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Machine learning techniques in hospital readmission prediction: A systematic review and bibliometric analysis (2012-2025)

computational-biology
computer-education
data-mining-and-machine-learning
data-science

Abstract

Introduction. This study conducts a comprehensive systematic review and bibliometric analysis of machine learning techniques for hospital readmission prediction, aiming to clarify current methodological practices, clinical focus areas, and future research opportunities.

Methods. Four major databases (Web of Science, Scopus, PubMed, and EBSCO) were searched for English-language articles published between 2012 and 2025. Following PRISMA guidelines, 173 eligible studies were identified. We synthesized evidence across disease categories, modeling methods, sample characteristics, imbalance-handling strategies, and feature selection practices, and conducted keyword co-occurrence analysis to reveal thematic structures in the literature.

Results. Research on readmission prediction has expanded rapidly in recent years, with the majority of studies focusing on heart failure, COPD, and ICU populations. Machine learning models dominate the methodological landscape, particularly logistic regression (115 studies), random forests (106 studies), and boosting-based ensemble algorithms （ 137 studies ）, which appear frequently across the included studies, while deep learning models have increasingly been used to leverage unstructured clinical text. However, 56.07% of studies did not address data imbalance, and 31.79% did not apply feature selection, revealing persistent methodological gaps. Bibliometric analysis shows five major research clusters centered on mortality-readmission interactions, risk-factor exploration, machine learning–based prediction, traditional scoring systems, and disease-specific readmission patterns.

Conclusions. Machine learning has substantially improved hospital readmission prediction, yet its clinical utility remains constrained by insufficient attention to data imbalance, feature dimensionality, and multi-source data integration. Future research should incorporate more advanced AI techniques such as graph neural networks, transfer learning, and multimodal fusion to improve model robustness, interpretability, and generalizability across diverse patient populations.

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