Automating venous thromboembolism risk assessment: a dual-branch deep learning method using electronic medical records
BackgroundVenous 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 assess...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-08-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2023.1237616/full |
| _version_ | 1797745403860353024 |
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| author | Jianhua Yang Jianfeng He Hongjiang Zhang |
| author_facet | Jianhua Yang Jianfeng He Hongjiang Zhang |
| author_sort | Jianhua Yang |
| collection | DOAJ |
| description | BackgroundVenous 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.MethodsThe 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 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).ResultsWe validated the accuracy of the method with the Padua assessment results of 7,690 Chinese clinical EMRs. First, our proposed method allows for a fully automated assessment, and the average time to assess one patient is only 0.37 s. Compared to the gold standard, our method has an Area Under Curve (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.ConclusionOur 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. |
| first_indexed | 2024-03-12T15:22:49Z |
| format | Article |
| id | doaj.art-64bf8f5ff4d042a285ff5cff0c6f8152 |
| institution | Directory Open Access Journal |
| issn | 2296-858X |
| language | English |
| last_indexed | 2024-03-12T15:22:49Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj.art-64bf8f5ff4d042a285ff5cff0c6f81522023-08-10T21:31:21ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2023-08-011010.3389/fmed.2023.12376161237616Automating venous thromboembolism risk assessment: a dual-branch deep learning method using electronic medical recordsJianhua Yang0Jianfeng He1Hongjiang Zhang2Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, ChinaFirst People's Hospital of Anning City (Jinfang Branch), Anning, ChinaBackgroundVenous 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.MethodsThe 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 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).ResultsWe validated the accuracy of the method with the Padua assessment results of 7,690 Chinese clinical EMRs. First, our proposed method allows for a fully automated assessment, and the average time to assess one patient is only 0.37 s. Compared to the gold standard, our method has an Area Under Curve (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.ConclusionOur 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.https://www.frontiersin.org/articles/10.3389/fmed.2023.1237616/fullvenous thromboembolismdeep learningelectronic medical recordintelligent assessmentPadua |
| spellingShingle | Jianhua Yang Jianfeng He Hongjiang Zhang Automating venous thromboembolism risk assessment: a dual-branch deep learning method using electronic medical records Frontiers in Medicine venous thromboembolism deep learning electronic medical record intelligent assessment Padua |
| title | Automating venous thromboembolism risk assessment: a dual-branch deep learning method using electronic medical records |
| title_full | Automating venous thromboembolism risk assessment: a dual-branch deep learning method using electronic medical records |
| title_fullStr | Automating venous thromboembolism risk assessment: a dual-branch deep learning method using electronic medical records |
| title_full_unstemmed | Automating venous thromboembolism risk assessment: a dual-branch deep learning method using electronic medical records |
| title_short | Automating venous thromboembolism risk assessment: a dual-branch deep learning method using electronic medical records |
| title_sort | automating venous thromboembolism risk assessment a dual branch deep learning method using electronic medical records |
| topic | venous thromboembolism deep learning electronic medical record intelligent assessment Padua |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2023.1237616/full |
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