Heart Failure Risk Prediction Using a Polarimetric Contexture Convolutional Network Based on Clinical and Physiological Parameters

Description:

Heart failure is a major cardiovascular condition in which early identification of high-risk patients is essential for timely clinical intervention and effective disease management. Conventional risk assessment methods may have difficulty capturing complex nonlinear relationships among demographic, physiological, cardiac, and biochemical parameters.

Input: Clinical parameters

Output: Heart Failure Risk Classification

Tags:

#HeartFailurePrediction #HeartFailureRisk #CardiovascularDisease #PCCN #DeepLearning #MachineLearning #MedicalAI #ClinicalPrediction #RiskPrediction #HealthcareAI #ExplainableAI #ArtificialIntelligence #ClinicalDecisionSupport #DigitalHealth

References:

1. Dhingra, L. S., Aminorroaya, A., Sangha, V., Pedroso, A. F., Asselbergs, F. W., Brant, L. C., ... & Khera, R. (2025). Heart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study. European heart journal, 46(11), 1044-1053.

2. Lyu, L., Zhu, H., Chen, H., Zhou, J., Chan, R. H., & Lu, L. (2025). PDSNet: Patient-disease dual spatial similarity neural networks for predicting heart failure risk using short electronic health records. IEEE Journal of Biomedical and Health Informatics.