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Dongdong Yang
Corresponding Author
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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.
Keywords
TCM, Insomnia, Machine learning, Diagnosis, Association rules, Cluster analysis, Random forest
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CURRENT STATUS: Published
View the published article at Chinese Medicine.
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Editorial decision: Accept
On 07 Dec, 2020
Reviewers invited
Invitations sent on 10 Nov, 2020
Editor assigned
On 09 Nov, 2020
Submission checks complete
On 09 Nov, 2020
Editor invited
On 09 Nov, 2020
Version 2
Posted 27 Oct, 2020
No comments provided
Editorial decision: Minor revision
On 01 Nov, 2020
Review #1 received
Received 28 Oct, 2020
Review #2 received
Received 22 Oct, 2020
Reviewer #2 agreed
On 21 Oct, 2020
Reviewers invited
Invitations sent on 18 Oct, 2020
Reviewer #1 agreed
On 18 Oct, 2020
Editor assigned
On 16 Oct, 2020
Submission checks complete
On 15 Oct, 2020
Editor invited
On 15 Oct, 2020
No comments provided
Editorial decision: Major revision
On 04 Sep, 2020
Review #3 received
Received 30 Aug, 2020
Review #2 received
Received 21 Aug, 2020
Reviewer #3 agreed
On 18 Aug, 2020
Review #1 received
Received 06 Aug, 2020
Reviewer #2 agreed
On 01 Aug, 2020
Reviewer #1 agreed
On 23 Jul, 2020
Reviewers invited
Invitations sent on 21 Jul, 2020
Editor assigned
On 16 Jul, 2020
Submission checks complete
On 15 Jul, 2020
Editor invited
On 15 Jul, 2020
First submitted
On 14 Jul, 2020
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Subject Areas
Neurology
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See the published version of this preprint at Chinese Medicine.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Dongdong Yang
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Chinese Medicine.
No community comments so far
Editorial decision: Accept
On 07 Dec, 2020
Reviewers invited
Invitations sent on 10 Nov, 2020
Editor assigned
On 09 Nov, 2020
Submission checks complete
On 09 Nov, 2020
Editor invited
On 09 Nov, 2020
Version 2
Posted 27 Oct, 2020
No comments provided
Editorial decision: Minor revision
On 01 Nov, 2020
Review #1 received
Received 28 Oct, 2020
Review #2 received
Received 22 Oct, 2020
Reviewer #2 agreed
On 21 Oct, 2020
Reviewers invited
Invitations sent on 18 Oct, 2020
Reviewer #1 agreed
On 18 Oct, 2020
Editor assigned
On 16 Oct, 2020
Submission checks complete
On 15 Oct, 2020
Editor invited
On 15 Oct, 2020
No comments provided
Editorial decision: Major revision
On 04 Sep, 2020
Review #3 received
Received 30 Aug, 2020
Review #2 received
Received 21 Aug, 2020
Reviewer #3 agreed
On 18 Aug, 2020
Review #1 received
Received 06 Aug, 2020
Reviewer #2 agreed
On 01 Aug, 2020
Reviewer #1 agreed
On 23 Jul, 2020
Reviewers invited
Invitations sent on 21 Jul, 2020
Editor assigned
On 16 Jul, 2020
Submission checks complete
On 15 Jul, 2020
Editor invited
On 15 Jul, 2020
First submitted
On 14 Jul, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Neurology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Chinese Medicine.
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.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
The full text of this article is available to read as a PDF.
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