Heterogeneity-Aware Regression with Variable Selection: Applications to Readmission Prediction

2293 

Contributed Abstract 

Poster 

Wei Wang (1), Angela Bailey (2), Jared Huling (3)

(1) University of Minnesota Twin Cities, N/A, (2) University of Minnesota Twin Cities, United States, (3) University of Minnesota, N/A

Angela Bailey  
University of Minnesota Twin Cities

Jared Huling  
University of Minnesota

Wei Wang  
University of Minnesota Twin Cities

Wei Wang  
University of Minnesota Twin Cities

Readmission prediction is a critical but challenging clinical task, as the inherent relationship between high-dimensional covariates and readmission is complex and heterogeneous. Despite this complexity, models should be interpretable to aid clinicians in understanding an individual's risk prediction. Readmissions are often heterogeneous, as individuals hospitalized for different reasons have materially different subsequent risks of readmission. To allow flexible yet interpretable modeling that accounts for patient heterogeneity, we propose hierarchical-group structure kernels that capture nonlinear and higher-order interactions via functional ANOVA, selecting variables through sparsity-inducing kernel summation, while modeling heterogeneity and allowing variable importance to vary across interactions. Extensive simulations and a Hematologic readmission dataset (N=18,096) demonstrate superior performance across subgroups of patients (AUROC, PRAUC) over the lasso and XGBoost. Additionally, our model provides interpretable insights into variable importance and group heterogeneity.

Readmission Prediction|Heterogeneity|Functional ANOVA|Kernel Methods|Sparsity-Inducing Regularization|

Health Policy Statistics Section

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