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- Title
Learning decision thresholds for risk stratification models from aggregate clinician behavior.
- Authors
Patel, Birju S; Steinberg, Ethan; Pfohl, Stephen R; Shah, Nigam H
- Abstract
Using a risk stratification model to guide clinical practice often requires the choice of a cutoff-called the decision threshold-on the model's output to trigger a subsequent action such as an electronic alert. Choosing this cutoff is not always straightforward. We propose a flexible approach that leverages the collective information in treatment decisions made in real life to learn reference decision thresholds from physician practice. Using the example of prescribing a statin for primary prevention of cardiovascular disease based on 10-year risk calculated by the 2013 pooled cohort equations, we demonstrate the feasibility of using real-world data to learn the implicit decision threshold that reflects existing physician behavior. Learning a decision threshold in this manner allows for evaluation of a proposed operating point against the threshold reflective of the community standard of care. Furthermore, this approach can be used to monitor and audit model-guided clinical decision making following model deployment.
- Subjects
MEDICAL personnel; IMPLICIT learning; PHYSICIANS; CARDIOVASCULAR diseases
- Publication
Journal of the American Medical Informatics Association, 2021, Vol 28, Issue 10, p2258
- ISSN
1067-5027
- Publication type
journal article
- DOI
10.1093/jamia/ocab159