The Impact of Artificial Intelligence on Traditional Chinese Medicine

2021 ◽  
pp. 1-18
Author(s):  
Yulin Wang ◽  
Xiuming Shi ◽  
Li Li ◽  
Thomas Efferth ◽  
Dong Shang
Keyword(s):  
Artificial Intelligence ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Modern Science ◽  
Diagnostic Methods ◽  
Digital Data ◽  
Syndrome Differentiation ◽  
Efficacy Evaluation ◽  
Combination Rules ◽  
The Impact

Traditional Chinese Medicine (TCM) is a well-established medical system with a long history. Currently, artificial intelligence (AI) is rapidly expanding in many fields including TCM. AI will significantly improve the reliability and accuracy of diagnostics, thus increasing the use of effective therapeutic methods for patients. This systematic review provides an updated overview on the major breakthroughs in the field of AI-assisted TCM four diagnostic methods, syndrome differentiation, and treatment. AI-assisted TCM diagnosis is mainly based on digital data collected by modern electronic instruments, which makes TCM diagnosis more quantitative, objective, and standardized. As a result, the diagnosis decisions made by different TCM doctors exhibit more consistency, accuracy, and reliability. Meanwhile, the therapeutic efficacy of TCM can be evaluated objectively. Therefore, AI is promoting TCM from experience to evidence-based medicine, a genuine scientific revolution. Furthermore, huge and non-uniform knowledge on formula-syndrome relationships and the combination rules of herbal TCM formulae could be better standardized with the help of AI analysis, which is necessary for the clinical efficacy evaluation and further optimization on the standardized TCM formulae. AI bridges the gap between TCM and modern science and technology. AI may bring clinical TCM diagnostics closer to western medicine. With the help of AI, more scientific evidence about TCM will be discovered. It can be expected that more unified guidelines for specific TCM syndromes will be issued with the development of AI-assisted TCM therapies in the future.

scholarly journals Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Changbo Zhao ◽  
Guo-Zheng Li ◽  
Chengjun Wang ◽  
Jinling Niu
Keyword(s):  
Machine Learning ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Machine Learning Algorithms ◽  
Diagnostic Methods ◽  
Machine Learning Techniques ◽  
Syndrome Differentiation ◽  
Reference List ◽  
Patient Classification ◽  
Research Issues

As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification.

scholarly journals Research on the diagnosis and treatment of the dominant diseases of traditional Chinese medicine based on machine learning

2020 ◽  
Author(s):  
yuqi tang ◽  
Dongdong Yang ◽  
Zechen Li ◽  
Yu Fang ◽  
Shanshan Gao
Keyword(s):  
Machine Learning ◽  
Chinese Medicine ◽  
Random Forest ◽  
Traditional Chinese Medicine ◽  
Hierarchical Cluster ◽  
Diagnostic Methods ◽  
Diagnosis And Treatment ◽  
Syndrome Differentiation ◽  
The Novel ◽  
Sample Data

Abstract Background: Insomnia as one of the dominant diseases of traditional Chinese medicine (TCM) has been extensively studied in recent years. To explore the novel approaches of research on TCM diagnosis and treatment, this paper presents a strategy for the research of insomnia based on machine learning. Methods: First of all, 654 insomnia cases have been collected from an experienced doctor of TCM as sample data. Secondly, in the light of the characteristics of TCM diagnosis and treatment, the contents of research samples have been divided into four parts: the basic information, the four diagnostic methods, the treatment based on syndrome differentiation and the main prescription. And then, these four parts have been analyzed by three analysis methods, including frequency analysis, association rules and hierarchical cluster analysis. Finally, a comprehensive study of the whole four parts has been conducted by random forest. Results: Researches of the above four parts revealed some essential connections. Simultaneously, based on the algorithm model established by the random forest, the accuracy of predicting the main prescription by the combination of the four diagnostic methods and the treatment based on syndrome differentiation was 0.85. Furthermore, having been extracted features through applying the random forest, the syndrome differentiation of five zang-organs was proven to be the most significant parameter of the TCM diagnosis and treatment.Conclusions: The results indicate that the machine learning methods are worthy of being adopted to study the dominant diseases of TCM for exploring the crucial rules of the diagnosis and treatment.

scholarly journals The Challenges of Ethical Review in Clinical Research of Traditional Chinese Medicine

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jie Zhang ◽  
Zong-Ming Zhang
Keyword(s):  
Risk Assessment ◽  
Informed Consent ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Clinical Research ◽  
Modern Medicine ◽  
Ethical Review ◽  
Syndrome Differentiation ◽  
Efficacy Evaluation ◽  
Five Elements

The ethical review of TCM (traditional Chinese medicine) clinical research in China is highly consistent with that of Western medicine, but it lacks the characteristics and culture of TCM. Compared with modern medicine, TCM has its own characteristics, such as the theory of Yin-Yang and the five elements, the treatment of syndrome differentiation, and the compatibility of prescriptions. These characteristics determine the ethical particularity of TCM clinical research. This article discusses the challenges in the ethical review of TCM clinical research, such as scientific design, efficacy evaluation, risk assessment, informed consent, and placebo use. We propose opportunities and difficulties in the ethical review of TCM clinical research and provide some relevant suggestions.

scholarly journals Research of insomnia on traditional Chinese medicine diagnosis and treatment based on machine learning

2020 ◽  
Author(s):  
Yuqi Tang ◽  
Zechen Li ◽  
Dongdong Yang ◽  
Yu Fang ◽  
Shanshan Gao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chinese Medicine ◽  
Random Forest ◽  
Traditional Chinese Medicine ◽  
Hierarchical Cluster ◽  
Diagnostic Methods ◽  
Diagnosis And Treatment ◽  
Syndrome Differentiation ◽  
The Novel ◽  
Sample Data

Abstract Background: Insomnia as one of the dominant diseases of traditional Chinese medicine (TCM) has been extensively studied in recent years. To explore the novel approaches of research on TCM diagnosis and treatment, this paper presents a strategy for the research of insomnia based on machine learning. Methods: First of all, 654 insomnia cases have been collected from an experienced doctor of TCM as sample data. Secondly, in the light of the characteristics of TCM diagnosis and treatment, the contents of research samples have been divided into four parts: the basic information, the four diagnostic methods, the treatment based on syndrome differentiation and the main prescription. And then, these four parts have been analyzed by three analysis methods, including frequency analysis, association rules and hierarchical cluster analysis. Finally, a comprehensive study of the whole four parts has been conducted by random forest. Results: Researches of the above four parts revealed some essential connections. Simultaneously, based on the algorithm model established by the random forest, the accuracy of predicting the main prescription by the combinations of the four diagnostic methods and the treatment based on syndrome differentiation was 0.85. Furthermore, having been extracted features through applying the random forest, the syndrome differentiation of five zang-organs was proven to be the most significant parameter of the TCM diagnosis and treatment.Conclusions: The results indicate that the machine learning methods are worthy of being adopted to study the dominant diseases of TCM for exploring the crucial rules of the diagnosis and treatment.

scholarly journals Research of insomnia on traditional Chinese medicine diagnosis and treatment based on machine learning

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Yuqi Tang ◽  
Zechen Li ◽  
Dongdong Yang ◽  
Yu Fang ◽  
Shanshan Gao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chinese Medicine ◽  
Random Forest ◽  
Traditional Chinese Medicine ◽  
Hierarchical Cluster ◽  
Diagnostic Methods ◽  
Diagnosis And Treatment ◽  
Syndrome Differentiation ◽  
The Novel ◽  
Sample Data

Abstract Background Insomnia as one of the dominant diseases of traditional Chinese medicine (TCM) has been extensively studied in recent years. To explore the novel approaches of research on TCM diagnosis and treatment, this paper presents a strategy for the research of insomnia based on machine learning. Methods First of all, 654 insomnia cases have been collected from an experienced doctor of TCM as sample data. Secondly, in the light of the characteristics of TCM diagnosis and treatment, the contents of research samples have been divided into four parts: the basic information, the four diagnostic methods, the treatment based on syndrome differentiation and the main prescription. And then, these four parts have been analyzed by three analysis methods, including frequency analysis, association rules and hierarchical cluster analysis. Finally, a comprehensive study of the whole four parts has been conducted by random forest. Results Researches of the above four parts revealed some essential connections. Simultaneously, based on the algorithm model established by the random forest, the accuracy of predicting the main prescription by the combinations of the four diagnostic methods and the treatment based on syndrome differentiation was 0.85. Furthermore, having been extracted features through applying the random forest, the syndrome differentiation of five zang-organs was proven to be the most significant parameter of the TCM diagnosis and treatment. Conclusions The results indicate that the machine learning methods are worthy of being adopted to study the dominant diseases of TCM for exploring the crucial rules of the diagnosis and treatment.

scholarly journals Research on the Diagnosis and Treatment of the Dominant Diseases of Traditional Chinese Medicine Based on Machine Learning

2020 ◽  
Author(s):  
Yuqi Tang ◽  
Dongdong Yang ◽  
Zechen Li ◽  
Yu Fang ◽  
Shanshan Gao
Keyword(s):  
Machine Learning ◽  
Chinese Medicine ◽  
Random Forest ◽  
Traditional Chinese Medicine ◽  
Hierarchical Cluster ◽  
Diagnostic Methods ◽  
Diagnosis And Treatment ◽  
Syndrome Differentiation ◽  
The Novel ◽  
Significant Relationships

Abstract Background: Insomnia as one of the dominant diseases of Traditional Chinese medicine (TCM) has been extensively studied in recent years. To explore the novel approaches of research on TCM diagnosis and treatment, this paper presents a strategy for the research of insomnia based on machine learning. Methods: First of all, 654 insomnia cases have been collected from an experienced doctor of TCM as sample data. Secondly, in the light of the characteristics of TCM diagnosis and treatment, the contents of research samples have been divided into four parts: the basic information, the four diagnostic methods, the treatment based on syndrome differentiation and the main prescription. And then, these four parts have been analyzed by three analysis methods, including frequency analysis, association rules and hierarchical cluster analysis. Finally, a comprehensive study of the whole four parts was conducted by random forest. Results: Analysis of the four parts revealed some significant relationships. Simultaneously, based on the algorithm model established by the random forest, the accuracy of predicting the main prescription by the combination of the four diagnostic methods and the treatment based on syndrome differentiation was 0.85. Furthermore, the syndrome differentiation of five zang-organs was proven to be the most significant parameter after the features extraction by the random forest.Conclusions: The results indicate that the machine learning method can be adopted to study the dominant diseases of TCM to explore the crucial rules of the diagnosis and the treatment.

scholarly journals Research of insomnia on traditional Chinese medicine diagnosis and treatment based on machine learning

2020 ◽  
Author(s):  
Yuqi Tang ◽  
Zechen Li ◽  
Dongdong Yang ◽  
Yu Fang ◽  
Shanshan Gao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chinese Medicine ◽  
Random Forest ◽  
Traditional Chinese Medicine ◽  
Hierarchical Cluster ◽  
Diagnostic Methods ◽  
Diagnosis And Treatment ◽  
Syndrome Differentiation ◽  
The Novel ◽  
Sample Data

Abstract Background: Insomnia as one of the dominant diseases of traditional Chinese medicine (TCM) has been extensively studied in recent years. To explore the novel approaches of research on TCM diagnosis and treatment, this paper presents a strategy for the research of insomnia based on machine learning. Methods: First of all, 654 insomnia cases have been collected from an experienced doctor of TCM as sample data. Secondly, in the light of the characteristics of TCM diagnosis and treatment, the contents of research samples have been divided into four parts: the basic information, the four diagnostic methods, the treatment based on syndrome differentiation and the main prescription. And then, these four parts have been analyzed by three analysis methods, including frequency analysis, association rules and hierarchical cluster analysis. Finally, a comprehensive study of the whole four parts has been conducted by random forest. Results: Researches of the above four parts revealed some essential connections. Simultaneously, based on the algorithm model established by the random forest, the accuracy of predicting the main prescription by the combinations of the four diagnostic methods and the treatment based on syndrome differentiation was 0.85. Furthermore, having been extracted features through applying the random forest, the syndrome differentiation of five zang-organs was proven to be the most significant parameter of the TCM diagnosis and treatment.Conclusions: The results indicate that the machine learning methods are worthy of being adopted to study the dominant diseases of TCM for exploring the crucial rules of the diagnosis and treatment.

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