Reliable Multi-Label Learning via Conformal Predictor and Random Forest for Syndrome Differentiation of Chronic Fatigue in Traditional Chinese Medicine

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.

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

10.21203/rs.3.rs-42369/v2 ◽

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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Advances in Clinical Research on Traditional Chinese Medicine Treatment of Chronic Fatigue Syndrome

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2020/4715679 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Xiaoyan Zhang ◽

Miao Wang ◽

Shigao Zhou

Keyword(s):

Chinese Medicine ◽

Chronic Fatigue Syndrome ◽

Traditional Chinese Medicine ◽

Clinical Research ◽

Modern Society ◽

Chronic Fatigue ◽

Syndrome Differentiation ◽

Fatigue Syndrome ◽

Chinese Medicine Treatment

Chronic fatigue syndrome (CFS) is one of the most common chronic diseases in modern society and affects patients’ quality of life to a certain extent. To date, the etiology and pathogenesis of CFS are still not completely clear. Various therapies have been developed, but there is still a lack of specific drugs or treatments. As a kind of adjuvant therapy, traditional Chinese medicine (TCM) has aroused widespread concern about the improvement of CFS. Although a large number of clinical randomized controlled trials have confirmed the therapeutic effect of TCM on CFS, the exact efficacy is still controversial. This article summarizes the clinical research methods and efficacy of TCM in the treatment of CFS over the past five years from the perspectives of syndrome differentiation, external treatment, and combination therapy.

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

Chinese Medicine ◽

10.1186/s13020-020-00409-8 ◽

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.

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

10.21203/rs.3.rs-39359/v1 ◽

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.

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

10.21203/rs.3.rs-42369/v3 ◽

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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