An MLI-Guided Framework for Subgroup-Aware Modeling in Electronic Health Records (AdaptHetero)
This addresses the need for more robust and equitable clinical deployment of AI models in healthcare, though it is incremental in applying existing interpretation methods to subgroup modeling.
The paper tackled the problem of using machine learning interpretation to guide subgroup-specific modeling in electronic health records, resulting in improved predictive performance with gains up to 174.39% across subpopulations.
Machine learning interpretation (MLI) has primarily been leveraged to foster clinician trust and extract insights from electronic health records (EHRs), rather than to guide subgroup-specific, operationalizable modeling strategies. To bridge this gap, we propose AdaptHetero, a novel MLI-driven framework that transforms interpretability insights into actionable guidance for tailoring model training and evaluation across subpopulations. Evaluated on three large-scale EHR datasets -- GOSSIS-1-eICU, WiDS, and MIMIC-IV -- AdaptHetero consistently uncovers heterogeneous model behaviors in predicting ICU mortality, in-hospital death, and hidden hypoxemia. Integrating SHAP-based interpretation with unsupervised clustering, AdaptHetero identifies clinically meaningful, subgroup-specific characteristics, improving predictive performance across many subpopulations (with gains up to 174.39 percent) while proactively flagging potential risks in others. These results highlight the framework's promise for more robust, equitable, and context-aware clinical deployment.