End-to-end Clinical Event Extraction from Chinese Electronic Health Record
This work addresses medical text processing for clinical event extraction, but it is incremental as it builds on existing methods with modest performance gains.
The paper tackled event extraction from Chinese electronic health records by using an end-to-end model to enhance output formatting, achieving an F1 score of 0.42 on a test set and placing second in a specific conference task.
Event extraction is an important work of medical text processing. According to the complex characteristics of medical text annotation, we use the end-to-end event extraction model to enhance the output formatting information of events. Through pre training and fine-tuning, we can extract the attributes of the four dimensions of medical text: anatomical position, subject word, description word and occurrence state. On the test set, the accuracy rate was 0.4511, the recall rate was 0.3928, and the F1 value was 0.42. The method of this model is simple, and it has won the second place in the task of mining clinical discovery events (task2) in the Chinese electronic medical record of the seventh China health information processing Conference (chip2021).