Machine learning models, particularly artificial neural networks and random forests, outperformed traditional regression methods in predicting 30-day hospital readmissions.
Systematic Review
Do machine learning models improve the prediction of 30-day hospital readmissions in general internal medicine patients compared to traditional regression models?
Machine learning models, particularly artificial neural networks and random forests, demonstrate superior predictive accuracy for 30-day hospital readmissions compared to traditional regression models, highlighting their potential to improve risk stratification.
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.
Sharda et al. (2025) conducted a systematic review in Hospital readmissions in general internal medicine. Machine learning models (e.g., Artificial Neural Networks, Random Forests) vs. Traditional regression-based models was evaluated on Predictive accuracy (AUC/C-statistic) for 30-day hospital readmissions. Machine learning models, particularly artificial neural networks and random forests, outperformed traditional regression methods in predicting 30-day hospital readmissions.