Predicting Unplanned Readmission Risk in Patients With Cirrhosis: Complication-Aware Dynamic Classifier Selection Approach
By leveraging the expertise of the most competent classifiers for each patient subgroup, our approach enables interpretable training and dynamic selection of heterogeneous predictive models. This advancement not only improves prediction accuracy but also highlights its considerable potential for clinical applications, facilitating the alignment of diverse patient subgroups with tailored decision-support algorithms.
Authors
- Zhichun Shi (ORCID: https://orcid.org/0000-0003-0793-3172)
- Haolin Wang (ORCID: https://orcid.org/0000-0002-1735-9525)
- Qingpeng Zhang (ORCID: https://orcid.org/0000-0002-6819-0686)
- Kexue Pu (ORCID: https://orcid.org/0000-0002-2339-3316)
- Xiaomei Xu (ORCID: https://orcid.org/0000-0001-9389-9112)
- Linjun Huang
Institutions
- Chengdu Fifth People's Hospital (CN)
- Hong Kong Footwear Federation (CN)
- Chongqing Medical University (CN)
- University of Hong Kong (HK)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2025-07-16
- DOI
- https://doi.org/10.2196/63581
- Citations
- 1
- Primary Topic
- Liver Disease Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.35