Heterogeneous readmission prediction with hierarchical effect decomposition and regularization

TL;DR

This paper introduces hierNest, a hierarchical modeling framework that improves hospital readmission risk prediction by leveraging the hierarchical structure of diagnoses, enhancing accuracy and interpretability.

Contribution

The paper presents a novel hierarchical modeling approach with structured regularization tailored for heterogeneous EHR data, improving prediction and interpretability.

Findings

HierNest outperforms existing methods in simulation studies.

It achieves higher accuracy with small subgroup sample sizes.

Application to Medicare data demonstrates practical effectiveness.

Abstract

Accurately predicting hospital readmission risks using electronic health records (EHRs) is critical for effective patient management and healthcare resource allocation. Patient populations in health systems are highly heterogeneous across different primary diagnoses, necessitating tailored yet interpretable prediction models. We propose a hierarchical modeling framework incorporating hierarchical nested re-parameterization and structured regularization methods, which we call hierNest. Specifically, our approach leverages the inherent hierarchical structure present in primary diagnoses and groupings of these diagnoses into major diagnostic categories. Our methodology facilitates information borrowing across related patient subgroups and preserves interpretability at different hierarchical levels. Simulation studies demonstrate superior predictive accuracy of the proposed method, particularly with small subgroup sample sizes and varying degrees of hierarchical effects. We apply our methods to a large EHR dataset comprising Medicare patients.