A Time-Guided Method for Constructing Combined Medical Event Chains

Medical events, such as diagnostic events, treatment events and examination events, and the relationships between these medical events are of importance in medical research. Moreover, the temporal relationship is one of the most basic medical event relationships. However, people often cannot clearly obtain the relationship between medical events directly from electronic medical records. This defect can lead to inconvenient query construction, inefficient query execution, and poor query performance when queries need to be made for certain diseases or symptoms. In this paper, we propose a method to construct a time-guided medical event chain, which combines medical events of patients with the same disease or symptoms according to the temporal relationship. We use BERT and Bi-LSTM joint encoding to learn contextual information to detect medical event trigger words, and test the effectiveness of our model on MLEE dataset. Finally, we extract medical event chains on the CCKS2020 evaluation dataset. Through the event chain, patients and doctors can more easily understand the trend of the disease and analyze the disease and the treatment effect, and it is helpful for some complex clinical research. Moreover, medical event chain can be easily integrated into existing medical knowledge graph (such as CMeKG) for auxiliary diagnosis.