Machine learning models demonstrated a greater Area Under the Curve for predicting 30-day all-cause hospital readmission compared with logistic regression (MD 0.03; 95% CI 0.01-0.05).
Meta-Analysis
Do machine learning algorithms improve the prediction of 30-day hospital readmissions compared to logistic regression in US patients?
Machine learning algorithms, particularly deep learning methods, demonstrated a small but statistically significant improvement in predicting 30-day hospital readmissions compared to traditional logistic regression.
Effect estimate: MD 0.03 (95% CI 0.01-0.05)
Machine learning algorithms are being increasingly used for predicting hospital readmissions. This meta-analysis evaluated the performance of logistic regression (LR) and machine learning (ML) models for the prediction of 30-day hospital readmission among patients in the US. Electronic databases (i.e., Medline, PubMed, and Embase) were searched from January 2015 to December 2019. Only studies in the English language were included. Two reviewers performed studies screening, quality appraisal, and data collection. The quality of the studies was assessed using the Quality in Prognosis Studies (QUIPS) tool. Model performance was evaluated using the Area Under Curve (AUC). A random-effects meta-analysis was performed using STATA 16.0MP. Nine studies were included based on the selection criteria. The most common ML methods applied were deep learning methods, classification trees, and artificial neural networks. Most of the studies had a low risk of bias (8/9). The AUC was greater with ML to predict 30-day all-cause hospital readmission compared with LR Mean Difference (MD): 0.03; 95% Confidence Interval (CI) 0.01–0.05. After sensitivity analysis, deep-learning methods had better performance compared with LR (MD 0.06; 95% CI, 0.04–0.09), followed by neuron networks (MD: 0.03; 95% CI, 0.03–0.03), while the AUCs of the tree-based (MD: 0.02; 95% CI -0.00-0.04) and kernel-based (MD: 0.02; 95% CI 0.02 (−0.13–0.16) methods were no different compared with LR. More than half of the studies evaluated heart failure-related rehospitalization (N = 5). For the readmission prediction among heart failure patients, ML performed better compared with LR, with a mean difference in AUC of 0.04 (95% CI, 0.01–0.07). The leave-one-out sensitivity analysis confirmed the robustness of the findings. Multiple ML methods were used to predict 30-day all-cause hospital readmission. Performance varied across the ML methods, with deep-learning methods showing the best performance over the LR.
Talwar et al. (2023) conducted a meta-analysis in Hospital readmission. Machine learning models vs. Logistic regression was evaluated on Area Under Curve (AUC) for predicting 30-day all-cause hospital readmission (MD 0.03, 95% CI 0.01-0.05). Machine learning models demonstrated a greater Area Under the Curve for predicting 30-day all-cause hospital readmission compared with logistic regression (MD 0.03; 95% CI 0.01-0.05).