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Natural Language Processing (1317) Complementary and Alternative Medicine (204) Electronic Health Records (1763) Ontologies, Classifications, and Coding (460) Applications of AI (1040)

Published on in Vol 14 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/85825, first published .
Doctor discusses patient's health with her in a clinic office.

Symptom Terminology Normalization in Traditional Chinese Medicine: Development and Evaluation of a 2-Stage Deep Learning Framework Based on Fine-Grained Semantic Classification

Symptom Terminology Normalization in Traditional Chinese Medicine: Development and Evaluation of a 2-Stage Deep Learning Framework Based on Fine-Grained Semantic Classification

Authors of this article:

Junyu Yao1 Author Orcid Image ;   Xingyue Gou1 Author Orcid Image ;   Wei Lai1 Author Orcid Image ;   Yuzhu Gao1 Author Orcid Image ;   Siqi Wang2 Author Orcid Image ;   Chuangan Zhou1 Author Orcid Image ;   Hui Ye1 Author Orcid Image ;   Jing Tian3 Author Orcid Image ;   Jun Yi4 Author Orcid Image ;   Dong Cao1 Author Orcid Image
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Junyu Yao   1 , MCM ;   Xingyue Gou   1 , MMed ;   Wei Lai   1 , PhD ;   Yuzhu Gao   1 , MCM ;   Siqi Wang   2 , MMed ;   Chuangan Zhou   1 , BMed ;   Hui Ye   1 , MEng ;   Jing Tian   3 , MA ;   Jun Yi   4 , MEng ;   Dong Cao   1 , PhD

1 School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China

2 Yunkang School of Medicine and Health, Guangzhou Nanfang College, Guangzhou, Guangdong, China

3 School of Foreign Studies, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China

4 School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, Guangdong, China

Corresponding Author:

  • Dong Cao, PhD
  • School of Medical Information Engineering
  • Guangzhou University of Chinese Medicine
  • 232 Outer Ring East Road, Guangzhou University City, Panyu District
  • Guangzhou, Guangdong 510006
  • China
  • Phone: 86 13246852860
  • Email: caodong9@163.com