Heterogeneous readmission prediction with hierarchical effect decomposition and regularization
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866908981015871488 |
|---|---|
| author | Jiang, Ziren Huo, Lingfeng Hou, Jue Vaughan-Sarrazin, Mary Smith, Maureen A. Huling, Jared D. |
| author_facet | Jiang, Ziren Huo, Lingfeng Hou, Jue Vaughan-Sarrazin, Mary Smith, Maureen A. Huling, Jared D. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_19569 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Heterogeneous readmission prediction with hierarchical effect decomposition and regularization Jiang, Ziren Huo, Lingfeng Hou, Jue Vaughan-Sarrazin, Mary Smith, Maureen A. Huling, Jared D. Methodology 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. |
| title | Heterogeneous readmission prediction with hierarchical effect decomposition and regularization |
| topic | Methodology |
| url | https://arxiv.org/abs/2603.19569 |