Acupuncture indication knowledge bases: meridian entity recognition and classification based on ACUBERT.

Autor:	Xu T; Key Laboratory of Acupuncture and Medicine Research of Ministry of Education, Nanjing University of Chinese Medicine, 138 Xianlin Road, Nanjing 210023, China.; School of Medical Information and Engineering, Xuzhou Medical University, 209 Tongshan Road, Xuzhou 221004, China., Wen J; Key Laboratory of Acupuncture and Medicine Research of Ministry of Education, Nanjing University of Chinese Medicine, 138 Xianlin Road, Nanjing 210023, China.; School of Medical Information and Engineering, Xuzhou Medical University, 209 Tongshan Road, Xuzhou 221004, China., Wang L; Nanjing KG Data Technology Co., Ltd., 1 Dongji Road, Nanjing 211100, China., Huang Y; Key Laboratory of Acupuncture and Medicine Research of Ministry of Education, Nanjing University of Chinese Medicine, 138 Xianlin Road, Nanjing 210023, China.; School of Medical Information and Engineering, Xuzhou Medical University, 209 Tongshan Road, Xuzhou 221004, China., Zhu Z; Key Laboratory of Acupuncture and Medicine Research of Ministry of Education, Nanjing University of Chinese Medicine, 138 Xianlin Road, Nanjing 210023, China.; School of Medical Information and Engineering, Xuzhou Medical University, 209 Tongshan Road, Xuzhou 221004, China., Zhu Q; School of Medical Information and Engineering, Xuzhou Medical University, 209 Tongshan Road, Xuzhou 221004, China.; Department of Traditional Chinese Medicine, Medical School, Qinghai University, 251 Ningda Road, Xining 810016, China., Fang Y; Key Laboratory of Acupuncture and Medicine Research of Ministry of Education, Nanjing University of Chinese Medicine, 138 Xianlin Road, Nanjing 210023, China.; School of Medical Information and Engineering, Xuzhou Medical University, 209 Tongshan Road, Xuzhou 221004, China., Yang C; Nanjing KG Data Technology Co., Ltd., 1 Dongji Road, Nanjing 211100, China.; School of Computer Science and Engineering, Southeast University, 2 Dongnandaxue Road, Nanjing 211102, China., Xia Y; Key Laboratory of Acupuncture and Medicine Research of Ministry of Education, Nanjing University of Chinese Medicine, 138 Xianlin Road, Nanjing 210023, China.; School of Medical Information and Engineering, Xuzhou Medical University, 209 Tongshan Road, Xuzhou 221004, China.
Jazyk:	angličtina
Zdroj:	Database : the journal of biological databases and curation [Database (Oxford)] 2024 Aug 30; Vol. 2024.
DOI:	10.1093/database/baae083
Abstrakt:	In acupuncture diagnosis and treatment, non-quantitative clinical descriptions have limited the development of standardized treatment methods. This study explores the effectiveness and the reasons for discrepancies in the entity recognition and classification of meridians in acupuncture indication using the Acupuncture Bidirectional Encoder Representations from Transformers (ACUBERT) model. During the research process, we selected 54 593 different entities from 82 acupuncture medical books as the pretraining corpus for medical literature, conducting classification research on Chinese medical literature using the BERT model. Additionally, we employed the support vector machine and Random Forest models as comparative benchmarks and optimized them through parameter tuning, ultimately leading to the development of the ACUBERT model. The results show that the ACUBERT model outperforms other baseline models in classification effectiveness, achieving the best performance at Epoch = 5. The model's "precision," "recall," and F1 scores reached above 0.8. Moreover, our study has a unique feature: it trains the meridian differentiation model based on the eight principles of differentiation and zang-fu differentiation as foundational labels. It establishes an acupuncture-indication knowledge base (ACU-IKD) and ACUBERT model with traditional Chinese medicine characteristics. In summary, the ACUBERT model significantly enhances the classification effectiveness of meridian attribution in the acupuncture indication database and also demonstrates the classification advantages of deep learning methods based on BERT in multi-category, large-scale training sets. Database URL: http://acuai.njucm.edu.cn:8081/#/user/login?tenantUrl=default. (© The Author(s) 2024. Published by Oxford University Press.)
Databáze:	MEDLINE
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