End-to-end Clinical Event Extraction from Chinese Electronic Health Record

TL;DR

This paper presents an end-to-end model for extracting clinical events from Chinese electronic health records, achieving moderate accuracy and recall, and demonstrating effectiveness in a medical text mining competition.

Contribution

The study introduces an end-to-end event extraction approach tailored for Chinese medical texts, improving output formatting and extracting key attributes through pre-training and fine-tuning.

Findings

Accuracy of 0.4511 on test set

Recall of 0.3928 on test set

Second place in a clinical event mining competition

Abstract

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).