March 26, 2024, 4:42 a.m. | Sergio Gonz\'alez, Abel Ko-Chun Yi, Wan-Ting Hsieh, Wei-Chao Chen, Chun-Li Wang, Victor Chien-Chia Wu, Shang-Hung Chang

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.15408v1 Announce Type: cross
Abstract: Cardiovascular diseases, including Heart Failure (HF), remain a leading global cause of mortality, often evading early detection. In this context, accessible and effective risk assessment is indispensable. Traditional approaches rely on resource-intensive diagnostic tests, typically administered after the onset of symptoms. The widespread availability of electrocardiogram (ECG) technology and the power of Machine Learning are emerging as viable alternatives within smart healthcare. In this paper, we propose several multi-modal approaches that combine 30-second ECG recordings …

abstract arxiv assessment context cs.ai cs.lg detection diagnostic diseases eess.sp failure global heart failure long-term modal mortality multi-modal risk risk assessment tests type

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