ArticleScandinavian journal of trauma, resuscitation and emergency medicine2020
Real-time AI prediction for major adverse cardiac events in emergency department patients with chest pain.
Article in Scandinavian journal of trauma, resuscitation and emergency medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 4 of them syntheses that pooled it.
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Who cites it
33 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- A meta-analysis of the diagnostic test accuracy of artificial intelligence predicting emergency department dispositions.BMC medical informatics and decision making · 2025Pooled it
- Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review.BMC medicine · 2024Pooled it
- Influence of artificial intelligence on the work design of emergency department clinicians a systematic literature review.BMC health services research · 2022Pooled it
- Applications of machine learning to undifferentiated chest pain in the emergency department: A systematic review.PloS one · 2021Pooled it
- Using artificial intelligence to predict adverse outcomes in emergency department patients with hyperglycemic crises in real time.BMC endocrine disorders · 2023Trial
- Evaluating the Efficacy of CPS, HEART and TIMI Score in Emergency Department Patients with Non-Traumatic Chest Pain: A Pilot Study.Medical sciences (Basel, Switzerland) · 2026Article
- Research trends and ethical perspectives on explainable artificial intelligence in emergency medicine: a bibliometric analysis.Scandinavian journal of trauma, resuscitation and emergency medicine · 2026Review
- Diagnostic Accuracy of Artificial Intelligence in Predicting Admission Status, Intensive Care Requirements, and Mortality in the Emergency Department: A Systematic Review and Meta-Analysis.Medical journal of the Islamic Republic of Iran · 2026Review
- The Utility of Troponin in Predicting Cardiac Dysfunction in Pediatric Patients: A Meta-Analysis.Cureus · 2025Review
- Artificial Intelligence (AI) and Emergency Medicine: Balancing Opportunities and Challenges.JMIR medical informatics · 2025Review
- Prognostic Significance of AI-Enhanced ECG for Emergency Department Patients.Diagnostics (Basel, Switzerland) · 2025Article
- Machine Learning in Predicting Cardiac Events for ESRD Patients: A Framework for Clinical Decision Support.Diagnostics (Basel, Switzerland) · 2025Article
- Adaptable graph neural networks design to support generalizability for clinical event prediction.Journal of biomedical informatics · 2025Article
- Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management.Advances in therapy · 2025Review
- Clinical and epidemiological characteristics of influenza and SARS-CoV-2 virus among patients with acute febrile illness in selected sites of Ethiopia 2021-2022.Frontiers in public health · 2025Article
- Getting to the HEART of Major Adverse Cardiac Events.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2024Article
- Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions.Journal of medical Internet research · 2024Article
- Implementing Explainable Machine Learning Models for Practical Prediction of Early Neonatal Hypoglycemia.Diagnostics (Basel, Switzerland) · 2024Article
- Self-Administration of Aspirin After Chest Pain for the Prevention of Premature Cardiovascular Mortality in the United States: A Population-Based Analysis.Journal of the American Heart Association · 2024Article
- Prediction of major adverse cardiac events in the emergency department using an artificial neural network with a systematic grid search.International journal of emergency medicine · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
backgroundA big-data-driven and artificial intelligence (AI) with machine learning (ML) approach has never been integrated with the hospital information system (HIS) for predicting major adverse cardiac events (MACE) in patients with chest pain in the emergency department (ED). Therefore, we conducted the present study to clarify it.
methodsIn total, 85,254 ED patients with chest pain in three hospitals between 2009 and 2018 were identified. We randomized the patients into a 70%/30% split for ML model training and testing. We used 14 clinical variables from their electronic health records to construct a random forest model with the synthetic minority oversampling technique preprocessing algorithm to predict acute myocardial infarction (AMI) < 1 month and all-cause mortality < 1 month. Comparisons of the predictive accuracies among random forest, logistic regression, support-vector clustering (SVC), and K-nearest neighbor (KNN) models were also performed.
resultsPredicting MACE using the random forest model produced areas under the curves (AUC) of 0.915 for AMI < 1 month and 0.999 for all-cause mortality < 1 month. The random forest model had better predictive accuracy than logistic regression, SVC, and KNN. We further integrated the AI prediction model with the HIS to assist physicians with decision-making in real time. Validation of the AI prediction model by new patients showed AUCs of 0.907 for AMI < 1 month and 0.888 for all-cause mortality < 1 month.
conclusionsAn AI real-time prediction model is a promising method for assisting physicians in predicting MACE in ED patients with chest pain. Further studies to evaluate the impact on clinical practice are warranted.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.