Classification and extraction of medical clinical trial screening standard texts based on Bi-LSTM and Attention mechanism

被引：3

作者：

Du, Lin ^{[1
]}

Cao, Dong ^{[1
]}

Li, Jinghua ^{[2
]}

Ye, Hui ^{[1
]}

机构：

[1] Guangzhou Univ Chinese Med, Sch Med Informat Engn, Guangzhou, Guangdong, Peoples R China

[2] China Acad Chinese Med Sci, Inst Informat Tradit Chinese Med IITCM, Beijing, Peoples R China

来源：

2020 ASIA CONFERENCE ON GEOLOGICAL RESEARCH AND ENVIRONMENTAL TECHNOLOGY | 2021年 / 632卷

关键词：

D O I：

10.1088/1755-1315/632/5/052088

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

The medical recruitment of human subjects in clinical trials often requires manual comparison of the subject's medical records and clinical trial screening criteria, which is a time-consuming and labor-consuming method to determine candidates. In order to solve this problem, it has a great clinical value to study a method of classifying clinical trial data screening standards automatically. This paper attempts to propose a medical short text classification model of clinical medicine based on BiL-Att (Bi-LSTM + Attention) model. It uses Word2vec prepossessing to get short text vector as the model input. The results showed that the classification effect of BiL-Att model reached the highest Average F1 value of 80.26%.

引用

页数：8