TY - GEN
T1 - TCM-SD
T2 - 21st China National Conference on Computational Linguistics, CCL 2022
AU - Ren, Mucheng
AU - Huang, Heyan
AU - Zhou, Yuxiang
AU - Cao, Qianwen
AU - Bu, Yuan
AU - Gao, Yang
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Traditional Chinese Medicine (TCM) is a natural, safe, and effective therapy that has spread and been applied worldwide. The unique TCM diagnosis and treatment system requires a comprehensive analysis of a patient’s symptoms hidden in the clinical record written in free text. Prior studies have shown that this system can be informationized and intelligentized with the aid of artificial intelligence (AI) technology, such as natural language processing (NLP). However, existing datasets are not of sufficient quality nor quantity to support the further development of data-driven AI technology in TCM. Therefore, in this paper, we focus on the core task of the TCM diagnosis and treatment system—syndrome differentiation (SD)—and we introduce the first public large-scale benchmark for SD, called TCM-SD. Our benchmark contains 54,152 real-world clinical records covering 148 syndromes. Furthermore, we collect a large-scale unlabelled textual corpus in the field of TCM and propose a domain-specific pre-trained language model, called ZY-BERT. We conducted experiments using deep neural networks to establish a strong performance baseline, reveal various challenges in SD, and prove the potential of domain-specific pre-trained language model. Our study and analysis reveal opportunities for incorporating computer science and linguistics knowledge to explore the empirical validity of TCM theories.
AB - Traditional Chinese Medicine (TCM) is a natural, safe, and effective therapy that has spread and been applied worldwide. The unique TCM diagnosis and treatment system requires a comprehensive analysis of a patient’s symptoms hidden in the clinical record written in free text. Prior studies have shown that this system can be informationized and intelligentized with the aid of artificial intelligence (AI) technology, such as natural language processing (NLP). However, existing datasets are not of sufficient quality nor quantity to support the further development of data-driven AI technology in TCM. Therefore, in this paper, we focus on the core task of the TCM diagnosis and treatment system—syndrome differentiation (SD)—and we introduce the first public large-scale benchmark for SD, called TCM-SD. Our benchmark contains 54,152 real-world clinical records covering 148 syndromes. Furthermore, we collect a large-scale unlabelled textual corpus in the field of TCM and propose a domain-specific pre-trained language model, called ZY-BERT. We conducted experiments using deep neural networks to establish a strong performance baseline, reveal various challenges in SD, and prove the potential of domain-specific pre-trained language model. Our study and analysis reveal opportunities for incorporating computer science and linguistics knowledge to explore the empirical validity of TCM theories.
KW - Bioinformatics
KW - Natural language processing
KW - Traditional chinese medicine
UR - http://www.scopus.com/inward/record.url?scp=85141744375&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-18315-7_16
DO - 10.1007/978-3-031-18315-7_16
M3 - Conference contribution
AN - SCOPUS:85141744375
SN - 9783031183140
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 247
EP - 263
BT - Chinese Computational Linguistics - 21st China National Conference, CCL 2022, Proceedings
A2 - Sun, Maosong
A2 - Liu, Yang
A2 - Che, Wanxiang
A2 - Feng, Yang
A2 - Qiu, Xipeng
A2 - Rao, Gaoqi
A2 - Chen, Yubo
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 14 October 2022 through 16 October 2022
ER -