AcuKG: a comprehensive knowledge graph for medical acupuncture

作者：Yiming Li, Xueqing Peng, Suyuan Peng, Jianfu Li, Donghong Pei, Qin Zhang, Yiwei Lu, Yan Hu, Fang Li, Li Zhou, Yongqun He, Cui Tao, Hua Xu, Na Hong · 发表于：Journal of the American Medical Informatics Association · 年份：2025 · DOI：10.1093/jamia/ocaf179 · 被引用次数：8 · 研究领域：Traditional Chinese Medicine Studies、Biomedical Text Mining and Ontologies、Machine Learning in Healthcare

BACKGROUND: Acupuncture, a key modality in traditional Chinese medicine, is gaining global recognition as a complementary therapy and a subject of increasing scientific interest. However, fragmented and unstructured acupuncture knowledge spread across diverse sources poses challenges for semantic retrieval, reasoning, and in-depth analysis. To address this gap, we developed AcuKG, a comprehensive knowledge graph that systematically organizes acupuncture-related knowledge to support sharing, discovery, and artificial intelligence-driven innovation in the field. METHODS: AcuKG integrates data from multiple sources, including online resources, guidelines, PubMed literature, ClinicalTrials.gov, and multiple ontologies (SNOMED CT, UBERON, and MeSH). We employed entity recognition, relation extraction, and ontology mapping to establish AcuKG, with human-in-the-loop to ensure data quality. Two cases evaluated AcuKG's usability: (1) how AcuKG advances acupuncture research for obesity and (2) how AcuKG enhances large language model (LLM) application on acupuncture question-answering. RESULTS: AcuKG comprises 1839 entities and 11 527 relations, mapped to 1836 standard concepts in 3 ontologies. Two use cases demonstrated AcuKG's effectiveness and potential in advancing acupuncture research and supporting LLM applications. In the obesity use case, AcuKG identified highly relevant acupoints (eg, ST25, ST36) and uncovered novel research insights based on evidence from clinical trials and l...