基于机器学习的结直肠手术部位感染预测模型建立

Establishment of predictive model for surgical site infection following colorectal surgery based on machine learning

目的尝试利用机器学习建立结直肠手术后手术部位感染(surgical site infection,SSI)预测模型。方法采用机器学习算法对杜克大学外部感染控制监测网登记的结直肠手术病例历史数据集进行分析建模。将全部数据集的80%作为训练数据集,20%作为测试数据集。为提升模型训练效果,再将全部数据集的90%作为训练数据集,10%作为测试数据集。预测结果与实际病例进行比对,计算模型的灵敏度、特异度、阳性预测值和阴性预测值,以受试者工作特征(receiver operating characteristic,ROC)曲线下面积作为模型参数评价模型预测能力,用比值比(odds ratio,OR)进行检验效度评价,检验水准α=0.05。结果数据集时间为2015年1月15日-2016年6月16日,共有患者7285例,其中234例发生SSI,SSI发生率为3.21%。采用随机森林法建立预测模型,使用全部数据集的90%进行训练,10%进行测试,该模型的灵敏度为76.9%,特异度为59.2%,阳性预测值为3.3%,阴性预测值为99.3%,ROC曲线下面积为0.767[OR=4.84,95%置信区间(1.32,17.74),P=0.02]。结论随机森林法建立的结直肠手术后SSI预测模型具有实现SSI半自动化监测的潜力,但需要更多数据训练提高模型的预测能力,实现临床应用。

Objective To establish a predictive model of surgical site infection(SSI)following colorectal surgery using machine learning.Methods Machine learning algorithm was used to analyze and model with the colorectal data set from Duke Infection Control Outreach Network Surveillance Network.The whole data set was divided into two parts,with 80%as the training data set and 20%as the testing data set.In order to improve the training effect,the whole data set was divided into two parts again,with 90%as the training data set and 10%as the testing data set.The predictive result of the model was compared with the actual infected cases,and the sensitivity,specificity,positive predictive value,and negative predictive value of the model were calculated,the area under receiver operating characteristic(ROC)curve was used to evaluate the predictive capacity of the model,odds ratio(OR)was calculated to tested the validity of evaluation with a significance level of 0.05.Results There were 7285 patients in the whole data set registered from January 15 th,2015 to June 16 th,2016,among whom 234 were SSI cases,with an incidence of SSI of 3.21%.The predictive model was established by random forest algorithm,which was trained by 90%of the whole data set and tested by 10%of that.The sensitivity,specificity,positive predictive value,and negative predictive value of the model were 76.9%,59.2%,3.3%,and99.3%,respectively,and the area under ROC curve was 0.767[OR=4.84,95%confidence interval(1.32,17.74),P=0.02].Conclusion The predictive model of SSI following colorectal surgery established by random forest algorithm has the potential to realize semi-automatic monitoring of SSIs,but more data training should be needed to improve the predictive capacity of the model before clinical application.