Performance of advanced machine learning algorithms overlogistic regression in predicting hospital readmissions: A meta-analysis

Ashna Talwar; Maria A Lopez-Olivo; Yinan Huang; Lin Ying; Rajender R Aparasu

doi:10.1016/j.rcsop.2023.100317

Performance of advanced machine learning algorithms overlogistic regression in predicting hospital readmissions: A meta-analysis

Explor Res Clin Soc Pharm. 2023 Aug 10:11:100317. doi: 10.1016/j.rcsop.2023.100317. eCollection 2023 Sep.

Authors

Ashna Talwar¹, Maria A Lopez-Olivo², Yinan Huang³, Lin Ying⁴, Rajender R Aparasu¹

Affiliations

¹ College of Pharmacy, University of Houston, Houston, TX, USA.
² Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
³ Department of Pharmacy Administration, The University of Mississippi, Oxford, MS, USA.
⁴ Department of Industrial Engineering, University of Houston, Houston, TX, USA.

Abstract

Objectives: 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.

Methods: 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 the Curve (AUC). A random-effects meta-analysis was performed using STATA 16.

Results: Nine studies were included based on the selection criteria. The most common ML techniques were tree-based methods such as boosting and random forest. 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]. Subgroup analyses found that deep-learning methods had a better performance compared with LR (MD 0.06; 95% CI, 0.04-0.09), followed by neural 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 to 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.

Conclusion: 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.

Keywords: Deep learning; Logistic regression; Machine learning; Neuron network; Prediction; Readmission.