TY - JOUR
T1 - A review of traditional Chinese medicine diagnosis using machine learning
T2 - Inspection, auscultation-olfaction, inquiry, and palpation
AU - Tian, Dingcheng
AU - Chen, Weihao
AU - Xu, Dechao
AU - Xu, Lisheng
AU - Xu, Gang
AU - Guo, Yaochen
AU - Yao, Yudong
N1 - Publisher Copyright:
© 2024 Elsevier Ltd
PY - 2024/3
Y1 - 2024/3
N2 - Traditional Chinese medicine (TCM) is an essential part of the Chinese medical system and is recognized by the World Health Organization as an important alternative medicine. As an important part of TCM, TCM diagnosis is a method to understand a patient's illness, analyze its state, and identify syndromes. In the long-term clinical diagnosis practice of TCM, four fundamental and effective diagnostic methods of inspection, auscultation-olfaction, inquiry, and palpation (IAOIP) have been formed. However, the diagnostic information in TCM is diverse, and the diagnostic process depends on doctors’ experience, which is subject to a high-level subjectivity. At present, the research on the automated diagnosis of TCM based on machine learning is booming. Machine learning, which includes deep learning, is an essential part of artificial intelligence (AI), which provides new ideas for the objective and AI-related research of TCM. This paper aims to review and summarize the current research status of machine learning in TCM diagnosis. First, we review some key factors for the application of machine learning in TCM diagnosis, including data, data preprocessing, machine learning models, and evaluation metrics. Second, we review and summarize the research and applications of machine learning methods in TCM IAOIP and the synthesis of the four diagnostic methods. Finally, we discuss the challenges and research directions of using machine learning methods for TCM diagnosis.
AB - Traditional Chinese medicine (TCM) is an essential part of the Chinese medical system and is recognized by the World Health Organization as an important alternative medicine. As an important part of TCM, TCM diagnosis is a method to understand a patient's illness, analyze its state, and identify syndromes. In the long-term clinical diagnosis practice of TCM, four fundamental and effective diagnostic methods of inspection, auscultation-olfaction, inquiry, and palpation (IAOIP) have been formed. However, the diagnostic information in TCM is diverse, and the diagnostic process depends on doctors’ experience, which is subject to a high-level subjectivity. At present, the research on the automated diagnosis of TCM based on machine learning is booming. Machine learning, which includes deep learning, is an essential part of artificial intelligence (AI), which provides new ideas for the objective and AI-related research of TCM. This paper aims to review and summarize the current research status of machine learning in TCM diagnosis. First, we review some key factors for the application of machine learning in TCM diagnosis, including data, data preprocessing, machine learning models, and evaluation metrics. Second, we review and summarize the research and applications of machine learning methods in TCM IAOIP and the synthesis of the four diagnostic methods. Finally, we discuss the challenges and research directions of using machine learning methods for TCM diagnosis.
KW - Deep learning
KW - Intelligent diagnosis
KW - Machine learning
KW - Traditional Chinese medicine
UR - http://www.scopus.com/inward/record.url?scp=85184517051&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85184517051&partnerID=8YFLogxK
U2 - 10.1016/j.compbiomed.2024.108074
DO - 10.1016/j.compbiomed.2024.108074
M3 - Review article
C2 - 38330826
AN - SCOPUS:85184517051
SN - 0010-4825
VL - 170
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
M1 - 108074
ER -