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Review
. 2025 May 24;17(5):e84761.
doi: 10.7759/cureus.84761. eCollection 2025 May.

The Role of Machine Learning in Predicting Hospital Readmissions Among General Internal Medicine Patients: A Systematic Review

Mukul Sharda  1 , Saujas Sharma  2 , Shaunak Raikar  3 , Nathaniel Verhagen  1 , Janavi Wagle  4 , Ritisha Mathur  5 , Saathvik Gowda  6 , Sharath Kommu  7 , Rupesh Prasad  8 , Sanjay Bhandari  1 , Pinky Jha  1
Affiliations
Review

The Role of Machine Learning in Predicting Hospital Readmissions Among General Internal Medicine Patients: A Systematic Review

Mukul Sharda et al. Cureus. .

Abstract

Hospital readmissions contribute significantly to healthcare costs. While traditional regression models for predicting 30-day readmission risk offer modest accuracy, machine learning (ML) presents an opportunity to capture complex relationships in healthcare data, potentially enhancing predictions. This review assesses the role of ML in predicting 30-day readmissions for general internal medicine admissions in the U.S. Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, a literature search of PubMed (2014-2023) was conducted using the keywords "artificial intelligence," "machine learning," and "readmission." The review focused on ML models predicting readmissions in general internal medicine patients in the U.S. Nine studies were reviewed, covering conditions like acute myocardial infarction (AMI), heart failure (HF), pneumonia (PNA), chronic obstructive pulmonary disease (COPD), and other general internal medicine cases. ML models such as artificial neural networks (ANN), random forests (RF), gradient boosting, logistic regression, and natural language processing (NLP) were used. ANN and RF models outperformed traditional regression methods, while NLP-based approaches showed limited success. Subgroup modeling provided marginal improvements in predictive accuracy. In conclusion, ML offers significant potential for improving 30-day readmission predictions by overcoming the limitations of traditional models. ANN and RF are particularly effective in predicting readmissions in general internal medicine. To advance predictive capabilities, future research should refine NLP, subgroup modeling, and focus on model generalizability, integration of diverse data sources, and the development of explainable AI for clinical adoption. Addressing these challenges could transform healthcare delivery, improve patient outcomes, and reduce costs.

Keywords: artificial intelligence; general internal medicine; machine learning; readmission; us based.

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Conflict of interest statement

Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.

Figures

Figure 1
Figure 1. PRISMA flowchart illustrating the selection process for studies included in the review
PRISMA: Preferred Reporting Items for Systematic reviews and Meta-Analyses
Figure 2
Figure 2. PRISMA risk of bias assessment for the included studies
PRISMA: Preferred Reporting Items for Systematic reviews and Meta-Analyses [9,12-19]
See this image and copyright information in PMC

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