摘要
目的建立利用术前指标基于机器学习算法预测腹部手术后死亡风险模型。方法选择我院2015年6月至2018年12月全麻下行腹部手术后死亡患者50例,根据手术类型和年龄,1∶3比例随机匹配同时间段内150例行腹部手术后康复出院患者。将200例患者资料随机分为训练数据集(n=140)和测试数据集(n=60)。利用术前指标(一般资料各指标、麻醉访视资料各指标和术前检查检验指标)基于AdaBoost、GBDT、LR、SVM四种机器学习算法建立预测腹部手术后死亡风险的模型,并在测试数据集中对模型进行评价。结果利用术前指标基于四种机器学习算法,预测腹部手术后死亡风险模型的受试者工作特征曲线下面积分别为0.796、0.794、0.846、0.781,均大于0.7;不同模型之间的受试者工作特征曲线下面积比较差异无统计学意义(P>0.05)。结论成功建立了利用术前指标基于机器学习算法预测腹部手术后死亡风险的模型。
Objective To establish the model for predicting the mortality risk after abdominal surgery using preoperative indices based on different machine learning algorithms.Methods Fifty patients died after abdominal surgery with general anesthesia from June 2015 to December 2018 in our hospital were enrolled in the study.Based on the types of surgery and age of dead patients,150 patients who were discharged from hospital upon recovery postoperatively were randomly selected from our database as control cases with a ratio of 1∶3.The total dataset of 200 patients was randomly divided into training dataset(n=140)and testing dataset(n=60).Preoperative indices(each index of baseline characteristics,each index of anesthesia interview information and indices of preoperative examination)were used to develop the model for predicting the mortality risk after abdominal surgery based on four machine learning algorithms AdaBoost,GBDT,LR,and SVM,and the model was evaluated in the testing dataset.Results The area under the receiver operating characteristic curves of models developed using preoperative index based on AdaBoost,GBDT,LR,and SVM for predicting the postoperative mortality risk were 0.796,0.794,0.846 and 0.781,respectively.There were no significant differences in area under the receiver operating characteristic curves among different models(P>0.05).Conclusion The model for predicting mortality risk after abdominal surgery using preoperative indicators based on different machine learning algorithms is successfully established.
作者
支鸿羽
辜梦月
李雨捷
杨智勇
钟坤华
陈芋文
张矩
易斌
鲁开智
Zhi Hongyu;Gu Mengyue;Li Yujie;Yang Zhiyong;Zhong Kunhua;Chen Yuwen;Zhang Ju;Yi Bin;Lu Kaizhi(Department of Anesthesiology,First Affiliated Hospital,Military Medical University of the Army(Third Military Medical University),Chongqing 400038,China;Chongqing Institute of Green and Intelligent Technology,Chinese Academy of Sciences,Chongqing 400714,China)
出处
《中华麻醉学杂志》
CAS
CSCD
北大核心
2019年第11期1287-1290,共4页
Chinese Journal of Anesthesiology
基金
国家重点研发计划(2018YFC0116701)。
关键词
人工智能
机器学习
预测
死亡
手术后并发症
Artificial intelligence
Machine learning
Forecasting
Death
Postoperative complications
