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

Music City Center 

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