Bibliometric Analysis of Machine Learning Applications in Ischemia Research

Area of Science:

• Biomedical research
• Artificial Intelligence
• Data Science

Background:

• Ischemia research is a critical area in cardiovascular and neurological sciences.
• Machine learning (ML) offers powerful tools for analyzing complex biomedical data.
• Understanding the integration of ML in ischemia research is essential for future advancements.

Purpose of the Study:

• To conduct a comprehensive bibliometric analysis of machine learning applications in ischemia research.
• To elucidate the current landscape, trends, and key players in this interdisciplinary field.

Main Methods:

• Bibliometric analysis of publications with "ischemia" and "machine learning" in titles.
• Examination of top 50 most cited papers for thematic focus and co-word dynamics.
• Analysis of publication trends, contributor institutions, geographical distribution, and co-authorship networks.

Main Results:

• Significant increase in publications over time, indicating growing research interest.
• Identification of leading contributors, institutions, and journals in ML for ischemia research.
• Detailed analysis of thematic trends and collaborative networks through co-word dynamics and co-authorship networks.

Conclusions:

• The study provides valuable insights into the evolving landscape of machine learning applications in ischemia research.
• It highlights the growing importance and collaborative nature of research at the intersection of ML and ischemia.