ArticleBMC medical informatics and decision making2025
A framework for normalized extraction of fine-grained traditional Chinese medicine symptom entities and relations.
Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn 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.
methodsA 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.
resultsThe 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%.
conclusionThe 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.