AIMC Topic: Predictive Value of Tests

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Utilization of Artificial Intelligence in Echocardiography.

Circulation journal : official journal of the Japanese Circulation Society Jun 29, 2019
Echocardiography has a central role in the diagnosis and management of cardiovascular disease. Precise and reliable echocardiographic assessment is required for clinical decision-making. Even if the development of new technologies (3-dimentional echo...
Diffusion of Innovation Echocardiography Image Interpretation, Computer-Assisted Diagnosis, Computer-Assisted Humans Deep Learning Machine Learning Predictive Value of Tests Forecasting Workflow Reproducibility of Results Cardiovascular Diseases
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A Machine Learning-Based Approach for the Prediction of Acute Coronary Syndrome Requiring Revascularization.

Journal of medical systems Jun 28, 2019
The aim of this study is to predict acute coronary syndrome (ACS) requiring revascularization in those patients presenting early-stage angina-like symptom using machine learning algorithms. We obtained data from 2344 ACS patients, who required revasc...
Acute Coronary Syndrome Algorithms Myocardial Revascularization Female Risk Assessment Humans Male ROC Curve Machine Learning Predictive Value of Tests
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Identification of a Multiplex Biomarker Panel for Hypertrophic Cardiomyopathy Using Quantitative Proteomics and Machine Learning.

Molecular & cellular proteomics : MCP Jun 26, 2019
Hypertrophic cardiomyopathy (HCM) is defined by pathological left ventricular hypertrophy (LVH). It is the commonest inherited cardiac condition and a significant number of high risk cases still go undetected until a sudden cardiac death (SCD) event....
Mutation Cardiomyopathy, Hypertrophic Hypertrophy, Left Ventricular Sarcomeres Peptides Severity of Illness Index Case-Control Studies Proteomics Predictive Value of Tests Prospective Studies Aged Biomarkers Young Adult Female Adult Humans Male Machine Learning Middle Aged Phenotype
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Leveraging Machine Learning Techniques to Forecast Patient Prognosis After Percutaneous Coronary Intervention.

JACC. Cardiovascular interventions Jun 26, 2019
OBJECTIVES: This study sought to determine whether machine learning can be used to better identify patients at risk for death or congestive heart failure (CHF) rehospitalization after percutaneous coronary intervention (PCI).
Treatment Outcome Humans Predictive Value of Tests Heart Failure Machine Learning Registries Decision Support Techniques Male Patient Readmission Percutaneous Coronary Intervention Clinical Decision-Making Middle Aged Minnesota Coronary Artery Disease Risk Assessment Female Reproducibility of Results Time Factors Hospital Mortality Risk Factors Aged
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Neural networks versus Logistic regression for 30 days all-cause readmission prediction.

Scientific reports Jun 26, 2019
Heart failure (HF) is one of the leading causes of hospital admissions in the US. Readmission within 30 days after a HF hospitalization is both a recognized indicator for disease progression and a source of considerable financial burden to the health...
Neural Networks, Computer Machine Learning Algorithms Humans ROC Curve Heart Failure Logistic Models Female Probability United States Aged Patient Readmission Predictive Value of Tests Male False Positive Reactions Area Under Curve Middle Aged Risk Deep Learning
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Gastroenterologist-Level Identification of Small-Bowel Diseases and Normal Variants by Capsule Endoscopy Using a Deep-Learning Model.

Gastroenterology Jun 25, 2019
BACKGROUND & AIMS: Capsule endoscopy has revolutionized investigation of the small bowel. However, this technique produces a video that is 8-10 hours long, so analysis is time consuming for gastroenterologists. Deep convolutional neural networks (CNN...
Predictive Value of Tests Image Interpretation, Computer-Assisted Observer Variation Capsule Endoscopy Intestine, Small Intestinal Diseases Gastroenterologists Reproducibility of Results Deep Learning China Clinical Competence Diagnosis, Differential Humans Prognosis Retrospective Studies
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Scoring of Coronary Artery Disease Characteristics on Coronary CT Angiograms by Using Machine Learning.

Radiology Jun 25, 2019
Background Coronary CT angiography contains prognostic information but the best method to extract these data remains unknown. Purpose To use machine learning to develop a model of vessel features to discriminate between patients with and without subs...
Predictive Value of Tests Coronary Angiography Coronary Artery Disease Image Interpretation, Computer-Assisted Coronary Vessels Young Adult Adult Male Middle Aged Severity of Illness Index Computed Tomography Angiography Risk Factors Humans Aged Aged, 80 and over Machine Learning Female Reproducibility of Results
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Machine Learning Prediction of Response to Cardiac Resynchronization Therapy: Improvement Versus Current Guidelines.

Circulation. Arrhythmia and electrophysiology Jun 20, 2019
BACKGROUND: Cardiac resynchronization therapy (CRT) has significant nonresponse rates. We assessed whether machine learning (ML) could predict CRT response beyond current guidelines.
Echocardiography Middle Aged Risk Assessment Disease Progression Time Factors Retrospective Studies Risk Factors Clinical Decision-Making Aged Humans Female Male Machine Learning Progression-Free Survival Ventricular Function, Left Recovery of Function Decision Support Techniques Practice Guidelines as Topic Heart Transplantation Stroke Volume Predictive Value of Tests Heart-Assist Devices Ohio Patient Selection Baltimore Heart Failure Cardiac Resynchronization Therapy
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Automatic Segmentation, Detection, and Diagnosis of Abdominal Aortic Aneurysm (AAA) Using Convolutional Neural Networks and Hough Circles Algorithm.

Cardiovascular engineering and technology Jun 19, 2019
PURPOSE: An abdominal aortic aneurysm (AAA) is known as a cardiovascular disease involving localized deformation (swelling or enlargement) of aorta occurring between the renal and iliac arteries. AAA would jeopardize patients' lives due to its ruptur...
Radiographic Image Interpretation, Computer-Assisted Automation Aorta, Abdominal Computed Tomography Angiography Aortography Prognosis Reproducibility of Results Case-Control Studies Predictive Value of Tests Humans Neural Networks, Computer Aortic Aneurysm, Abdominal
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Using natural language processing of clinical text to enhance identification of opioid-related overdoses in electronic health records data.

Pharmacoepidemiology and drug safety Jun 19, 2019
PURPOSE: To enhance automated methods for accurately identifying opioid-related overdoses and classifying types of overdose using electronic health record (EHR) databases.
Natural Language Processing Analgesics, Opioid Opioid-Related Disorders Datasets as Topic Drug Overdose Self-Injurious Behavior Humans Electronic Health Records Risk Heroin Washington Sensitivity and Specificity Predictive Value of Tests
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