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

By PulseAugur Editorial · [1 sources] ·

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 →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

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

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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]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Eran Zvuloni, Ronit Almog, Michael Glikson, Shany Brimer Biton, Ilan Green, Izhar Laufer, Offer Amir, Joachim A. Behar ·

    Modeling Day-Long ECG Signals to Predict Heart Failure Risk with Explainable AI

    arXiv:2601.00014v2 Announce Type: replace-cross Abstract: Heart failure (HF) affects 11.8% of adults aged 65 and older, reducing quality of life and longevity. Preventing HF can reduce morbidity and mortality. We hypothesized that artificial intelligence (AI) applied to 24-hour s…

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