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Updated: Jul 10, 2026
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Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
Published on: February 13, 2021
Time-Adaptive Machine Learning Models for Predicting the Severity of Heart Failure with Reduced Ejection Fraction
Trevor Winger1,2, Cagri Ozdemir3, Shanti L Narasimhan4
1Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
Diagnostics (Basel, Switzerland)
|March 28, 2025
Summary
A time-adaptive machine learning model effectively predicts heart failure with reduced ejection fraction severity. Personalizing predictions with patient data significantly improves accuracy, aiding tailored interventions for chronic disease management.
Area of Science:
- Cardiology
- Artificial Intelligence
- Machine Learning
Background:
- Heart failure with reduced ejection fraction (HFrEF) requires individualized management.
- Predicting HFrEF severity and disease progression presents a clinical challenge.
Purpose of the Study:
- To evaluate a time-adaptive Passive-Aggressive classifier for HFrEF severity prediction.
- To assess the model's ability to capture patient-specific disease trajectories.
Main Methods:
- Utilized a time-adaptive Passive-Aggressive classifier with clinical data and Brain Natriuretic Peptide levels.
- Personalized the model by sequentially incorporating 0-9 clinical visits per patient.
- Assessed model adaptability and effectiveness using accuracy and reliability metrics.
Main Results:
- Model accuracy and reliability significantly improved with progressive patient data incorporation.
- One-Versus-Rest AUC increased from 0.4884 (zero visits) to 0.8253 (nine visits).
- Demonstrated ability to handle diverse patient presentations and dynamic disease progression.
Conclusions:
- Time-adaptive machine learning, specifically the Passive-Aggressive classifier, shows promise for HFrEF management.
- Patient-specific predictions facilitate early detection and tailored interventions.
- Adaptive models can enhance clinical workflows for chronic disease management.
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