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).
@article{arxiv.2208.09354,
title = {End-to-end Clinical Event Extraction from Chinese Electronic Health Record},
author = {Wei Feng and Ruochen Huang and Yun Yu and Huiting Sun and Yun Liu},
journal= {arXiv preprint arXiv:2208.09354},
year = {2022}
}