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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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Personalized Management for Heart Failure with Preserved Ejection Fraction.
Chang-Yi Lin1, Heng-You Sung1, Ying-Ju Chen2
1Division of Cardiology, Department of Internal Medicine, MacKay Memorial Hospital, No. 92, Sec. 2, Zhongshan N. Road, Taipei 10449, Taiwan.
Journal of Personalized Medicine
|May 27, 2023
Summary
Artificial intelligence (AI) can identify distinct patient groups in heart failure with preserved ejection fraction (HFpEF). Further research is needed to integrate AI-driven phenotyping into clinical practice for better HFpEF management.
Area of Science:
- Cardiology
- Medical Informatics
- Artificial Intelligence
Background:
- Heart failure with preserved ejection fraction (HFpEF) presents diverse clinical phenotypes due to multiple underlying mechanisms and comorbidities.
- Understanding these phenotypes is crucial for advancing HFpEF pathophysiology, treatment strategies, and patient outcomes.
Purpose of the Study:
- To explore the potential of artificial intelligence (AI) in phenotyping HFpEF using multi-dimensional data.
- To highlight the gap between AI capabilities and current clinical practice guidelines for HFpEF.
Main Methods:
- AI-based phenotyping utilizing clinical, biomarker, and imaging data.
- Analysis of existing data to identify distinct HFpEF phenotypes.
Main Results:
- Accumulating data suggests AI can effectively phenotype HFpEF patients.
- Current guidelines and consensus lack integration of AI-driven phenotyping in daily practice.
Conclusions:
- AI-based phenotyping holds promise for advancing HFpEF management.
- Further studies are required to validate AI findings and standardize clinical implementation for HFpEF.
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