Applications of artificial intelligence and machine learning in heart failure
Tauben Averbuch1, Kristen Sullivan1, Andrew Sauer2
1Department of Medicine, McMaster University, Hamilton, Ontario, Canada.
European Heart Journal. Digital Health
|January 30, 2023
Summary
Machine learning (ML) offers advanced pattern extraction for complex datasets, outperforming traditional methods in heart failure (HF) care. External validation is crucial for ML
Area of Science:
- Artificial Intelligence and Machine Learning in Cardiovascular Medicine
- Computational Biology and Bioinformatics
- Health Informatics and Data Science
Background:
- Traditional statistical methods struggle with large, noisy datasets common in heart failure (HF).
- Machine learning (ML) offers a powerful alternative, handling complexity with fewer assumptions.
- ML enables detection of novel relationships without pre-specified predictors.
Purpose of the Study:
- To review the rationale and applications of ML in heart failure management.
- To explore ML's role in disease classification, diagnosis, risk stratification, and treatment optimization.
- To discuss ML's potential in learning healthcare systems and clinical trial recruitment.
Main Methods:
- Review of existing literature on machine learning applications in heart failure.
- Discussion of ML's advantages over traditional statistical models for complex data.
- Analysis of ML's utility across various aspects of HF care, from diagnosis to patient selection.
Main Results:
- ML can enhance HF disease classification, early diagnosis, and decompensation detection.
- ML aids in risk stratification, optimizing medical therapy titration, and patient selection for devices.
- ML shows potential for improving clinical trial recruitment and expediting healthcare system learning.
Conclusions:
- Machine learning holds significant promise for advancing heart failure clinical care and research.
- Limitations include opaque logic and potential unreliability with data errors or shifts.
- External validation through prospective studies is essential for widespread adoption of ML in HF.
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