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基于机器学习算法建立非心脏手术术后认知功能障碍风险预测模型
Establishment of prediction model for risk of postoperative cognitive dysfunction after non-cardiac surgery based on different machine learning algorithms
- 陆军军医大学学报 2023年45卷第8期 页码：759-764
- 作者机构：
1.400038 重庆, 陆军军医大学(第三军医大学)第一附属医院麻醉科
2.400714 重庆, 中国科学院重庆绿色智能技术研究院
- 作者简介：
[易斌，E-mail：yibin1974@163.com YI Bin, E-mail: yibin1974@163.com
[鲁开智，E-mail：lukaizhi2010@163.com LU Kaizhi, E-mail: lukaizhi2010@163.com
- 基金信息：国家重点研发计划(2018YFC0116702)
DOI：10.16016/j.2097-0927.202301048
中图分类号： R319;R619.9;R749收稿：2023-01-01，
修回：2023-02-15，
纸质出版：2023-04-30
- 稿件说明：
移动端阅览
宋艾璘, 李雨捷, 舒欣, 等. 基于机器学习算法建立非心脏手术术后认知功能障碍风险预测模型[J]. 陆军军医大学学报, 2023,45(8):759-764.
Ailin SONG, Yujie LI, Xin SHU, et al. Establishment of prediction model for risk of postoperative cognitive dysfunction after non-cardiac surgery based on different machine learning algorithms[J]. Journal of Army Medical University, 2023, 45(8): 759-764.
宋艾璘, 李雨捷, 舒欣, 等. 基于机器学习算法建立非心脏手术术后认知功能障碍风险预测模型[J]. 陆军军医大学学报, 2023,45(8):759-764. DOI： 10.16016/j.2097-0927.202301048.
Ailin SONG, Yujie LI, Xin SHU, et al. Establishment of prediction model for risk of postoperative cognitive dysfunction after non-cardiac surgery based on different machine learning algorithms[J]. Journal of Army Medical University, 2023, 45(8): 759-764. DOI： 10.16016/j.2097-0927.202301048.
摘要
目的
2
利用术前指标基于机器学习算法建立预测非心脏手术术后认知功能障碍(postoperative cognitive dysfunction，POCD)的风险模型。
方法
2
采用病例-对照研究设计方案，在我院2014年1月至2019年1月医疗大数据平台上筛选行非心脏手术术后发生POCD的患者，最终纳入92例患者为POCD组，在同样的大数据平台上依照POCD组手术类型和年龄段，按1∶3比例随机匹配同时间段的276例术后未发生POCD且治愈出院的患者为非POCD组。同时，将这368例患者按照7∶3比例随机分为建模组(
n
=259)和验证组(
n
=109)。在数据预处理和特征选择术前临床指标(一般资料、相关评分表、手术相关信息和术前检验指标)之后，基于逻辑回归(logistic regression，LR)、支持向量机(support vector machine，SVM)、决策树(Decision Tree)3种机器学习算法建立非心脏手术POCD的风险预测模型。模型效能通过敏感性、特异性、F1分数、受试者工作特征曲线下面积(area under the receiver operating characteristic curve，AUC)进行评估。
结果
2
3种机器学习算法建立的非心脏手术POCD的预测模型以SVM算法最优，验证组中AUC为0.957(95%
CI
：0.905~1.000)，敏感度和特异度分别为92.6%和98.8%。
结论
2
初步构建出基于机器学习算法建立非心脏手术POCD的预测模型，该模型对POCD有良好的预测能力。
Abstract
Objective
2
To establish a risk model for predicting postoperative cognitive dysfunction (POCD) after non-cardiac surgery using preoperative indicators based on machine learning algorithm.
Methods
2
A case-control study was designed
and conducted on the POCD patients after non-cardiac surgery in the medical big data platform of our hospital from January 2014 to January 2019. Finally
92 patients were included in the POCD group. According to surgical type and age matched of the POCD group
another 276 patients who did not develop POCD after surgery and discharged from the hospital during the same time period from the same big data platform were assigned into the non-POCD group at a ratio of 1∶3. At the same time
these 368 patients were randomly divided into modeling group (
n
=259) and validation group (
n
=109) at a ratio of 7∶3. After data preprocessing and feature selection of preoperative clinical indicators (general data
relevant scoring scales
surgical-related data and results of preoperative laboratory tests)
the risk prediction model of POCD for non-cardiac surgery was established based on 3 machine learning algorithms
that is
logistic regression (LR)
support vector machine (SVM) and Decision Tree. The model efficacy was evaluated by sensitivity
specificity
F1 score
and the area under the receiver operating characteristic curve (AUC).
Results
2
The SVM algorithm was the best model among the 3 machine learning algorithms to predict the risk of POCD after non-cardiac surgery. The AUC value of the model in the validation group was 0.957 (95%
CI
: 0.905~1.000)
with a sensitivity and specificity of 92.6% and 98.8%
respectively.
Conclusion
2
A prediction model of POCD after non-cardiac surgery is successfully established based on machine learning algorithm. This model shows good predictive performance for POCD.
关键词
Keywords
references
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