Fast and interpretable mortality risk scores for critical care patients.
Journal:
Journal of the American Medical Informatics Association : JAMIA
PMID:
39873685
Abstract
OBJECTIVE: Prediction of mortality in intensive care unit (ICU) patients typically relies on black box models (that are unacceptable for use in hospitals) or hand-tuned interpretable models (that might lead to the loss in performance). We aim to bridge the gap between these 2 categories by building on modern interpretable machine learning (ML) techniques to design interpretable mortality risk scores that are as accurate as black boxes.