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Application effect of machine learning models in predicting intensive care unit length of stay in adult patients: a systematic review and Meta-analysis
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Yu-jia ZHU1, 2, Yue MA1, 2, Ling-gen GAO3, Hong-wei WANG4, Yan YUAN1, Qun-xia MAO1
Modern Preventive Medicine | 2026, 53(9) : 1574 - 1578
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Modern Preventive Medicine | 2026, 53(9): 1574-1578
• Epidemiology and Statistical Methods •
Application effect of machine learning models in predicting intensive care unit length of stay in adult patients: a systematic review and Meta-analysis
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Yu-jia ZHU1, 2, Yue MA1, 2, Ling-gen GAO3, Hong-wei WANG4, Yan YUAN1, Qun-xia MAO1
Affiliations
  • National Research Institute for Family Planning, Social Medicine Center, Beijing, 100081, China
Published: 2026-05-10 doi: 10.20043/j.cnki.MPM.202508086
Outline
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Objective

To systematically evaluate the application effects of different types of machine learning (ML) models in predicting intensive care unit length of stay (ICULOS) in adult patients.

Methods

By searching China National Knowledge Infrastructure (CNKI), Wanfang Database, and English databases PubMed and Web of Science, we collected studies on machine learning to predict ICULOS in adult patients published from the establishment of the database to October 2025, screened the included literature, and performed Meta-analysis using RStudio.

Results

A total of 40 articles were included, among which the three most frequently used models were random forest (RF), extreme gradient boosting (XGBoost), and gradient boosting machine (GBM) models. The overall prediction performance was good, with a combined area under curve (AUC) of 0.82 (95% CI: 0.79-0.85). Among the prediction models for ICULOS in adult patients with cardiovascular-related diseases, XGBoost had the best prediction effect (AUC=0.88, 95% CI: 0.86-0.93). Among the prediction models for ICULOS in adult patients with central nervous system-related diseases, mixed models showed the best predictive performance (AUC=0.90). In the prediction models of ICULOS of adult patients with other diseases and undifferentiated disease types, RF had the best prediction effect (AUCother specific diseases=0.89, 95% CI: 0.84-0.94; AUCundifferentiated diseases=0.95).

Conclusion

ML showed good performance in predicting ICULOS of adult patients. In the prediction of ICULOS of adult patients with different diseases, the appropriate ML model should be selected in combination with the specific disease and data structure.

Intensive care unit  /  Length of stay  /  Machine learning  /  Prediction model  /  Meta-analysis
朱昱嘉, 马跃, 高凌根, 王宏伟, 袁艳, 茅群霞. 基于机器学习模型预测成年患者重症监护室住院时间的应用效果——系统综述和Meta分析. 现代预防医学, 2026 , 53 (9) : 1574 -1578 . DOI: 10.20043/j.cnki.MPM.202508086
Yu-jia ZHU, Yue MA, Ling-gen GAO, Hong-wei WANG, Yan YUAN, Qun-xia MAO. Application effect of machine learning models in predicting intensive care unit length of stay in adult patients: a systematic review and Meta-analysis[J]. Modern Preventive Medicine, 2026 , 53 (9) : 1574 -1578 . DOI: 10.20043/j.cnki.MPM.202508086
Year 2026 volume 53 Issue 9
25
14
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doi: 10.20043/j.cnki.MPM.202508086
  • Receive Date:2025-08-08
  • Online Date:2026-11-02
  • Published:2026-05-10
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  • Received:2025-08-08
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    National Research Institute for Family Planning, Social Medicine Center, Beijing, 100081, China
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表12种不同金属材料的力学参数
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Family
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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