摘要
Abstract
Objective:To develop a machine-learning-based predictive model for diagnosing tuberculous pleurisy(TBP)to improve clinical diagnostic accuracy.Methods:We retrospectively collected clinical data of 523 pleural effusion patients(375 with TBP and 148 with non-TBP)admitted in Xi'an Chest Hospital between January 2020 and December 2021.Fifteen indicators,including adenosine deaminase(ADA),tuberculosis infection T-cell spot test(T-SPOT.TB),and C-reactive protein(CRP),were incorporated.Seven machine learning algorithms,including random forest,support vector machine,and neural network,were employed to construct predictive models.Model performances were evaluated using 5-fold cross-validation.Feature importance was analyzed using SHapley Additive exPlanations(SHAP).Results:The model developed with Neural Network demonstrated optimal performance,achieving an area under the curve(AUC)of 0.932 on the test set,with an accuracy of 88.6％,precision of 94.4％,and recall rates of 89.3％.SHAP analysis identified ADA(SHAP value=0.12～0.18)and T-SPOT.TB(SHAP value=0.10～0.15)as two most significant predictors,with a notable synergistic effect(P＜0.001).Conclusion:The Neural Network machine learning model developed in this study exhibited excellent diagnostic performance.Through interpretable analysis,key predictive factors and their interactions were elucidated,providing a novel tool for precise diagnosis of TBP.This model can assist clinical decision-making,particularly for cases in the"gray zone"under conventional diagnostic criteria.关键词
结核/胸膜炎/诊断,计算机辅助/模型,统计学/人工智能Key words
Tuberculosis/Pleurisy/Diagnosis,computer-assisted/Models,statistical/Artificial intelligence algorithms分类
临床医学
