朴素贝叶斯分类器在化疗所致恶心呕吐风险预测模型上的应用  

Establishment of naive Bayes classifier-based risk prediction model for chemotherapyinduced nausea and vomiting

作　　者：曹众平 熊习安 杨群[1,2] CAO Zhongping;XIONG Xi'an;YANG Qun(Clinical Nursing Teaching and Research Section of the Second XiangYa Hospital of Central South University,Changsha 410011,China;Department of Oncology of the Second XiangYa Hospital Central of South University,Changsha 410011 China;Department of Experimental Physics of Institute of High Energy Physics Chinese Academy of Sciences,Beijing 100043,China)

机构地区：[1]中南大学湘雅二医院临床护理教研室,湖南长沙410011 [2]中南大学湘雅二医院肿瘤中心,湖南长沙410011 [3]中国科学院高能物理研究所实验物理中心,北京100043

出　　处：《南方医科大学学报》2021年第4期607-612,共6页Journal of Southern Medical University

基　　金：湖南省科学技术厅面上项目(2020JJ4821)。

摘　　要：目的研究朴素贝叶斯分类器在化疗所致恶性呕吐风险预测模型上的应用,构建基于中国患者的化疗所致恶性呕吐风险预测模型。方法收集2020年7~9月中南大学湘雅二医院肿瘤中心300例住院化疗患者的基本资料和治疗方案,并随访患者出院后临床资料。对治疗方案中的特征和患者个体特征进行相关性分析,对相关性大于0.8的两个特征,分别计算两个特征对模型评价指标曲线下面积的贡献,去除其中贡献相对较小的特征。使用机器学习库scikit-learn中的朴素贝叶斯分类器作为化疗所致恶心呕吐风险预测模型,使用10折分层随机分割交叉验证得到模型最终结果。其中70%的样本用来训练机器学习模型,30%的样本作为测试集用来衡量模型的表现。结果急性化疗所致恶心呕吐风险预测灵敏度为0.83±0.04(95%CI:0.80~0.86),特异度为0.45±0.03(95%CI:0.42~0.47),曲面下面积为0.72±0.04(95%CI:0.69~0.75)。延迟性化疗所致恶心呕吐风险预测灵敏度为0.84±0.01(95%CI:0.83~0.86),特异度为0.48±0.03(95%CI:0.45~0.52),曲面下面积为0.74±0.02(95%CI:0.72~0.77)。结论在本研究中,基于朴素贝叶斯分类器构建了适用于中国肿瘤患者的化疗致所致恶心呕吐风险预测模型,具有较好的预测效果,为化疗致所致恶心呕吐风险预测模型提供新的研究方向和思路。Objective To establish a risk prediction model of chemotherapy-induced nausea and vomiting based on naive Bayes classifier.Methods We collected the basic information,treatment protocols and follow-up data from 300 patients receiving chemotherapy in the Oncology Department of Second Xiangya Hospital from July to September,2020.Correlation analysis was carried out between the potential factors related to nausea and vomiting in the treatment plan and the individual characteristics of the patients.For the two characteristics with a correlation coefficient greater than 0.8,their contribution to the area under curve(AUC)was calculated,and the characteristic with a smaller contribution was removed.The naive Bayes classifier in the machine learning library scikit-learn was used as the prediction model of chemotherapy-induced nausea and vomiting,and 10-fold stratified-shuffled-split cross-validation was used to obtain the final result of the model.The machine learning model was trained using 70%of the samples,and 30%of the samples were used as the test set to assess the performance of the model.Results The sensitivity of the model for predicting the risk of nausea and vomiting due to acute chemotherapy was 0.83±0.04(95%CI:0.80-0.86)with a specificity of 0.45±0.03(95%CI:0.42-0.47)and an AUC of 0.72±0.04(95%CI:0.69-0.75).The sensitivity of the model for predicting the risk of delayed chemotherapy-induced nausea and vomiting was 0.84±0.01(95%CI:0.83-0.86)with a specificity of 0.48±0.03(95%CI:0.45-0.52)and an AUC of 0.74±0.02(95%CI:0.72-0.77).Conclusion The naive Bayes classifier model has a good performance in predicting the risk of chemotherapy-induced nausea and vomiting in Chinese cancer patients.

关 键 词：化疗 恶心 呕吐 预测模型 机器学习 朴素贝叶斯分类器 

分 类 号：R730.5[医药卫生—肿瘤]