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Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
Published on: February 13, 2021
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Machine-Learning-Driven Phenotyping in Heart Failure with Preserved Ejection Fraction: Current Approaches and Future
Victoria Potoupni1, Athanasios Samaras2, Christodoulos Papadopoulos1
1Third Department of Cardiology, Ippokratio General Hospital, Aristotle University of Thessaloniki, 546 42 Thessaloniki, Greece.
Medicina (Kaunas, Lithuania)
|November 27, 2025
Summary
Machine learning (ML) helps identify heart failure with preserved ejection fraction (HFpEF) subgroups by analyzing diverse patient data. This approach improves diagnosis, risk stratification, and personalized treatment for better patient outcomes.
Area of Science:
- Cardiology
- Biomedical Informatics
- Artificial Intelligence
Background:
- Heart failure with preserved ejection fraction (HFpEF) presents a significant clinical challenge due to its varied nature and few treatment choices.
- Effective patient phenotyping is crucial for enhancing diagnosis, prognosis, and personalized treatment strategies in HFpEF.
Purpose of the Study:
- To explore the application of machine learning (ML) in identifying distinct HFpEF subgroups.
- To demonstrate how ML can integrate multifaceted data for improved patient stratification and management.
Main Methods:
- Utilized machine learning models to analyze integrated datasets including clinical, imaging, biomarker, and physiological parameters.
- Developed algorithms to uncover complex patterns within HFpEF patient data.
Main Results:
- ML models successfully identified clinically relevant HFpEF subgroups, revealing patterns not apparent through traditional methods.
- The approach demonstrated potential for enhanced risk stratification and personalized therapeutic guidance.
Conclusions:
- Machine learning offers a powerful approach to refine HFpEF phenotyping, leading to improved diagnostic accuracy and treatment personalization.
- Future advancements in AI, data standardization, and interdisciplinary collaboration are key to realizing ML's full potential in managing HFpEF.
Keywords:
HFpEFcardiac dysfunctioncardiac phenotypingheart failuremachine learningpatient stratificationMore Related Videos
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