Prediction of Venous Thrombosis Chinese Electronic Medical Records Based on Deep Learning and 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.

Automated summary

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.