Machine learning in cardiology: Clinical application and basic research.

Area of Science:

• Artificial Intelligence
• Medical Informatics
• Bioinformatics

Background:

• Machine learning (ML) is a rapidly advancing subfield of artificial intelligence with broad societal impact.
• Its application is expanding significantly within the medical field, transforming data analysis and research.
• ML encompasses supervised, unsupervised, and reinforcement learning, chosen based on data type and research goals.

Purpose of the Study:

• To review recent advancements in machine learning applications within medicine.
• To specifically focus on the use of ML in both clinical and basic cardiovascular research.
• To underscore the growing relevance of ML in analyzing diverse medical data.

Main Methods:

• Review of current literature on machine learning in medical and cardiovascular research.
• Categorization of ML types (supervised, unsupervised, reinforcement) and their applications.
• Analysis of ML utilization across various data types including electronic health records and omics data.

Main Results:

• Machine learning is crucial for analyzing complex datasets like electronic health records, microarray data, and RNA sequences.
• Significant progress has been made in applying ML to clinical cardiovascular studies.
• ML is indispensable for advanced genomic and multi-omics analyses in basic research.

Conclusions:

• Machine learning is a key technology driving innovation in cardiovascular medicine and research.
• Its versatility allows for effective analysis of diverse data, from clinical records to genomic sequences.
• Continued advancements in ML promise further breakthroughs in understanding and treating cardiovascular diseases.