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A machine learning model for diagnosing acute pulmonary embolism and comparison with Wells score,revised Geneva score,and years algorithm

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Chinese Medical Journal 2024年第6期137卷 676-682页

作者： Linfeng Xi Han Kang Mei Deng Wenqing Xu Feiya Xu Qian Gao Wanmu Xie Rongguo Zhang Min Liu Zhenguo Zhai Chen Wang Capital Medical University Beijing 100069China National Center for Respiratory Medicine State Key Laboratory of Respiratory Health and MultimorbidityNational Clinical Research Center for Respiratory DiseasesInstitute of Respiratory MedicineChinese Academy of Medical SciencesDepartment of Pulmonary and Critical Care MedicineCenter of Respiratory MedicineChina-Japan Friendship HospitalBeijing 100029China Institute of Advanced Research Infervision Medical Technology Co.Ltd.Beijing 100025China Department of Radiology China-Japan Friendship HospitalBeijing 100029China Chinese Academy of Medical Sciences and Peking Union Medical College Beijing 100730China Department of Radiology Peking University China-Japan Friendship School of Clinical MedicineBeijing 100191China

Background:Acute pulmonary embolism(APE)is a fatal cardiovascular disease,yet missed diagnosis and misdiagnosis often occur due to non-specific symptoms and signs.A simple,objective technique will help clinicians make a quick and precise *** population studies,machine learning(ML)plays a critical role in characterizing cardiovascular risks,predicting outcomes,and identifying *** work sought to develop an ML model for helping APE diagnosis and compare it against current clinical probability assessment ***:This is a single-center retrospective *** with suspected APE were continuously enrolled and randomly divided into two groups including training and testing sets.A total of 8 ML models,including random forest(RF),Naïve Bayes,decision tree,K-nearest neighbors,logistic regression,multi-layer perceptron,support vector machine,and gradient boosting decision tree were developed based on the training set to diagnose ***,the model with the best diagnostic performance was selected and evaluated against the current clinical assessment strategies,including the Wells score,revised Geneva score,and years ***,the ML model was internally validated to assess the diagnostic performance using receiver operating characteristic(ROC)***:The ML models were constructed using eight clinical features,including D-dimer,cardiac troponin T(cTNT),arterial oxygen saturation,heart rate,chest pain,lower limb pain,hemoptysis,and chronic heart *** eight ML models,the RF model achieved the best performance with the highest area under the curve(AUC)(AUC=0.774).Compared to the current clinical assessment strategies,the RF model outperformed the Wells score(P=0.030)and was not inferior to any other clinical probability assessment *** AUC of the RF model for diagnosing APE onset in internal validation set was ***:Based on RF algorithm,a novel prediction model was finally constructed for AP

关键词： Acute pulmonary embolism Machine learning Wells score Revised Geneva score years algorithm

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