A comparison of approaches to improve worst-case predictive model performance over patient subpopulations.

ABSTRACT: Predictive models for clinical outcomes that are accurate on average in a patient population may underperform drastically for some subpopulations, potentially introducing or reinforcing inequities in care access and quality. Model training approaches that aim to maximize worst-case model performance across subpopulations, such as distributionally robust optimization (DRO), attempt to address this problem without introducing additional harms. We conduct a large-scale empirical study of DRO and several variations of standard learning procedures to identify approaches for model development and selection that consistently improve disaggregated and worst-case performance over subpopulations compared to standard approaches for learning predictive models from electronic health records data. In th

SUBMITTER: Pfohl SR 

PROVIDER: S-EPMC8885701 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature