A Comparison of Interpretable Machine Learning Approaches to Identify Outpatient Clinical Phenotypes Predictive of First Acute Myocardial Infarction
Computational phenotyping of longitudinal health records can improve classifier performance and identify predictive clinical concepts. State-of-the-art interpretable machine learning approaches can augment acute myocardial infarction risk assessment and prioritize potential risk factors for further investigation and validation.
Authors
- Michael R. Mathis (ORCID: https://orcid.org/0000-0002-9697-2212)
- Cristian Minoccheri (ORCID: https://orcid.org/0000-0001-5910-1066)
- Emily Wittrup (ORCID: https://orcid.org/0000-0002-5314-5903)
- Matthew Hodgman (ORCID: https://orcid.org/0009-0005-3757-4141)
- Kayvan Najarian (ORCID: https://orcid.org/0000-0003-4485-6612)
Institutions
- University of Michigan (US)
Publication Details
- Journal
- Diagnostics
- Published
- 2024-08-10
- DOI
- https://doi.org/10.3390/diagnostics14161741
- Citations
- 1
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.35