Lingdan: enhancing encoding of traditional Chinese medicine knowledge for clinical reasoning tasks with large language models.

Autor:	Hua R; Institute of Medical Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer Science & Technology, Beijing Jiaotong University, Beijing 100044, China., Dong X; Institute of Medical Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer Science & Technology, Beijing Jiaotong University, Beijing 100044, China., Wei Y; Innovation Center of Digital & Intelligent Chinese Medicine, Tasly Pharmaceutical Group Co., Ltd., Tianjin 300410, China., Shu Z; Institute of Medical Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer Science & Technology, Beijing Jiaotong University, Beijing 100044, China., Yang P; Innovation Center of Digital & Intelligent Chinese Medicine, Tasly Pharmaceutical Group Co., Ltd., Tianjin 300410, China., Hu Y; Innovation Center of Digital & Intelligent Chinese Medicine, Tasly Pharmaceutical Group Co., Ltd., Tianjin 300410, China., Zhou S; Innovation Center of Digital & Intelligent Chinese Medicine, Tasly Pharmaceutical Group Co., Ltd., Tianjin 300410, China., Sun H; Innovation Center of Digital & Intelligent Chinese Medicine, Tasly Pharmaceutical Group Co., Ltd., Tianjin 300410, China., Yan K; Innovation Center of Digital & Intelligent Chinese Medicine, Tasly Pharmaceutical Group Co., Ltd., Tianjin 300410, China., Yan X; Innovation Center of Digital & Intelligent Chinese Medicine, Tasly Pharmaceutical Group Co., Ltd., Tianjin 300410, China., Chang K; Institute of Medical Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer Science & Technology, Beijing Jiaotong University, Beijing 100044, China., Li X; Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan 430065, China.; Hubei Academy of Chinese Medicine, Wuhan 430061, China.; Institute of Liver Diseases, Hubei Key Laboratory of Theoretical and Applied Research of Liver and Kidney in Traditional Chinese Medicine, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan 430061, China., Bai Y; Department of Gastroenterology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China., Zhang R; Department of Gastroenterology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China., Wang W; Innovation Center of Digital & Intelligent Chinese Medicine, Tasly Pharmaceutical Group Co., Ltd., Tianjin 300410, China., Zhou X; Institute of Medical Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer Science & Technology, Beijing Jiaotong University, Beijing 100044, China.
Jazyk:	angličtina
Zdroj:	Journal of the American Medical Informatics Association : JAMIA [J Am Med Inform Assoc] 2024 Sep 01; Vol. 31 (9), pp. 2019-2029.
DOI:	10.1093/jamia/ocae087
Abstrakt:	Objective: The recent surge in large language models (LLMs) across various fields has yet to be fully realized in traditional Chinese medicine (TCM). This study aims to bridge this gap by developing a large language model tailored to TCM knowledge, enhancing its performance and accuracy in clinical reasoning tasks such as diagnosis, treatment, and prescription recommendations. Materials and Methods: This study harnessed a wide array of TCM data resources, including TCM ancient books, textbooks, and clinical data, to create 3 key datasets: the TCM Pre-trained Dataset, the Traditional Chinese Patent Medicine (TCPM) Question Answering Dataset, and the Spleen and Stomach Herbal Prescription Recommendation Dataset. These datasets underpinned the development of the Lingdan Pre-trained LLM and 2 specialized models: the Lingdan-TCPM-Chat Model, which uses a Chain-of-Thought process for symptom analysis and TCPM recommendation, and a Lingdan Prescription Recommendation model (Lingdan-PR) that proposes herbal prescriptions based on electronic medical records. Results: The Lingdan-TCPM-Chat and the Lingdan-PR Model, fine-tuned on the Lingdan Pre-trained LLM, demonstrated state-of-the art performances for the tasks of TCM clinical knowledge answering and herbal prescription recommendation. Notably, Lingdan-PR outperformed all state-of-the-art baseline models, achieving an improvement of 18.39% in the Top@20 F1-score compared with the best baseline. Conclusion: This study marks a pivotal step in merging advanced LLMs with TCM, showcasing the potential of artificial intelligence to help improve clinical decision-making of medical diagnostics and treatment strategies. The success of the Lingdan Pre-trained LLM and its derivative models, Lingdan-TCPM-Chat and Lingdan-PR, not only revolutionizes TCM practices but also opens new avenues for the application of artificial intelligence in other specialized medical fields. Our project is available at https://github.com/TCMAI-BJTU/LingdanLLM. (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)
Databáze:	MEDLINE
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