心外科手术患者重症监护室住院时间预测模型研究  

Prediction Model of Intensive Care Unit Length of Stay for Patients with Cardiac Surgery

作　　者：张平 吴念悦 张浩天 李功利 刘加林 李科[1] 无 ZHANG Ping;WU Nianyue;ZHANG Haotian;LI Gongli;LIU Jialin;LI Ke;无(School of Life Science&Technology,University of Electronic Science&Technology of China,Chengdu 610054;West China Medical School,Sichuan University,Chengdu 610041;The People's Hospital of Pingshan in Sichuan Province,Yibin Sichuan 645353)

机构地区：[1]电子科技大学生命科学与技术学院,成都610054 [2]四川大学华西医学院,成都610041 [3]四川省屏山县人民医院,四川宜宾645353

出　　处：《电子科技大学学报》2022年第4期500-505,共6页Journal of University of Electronic Science and Technology of China

基　　金：四川省科技支撑计划(2020YSF0546,21ZDYF3601,21ZDYF2028)。

摘　　要：心外科患者重症监护室ICU住院时间的影响因素分析和预测有利于住院患者的早期干预和成本控制,对心脏外科患者的治疗和护理具有重要意义。本文使用重症监护数据库MIMIC-IV作为实验数据集,纳入7 567名患者数据,采用最小绝对收缩选择算子Lasso从126个影响因子中筛选出41个重要预测因子。基于梯度增强决策树GBDT算法构建了心外科重症监护室住院时间预测模型。实验结果显示,训练全部预测因子的GBDT模型平均准确率为0.688,高于传统逻辑回归LR算法平均准确率0.603,基于筛选出的重要预测因子的GBDT算法与基于全体因子的GBDT算法在最终平均准确率上效果相同,说明该方法可优化数据采集,准确预测住院时间,为临床决策支持系统提供算法支撑。The analysis and prediction of influencing factors of length of stay in intensive care unit(ICU) of cardiac surgery patients is conducive to the early intervention and cost control of inpatients, and is of great significance to the treatment and nursing of cardiac surgery patients. This paper uses the intensive care database medical information mart for intensive care IV(MIMIC-IV) as the experimental data set, 7 567 patients were included. 41 important predictors were selected from 126 influencing factors by least absolute shrinkage and selection operator(Lasso). This paper constructs a prediction model of length of stay in cardiac surgery intensive care unit based on gradient enhanced decision tree(GBDT) algorithm. The experimental results show that under the condition of training all predictors, the average accuracy of GBDT model is 0.688 higher than that of traditional logistic regression algorithm, which is 0.603. The GBDT algorithm with the selected important predictors has the same effect on the final average accuracy as that with all factors, which shows that this method can optimize data collection, accurately predict length of stay in ICU, and provide algorithm support for clinical decision support system.

关 键 词：心脏手术 重症监护室 住院时间 机器学习 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]