Artificial intelligence based Chinese clinical trials eligibility criteria classification.
10.7507/1001-5515.202006035
- Author:
Hui ZONG
1
;
Zeyu ZHANG
1
;
Jinxuan YANG
1
;
Jianbo LEI
2
;
Zuofeng LI
3
;
Tianyong HAO
4
;
Xiaoyan ZHANG
1
Author Information
1. School of Life Sciences and Technology, Tongji University, Shanghai 200092, P.R.China.
2. Center for Medical Informatics, Peking University, Beijing 100080, P.R.China.
3. Philips Research China, Shanghai 200072, P.R.China.
4. School of Computer Science, South China Normal University, Guangzhou 510631, P.R.China.
- Publication Type:Journal Article
- Keywords:
artificial intelligence;
clinical trial;
eligibility criteria;
natural language processing;
text classification
- MeSH:
Artificial Intelligence;
China;
Humans;
Language;
Natural Language Processing;
Neural Networks, Computer
- From:
Journal of Biomedical Engineering
2021;38(1):105-110
- CountryChina
- Language:Chinese
-
Abstract:
Subject recruitment is a key component that affects the progress and results of clinical trials, and generally conducted with eligibility criteria (includes inclusion criteria and exclusion criteria). The semantic category analysis of eligibility criteria can help optimizing clinical trials design and building automated patient recruitment system. This study explored the automatic semantic categories classification of Chinese eligibility criteria based on artificial intelligence by academic shared task. We totally collected 38 341 annotated eligibility criteria sentences and predefined 44 semantic categories. A total of 75 teams participated in competition, with 27 teams having submitted system outputs. Based on the results, we found out that most teams adopted mixed models. The mainstream resolution was applying pre-trained language models capable of providing rich semantic representation, which were combined with neural network models and used to fine-tune the models with reference to classifier tasks, and finally improved classification performance could be obtained by ensemble modeling. The best-performing system achieved a macro
