Classifying Chinese Medicine Constitution Using Multimodal Deep-Learning Model

Gu, Tianyu; Yan, Zhuangzhi; Jiang, Jiehui

doi:10.1007/s11655-022-3541-8

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…29 Meanwhile, the inherent mode of "same disease treated with different treatment, different disease with similar treatment" led to many technical barriers to be solved for intelligent inheritance of famous TCM doctors. 30 Therefore, a new method with the help of artificial intelligence technology was badly needed for inheriting the academic thoughts and clinical experience of famous veteran doctors. Many scholars had discussed development of artificial intelligent techniques for TCM.…”

Section: Discussionmentioning

confidence: 99%

“…In the past, the medical literature and records inherited by the “Master‐Prentice” schema lacked standardization and systematization 29 . Meanwhile, the inherent mode of “same disease treated with different treatment, different disease with similar treatment” led to many technical barriers to be solved for intelligent inheritance of famous TCM doctors 30 . Therefore, a new method with the help of artificial intelligence technology was badly needed for inheriting the academic thoughts and clinical experience of famous veteran doctors.…”

Section: Discussionmentioning

confidence: 99%

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The intelligent experience inheritance system for Traditional Chinese Medicine

Ren¹,

Guo

Wang

et al. 2023

J Evidence Based Medicine

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The inheritance of knowledge and experience was crucial to the development of Traditional Chinese Medicine (TCM). However, the existing methods of inheriting the unique clinical experience of famous veteran TCM doctors still followed the outdated and inefficient Master‐Prentice schema. In addition, the inherited medical books and records were usually lack of standardization and systematization. In this article, a new method for inheriting the academic thoughts and clinical experience of famous veteran doctors with the help of artificial intelligence technology was explored. Due to the individualized treatment characteristics namely “same disease with different treatments, different diseases with the same treatment,” the intelligent inheritance of TCM faced many technical barriers. To tackle these problems, we proposed a prototype system framework for the intelligent inheritance of famous veteran doctors based on rules and deep learning models and performed a case study on the treatment of pediatric asthma. The architecture could not only make full use of the advantages of deep learning, but also integrate the valuable knowledge and experience analysis of famous veteran doctors from injected rules. Specifically, the study took pediatric asthma medical records as training and test samples and calculated the similarity between the generated prescriptions and the real‐world clinical prescriptions from the famous veteran doctors. Experimental results showed that the generated prescription could achieve a similarity of more than 90%. It proved that the proposed framework provided a feasible way for the intelligent inheritance and research of the academic thoughts and clinical experience of famous veteran TCM doctors.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The intelligent experience inheritance system for Traditional Chinese Medicine

Ren¹,

Guo

Wang

et al. 2023

J Evidence Based Medicine

View full text Add to dashboard Cite

show abstract

“… 14 , 15 , 16 Several TCM research institutions have integrated AI techniques with distinctive TCM syndrome differentiation, and have achieved better predictive performance of the model in several tasks, such as TCM syndrome prediction, tongue diagnosis, and constitution identification, among others. 17 , 18 , 19 , 20 TCM syndrome differentiation is not limited to a single disease, and a diagnostic approach solely focused on a particular disease cannot adequately capture the clinical diagnostic characteristics of TCM holistic syndrome differentiation. 21 …”

Section: Introductionmentioning

confidence: 99%