Lingdan: enhancing encoding of traditional Chinese medicine knowledge for clinical reasoning tasks with large language models (2024)

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Volume 31 Issue 9 September 2024
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Rui Hua, ME

Institute of Medical Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer Science & Technology, Beijing Jiaotong University

, Beijing 100044,

China

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Xin Dong, ME

Institute of Medical Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer Science & Technology, Beijing Jiaotong University

, Beijing 100044,

China

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Yu Wei, PhD

Innovation Center of Digital & Intelligent Chinese Medicine, Tasly Pharmaceutical Group Co., Ltd.

, Tianjin 300410,

China

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Zixin Shu, MM

Institute of Medical Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer Science & Technology, Beijing Jiaotong University

, Beijing 100044,

China

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Pengcheng Yang, PhD

Innovation Center of Digital & Intelligent Chinese Medicine, Tasly Pharmaceutical Group Co., Ltd.

, Tianjin 300410,

China

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Yunhui Hu, PhD

Innovation Center of Digital & Intelligent Chinese Medicine, Tasly Pharmaceutical Group Co., Ltd.

, Tianjin 300410,

China

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Shuiping Zhou, PhD

Innovation Center of Digital & Intelligent Chinese Medicine, Tasly Pharmaceutical Group Co., Ltd.

, Tianjin 300410,

China

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Kaijing Yan, PhD

Innovation Center of Digital & Intelligent Chinese Medicine, Tasly Pharmaceutical Group Co., Ltd.

, Tianjin 300410,

China

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Xijun Yan, ME

Innovation Center of Digital & Intelligent Chinese Medicine, Tasly Pharmaceutical Group Co., Ltd.

, Tianjin 300410,

China

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Kai Chang, ME

Institute of Medical Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer Science & Technology, Beijing Jiaotong University

, Beijing 100044,

China

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Xiaodong Li, MD

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

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Yuning Bai, MD

Department of Gastroenterology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences

, Beijing 100053,

China

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Runshun Zhang, MD

Department of Gastroenterology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences

, Beijing 100053,

China

Corresponding authors: Xuezhong Zhou, PhD, Institute of Medical Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer Science & Technology, Beijing Jiaotong University, No. 3 Shangyuancun, Haidian District, Beijing 100044, China (xzzhou@bjtu.edu.cn); Wenjia Wang, PhD, Tasly Pharmaceutical Group Co., Ltd., Tianjin 518000, China (tsl-wangwenjia@tasly.com); and Runshun Zhang, PhD, Department of Gastroenterology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China (runshunzhang@139.com)

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Wenjia Wang, PhD

Innovation Center of Digital & Intelligent Chinese Medicine, Tasly Pharmaceutical Group Co., Ltd.

, Tianjin 300410,

China

Corresponding authors: Xuezhong Zhou, PhD, Institute of Medical Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer Science & Technology, Beijing Jiaotong University, No. 3 Shangyuancun, Haidian District, Beijing 100044, China (xzzhou@bjtu.edu.cn); Wenjia Wang, PhD, Tasly Pharmaceutical Group Co., Ltd., Tianjin 518000, China (tsl-wangwenjia@tasly.com); and Runshun Zhang, PhD, Department of Gastroenterology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China (runshunzhang@139.com)

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Xuezhong Zhou, PhD

Institute of Medical Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer Science & Technology, Beijing Jiaotong University

, Beijing 100044,

China

Corresponding authors: Xuezhong Zhou, PhD, Institute of Medical Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer Science & Technology, Beijing Jiaotong University, No. 3 Shangyuancun, Haidian District, Beijing 100044, China (xzzhou@bjtu.edu.cn); Wenjia Wang, PhD, Tasly Pharmaceutical Group Co., Ltd., Tianjin 518000, China (tsl-wangwenjia@tasly.com); and Runshun Zhang, PhD, Department of Gastroenterology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China (runshunzhang@139.com)

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Journal of the American Medical Informatics Association, Volume 31, Issue 9, September 2024, Pages 2019–2029, https://doi.org/10.1093/jamia/ocae087

Published:

22 July 2024

Article history

Received:

15 December 2023

Revision received:

22 February 2024

Editorial decision:

25 March 2024

Accepted:

06 April 2024

Published:

22 July 2024

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    Rui Hua, Xin Dong, Yu Wei, Zixin Shu, Pengcheng Yang, Yunhui Hu, Shuiping Zhou, He Sun, Kaijing Yan, Xijun Yan, Kai Chang, Xiaodong Li, Yuning Bai, Runshun Zhang, Wenjia Wang, Xuezhong Zhou, Lingdan: enhancing encoding of traditional Chinese medicine knowledge for clinical reasoning tasks with large language models, Journal of the American Medical Informatics Association, Volume 31, Issue 9, September 2024, Pages 2019–2029, https://doi.org/10.1093/jamia/ocae087

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Abstract

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.

traditional Chinese medicine, large language model, pre-training, clinical reasoning, prescription recommendation

© 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

This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/pages/standard-publication-reuse-rights)

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Research and applications

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