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Advances in Machine Learning Approaches to Heart Failure with Preserved Ejection Fraction
Faraz S Ahmad1, Yuan Luo2, Ramsey M Wehbe3
1Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA; Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA; Bluhm Cardiovascular Institute Center for Artificial Intelligence, Northwestern Medicine, Chicago, IL, USA. Electronic address: https://twitter.com/FarazA.
Heart Failure Clinics
|March 28, 2022
Summary
Machine learning offers promise for understanding and treating heart failure with preserved ejection fraction (HFpEF). Careful consideration of potential pitfalls is crucial for appropriate application and interpretation of these advanced computational methods.
Area of Science:
- Cardiology
- Biomedical Informatics
- Computational Medicine
Background:
- Heart failure with preserved ejection fraction (HFpEF) is a complex cardiovascular condition.
- Current understanding of HFpEF pathogenesis and targeted therapies remains limited.
- Machine learning (ML) presents novel opportunities for advancing HFpEF research.
Purpose of the Study:
- To explore the potential utility of machine learning in understanding HFpEF.
- To identify common pitfalls associated with machine learning application in HFpEF research.
- To guide appropriate interpretation and application of ML in precision medicine for HFpEF.
Main Methods:
- Review of machine learning principles and their application in complex clinical syndromes.
- Discussion of potential benefits of ML for targeted therapies and mechanistic insights in HFpEF.
- Analysis of common challenges and limitations in ML studies relevant to cardiovascular research.
Main Results:
- Machine learning algorithms can learn from data to potentially improve HFpEF therapies.
- ML holds promise for guiding precision medicine approaches in complex conditions like HFpEF.
- Several potential pitfalls exist in ML studies that require careful consideration.
Conclusions:
- Machine learning has considerable promise for advancing the mechanistic understanding and treatment of HFpEF.
- Awareness of ML pitfalls is essential for accurate interpretation and effective clinical application.
- Appropriate use of ML can enhance targeted therapies and precision medicine for HFpEF patients.
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