A flexible symbolic regression method for constructing interpretable clinical prediction models

Machine learning (ML) models trained for triggering clinical decision support (CDS) are typically either accurate or interpretable but not both. Scaling CDS to the panoply of clinical use cases while mitigating risks to patients will require many ML models be intuitively interpretable for clinicians...

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Bibliographic Details
Published in:NPJ digital medicine 2023-06, Vol.6 (1), p.107-14, Article 107
Main Authors: La Cava, William G., Lee, Paul C., Ajmal, Imran, Ding, Xiruo, Solanki, Priyanka, Cohen, Jordana B., Moore, Jason H., Herman, Daniel S.
Format: Article
Language:English
Subjects:
639/705/1042
639/705/531
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692/700/139
Biomedicine
Biotechnology
Clinical decision making
Electronic health records
Hypertension
Hypokalemia
Machine learning
Medicine
Medicine & Public Health
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Summary:Machine learning (ML) models trained for triggering clinical decision support (CDS) are typically either accurate or interpretable but not both. Scaling CDS to the panoply of clinical use cases while mitigating risks to patients will require many ML models be intuitively interpretable for clinicians. To this end, we adapted a symbolic regression method, coined the feature engineering automation tool (FEAT), to train concise and accurate models from high-dimensional electronic health record (EHR) data. We first present an in-depth application of FEAT to classify hypertension, hypertension with unexplained hypokalemia, and apparent treatment-resistant hypertension (aTRH) using EHR data for 1200 subjects receiving longitudinal care in a large healthcare system. FEAT models trained to predict phenotypes adjudicated by chart review had equivalent or higher discriminative performance ( p  
ISSN:2398-6352
2398-6352
DOI:10.1038/s41746-023-00833-8