AI model predicts heart failure risk using 24-hour ECG data

Researchers have developed a deep learning model called DeepHHF that can predict the risk of heart failure within five years using 24-hour electrocardiogram (ECG) data. The model, trained on the Technion-Leumit Holter ECG (TLHE) dataset, achieved an area under the receiver operating characteristic curve of 0.80, outperforming traditional methods. Explainability analysis revealed that DeepHHF focuses on arrhythmias and heart abnormalities, highlighting the potential of AI in non-invasive and accessible heart failure risk prediction. AI

IMPACT This research demonstrates the potential for AI to improve non-invasive and accessible heart failure risk prediction, potentially leading to earlier interventions and better patient outcomes.

RANK_REASON The cluster contains an academic paper detailing a new AI model and its performance on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• DeepHHF
• Eran Zvuloni
• heart failure
• Hugging Face
• Technion-Leumit Holter ECG (TLHE) dataset

• paper
• model release
• safety

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