Detection of calibration drift in clinical prediction models to inform model updating.

ABSTRACT: Model calibration, critical to the success and safety of clinical prediction models, deteriorates over time in response to the dynamic nature of clinical environments. To support informed, data-driven model updating strategies, we present and evaluate a calibration drift detection system. Methods are developed for maintaining dynamic calibration curves with optimized online stochastic gradient descent and for detecting increasing miscalibration with adaptive sliding windows. These methods are generalizable to support diverse prediction models developed using a variety of learning algorithms and customizable to address the unique needs of clinical use cases. In both simulation and case studies, our system accurately detected calibration drift. When drift is detected, our system further prov

SUBMITTER: Davis SE 

PROVIDER: S-EPMC8627243 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature