Predictive Models for Chronic Heart Failure

2025-04-07

AI Summary

To address the clinical challenge of early risk identification in chronic heart failure (HF) patients and the suboptimal sensitivity of existing predictive models, this study proposes a dual-path decoupled stacked ensemble framework integrating clinical and echocardiographic features. Distinct base learners model each heterogeneous feature modality separately, and a meta-learner fuses their outputs into a calibrated risk score—prioritizing high sensitivity as required in clinical screening. Evaluated on real-world data, the model achieves 95% sensitivity and 84% accuracy, enabling precise high-risk patient identification in the PrediHealth remote monitoring program. The core contributions are: (1) the first dual-path feature decoupling mechanism for HF risk prediction, preserving modality-specific signal integrity; and (2) a clinically optimized stacked architecture that enhances recall while maintaining interpretability. This yields a robust, explainable, and high-recall risk stratification tool tailored to clinical deployment requirements.

Technology Category

• Machine Learning: Ensemble Methods
• Humans and AI: Human-in-the-loop Machine Learning
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

The management of chronic Heart Failure (HF) presents significant challenges in modern healthcare, requiring continuous monitoring, early detection of exacerbations, and personalized treatment strategies. In this paper, we present a predictive model founded on Machine Learning (ML) techniques to identify patients at HF risk. This model is an ensemble learning approach, a modified stacking technique, that uses two specialized models leveraging clinical and echocardiographic features and then a meta-model to combine the predictions of these two models. We initially assess the model on a real dataset and the obtained results suggest that it performs well in the stratification of patients at HR risk. Specifically, we obtained high sensitivity (95%), ensuring that nearly all high-risk patients are identified. As for accuracy, we obtained 84%, which can be considered moderate in some ML contexts. However, it is acceptable given our priority of identifying patients at risk of HF because they will be asked to participate in the telemonitoring program of the PrediHealth research project on which some of the authors of this paper are working. The initial findings also suggest that ML-based risk stratification models can serve as valuable decision-support tools not only in the PrediHealth project but also for healthcare professionals, aiding in early intervention and personalized patient management. To have a better understanding of the value and of potentiality of our predictive model, we also contrasted its results with those obtained by using three baseline models. The preliminary results indicate that our predictive model outperforms these baselines that flatly consider features, ie not grouping them in clinical and echocardiographic features.

Problem

• Predicts chronic Heart Failure risk using Machine Learning
• Combines clinical and echocardiographic features via ensemble learning
• Aims to improve early intervention and personalized patient management

Innovation

• Ensemble learning with modified stacking technique
• Combines clinical and echocardiographic features
• High sensitivity for HF risk stratification