Predictive value of artificial intelligence model in diagnosis of venous thromboembolism in lower extremities of trauma patients / 中华创伤杂志

ABSTRACT

Objective:To explore the predictive value of artificial intelligence algorithm model for venous thromboembolism（VTE）in lower extremities of trauma patients.Methods:The data of 15，856 orthopedic inpatients were retrospectively collected from the information system database in Chinese PLA General Hospital from December 1992 to November 2017. The patients were divided according to whether they had thrombosis or not. Data pretreatment and feature extraction were carried out. Four artificial intelligence algorithms including Random Forest（RF），Bayes（Bayes），Decision Tree（DTC）and Gradient Boosting Tree（GBDT）were constructed to evaluate their clinical diagnostic efficacy in VTE. The original data were divided into training set and test set according to the ratio of 8∶2 by random stratified sampling method. By comparing the area under receiver operating characteristic curve（ROC）（AUC），true positive rate（TPR）and accuracy in the above methods，the efficiency of different models in clinical diagnosis of VTE was evaluated. According to the contribution degree of the features in the model，the important features were ranked to screen the independent risk factors of VTE.Results:For RF，Bayes，DTC and GBDT algorithm models，the AUC was 0.89，0.86，0.68，0.71，with the TPR for 0.29，0.44，0.38，0.66 and the accuracy for 0.97，0.94，0.95，0.76，respectively. The RF algorithm model had the highest accuracy and the largest AUC. Analysis of important features of artificial intelligence prediction models for VTE showed that the history of thrombosis was the primary predictor of adverse outcomes. The ranking of important clinical features represented by the RF model showed that the history of thrombosis，enoxaparin sodium injection dose，last glucose measurement and first glucose measurement after surgery were important predictive characteristics of VTE.Conclusions:The RF model has the highest accuracy in risk prediction of VTE in trauma patients，which can provide a reference for the formulation of VTE prevention strategies.