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Wu J, Lin Y, Li P, Hu Y, Zhang L, Kong G. Predicting Prolonged Length of ICU Stay through Machine Learning. Diagnostics (Basel) 2021; 11:diagnostics11122242. [PMID: 34943479 PMCID: PMC8700580 DOI: 10.3390/diagnostics11122242] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 11/22/2021] [Accepted: 11/24/2021] [Indexed: 12/12/2022] Open
Abstract
This study aimed to construct machine learning (ML) models for predicting prolonged length of stay (pLOS) in intensive care units (ICU) among general ICU patients. A multicenter database called eICU (Collaborative Research Database) was used for model derivation and internal validation, and the Medical Information Mart for Intensive Care (MIMIC) III database was used for external validation. We used four different ML methods (random forest, support vector machine, deep learning, and gradient boosting decision tree (GBDT)) to develop prediction models. The prediction performance of the four models were compared with the customized simplified acute physiology score (SAPS) II. The area under the receiver operation characteristic curve (AUROC), area under the precision-recall curve (AUPRC), estimated calibration index (ECI), and Brier score were used to measure performance. In internal validation, the GBDT model achieved the best overall performance (Brier score, 0.164), discrimination (AUROC, 0.742; AUPRC, 0.537), and calibration (ECI, 8.224). In external validation, the GBDT model also achieved the best overall performance (Brier score, 0.166), discrimination (AUROC, 0.747; AUPRC, 0.536), and calibration (ECI, 8.294). External validation showed that the calibration curve of the GBDT model was an optimal fit, and four ML models outperformed the customized SAPS II model. The GBDT-based pLOS-ICU prediction model had the best prediction performance among the five models on both internal and external datasets. Furthermore, it has the potential to assist ICU physicians to identify patients with pLOS-ICU risk and provide appropriate clinical interventions to improve patient outcomes.
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Affiliation(s)
- Jingyi Wu
- National Institute of Health Data Science, Peking University, Beijing 100191, China; (J.W.); (L.Z.)
- Advanced Institute of Information Technology, Peking University, Hangzhou 311215, China;
| | - Yu Lin
- Department of Medicine and Therapeutics, LKS Institute of Health Science, The Chinese University of Hong Kong, Hong Kong, China;
| | - Pengfei Li
- Advanced Institute of Information Technology, Peking University, Hangzhou 311215, China;
| | - Yonghua Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China;
- Medical Informatics Center, Peking University, Beijing 100191, China
| | - Luxia Zhang
- National Institute of Health Data Science, Peking University, Beijing 100191, China; (J.W.); (L.Z.)
- Advanced Institute of Information Technology, Peking University, Hangzhou 311215, China;
- Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing 100034, China
| | - Guilan Kong
- National Institute of Health Data Science, Peking University, Beijing 100191, China; (J.W.); (L.Z.)
- Advanced Institute of Information Technology, Peking University, Hangzhou 311215, China;
- Correspondence: ; Tel.: +86-18710098511
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