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Article: Artificial intelligence and its application for cardiovascular diseases in Chinese medicine

TitleArtificial intelligence and its application for cardiovascular diseases in Chinese medicine
Authors
KeywordsArtificial intelligence (AI)
Cardiovascular diseases (CVDs)
Deep learning (DL)
Machine learning (ML)
Traditional Chinese medicine (TCM)
Issue Date5-Dec-2022
PublisherElsevier
Citation
Digital Chinese Medicine, 2022, v. 5, n. 4, p. 367-376 How to Cite?
Abstract

Cardiovascular diseases (CVDs) are major disease burdens with high mortality worldwide. Early prediction of cardiovascular events can reduce the incidence of acute myocardial infarction and decrease the mortality rates of patients with CVDs. The pathological mechanisms and multiple factors involved in CVDs are complex; thus, traditional data analysis is insufficient and inefficient to manage multidimensional data for the risk prediction of CVDs and heart attacks, medical image interpretations, therapeutic decision-making, and disease prognosis prediction. Meanwhile, traditional Chinese medicine (TCM) has been widely used for treating CVDs. TCM offers unique theoretical and practical applications in the diagnosis and treatment of CVDs. Big data have been generated to investigate the scientific basis of TCM diagnostic methods. TCM formulae contain multiple herbal items. Elucidating the complicated interactions between the active compounds and network modulations requires advanced data-analysis capability. Recent progress in artificial intelligence (AI) technology has allowed these challenges to be resolved, which significantly facilitates the development of integrative diagnostic and therapeutic strategies for CVDs and the understanding of the therapeutic principles of TCM formulae. Herein, we briefly introduce the basic concept and current progress of AI and machine learning (ML) technology, and summarize the applications of advanced AI and ML for the diagnosis and treatment of CVDs. Furthermore, we review the progress of AI and ML technology for investigating the scientific basis of TCM diagnosis and treatment for CVDs. We expect the application of AI and ML technology to promote synergy between western medicine and TCM, which can then boost the development of integrative medicine for the diagnosis and treatment of CVDs.


Persistent Identifierhttp://hdl.handle.net/10722/340709
ISSN
2023 SCImago Journal Rankings: 0.230

 

DC FieldValueLanguage
dc.contributor.authorChen, X-
dc.contributor.authorLeung, YLA-
dc.contributor.authorShen J-
dc.date.accessioned2024-03-11T10:46:33Z-
dc.date.available2024-03-11T10:46:33Z-
dc.date.issued2022-12-05-
dc.identifier.citationDigital Chinese Medicine, 2022, v. 5, n. 4, p. 367-376-
dc.identifier.issn2096-479X-
dc.identifier.urihttp://hdl.handle.net/10722/340709-
dc.description.abstract<p>Cardiovascular diseases (CVDs) are major <a href="https://www.sciencedirect.com/topics/medicine-and-dentistry/disease-burden" title="Learn more about disease burdens from ScienceDirect's AI-generated Topic Pages">disease burdens</a> with high mortality worldwide. Early prediction of cardiovascular events can reduce the incidence of <a href="https://www.sciencedirect.com/topics/medicine-and-dentistry/acute-heart-infarction" title="Learn more about acute myocardial infarction from ScienceDirect's AI-generated Topic Pages">acute myocardial infarction</a> and decrease the mortality rates of patients with CVDs. The pathological mechanisms and multiple factors involved in CVDs are complex; thus, traditional data analysis is insufficient and inefficient to manage multidimensional data for the risk prediction of CVDs and heart attacks, medical image interpretations, therapeutic decision-making, and disease prognosis prediction. Meanwhile, <a href="https://www.sciencedirect.com/topics/medicine-and-dentistry/traditional-chinese-medicine" title="Learn more about traditional Chinese medicine from ScienceDirect's AI-generated Topic Pages">traditional Chinese medicine</a> (TCM) has been widely used for treating CVDs. TCM offers unique theoretical and practical applications in the diagnosis and <a href="https://www.sciencedirect.com/topics/medicine-and-dentistry/therapeutic-procedure" title="Learn more about treatment from ScienceDirect's AI-generated Topic Pages">treatment</a> of CVDs. Big data have been generated to investigate the scientific basis of TCM diagnostic methods. TCM formulae contain multiple herbal items. Elucidating the complicated interactions between the active compounds and network modulations requires advanced data-analysis capability. Recent progress in artificial intelligence (AI) technology has allowed these challenges to be resolved, which significantly facilitates the development of integrative diagnostic and therapeutic strategies for CVDs and the understanding of the therapeutic principles of TCM formulae. Herein, we briefly introduce the basic concept and current progress of AI and machine learning (ML) technology, and summarize the applications of advanced AI and ML for the diagnosis and treatment of CVDs. Furthermore, we review the progress of AI and ML technology for investigating the scientific basis of TCM diagnosis and treatment for CVDs. We expect the application of AI and ML technology to promote synergy between western medicine and TCM, which can then boost the development of <a href="https://www.sciencedirect.com/topics/medicine-and-dentistry/integrative-medicine" title="Learn more about integrative medicine from ScienceDirect's AI-generated Topic Pages">integrative medicine</a> for the diagnosis and treatment of CVDs.<br></p>-
dc.languageeng-
dc.publisherElsevier-
dc.relation.ispartofDigital Chinese Medicine-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectArtificial intelligence (AI)-
dc.subjectCardiovascular diseases (CVDs)-
dc.subjectDeep learning (DL)-
dc.subjectMachine learning (ML)-
dc.subjectTraditional Chinese medicine (TCM)-
dc.titleArtificial intelligence and its application for cardiovascular diseases in Chinese medicine-
dc.typeArticle-
dc.identifier.doi10.1016/j.dcmed.2022.12.003-
dc.identifier.scopuseid_2-s2.0-85149804453-
dc.identifier.volume5-
dc.identifier.issue4-
dc.identifier.spage367-
dc.identifier.epage376-
dc.identifier.eissn2589-3777-
dc.identifier.issnl2589-3777-

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