Risk stratification of chest pain in the emergency department using artificial intelligence applied to electrocardiograms
CP-AI, a fully automated neural network classifier, demonstrated superior performance in the prediction of 7-day major cardiovascular diagnoses for patients presenting with acute chest pain compared with conventional models trained on demographics and cardiac biomarkers. CP-AI may standardise and expedite risk stratification of patients presenting to the ED with chest pain.
Authors
- Aaron D. Aguirre (ORCID: https://orcid.org/0000-0002-5509-1646)
- Márton Kolossváry (ORCID: https://orcid.org/0000-0002-5570-991X)
- Ridwan Alam (ORCID: https://orcid.org/0000-0002-4332-4051)
- Raimon Padrós-Valls (ORCID: https://orcid.org/0000-0001-7321-8973)
- Michael T. Lu (ORCID: https://orcid.org/0000-0003-4696-9610)
- Julian S. Haimovich (ORCID: https://orcid.org/0000-0003-4346-6241)
Institutions
- Obuda University (HU)
- Harvard University (US)
- Gottsegen National Cardiovascular Center (HU)
- University of California San Diego (US)
- Massachusetts General Hospital (US)
- Center for Systems Biology (US)
- Mass General Brigham (US)
- Massachusetts Institute of Technology (US)
Publication Details
- Journal
- Open Heart
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1136/openhrt-2025-003343
- Citations
- 3
- Primary Topic
- Acute Myocardial Infarction Research
- Type
- article
- Field-Weighted Citation Impact
- 4.16
Funders
- Foundation for the National Institutes of Health
- Magyar Tudományos Akadémia
- National Institutes of Health