ultrasonographic Periodontal Probe

NASA Astrophysics Data System (ADS)

Bertoncini, C. A.; Hinders, M. K.

2010-02-01

Periodontal disease, commonly known as gum disease, affects millions of people. The current method of detecting periodontal pocket depth is painful, invasive, and inaccurate. As an alternative to manual probing, an ultrasonographic periodontal probe is being developed to use ultrasound echo waveforms to measure periodontal pocket depth, which is the main measure of periodontal disease. Wavelet transforms and pattern classification techniques are implemented in artificial intelligence routines that can automatically detect pocket depth. The main pattern classification technique used here, called a binary classification algorithm, compares test objects with only two possible pocket depth measurements at a time and relies on dimensionality reduction for the final determination. This method correctly identifies up to 90% of the ultrasonographic probe measurements within the manual probe's tolerance.

Newer approaches for optimal bioavailability of ocularly delivered drugs: review.

PubMed

Kesavan, K; Balasubramaniam, J; Kant, S; Singh, P N; Pandit, J K

2011-03-01

Eye diseases can cause discomfort and anxiety in patients, with the ultimate fear of loss of vision and facial disfigurement. Many regions of the eye are relatively inaccessible to systemically administered drugs and, as a result, topical drug delivery remains the preferred route in most cases. Drugs may be delivered to treat the precorneal region for conjunctivitis and blepharitis, or to provide intraocular diseases such as glaucoma, uveitis, and cytomegalovirus retinitis. Most of the ophthalmic formulation strategies aim at maximizing ocular drug permeability through prolongation of the drug residence time in the cornea and conjunctival sac, as well as minimizing precorneal drug loss. The conventional topical ocular drug delivery systems show drawbacks such as increased precorneal elimination and high variability in efficacy. Attempts have been made to overcome these problems and enhance ocular bioavailability by the development of newer drug delivery systems. This review is concerned with classification, recent findings and applications and biocompatibility of newer drug delivery systems for the treatment of ocular diseases.

Disease-specific differences in the use of traditional Korean medicine in Korea.

PubMed

Oh, In-Hwan; Yoon, Seok-Jun; Park, Minjung; An, SoHee

2015-05-03

Though traditional Korean medicine plays an important role in the Korean parallel health care system, there is limited information about the preference and usage of traditional Korean medicine compared to Western medicine because they have different disease classification systems. The aim of this study is to determine the relative preference for traditional Korean medicine using data acquired nationwide. Data from the 2008 Korea Health Panel were analyzed to determine the preference of medical services by disease. The use of traditional Korean medicine use is defined by the type of medical institution they used. Disease types, number of visits and out of pocket expenditures were analyzed. Traditional Korean medicine was used in only a small number of cases that were emergencies or hospitalization. However, in terms of outpatient services, traditional Korean medicine was used in 7.8% of all cases and represented 9.9% of total medical costs. Among disease groups, traditional Korean medicine use was higher in patients with nervous system and musculoskeletal system diseases. And patients with musculoskeletal and nervous system diseases such as arthrosis were the most likely to use traditional Korean medicine particularly in an outpatient setting. Korean characteristics of service use resemble the complementary and alternative medicine use in other countries in terms of disease group, and the complementary and alternative medicine should be considered to estimate the burden of disease in countries with parallel health care systems, such as Korea. This is the first study determined the actual preference of traditional Korean medicine for specific chronic diseases.

Ecological Land Classification: Applications to Identify the Productive Potential of Southern Forests

Treesearch

Dennis L. Mengel; D. Thompson Tew; [Editors

1991-01-01

Eighteen papers representing four categories-Regional Overviews; Classification System Development; Classification System Interpretation; Mapping/GIS Applications in Classification Systems-present the state of the art in forest-land classification and evaluation in the South. In addition, nine poster papers are presented.

Module modified acute physiology and chronic health evaluation II: predicting the mortality of neuro-critical disease.

PubMed

Su, Yingying; Wang, Miao; Liu, Yifei; Ye, Hong; Gao, Daiquan; Chen, Weibi; Zhang, Yunzhou; Zhang, Yan

2014-12-01

This study aimed to conduct and assess a module modified acute physiology and chronic health evaluation (MM-APACHE) II model, based on disease categories modified-acute physiology and chronic health evaluation (DCM-APACHE) II model, in predicting mortality more accurately in neuro-intensive care units (N-ICUs). In total, 1686 patients entered into this prospective study. Acute physiology and chronic health evaluation (APACHE) II scores of all patients on admission and worst 24-, 48-, 72-hour scores were obtained. Neurological diagnosis on admission was classified into five categories: cerebral infarction, intracranial hemorrhage, neurological infection, spinal neuromuscular (SNM) disease, and other neurological diseases. The APACHE II scores of cerebral infarction, intracranial hemorrhage, and neurological infection patients were used for building the MM-APACHE II model. There were 1386 cases for cerebral infarction disease, intracranial hemorrhage disease, and neurological infection disease. The logistic linear regression showed that 72-hour APACHE II score (Wals  =  173.04, P < 0.001) and disease classification (Wals  =  12.51, P  =  0.02) were of importance in forecasting hospital mortality. Module modified acute physiology and chronic health evaluation II model, built on the variables of the 72-hour APACHE II score and disease category, had good discrimination (area under the receiver operating characteristic curve (AU-ROC  =  0.830)) and calibration (χ2  =  12.518, P  =  0.20), and was better than the Knaus APACHE II model (AU-ROC  =  0.778). The APACHE II severity of disease classification system cannot provide accurate prognosis for all kinds of the diseases. A MM-APACHE II model can accurately predict hospital mortality for cerebral infarction, intracranial hemorrhage, and neurologic infection patients in N-ICU.

[Technologies for Complex Intelligent Clinical Data Analysis].

PubMed

Baranov, A A; Namazova-Baranova, L S; Smirnov, I V; Devyatkin, D A; Shelmanov, A O; Vishneva, E A; Antonova, E V; Smirnov, V I

2016-01-01

The paper presents the system for intelligent analysis of clinical information. Authors describe methods implemented in the system for clinical information retrieval, intelligent diagnostics of chronic diseases, patient's features importance and for detection of hidden dependencies between features. Results of the experimental evaluation of these methods are also presented. Healthcare facilities generate a large flow of both structured and unstructured data which contain important information about patients. Test results are usually retained as structured data but some data is retained in the form of natural language texts (medical history, the results of physical examination, and the results of other examinations, such as ultrasound, ECG or X-ray studies). Many tasks arising in clinical practice can be automated applying methods for intelligent analysis of accumulated structured array and unstructured data that leads to improvement of the healthcare quality. the creation of the complex system for intelligent data analysis in the multi-disciplinary pediatric center. Authors propose methods for information extraction from clinical texts in Russian. The methods are carried out on the basis of deep linguistic analysis. They retrieve terms of diseases, symptoms, areas of the body and drugs. The methods can recognize additional attributes such as "negation" (indicates that the disease is absent), "no patient" (indicates that the disease refers to the patient's family member, but not to the patient), "severity of illness", disease course", "body region to which the disease refers". Authors use a set of hand-drawn templates and various techniques based on machine learning to retrieve information using a medical thesaurus. The extracted information is used to solve the problem of automatic diagnosis of chronic diseases. A machine learning method for classification of patients with similar nosology and the methodfor determining the most informative patients'features are also proposed. Authors have processed anonymized health records from the pediatric center to estimate the proposed methods. The results show the applicability of the information extracted from the texts for solving practical problems. The records ofpatients with allergic, glomerular and rheumatic diseases were used for experimental assessment of the method of automatic diagnostic. Authors have also determined the most appropriate machine learning methods for classification of patients for each group of diseases, as well as the most informative disease signs. It has been found that using additional information extracted from clinical texts, together with structured data helps to improve the quality of diagnosis of chronic diseases. Authors have also obtained pattern combinations of signs of diseases. The proposed methods have been implemented in the intelligent data processing system for a multidisciplinary pediatric center. The experimental results show the availability of the system to improve the quality of pediatric healthcare.

[Systematization of diseases and lesions of the endometrium by etiological and pathogenetical mechanisms of development to choose an optimal treatment].

PubMed

Boroda, A M

2004-03-01

Current clinical gynecology considers pathological states of endometrium (PSE) as one of the most challenging issue of the day. Many questions of etiology, pathogenesis, diagnostics, and treatment of PSE are still under discussion. Nowadays there isn't a whole agreed classification of PSE. Morphological classification remains the most widely used one, but morphological changes occurring in the endometrium don't show a wide variety of disorders related to these pathological states. A new clinicopathogenetic classification of PSE was proposed, which is based on choosing the optimal treatment with functional state of the disease taken into account. This classification helps us to perceive the problem as a whole with choosing functionally based treatment for each patient.

Development and validation of an endoscopic classification of diverticular disease of the colon: the DICA classification.

PubMed

Tursi, Antonio; Brandimarte, Giovanni; Di Mario, Francesco; Andreoli, Arnaldo; Annunziata, Maria Laura; Astegiano, Marco; Bianco, Maria Antonietta; Buri, Luigi; Cammarota, Giovanni; Capezzuto, Erminio; Chilovi, Fausto; Cianci, Massimo; Conigliaro, Rita; Del Favero, Giuseppe; Di Cesare, Luigi; Di Fonzo, Michela; Elisei, Walter; Faggiani, Roberto; Farroni, Ferruccio; Forti, Giacomo; Germanà, Bastianello; Giorgetti, Gian Marco; Giovannone, Maurizio; Lecca, Piera Giuseppina; Loperfido, Silvano; Marmo, Riccardo; Morucci, Piero; Occhigrossi, Giuseppe; Penna, Antonio; Rossi, Alfredo Francesco; Spadaccini, Antonio; Zampaletta, Costantino; Zilli, Maurizio; Zullo, Angelo; Scarpignato, Carmelo; Picchio, Marcello

2015-01-01

A validated endoscopic classification of diverticular disease (DD) of the colon is lacking at present. Our aim was to develop a simple endoscopic score of DD: the Diverticular Inflammation and Complication Assessment (DICA) score. The DICA score for DD resulted in the sum of the scores for the extension of diverticulosis, the number of diverticula per region, the presence and type of inflammation, and the presence and type of complications: DICA 1 (≤ 3), DICA 2 (4-7) and DICA 3 (>7). A comparison with abdominal pain and inflammatory marker expression was also performed. A total of 50 videos of DD patients were reassessed in order to investigate the predictive role of DICA on the outcome of the disease. Overall agreement in using DICA was 0.847 (95% confidence interval, CI, 0.812-0.893): 0.878 (95% CI 0.832-0.895) for DICA 1, 0.765 (95% CI 0.735-0.786) for DICA 2 and 0.891 (95% CI 0.845-0.7923) for DICA 3. Intra-observer agreement (kappa) was 0.91 (95% CI 0.886-0.947). A significant correlation was found between the DICA score and C-reactive protein values (p = 0.0001), as well as between the median pain score and the DICA score (p = 0.0001). With respect to the 50 patients retrospectively reassessed, occurrence/recurrence of disease complications was recorded in 29 patients (58%): 10 (34.5%) were classified as DICA 1 and 19 (65.5%) as DICA 2 (p = 0.036). The DICA score is a simple, reproducible, validated and easy-to-use endoscopic scoring system for DD of the colon. © 2014 S. Karger AG, Basel.

A classification tree for the prediction of benign versus malignant disease in patients with small renal masses.

PubMed

Rendon, Ricardo A; Mason, Ross J; Kirkland, Susan; Lawen, Joseph G; Abdolell, Mohamed

2014-08-01

To develop a classification tree for the preoperative prediction of benign versus malignant disease in patients with small renal masses. This is a retrospective study including 395 consecutive patients who underwent surgical treatment for a renal mass < 5 cm in maximum diameter between July 1st 2001 and June 30th 2010. A classification tree to predict the risk of having a benign renal mass preoperatively was developed using recursive partitioning analysis for repeated measures outcomes. Age, sex, volume on preoperative imaging, tumor location (central/peripheral), degree of endophytic component (1%-100%), and tumor axis position were used as potential predictors to develop the model. Forty-five patients (11.4%) were found to have a benign mass postoperatively. A classification tree has been developed which can predict the risk of benign disease with an accuracy of 88.9% (95% CI: 85.3 to 91.8). The significant prognostic factors in the classification tree are tumor volume, degree of endophytic component and symptoms at diagnosis. As an example of its utilization, a renal mass with a volume of < 5.67 cm3 that is < 45% endophytic has a 52.6% chance of having benign pathology. Conversely, a renal mass with a volume ≥ 5.67 cm3 that is ≥ 35% endophytic has only a 5.3% possibility of being benign. A classification tree to predict the risk of benign disease in small renal masses has been developed to aid the clinician when deciding on treatment strategies for small renal masses.

[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'.

A Study on the Correlation of Pertrochanteric Osteoporotic Fracture Severity with the Severity of Osteoporosis.

PubMed

Hayer, Prabhnoor Singh; Deane, Anit Kumar Samuel; Agrawal, Atul; Maheshwari, Rajesh; Juyal, Anil

2016-04-01

Osteoporosis is a metabolic bone disease caused by progressive bone loss. It is characterized by low Bone Mineral Density (BMD) and structural deterioration of bone tissue leading to bone fragility and increased risk of fractures. When classifying a fracture, high reliability and validity are crucial for successful treatment. Furthermore, a classification system should include severity, method of treatment, and prognosis for any given fracture. Since it is known that treatment significantly influences prognosis, a classification system claiming to include both would be desirable. Since there is no such classification system, which includes both the fracture type and the osteoporosis severity, we tried to find a correlation between fracture severity and osteoporosis severity. The aim of the study was to evaluate whether the AO/ASIF fracture classification system, which indicates the severity of fractures, has any relationship with the bone mineral status in patients with primary osteoporosis. We hypothesized that fracture severity and severity of osteoporosis should show some correlation. An observational analytical study was conducted over a period of one year during which 49 patients were included in the study at HIMS, SRH University, Dehradun. The osteoporosis status of all the included patients with a pertrochanteric fracture was documented using a DEXA scan and T-Score (BMD) was calculated. All patients had a trivial trauma. All the fractures were classified as per AO/ASIF classification. Pearson Correlation between BMD and fracture type was calculated. Data was entered on Microsoft Office Excel version 2007 and Interpretation and analysis of obtained data was done using summary statistics. Pearson Correlation between BMD and fracture type was calculated using the SPSS software version 22.0. The average age of the patients included in the study was 71.2 years and the average bone mineral density was -4.9. The correlation between BMD and fracture type was calculated and the r-values obtained was 0.180, which showed low a correlation and p-value was 0.215, which was insignificant. Statistically the pertrochanteric fracture configuration as per AO Classification does not correlate with the osteoporosis severity of the patient.

42 CFR 412.513 - Patient classification system.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 42 Public Health 2 2010-10-01 2010-10-01 false Patient classification system. 412.513 Section 412... Long-Term Care Hospitals § 412.513 Patient classification system. (a) Classification methodology. CMS...-DRGs. (1) The classification of a particular discharge is based, as appropriate, on the patient's age...

42 CFR 412.513 - Patient classification system.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 42 Public Health 2 2011-10-01 2011-10-01 false Patient classification system. 412.513 Section 412... Long-Term Care Hospitals § 412.513 Patient classification system. (a) Classification methodology. CMS...-DRGs. (1) The classification of a particular discharge is based, as appropriate, on the patient's age...

Otitis Media Diagnosis for Developing Countries Using Tympanic Membrane Image-Analysis.

PubMed

Myburgh, Hermanus C; van Zijl, Willemien H; Swanepoel, DeWet; Hellström, Sten; Laurent, Claude

2016-03-01

Otitis media is one of the most common childhood diseases worldwide, but because of lack of doctors and health personnel in developing countries it is often misdiagnosed or not diagnosed at all. This may lead to serious, and life-threatening complications. There is, thus a need for an automated computer based image-analyzing system that could assist in making accurate otitis media diagnoses anywhere. A method for automated diagnosis of otitis media is proposed. The method uses image-processing techniques to classify otitis media. The system is trained using high quality pre-assessed images of tympanic membranes, captured by digital video-otoscopes, and classifies undiagnosed images into five otitis media categories based on predefined signs. Several verification tests analyzed the classification capability of the method. An accuracy of 80.6% was achieved for images taken with commercial video-otoscopes, while an accuracy of 78.7% was achieved for images captured on-site with a low cost custom-made video-otoscope. The high accuracy of the proposed otitis media classification system compares well with the classification accuracy of general practitioners and pediatricians (~64% to 80%) using traditional otoscopes, and therefore holds promise for the future in making automated diagnosis of otitis media in medically underserved populations.

Otitis Media Diagnosis for Developing Countries Using Tympanic Membrane Image-Analysis

PubMed Central

Myburgh, Hermanus C.; van Zijl, Willemien H.; Swanepoel, DeWet; Hellström, Sten; Laurent, Claude

2016-01-01

Background Otitis media is one of the most common childhood diseases worldwide, but because of lack of doctors and health personnel in developing countries it is often misdiagnosed or not diagnosed at all. This may lead to serious, and life-threatening complications. There is, thus a need for an automated computer based image-analyzing system that could assist in making accurate otitis media diagnoses anywhere. Methods A method for automated diagnosis of otitis media is proposed. The method uses image-processing techniques to classify otitis media. The system is trained using high quality pre-assessed images of tympanic membranes, captured by digital video-otoscopes, and classifies undiagnosed images into five otitis media categories based on predefined signs. Several verification tests analyzed the classification capability of the method. Findings An accuracy of 80.6% was achieved for images taken with commercial video-otoscopes, while an accuracy of 78.7% was achieved for images captured on-site with a low cost custom-made video-otoscope. Interpretation The high accuracy of the proposed otitis media classification system compares well with the classification accuracy of general practitioners and pediatricians (~ 64% to 80%) using traditional otoscopes, and therefore holds promise for the future in making automated diagnosis of otitis media in medically underserved populations. PMID:27077122

Using electronic patient records to discover disease correlations and stratify patient cohorts.

PubMed

Roque, Francisco S; Jensen, Peter B; Schmock, Henriette; Dalgaard, Marlene; Andreatta, Massimo; Hansen, Thomas; Søeby, Karen; Bredkjær, Søren; Juul, Anders; Werge, Thomas; Jensen, Lars J; Brunak, Søren

2011-08-01

Electronic patient records remain a rather unexplored, but potentially rich data source for discovering correlations between diseases. We describe a general approach for gathering phenotypic descriptions of patients from medical records in a systematic and non-cohort dependent manner. By extracting phenotype information from the free-text in such records we demonstrate that we can extend the information contained in the structured record data, and use it for producing fine-grained patient stratification and disease co-occurrence statistics. The approach uses a dictionary based on the International Classification of Disease ontology and is therefore in principle language independent. As a use case we show how records from a Danish psychiatric hospital lead to the identification of disease correlations, which subsequently can be mapped to systems biology frameworks.

Fuzzy logic algorithm for quantitative tissue characterization of diffuse liver diseases from ultrasound images.

PubMed

Badawi, A M; Derbala, A S; Youssef, A M

1999-08-01

Computerized ultrasound tissue characterization has become an objective means for diagnosis of liver diseases. It is difficult to differentiate diffuse liver diseases, namely cirrhotic and fatty liver by visual inspection from the ultrasound images. The visual criteria for differentiating diffused diseases are rather confusing and highly dependent upon the sonographer's experience. This often causes a bias effects in the diagnostic procedure and limits its objectivity and reproducibility. Computerized tissue characterization to assist quantitatively the sonographer for the accurate differentiation and to minimize the degree of risk is thus justified. Fuzzy logic has emerged as one of the most active area in classification. In this paper, we present an approach that employs Fuzzy reasoning techniques to automatically differentiate diffuse liver diseases using numerical quantitative features measured from the ultrasound images. Fuzzy rules were generated from over 140 cases consisting of normal, fatty, and cirrhotic livers. The input to the fuzzy system is an eight dimensional vector of feature values: the mean gray level (MGL), the percentile 10%, the contrast (CON), the angular second moment (ASM), the entropy (ENT), the correlation (COR), the attenuation (ATTEN) and the speckle separation. The output of the fuzzy system is one of the three categories: cirrhosis, fatty or normal. The steps done for differentiating the pathologies are data acquisition and feature extraction, dividing the input spaces of the measured quantitative data into fuzzy sets. Based on the expert knowledge, the fuzzy rules are generated and applied using the fuzzy inference procedures to determine the pathology. Different membership functions are developed for the input spaces. This approach has resulted in very good sensitivities and specificity for classifying diffused liver pathologies. This classification technique can be used in the diagnostic process, together with the history information, laboratory, clinical and pathological examinations.