Výsledky vyhledávání - "Aguirre, Aaron A."

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An Electrocardiogram Foundation Model Built on over 10 Million Recordings with External Evaluation across Multiple Domains

Autor: Li, Jun, Aguirre, Aaron, Moura, Junior, Liu, Che, Zhong, Lanhai, Sun, Chenxi, Clifford, Gari, Westover, Brandon, Hong, Shenda

Artificial intelligence (AI) has demonstrated significant potential in ECG analysis and cardiovascular disease assessment. Recently, foundation models have played a remarkable role in advancing medical AI. The development of an ECG foundation model h

Externí odkaz: http://arxiv.org/abs/2410.04133

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Unlocking Telemetry Potential: Self-Supervised Learning for Continuous Clinical Electrocardiogram Monitoring

Autor: Kite, Thomas, Siam, Uzair Tahamid, Ayers, Brian, Houstis, Nicholas, Aguirre, Aaron D

Machine learning (ML) applied to routine patient monitoring within intensive care units (ICUs) has the potential to improve care by providing clinicians with novel insights into each patient's health and expected response to interventions. This paper

Externí odkaz: http://arxiv.org/abs/2406.16915

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Detecting QT prolongation From a Single-lead ECG With Deep Learning

Autor: Alam, Ridwan, Aguirre, Aaron, Stultz, Collin

For a number of antiarrhythmics, drug loading requires a 3 day hospitalization with monitoring for QT prolongation. Automated QT monitoring with wearable ECG monitors would facilitate out-of-hospital care. We develop a deep learning model that infers

Externí odkaz: http://arxiv.org/abs/2401.05378

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Patient Contrastive Learning: a Performant, Expressive, and Practical Approach to ECG Modeling

Autor: Diamant, Nathaniel, Reinertsen, Erik, Song, Steven, Aguirre, Aaron, Stultz, Collin, Batra, Puneet

Supervised machine learning applications in health care are often limited due to a scarcity of labeled training data. To mitigate this effect of small sample size, we introduce a pre-training approach, Patient Contrastive Learning of Representations

Externí odkaz: http://arxiv.org/abs/2104.04569

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