Automating venous thromboembolism risk assessment: a dual-branch deep learning method using electronic medical records - Yang, Jianhua; He, Jianfeng; Zhang, Hongjiang (Frontiers in Medicine, 2023)
Abstract Background:Venous thromboembolism(VTE) is a prevalent cardiovascular disease. Although risk assessment and preventive measures are effective, manual assessment is inefficient and covers a small population in clinical practice. Hence,it is necessary to explore intelligent methods for VTE risk assessment. Methods: The Padua scale has been widely used in VTE risk assessment, and we divided its assessment into disease category judgment and comprehensive clinical information judgment according to the characteristics of the Padua scale. We proposed a dual-branch deep learning(DB-DL) assessment method. First, in the disease category branch, we propose a deep learning-based Padua Disease Category Classification Model (PDCM) for determining patients' Padua disease categories by considering patients' diagnosis, symptoms, and symptom weights. In the branch of comprehensive clinical information, we use the Chinese lexical analysis(LAC) word separation technique, combined with professional corpus and rules, to extract and judge the comprehensive clinical factors in the electronic medical record (EMR). Results:We validated the accuracy of the method with the Padua assessment results of 7690 Chinese clinical EMRs. First, our proposed method allows for a fully automated assessment and the average time to assess 1 patient is only 0.37 seconds. Compared to the gold standard, our method has an AUC value of 0.883 ,a specificity value of 0.957 and a sensitivity value of 0.816 for assessing the Padua risk patient class. Conclusion: Our DB-DL assessment method automates VTE risk assessment, thereby addressing the challenges of time-consuming evaluation and limited population coverage. Thus, this method is highly clinically valuable.