AIMC Topic: Coronary Artery Disease

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Serum cystatin C and neutrophil gelatinase-associated lipocalin in predicting the severity of coronary artery disease in diabetic patients.

Anatolian journal of cardiology Nov 26, 2015
OBJECTIVE: Cystatin C and neutrophil gelatinase-associated lipocalin (NGAL) are biomarkers of renal functions. We evaluated their roles in predicting the severity of coronary artery disease (CAD).
Lipocalin-2 Coronary Artery Disease Lipocalins Diabetes Complications Cross-Sectional Studies Male Middle Aged Glomerular Filtration Rate Diabetes Mellitus Cystatin C Humans Prospective Studies Biomarkers Female Aged
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Creation of a new longitudinal corpus of clinical narratives.

Journal of biomedical informatics Oct 1, 2015
The 2014 i2b2/UTHealth Natural Language Processing (NLP) shared task featured a new longitudinal corpus of 1304 records representing 296 diabetic patients. The corpus contains three cohorts: patients who have a diagnosis of coronary artery disease (C...
Electronic Health Records Female Aged Male Middle Aged Risk Assessment Humans Confidentiality Data Mining Computer Security Vocabulary, Controlled Diabetes Complications Boston Comorbidity Incidence Cohort Studies Narration Natural Language Processing Coronary Artery Disease
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A bifurcation identifier for IV-OCT using orthogonal least squares and supervised machine learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Sep 21, 2015
Intravascular optical coherence tomography (IV-OCT) is an in-vivo imaging modality based on the intravascular introduction of a catheter which provides a view of the inner wall of blood vessels with a spatial resolution of 10-20 μm. Recent studies in...
Image Enhancement Least-Squares Analysis Coronary Artery Disease Supervised Machine Learning Image Interpretation, Computer-Assisted Pattern Recognition, Automated Humans Reproducibility of Results Sensitivity and Specificity Data Interpretation, Statistical Radiography Tomography, Optical Coherence
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Fuzzy Rule-Based Classification System for Assessing Coronary Artery Disease.

Computational and mathematical methods in medicine Sep 13, 2015
The aim of this study was to determine the accuracy of fuzzy rule-based classification that could noninvasively predict CAD based on myocardial perfusion scan test and clinical-epidemiological variables. This was a cross-sectional study in which the ...
Iran Myocardial Perfusion Imaging Models, Cardiovascular Male Middle Aged Fuzzy Logic Coronary Angiography Coronary Artery Disease Humans Machine Learning Computer Simulation Female Aged Adult Cross-Sectional Studies
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Comparison of UMLS terminologies to identify risk of heart disease using clinical notes.

Journal of biomedical informatics Sep 12, 2015
The second track of the 2014 i2b2 challenge asked participants to automatically identify risk factors for heart disease among diabetic patients using natural language processing techniques for clinical notes. This paper describes a rule-based system ...
Humans Electronic Health Records Female Aged Male Narration Natural Language Processing Coronary Artery Disease Confidentiality Data Mining Pattern Recognition, Automated Middle Aged Risk Assessment Comorbidity Incidence Cohort Studies Longitudinal Studies Ohio Terminology as Topic Unified Medical Language System Computer Security Vocabulary, Controlled Diabetes Complications
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A hybrid model for automatic identification of risk factors for heart disease.

Journal of biomedical informatics Sep 12, 2015
Coronary artery disease (CAD) is the leading cause of death in both the UK and worldwide. The detection of related risk factors and tracking their progress over time is of great importance for early prevention and treatment of CAD. This paper describ...
United Kingdom Pattern Recognition, Automated Computer Security Vocabulary, Controlled Middle Aged Risk Assessment Comorbidity Incidence Cohort Studies Longitudinal Studies Narration Natural Language Processing Coronary Artery Disease Confidentiality Data Mining Humans Electronic Health Records Female Aged Male
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Identifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2.

Journal of biomedical informatics Jul 22, 2015
The second track of the 2014 i2b2/UTHealth natural language processing shared task focused on identifying medical risk factors related to Coronary Artery Disease (CAD) in the narratives of longitudinal medical records of diabetic patients. The risk f...
Pattern Recognition, Automated Computer Security Vocabulary, Controlled Diabetes Complications Boston Longitudinal Studies Narration Natural Language Processing Coronary Artery Disease Confidentiality Data Mining Middle Aged Risk Assessment Comorbidity Incidence Cohort Studies Humans Electronic Health Records Female Aged Male
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The role of fine-grained annotations in supervised recognition of risk factors for heart disease from EHRs.

Journal of biomedical informatics Jun 26, 2015
This paper describes a supervised machine learning approach for identifying heart disease risk factors in clinical text, and assessing the impact of annotation granularity and quality on the system's ability to recognize these risk factors. We utiliz...
Female Aged Male Middle Aged Risk Assessment Comorbidity Incidence Cohort Studies Longitudinal Studies Narration Natural Language Processing Humans Electronic Health Records Computer Security Vocabulary, Controlled Diabetes Complications Maryland Coronary Artery Disease Confidentiality Supervised Machine Learning Data Mining Pattern Recognition, Automated
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Fast Computation of Hemodynamic Sensitivity to Lumen Segmentation Uncertainty.

IEEE transactions on medical imaging Jun 16, 2015
Patient-specific blood flow modeling combining imaging data and computational fluid dynamics can aid in the assessment of coronary artery disease. Accurate coronary segmentation and realistic physiologic modeling of boundary conditions are important ...
Algorithms Tomography, X-Ray Computed Hemodynamics Imaging, Three-Dimensional Coronary Angiography Coronary Artery Disease Coronary Vessels Humans Machine Learning
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Annotating risk factors for heart disease in clinical narratives for diabetic patients.

Journal of biomedical informatics May 21, 2015
The 2014 i2b2/UTHealth natural language processing shared task featured a track focused on identifying risk factors for heart disease (specifically, Cardiac Artery Disease) in clinical narratives. For this track, we used a "light" annotation paradigm...
Computer Security Vocabulary, Controlled Diabetes Complications Boston Confidentiality Data Mining Documentation New York Pattern Recognition, Automated Humans Electronic Health Records Female Incidence Cohort Studies Longitudinal Studies Narration Natural Language Processing Coronary Artery Disease Aged Male Middle Aged Risk Assessment Comorbidity
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