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Comparison of logistic regression and machine learning methods for predicting postoperative delirium in elderly patients: A retrospective study

文献类型：期刊论文

作者Song, Yu-Xiang2,3; Yang, Xiao-Dong1; Luo, Yun-Gen2,3; Ouyang, Chun-Lei3; Yu, Yao3; Ma, Yu-Long3; Li, Hao3; Lou, Jing-Sheng3; Liu, Yan-Hong3; Chen, Yi-Qiang1
刊名CNS NEUROSCIENCE & THERAPEUTICS
出版日期2022-10-11
页码10
关键词aged delirium machine learning nomograms risk assessment
ISSN号1755-5930
DOI10.1111/cns.13991
英文摘要Aims To compare the performance of logistic regression and machine learning methods in predicting postoperative delirium (POD) in elderly patients. Method This was a retrospective study of perioperative medical data from patients undergoing non-cardiac and non-neurology surgery over 65 years old from January 2014 to August 2019. Forty-six perioperative variables were used to predict POD. A traditional logistic regression and five machine learning models (Random Forest, GBM, AdaBoost, XGBoost, and a stacking ensemble model) were compared by the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, and precision. Results In total, 29,756 patients were enrolled, and the incidence of POD was 3.22% after variable screening. AUCs were 0.783 (0.765-0.8) for the logistic regression method, 0.78 for random forest, 0.76 for GBM, 0.74 for AdaBoost, 0.73 for XGBoost, and 0.77 for the stacking ensemble model. The respective sensitivities for the 6 aforementioned models were 74.2%, 72.2%, 76.8%, 63.6%, 71.6%, and 67.4%. The respective specificities for the 6 aforementioned models were 70.7%, 99.8%, 96.5%, 98.8%, 96.5%, and 96.1%. The respective precision values for the 6 aforementioned models were 7.8%, 52.3%, 55.6%, 57%, 54.5%, and 56.4%. Conclusions The optimal application of the logistic regression model could provide quick and convenient POD risk identification to help improve the perioperative management of surgical patients because of its better sensitivity, fewer variables, and easier interpretability than the machine learning model.
资助项目National Key Research and Development Program of China[2018YFC2001901]
WOS研究方向Neurosciences & Neurology ; Pharmacology & Pharmacy
语种英语
WOS记录号WOS:000865651300001
出版者WILEY
源URL[http://119.78.100.204/handle/2XEOYT63/19802]  
专题中国科学院计算技术研究所期刊论文
通讯作者Chen, Yi-Qiang; Cao, Jiang-Bei; Mi, Wei-Dong
作者单位1.Chinese Acad Sci, Inst Comp Technol, 6 Kexueyuan South Rd, Beijing 100190, Peoples R China
2.Chinese Peoples Liberat Army, Med Sch, Beijing, Peoples R China
3.Chinese Peoples Liberat Army Gen Hosp, Dept Anesthesiol, Med Ctr 1, 28 Fuxing Rd, Beijing 100853, Peoples R China
推荐引用方式
GB/T 7714		 Song, Yu-Xiang,Yang, Xiao-Dong,Luo, Yun-Gen,et al. Comparison of logistic regression and machine learning methods for predicting postoperative delirium in elderly patients: A retrospective study[J]. CNS NEUROSCIENCE & THERAPEUTICS,2022:10.
APA Song, Yu-Xiang.,Yang, Xiao-Dong.,Luo, Yun-Gen.,Ouyang, Chun-Lei.,Yu, Yao.,...&Mi, Wei-Dong.(2022).Comparison of logistic regression and machine learning methods for predicting postoperative delirium in elderly patients: A retrospective study.CNS NEUROSCIENCE & THERAPEUTICS,10.
MLA Song, Yu-Xiang,et al."Comparison of logistic regression and machine learning methods for predicting postoperative delirium in elderly patients: A retrospective study".CNS NEUROSCIENCE & THERAPEUTICS (2022):10.

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来源：计算技术研究所

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