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Machine learning-based myocardial infarction bibliometric analysis.

Ying Fang1, Yuedi Wu1, Lijuan Gao1

  • 1Xiaoshan District Hospital of Traditional Chinese Medicine, Hangzhou, Zhejiang Province, China.

Frontiers in Medicine
|February 21, 2025
PubMed
Summary
This summary is machine-generated.

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Machine learning (ML) for myocardial infarction (MI) research has rapidly advanced since 2016, with the United States leading in quality. Deep learning and neural networks are key emerging trends for MI diagnosis and treatment.

Area of Science:

  • * **Cardiology and Medical Informatics:** Focus on the intersection of machine learning (ML) and myocardial infarction (MI) research.
  • * **Bibliometric Analysis:** Examination of research output, trends, and collaborations in ML for MI.

Background:

  • * **Growing Interest:** Research in ML for MI has surged, particularly after 2016.
  • * **Key Players:** The United States and China are significant contributors, with the US showing higher research impact.

Purpose of the Study:

  • * **Trend Identification:** To analyze research trends and hotspots in ML for MI from 2008 to 2024.
  • * **Contribution Assessment:** To compare the contributions of countries, authors, and agencies.
  • * **Future Outlook:** To provide insights into future research and development directions.

Main Methods:

  • * **Data Source:** 1,036 publications from the Web of Science Core Collection.
Keywords:
CiteSpacebibliometricsdeep learningmachine learningmyocardial infarction

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  • * **Analytical Tools:** CiteSpace, Bibliometrix, and VOSviewer used for bibliometric analysis and visualization.
  • * **Metrics:** Analysis of publications, countries, institutions, authors, keywords, and citations.

    • Main Results:

    • * **Rapid Growth:** Significant research development in ML for MI post-2015, especially in the US and China.
    • * **US Dominance:** The United States leads in research quality, evidenced by higher impact factors and citation counts.
    • * **Collaboration:** Emerging institutional collaborations, with a need for enhanced international cooperation.

    Conclusions:

    • * **Emerging Trends:** Deep learning and neural networks are pivotal for early diagnosis, risk assessment, and rehabilitation in MI.
    • * **Research Focus:** Future research is directed towards Medicine, Medical Sciences, Molecular Biology, and Genetics.
    • * **Publication Impact:** Key journals like "Circulation" and "Computers in Biology and Medicine" are prominent.