期刊
APPLIED SCIENCES-BASEL
卷 12, 期 21, 页码 -出版社
MDPI
DOI: 10.3390/app122110824
关键词
venous thromboembolism (VTE); deep learning; information extraction; electronic medical record (EMR); joint extraction
类别
资金
- National Natural Science Foundation of China [82160347]
- Yunnan Key Laboratory of Smart City in Cyberspace Security [202102AE090031]
This study proposes a joint extraction model of Chinese electronic medical records based on deep learning, which effectively improves the effect of medical information extraction and determines the risk factors of venous thrombosis through rule reasoning.
Aiming at the problems of heavy workload of medical staff in the process of venous thrombosis prevention and treatment, error evaluation, missed evaluation, and inconsistent evaluation, we propose a joint extraction model of Chinese electronic medical records based on deep learning. The approach was to first construct the handshake annotation, then use bidirectional encoder representations from transformers (BERT) as the word vector embedding, then use the bidirectional long short-term memory network (BiLSTM) to extract the contextual features, and then integrate the contextual information into the process of normalizing the word vector. Experiments show that our proposed method achieves 93.3% and 94.3% of entity and relation F1 on the constructed electronic medical record dataset, which effectively improves the effect of medical information extraction. At the same time, the venous thromboembolism (VTE) risk factors extracted from the electronic medical record were used to judge the risk factors of venous thrombosis by means of rule reasoning. Compared with the assessment of clinicians on the Wells and Geneva scales, the accuracy rates of 84.7% and 86.1% were obtained.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据