BIOMAT Database Logo BIOMAT Database Logo

Machine Learning Based Risk Prediction for Major Adverse Cardiovascular Events. 2021

Michael Schrempf, and Diether Kramer, and Stefanie Jauk, and Sai P K Veeranki, and Werner Leodolter, and Peter P Rainer
Steiermärkische Krankenanstaltengesellschaft m. b. H., Graz, Austria.

BACKGROUND Patients with major adverse cardiovascular events (MACE) such as myocardial infarction or stroke suffer from frequent hospitalizations and have high mortality rates. By identifying patients at risk at an early stage, MACE can be prevented with the right interventions. OBJECTIVE The aim of this study was to develop machine learning-based models for the 5-year risk prediction of MACE. METHODS The data used for modelling included electronic medical records of more than 128,000 patients including 29,262 patients with MACE. A feature selection based on filter and embedded methods resulted in 826 features for modelling. Different machine learning methods were used for modelling on the training data. RESULTS A random forest model achieved the best calibration and discriminative performance on a separate test data set with an AUROC of 0.88. CONCLUSIONS The developed risk prediction models achieved an excellent performance in the test data. Future research is needed to determine the performance of these models and their clinical benefit in prospective settings.

UI MeSH Term Description Entries
D009203 Myocardial Infarction NECROSIS of the MYOCARDIUM caused by an obstruction of the blood supply to the heart (CORONARY CIRCULATION). Cardiovascular Stroke,Heart Attack,Myocardial Infarct,Cardiovascular Strokes,Heart Attacks,Infarct, Myocardial,Infarction, Myocardial,Infarctions, Myocardial,Infarcts, Myocardial,Myocardial Infarctions,Myocardial Infarcts,Stroke, Cardiovascular,Strokes, Cardiovascular
D011446 Prospective Studies Observation of a population for a sufficient number of persons over a sufficient number of years to generate incidence or mortality rates subsequent to the selection of the study group. Prospective Study,Studies, Prospective,Study, Prospective
D006760 Hospitalization The confinement of a patient in a hospital. Hospitalizations
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000069550 Machine Learning A type of ARTIFICIAL INTELLIGENCE that enable COMPUTERS to independently initiate and execute LEARNING when exposed to new data. Transfer Learning,Learning, Machine,Learning, Transfer
D057286 Electronic Health Records Media that facilitate transportability of pertinent information concerning patient's illness across varied providers and geographic locations. Some versions include direct linkages to online CONSUMER HEALTH INFORMATION that is relevant to the health conditions and treatments related to a specific patient. Electronic Health Record Data,Electronic Medical Record,Electronic Medical Records,Computerized Medical Record,Computerized Medical Records,Electronic Health Record,Medical Record, Computerized,Medical Records, Computerized,Health Record, Electronic,Health Records, Electronic,Medical Record, Electronic,Medical Records, Electronic
D018570 Risk Assessment The qualitative or quantitative estimation of the likelihood of adverse effects that may result from exposure to specified health hazards or from the absence of beneficial influences. (Last, Dictionary of Epidemiology, 1988) Assessment, Risk,Benefit-Risk Assessment,Risk Analysis,Risk-Benefit Assessment,Health Risk Assessment,Risks and Benefits,Analysis, Risk,Assessment, Benefit-Risk,Assessment, Health Risk,Assessment, Risk-Benefit,Benefit Risk Assessment,Benefit-Risk Assessments,Benefits and Risks,Health Risk Assessments,Risk Analyses,Risk Assessment, Health,Risk Assessments,Risk Benefit Assessment,Risk-Benefit Assessments

Related Publications

Michael Schrempf, and Diether Kramer, and Stefanie Jauk, and Sai P K Veeranki, and Werner Leodolter, and Peter P Rainer
Risk Prediction of Major Adverse Cardiovascular Events Occurrence Within 6 Months After Coronary Revascularization: Machine Learning Study.
April 2022, JMIR medical informatics,
Michael Schrempf, and Diether Kramer, and Stefanie Jauk, and Sai P K Veeranki, and Werner Leodolter, and Peter P Rainer
Predicting Major Adverse Cardiovascular Events Following Carotid Endarterectomy Using Machine Learning.
October 2023, Journal of the American Heart Association,
Michael Schrempf, and Diether Kramer, and Stefanie Jauk, and Sai P K Veeranki, and Werner Leodolter, and Peter P Rainer
Machine learning prediction of postoperative major adverse cardiovascular events in geriatric patients: a prospective cohort study.
September 2022, BMC anesthesiology,
Michael Schrempf, and Diether Kramer, and Stefanie Jauk, and Sai P K Veeranki, and Werner Leodolter, and Peter P Rainer
Machine learning prediction of major adverse cardiac events after elective bariatric surgery.
January 2024, Surgical endoscopy,
Michael Schrempf, and Diether Kramer, and Stefanie Jauk, and Sai P K Veeranki, and Werner Leodolter, and Peter P Rainer
Machine Learning Models for Cardiovascular Disease Events Prediction.
July 2022, Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference,
Michael Schrempf, and Diether Kramer, and Stefanie Jauk, and Sai P K Veeranki, and Werner Leodolter, and Peter P Rainer
Attention-Based Deep Learning Model for Prediction of Major Adverse Cardiovascular Events in Peritoneal Dialysis Patients.
February 2024, IEEE journal of biomedical and health informatics,
Michael Schrempf, and Diether Kramer, and Stefanie Jauk, and Sai P K Veeranki, and Werner Leodolter, and Peter P Rainer
Radiomics-based machine learning models in STEMI: a promising tool for the prediction of major adverse cardiac events.
July 2023, European radiology,
Michael Schrempf, and Diether Kramer, and Stefanie Jauk, and Sai P K Veeranki, and Werner Leodolter, and Peter P Rainer
Machine learning algorithms to predict major adverse cardiovascular events in patients with diabetes.
September 2023, Computers in biology and medicine,
Michael Schrempf, and Diether Kramer, and Stefanie Jauk, and Sai P K Veeranki, and Werner Leodolter, and Peter P Rainer
Pre-existing and machine learning-based models for cardiovascular risk prediction.
April 2021, Scientific reports,
Michael Schrempf, and Diether Kramer, and Stefanie Jauk, and Sai P K Veeranki, and Werner Leodolter, and Peter P Rainer
MultiGML: Multimodal graph machine learning for prediction of adverse drug events.
September 2023, Heliyon,
Copied contents to your clipboard!