ArticleBMC medical informatics and decision making2020
Constructing fine-grained entity recognition corpora based on clinical records of traditional Chinese medicine.
Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- From narrative evidence to computable knowledge: a decision-relevant corpus for medicinal herb-disease relationships.Journal of biomedical semantics · 2026Article
- A framework for normalized extraction of fine-grained traditional Chinese medicine symptom entities and relations.BMC medical informatics and decision making · 2025Article
- Annotated corpus for traditional formula-disease relationships in biomedical articles.Scientific data · 2025Article
- Predicting new-onset stroke with machine learning: development of a model integrating traditional Chinese and western medicine.Frontiers in pharmacology · 2025Article
- Artificial intelligence in acupuncture: bridging traditional knowledge and precision integrative medicine.Frontiers in medicine · 2025Review
- A Review on Electronic Health Record Text-Mining for Biomedical Name Entity Recognition in Healthcare Domain.Healthcare (Basel, Switzerland) · 2023Review
- Information Extraction from the Text Data on Traditional Chinese Medicine: A Review on Tasks, Challenges, and Methods from 2010 to 2021.Evidence-based complementary and alternative medicine : eCAM · 2022Review
- Extraction of Traditional Chinese Medicine Entity: Design of a Novel Span-Level Named Entity Recognition Method With Distant Supervision.JMIR medical informatics · 2021Article
- TCMNER and PubMed: A Novel Chinese Character-Level-Based Model and a Dataset for TCM Named Entity Recognition.Journal of healthcare engineering · 2021Article
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5 authors.
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Abstract
backgroundIn this study, we focus on building a fine-grained entity annotation corpus with the corresponding annotation guideline of traditional Chinese medicine (TCM) clinical records. Our aim is to provide a basis for the fine-grained corpus construction of TCM clinical records in future.
methodsWe developed a four-step approach that is suitable for the construction of TCM medical records in our corpus. First, we determined the entity types included in this study through sample annotation. Then, we drafted a fine-grained annotation guideline by summarizing the characteristics of the dataset and referring to some existing guidelines. We iteratively updated the guidelines until the inter-annotator agreement (IAA) exceeded a Cohen's kappa value of 0.9. Comprehensive annotations were performed while keeping the IAA value above 0.9.
resultsWe annotated the 10,197 clinical records in five rounds. Four entity categories involving 13 entity types were employed. The final fine-grained annotated entity corpus consists of 1104 entities and 67,799 tokens. The final IAAs are 0.936 on average (for three annotators), indicating that the fine-grained entity recognition corpus is of high quality.
conclusionsThese results will provide a foundation for future research on corpus construction and named entity recognition tasks in the TCM clinical domain.
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