Observational studyScientific reports2022
Prehospital diagnostic algorithm for acute coronary syndrome using machine learning: a prospective observational study.
Observational study in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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The trial behind it
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Who cites it
14 citing papers in PubMed, 24 citations in OpenAlex.
- Artificial intelligence in prehospital assessment of acute coronary syndrome: a scoping review.BMC emergency medicine · 2026Article
- Selective classification with machine learning uncertainty estimates improves ACS prediction: a retrospective study in the prehospital setting.Scientific reports · 2026Article
- AI-guided refinement of coronary revascularization need in patients suspected of acute coronary syndrome.European heart journal. Digital health · 2025Article
- Early auxiliary diagnosis model for chest pain triad based on artificial intelligence multimodal fusion.JAMIA open · 2025Article
- Review
- Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management.Advances in therapy · 2025Review
- Prehospital ECG Interpretation Methods for ST-Elevation MI Detection and Catheterization Laboratory Activation: A Systematic Review and Meta-Analysis.Archives of academic emergency medicine · 2025Review
- Prediction of emergency department presentations for acute coronary syndrome using a machine learning approach.Scientific reports · 2024Article
- Preclinical identification of acute coronary syndrome without high sensitivity troponin assays using machine learning algorithms.Scientific reports · 2024Article
- International evaluation of an artificial intelligence-powered electrocardiogram model detecting acute coronary occlusion myocardial infarction.European heart journal. Digital health · 2024Article
- Identifying and Solving Gaps in Pre- and In-Hospital Acute Myocardial Infarction Care in Asia-Pacific Countries.Korean circulation journal · 2023Review
- A Review of the Relationship between the Immune Response, Inflammation, Oxidative Stress, and the Pathogenesis of Sickle Cell Anaemia.Biomedicines · 2023Review
- A bibliometric analysis of acute myocardial infarction in women from 2000 to 2022.Frontiers in cardiovascular medicine · 2023Article
- Artificial intelligence-assisted remote detection of ST-elevation myocardial infarction using a mini-12-lead electrocardiogram device in prehospital ambulance care.Frontiers in cardiovascular medicine · 2022Article
Corrections and comments
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Authors and funding
11 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rapid and precise prehospital recognition of acute coronary syndrome (ACS) is key to improving clinical outcomes. The aim of this study was to investigate a predictive power for predicting ACS using the machine learning-based prehospital algorithm. We conducted a multicenter observational prospective study that included 10 participating facilities in an urban area of Japan. The data from consecutive adult patients, identified by emergency medical service personnel with suspected ACS, were analyzed. In this study, we used nested cross-validation to evaluate the predictive performance of the model. The primary outcomes were binary classification models for ACS prediction based on the nine machine learning algorithms. The voting classifier model for ACS using 43 features had the highest area under the receiver operating curve (AUC) (0.861 [95% CI 0.775-0.832]) in the test score. After validating the accuracy of the model using the external cohort, we repeated the analysis with a limited number of selected features. The performance of the algorithms using 17 features remained high AUC (voting classifier, 0.864 [95% CI 0.830-0.898], support vector machine (radial basis function), 0.864 [95% CI 0.829-0.887]) in the test score. We found that the machine learning-based prehospital algorithms showed a high predictive power for predicting ACS.
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Registered trials
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.