Tag: Machine learning

Identification of Potential Targets of Stress Cardiomyopathy by a Machine Learning Algorithm

Announcing a new article publication for Cardiovascular Innovations and Applications journal.  Stress cardiomyopathy (SCM) is a reversible, self-limiting condition that manifests as left ventricular insufficiency. The incidence of stress cardiomyopathy has increased because of increasing mental and social stress, but the exact pathophysiological mechanisms remain unclear.

To elucidate the critical molecules in the pathogenesis of SCM and the functional changes that they mediate, data for a healthy control group and stress cardiomyopathy (SCM) group was downloaded from the Gene Expression Omnibus database, differential analysis was performed, and the results of GO and KEGG enrichment analysis was analysed to describe SCM-associated genes and functions. Lasso, random forest, SVM-RFM, and Friends analysis were used to screen hub genes; CIBERSORT and MCPcounter were used to explore the relationship between SCM and immunity; and an animal model of SCM was constructed to conduct bidirectional verification of the obtained results.

In total, 21 samples (6 healthy, 15 SCM) were used in this study. Overall, 39 DEGs (absolute fold change ≥ 1; P < 0.05), including 23 upregulated and 16 downregulated genes in SCM, were extracted. Three common hub genes (PLAT, SEMA6B, and CRP) were finally screened. It was further confirmed that functional changes in SCM were concentrated in immunity and coagulation functions.

Three key genes (PLAT, SEMA6B, and CRP) in SCM were identified by machine learning, and the major functional changes leading to SCM, and relationships of SCM with immunity, were identified.

https://www.scienceopen.com/hosted-document?doi=10.15212/CVIA.2024.0011

CVIA is available on the ScienceOpen platform and at Cardiovascular Innovations and Applications. Submissions may be made using ScholarOne Manuscripts. There are no author submission or article processing fees. Cardiovascular Innovations and Applications is indexed in the EMBASE, EBSCO, ESCI, OCLC, Primo Central (Ex Libris), Sherpa Romeo, NISC (National Information Services Corporation), DOAJ, Index Copernicus, Research4Life and Ulrich’s web Databases. Follow CVIA on Twitter @CVIA_Journal; or Facebook.

Xuexin Jin, Xuanrui Ji and Hongpeng Yin et al. Identification of Potential Targets of Stress Cardiomyopathy by a Machine Learning Algorithm. CVIA. 2024. Vol. 9(1). DOI: 10.15212/CVIA.2024.0011

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Artificial Intelligence Solutions for Cardiovascular Disease Detection and Management in Women

Announcing a new article publication for Cardiovascular Innovations and Applications journal. Artificial intelligence (AI) is a method of data analysis that enables machines to learn patterns from datasets and make predictions. With advances in computer chip technology for data processing and the increasing availability of big data, AI can be leveraged to improve cardiovascular care for women – an often understudied and undertreated population. The authors of this article briefly discuss the potential benefits of AI-based solutions in cardiovascular care for women and also highlight inadvertent drawbacks to the use of AI and novel digital technologies in women.

https://www.scienceopen.com/hosted-document?doi=10.15212/CVIA.2023.0024

CVIA is available on the ScienceOpen platform and at Cardiovascular Innovations and Applications. Submissions may be made using ScholarOne Manuscripts. There are no author submission or article processing fees. Cardiovascular Innovations and Applications is indexed in the EMBASE, EBSCO, ESCI, OCLC, Primo Central (Ex Libris), Sherpa Romeo, NISC (National Information Services Corporation), DOAJ, Index Copernicus, Research4Life and Ulrich’s web Databases. Follow CVIA on Twitter @CVIA_Journal; or Facebook.

Wendy Tatiana Garzon-Siatoya, Andrea Carolina Morales-Lara and Demilade Adedinsewo. Artificial Intelligence Solutions for Cardiovascular Disease Detection and Management in Women: Promise and Perils. CVIA. 2023. Vol. 8(1). DOI: 10.15212/CVIA.2023.0024

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Machine Learning Methods in Real-World Studies of Cardiovascular Disease

Announcing a new article publication for Cardiovascular Innovations and Applications journal.  Cardiovascular disease (CVD) is one of the leading causes of death worldwide, and answers are urgently needed regarding many aspects, particularly risk identification and prognosis prediction. Real-world studies with large numbers of observations provide an important basis for CVD research but are constrained by high dimensionality, and missing or unstructured data. Machine learning (ML) methods, including a variety of supervised and unsupervised algorithms, are useful for data governance, and are effective for high dimensional data analysis and imputation in real-world studies. This article reviews the theory, strengths and limitations, and applications of several commonly used ML methods in the CVD field, to provide a reference for further application.

This article introduces the origin, purpose, theory, advantages and limitations, and applications of multiple commonly used ML algorithms, including hierarchical and k-means clustering, principal component analysis, random forest, support vector machine, and neural networks. An example uses a random forest on the Systolic Blood Pressure Intervention Trial (SPRINT) data to demonstrate the process and main results of ML application in CVD.

ML methods are effective tools for producing real-world evidence to support clinical decisions and meet clinical needs. This review explains the principles of multiple ML methods in plain language, to provide a reference for further application. Future research is warranted to develop accurate ensemble learning methods for wide application in the medical field.

https://www.scienceopen.com/hosted-document?doi=10.15212/CVIA.2023.0011

CVIA is available on the ScienceOpen platform and at Cardiovascular Innovations and Applications. Submissions may be made using ScholarOne Manuscripts. There are no author submission or article processing fees. Cardiovascular Innovations and Applications is indexed in the EMBASE, EBSCO, ESCI, OCLC, Primo Central (Ex Libris), Sherpa Romeo, NISC (National Information Services Corporation), DOAJ, Index Copernicus, Research4Life and Ulrich’s web Databases. Follow CVIA on Twitter @CVIA_Journal; or Facebook.

Jiawei Zhou, Dongfang You and Jianling Bai et al. Machine Learning Methods in Real-World Studies of Cardiovascular Disease. CVIA. 2023. Vol. 7(1). DOI: 10.15212/CVIA.2023.0011

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