摘要
Abstract
Objective To establish the optimum predictive model of systemic lupus erythematosus(SLE) and database for the standardized interpretation of the autoantibodies test report by data mining,so as to im-prove the diagnostic efficiency.Methods Autoantibodies test results of the 8 904 subjects who were detected at first time and had a definite diagnosis were collected from January 2015 to September 2016,including 668 cases of SLE patients,1 279 cases of other autoimmune disease(AID)patients and 6 957 cases of non-AID pa-tients.ROC curve analysis was used to screen valuable indexes for diagnosis of SLE from age,sex and 16 kinds of autoantibodies,followed by application of decision tree,Logistic regression and artificial neural network (ANN)to establish predictive models of SLE respectively.The optimal model was selected,and the extended database of performance indicators such as posterior probability,misdiagnosis rate and missed diagnosis rate was established for interpretation of the antibodies test report.Results According to the analysis of ROC curve,age,gender,ANA,SSA,nRNP/Sm,Ro-52,Histone,Nuclesome,Rib· P,ds-DNA,Sm,SSB and AMA-M2 showed application value in different degrees(P<0.01).The Logistic model was better than the other two models,as well as any single antibody test,and the difference was statistically significant(P<0.05).Con-clusion The Logistic model established by combining the antibodies with the sex and age factors and the pre-diction performance index database established according to the local prevalence,could effectively improve the diagnostic efficiency of the disease.关键词
系统性红斑狼疮/自身抗体谱/数据挖掘/预测模型Key words
systemic lupus erythematosus/autoantibodies/data mining/prediction model分类
临床医学
