Machine learning for patient risk stratification: standing on, or looking over, the shoulders of clinicians?

• Published in: NPJ digital medicine Vol. 4; no. 1; p. 62
• Main Authors: Beaulieu-Jones, Brett K., Yuan, William, Brat, Gabriel A., Beam, Andrew L., Weber, Griffin, Ruffin, Marshall, Kohane, Isaac S.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 30.03.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 692/53/2423; 692/700/1750; Benchmarks; Biomedicine; Biotechnology; Digital technology; Health informatics; Health risk assessment; Health risks; Machine learning; Medicine; Medicine & Public Health
• ISSN: 2398-6352; 2398-6352
• DOI: 10.1038/s41746-021-00426-3

Machine learning can help clinicians to make individualized patient predictions only if researchers demonstrate models that contribute novel insights, rather than learning the most likely next step in a set of actions a clinician will take. We trained deep learning models using only clinician-initiated, administrative data for 42.9 million admissions using three subsets of data: demographic data only, demographic data and information available at admission, and the previous data plus charges recorded during the first day of admission. Models trained on charges during the first day of admission achieve performance close to published full EMR-based benchmarks for inpatient outcomes: inhospital mortality (0.89 AUC), prolonged length of stay (0.82 AUC), and 30-day readmission rate (0.71 AUC). Similar performance between models trained with only clinician-initiated data and those trained with full EMR data purporting to include information about patient state and physiology should raise concern in the deployment of these models. Furthermore, these models exhibited significant declines in performance when evaluated over only myocardial infarction (MI) patients relative to models trained over MI patients alone, highlighting the importance of physician diagnosis in the prognostic performance of these models. These results provide a benchmark for predictive accuracy trained only on prior clinical actions and indicate that models with similar performance may derive their signal by looking over clinician’s shoulders—using clinical behavior as the expression of preexisting intuition and suspicion to generate a prediction. For models to guide clinicians in individual decisions, performance exceeding these benchmarks is necessary.