Machine learning models that integrate cardiac troponin concentrations and clinical features to compute the probability of myocardial infarction outperform current care pathways that use fixed troponin thresholds or risk scores. Adoption of these models could reduce inequalities, prevent unnecessary admissions, and accelerate the diagnosis and treatment of patients with myocardial infarction.
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This is a summary of: Doudesis, D. et al. Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations. Nat. Med. https://doi.org/10.1038/s41591-023-02325-4 (2023).
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Improving the diagnosis of myocardial infarction with machine learning. Nat Med 29, 1070–1071 (2023). https://doi.org/10.1038/s41591-023-02331-6
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DOI: https://doi.org/10.1038/s41591-023-02331-6
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