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Open AccessArticle
A Comparison of Interpretable Machine Learning Approaches to Identify Outpatient Clinical Phenotypes Predictive of First Acute Myocardial Infarction
by
Matthew Hodgman
Matthew Hodgman 1,*
,
Cristian Minoccheri
Cristian Minoccheri 1
,
Michael Mathis
Michael Mathis 2,
Emily Wittrup
Emily Wittrup 1 and
Kayvan Najarian
Kayvan Najarian 1,3,4,5,6
1
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
2
Department of Anesthesiology, University of Michigan, Ann Arbor, MI 48109, USA
3
Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI 48109, USA
4
Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Ann Arbor, MI 48109, USA
5
Department of Emergency Medicine, University of Michigan, Ann Arbor, MI 48109, USA
6
Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA
*
Author to whom correspondence should be addressed.
Diagnostics 2024, 14(16), 1741; https://doi.org/10.3390/diagnostics14161741 (registering DOI)
Submission received: 15 June 2024
/
Revised: 30 July 2024
/
Accepted: 6 August 2024
/
Published: 10 August 2024
Abstract
Background: Acute myocardial infarctions are deadly to patients and burdensome to healthcare systems. Most recorded infarctions are patients’ first, occur out of the hospital, and often are not accompanied by cardiac comorbidities. The clinical manifestations of the underlying pathophysiology leading to an infarction are not fully understood and little effort exists to use explainable machine learning to learn predictive clinical phenotypes before hospitalization is needed. Methods: We extracted outpatient electronic health record data for 2641 case and 5287 matched-control patients, all without pre-existing cardiac diagnoses, from the Michigan Medicine Health System. We compare six different interpretable, feature extraction approaches, including temporal computational phenotyping, and train seven interpretable machine learning models to predict the onset of first acute myocardial infarction within six months. Results: Using temporal computational phenotypes significantly improved the model performance compared to alternative approaches. The mean cross-validation test set performance exhibited area under the receiver operating characteristic curve values as high as 0.674. The most consistently predictive phenotypes of a future infarction include back pain, cardiometabolic syndrome, family history of cardiovascular diseases, and high blood pressure. Conclusions: 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.
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MDPI and ACS Style
Hodgman, M.; Minoccheri, C.; Mathis, M.; Wittrup, E.; Najarian, K.
A Comparison of Interpretable Machine Learning Approaches to Identify Outpatient Clinical Phenotypes Predictive of First Acute Myocardial Infarction. Diagnostics 2024, 14, 1741.
https://doi.org/10.3390/diagnostics14161741
AMA Style
Hodgman M, Minoccheri C, Mathis M, Wittrup E, Najarian K.
A Comparison of Interpretable Machine Learning Approaches to Identify Outpatient Clinical Phenotypes Predictive of First Acute Myocardial Infarction. Diagnostics. 2024; 14(16):1741.
https://doi.org/10.3390/diagnostics14161741
Chicago/Turabian Style
Hodgman, Matthew, Cristian Minoccheri, Michael Mathis, Emily Wittrup, and Kayvan Najarian.
2024. "A Comparison of Interpretable Machine Learning Approaches to Identify Outpatient Clinical Phenotypes Predictive of First Acute Myocardial Infarction" Diagnostics 14, no. 16: 1741.
https://doi.org/10.3390/diagnostics14161741
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