A framework for normalized extraction of fine-grained traditional Chinese medicine symptom entities and relations
Xingyue Gou1, Junyu Yao1, Wei Lai1
1School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong Province, 510006, China.
Background:
In Traditional Chinese Medicine Electronic Medical Records (TCM EMRs), symptom descriptions are often semi-structured, and coarse-grained annotation can lead to symptom nesting and information loss. To address these limitations and improve the precision of symptom representation, this study proposes a fine-grained symptom entity annotation system. Its objective is to convert unstandardized symptom expressions into structured data, thereby enhancing the correlation and standardization of symptom information to support intelligent TCM diagnosis and treatment.
Methods:
A five-step approach was employed: First, we drafted a fine-grained annotation guideline based on existing research. Second, we annotated symptom entities and iteratively refined the annotations through trial runs, multiple revisions, and evaluations. Third, we trained and assessed Named Entity Recognition (NER) models. Fourth, we extracted relations using predefined rules. Finally, we generated normalized outputs by integrating these rules and manually validated the extraction results.
Results:
The study annotated 500 TCM EMRs over five trials, identified 12 entity categories and 10 relation types. The inter-annotator agreement (IAA) F1 scores for entities and relations were 93.56% and 91.23%, respectively. The final corpus comprises 39,097 entities and 41,373 relation pairs, with 15,853 normalized symptom sentences generated through rule-based combination. Compared to prior studies, TCM symptom information utilization (TCM-SIU) increased by 8.24%. The best-performing NER model achieved an F1 score of 92.29%, while rule-based Relation Extraction (RE) attained an F1 score of 88.17%.
Conclusion:
The proposed fine-grained symptom annotation system significantly enhances the utilization of symptom information. It effectively mitigates symptom nesting issues, supports comprehensive association, and facilitates structured output, thereby providing robust data for symptom standardization and precise syndrome differentiation.
