AIMC Topic: Acute Coronary Syndrome

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Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram.

Nature communications Aug 7, 2020
Prompt identification of acute coronary syndrome is a challenge in clinical practice. The 12-lead electrocardiogram (ECG) is readily available during initial patient evaluation, but current rule-based interpretation approaches lack sufficient accurac...
Machine Learning Algorithms Female Male Humans Electrocardiography Acute Coronary Syndrome Databases as Topic Reference Standards Middle Aged Hospitals ROC Curve
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Choosing Clinical Variables for Risk Stratification Post-Acute Coronary Syndrome.

Scientific reports Oct 10, 2019
Most risk stratification methods use expert opinion to identify a fixed number of clinical variables that have prognostic significance. In this study our goal was to develop improved metrics that utilize a variable number of input parameters. We firs...
Middle Aged Patient Selection Risk Assessment Acute Coronary Syndrome Female Cohort Studies Humans Risk Factors Registries Machine Learning Aged Percutaneous Coronary Intervention Prognosis Logistic Models Male Follow-Up Studies
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Artificial Intelligence for Diagnosis of Acute Coronary Syndromes: A Meta-analysis of Machine Learning Approaches.

The Canadian journal of cardiology Sep 27, 2019
BACKGROUND: Machine learning (ML) encompasses a wide variety of methods by which artificial intelligence learns to perform tasks when exposed to data. Although detection of myocardial infarction has been facilitated with introduction of troponins, th...
Acute Coronary Syndrome Humans Artificial Intelligence Machine Learning
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Machine Intelligence for Management of Acute Coronary Syndromes: Neural or Nervous Times?

The Canadian journal of cardiology Sep 20, 2019
Humans Artificial Intelligence Machine Learning Acute Coronary Syndrome
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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...
Female Male Risk Assessment ROC Curve Humans Machine Learning Algorithms Predictive Value of Tests Acute Coronary Syndrome Myocardial Revascularization
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Extensive phenotype data and machine learning in prediction of mortality in acute coronary syndrome - the MADDEC study.

Annals of medicine Apr 27, 2019
Investigation of the clinical potential of extensive phenotype data and machine learning (ML) in the prediction of mortality in acute coronary syndrome (ACS). The value of ML and extensive clinical data was analyzed in a retrospective registry stud...
Acute Coronary Syndrome Logistic Models Coronary Angiography Registries Electronic Health Records Female Aged Risk Assessment Comorbidity Male Middle Aged Humans Machine Learning Phenotype Retrospective Studies
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Evidential MACE prediction of acute coronary syndrome using electronic health records.

BMC medical informatics and decision making Apr 9, 2019
BACKGROUND: Major adverse cardiac event (MACE) prediction plays a key role in providing efficient and effective treatment strategies for patients with acute coronary syndrome (ACS) during their hospitalizations. Existing prediction models have limita...
Prognosis Electronic Health Records Hospitalization Predictive Value of Tests Acute Coronary Syndrome Risk Assessment Logistic Models Humans Machine Learning
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Predicting Long-Term Mortality after Acute Coronary Syndrome Using Machine Learning Techniques and Hematological Markers.

Disease markers Jan 30, 2019
INTRODUCTION: Hematological indices including red cell distribution width and neutrophil to lymphocyte ratio are proven to be associated with outcomes of acute coronary syndrome. The usefulness of machine learning techniques in predicting mortality a...
Biomarkers Acute Coronary Syndrome Humans Female Lymphocyte Count Middle Aged Machine Learning Aged Erythrocyte Count Survival Rate Hospital Mortality Male
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Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome.

BMC medical informatics and decision making Jan 9, 2019
BACKGROUND: Main adverse cardiac events (MACE) are essentially composite endpoints for assessing safety and efficacy of treatment processes of acute coronary syndrome (ACS) patients. Timely prediction of MACE is highly valuable for improving the effe...
Humans Acute Coronary Syndrome Neural Networks, Computer Prognosis Electronic Health Records Male Middle Aged Female Deep Learning Models, Theoretical Hospitalization Aged
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Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients.

PloS one Jan 9, 2019
BACKGROUND: Patient with acute coronary syndrome benefits from early revascularization. However, methods for the selection of patients who require urgent revascularization from a variety of patients visiting the emergency room with chest symptoms is ...
Electrocardiography Prognosis Humans Datasets as Topic Chest Pain Diagnosis, Differential Diagnosis, Computer-Assisted Myocardial Revascularization Neural Networks, Computer Predictive Value of Tests Emergency Service, Hospital Acute Coronary Syndrome Decision Making, Computer-Assisted Patient Selection Time Factors Sensitivity and Specificity
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