Performance of advanced machine learning algorithms overlogistic regression in predicting hospital readmissions: A meta-analysis
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: Ele...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | Exploratory Research in Clinical and Social Pharmacy |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667276623000987 |
| _version_ | 1797660060369813504 |
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| author | Ashna Talwar Maria A. Lopez-Olivo Yinan Huang Lin Ying Rajender R. Aparasu |
| author_facet | Ashna Talwar Maria A. Lopez-Olivo Yinan Huang Lin Ying Rajender R. Aparasu |
| author_sort | Ashna Talwar |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T18:25:18Z |
| format | Article |
| id | doaj.art-af73eb14aeb1465a8ced1dbd2a70b4f4 |
| institution | Directory Open Access Journal |
| issn | 2667-2766 |
| language | English |
| last_indexed | 2024-03-11T18:25:18Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Exploratory Research in Clinical and Social Pharmacy |
| spelling | doaj.art-af73eb14aeb1465a8ced1dbd2a70b4f42023-10-14T04:45:48ZengElsevierExploratory Research in Clinical and Social Pharmacy2667-27662023-09-0111100317Performance of advanced machine learning algorithms overlogistic regression in predicting hospital readmissions: A meta-analysisAshna Talwar0Maria A. Lopez-Olivo1Yinan Huang2Lin Ying3Rajender R. Aparasu4College of Pharmacy, University of Houston, Houston, TX, USADepartment of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, TX, USADepartment of Pharmacy Administration, The University of Mississippi, Oxford, MS, USADepartment of Industrial Engineering, University of Houston, Houston, TX, USACollege of Pharmacy, University of Houston, Houston, TX, USA; Corresponding author at: Department of Pharmaceutical Health Outcomes and Policy, Geriatrics, UTHealth McGovern Medical School, Health and Biomedical Sciences Building 2 – Office 4052, College of Pharmacy, University of Houston, 4349 Martin Luther King Boulevard, Houston, TX 77204-5047, USA.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.http://www.sciencedirect.com/science/article/pii/S2667276623000987ReadmissionMachine learningLogistic regressionDeep learningPredictionNeuron network |
| spellingShingle | Ashna Talwar Maria A. Lopez-Olivo Yinan Huang Lin Ying Rajender R. Aparasu Performance of advanced machine learning algorithms overlogistic regression in predicting hospital readmissions: A meta-analysis Exploratory Research in Clinical and Social Pharmacy Readmission Machine learning Logistic regression Deep learning Prediction Neuron network |
| title | Performance of advanced machine learning algorithms overlogistic regression in predicting hospital readmissions: A meta-analysis |
| title_full | Performance of advanced machine learning algorithms overlogistic regression in predicting hospital readmissions: A meta-analysis |
| title_fullStr | Performance of advanced machine learning algorithms overlogistic regression in predicting hospital readmissions: A meta-analysis |
| title_full_unstemmed | Performance of advanced machine learning algorithms overlogistic regression in predicting hospital readmissions: A meta-analysis |
| title_short | Performance of advanced machine learning algorithms overlogistic regression in predicting hospital readmissions: A meta-analysis |
| title_sort | performance of advanced machine learning algorithms overlogistic regression in predicting hospital readmissions a meta analysis |
| topic | Readmission Machine learning Logistic regression Deep learning Prediction Neuron network |
| url | http://www.sciencedirect.com/science/article/pii/S2667276623000987 |
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