Prognostic Significance and Associations of Neural Network–Derived Electrocardiographic Features

BACKGROUND: Subtle, prognostically important ECG features may not be apparent to physicians. In the course of supervised machine learning, thousands of ECG features are identified. These are not limited to conventional ECG parameters and morphology. We aimed to investigate whether neural network–der...

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Wedi'i Gadw mewn:
Manylion Llyfryddiaeth
Cyhoeddwyd yn:Circ Cardiovasc Qual Outcomes
Prif Awduron: Sau, Arunashis, Ribeiro, Antônio H., McGurk, Kathryn A., Pastika, Libor, Bajaj, Nikesh, Gurnani, Mehak, Sieliwonczyk, Ewa, Patlatzoglou, Konstantinos, Ardissino, Maddalena, Chen, Jun Yu, Wu, Huiyi, Shi, Xili, Hnatkova, Katerina, Zheng, Sean L., Britton, Annie, Shipley, Martin, Andršová, Irena, Novotný, Tomáš, Sabino, Ester C., Giatti, Luana, Barreto, Sandhi M., Waks, Jonathan W., Kramer, Daniel B., Mandic, Danilo, Peters, Nicholas S., O’Regan, Declan P., Malik, Marek, Ware, James S., Ribeiro, Antonio Luiz P., Ng, Fu Siong
Fformat: Artigo
Iaith:Inglês
Cyhoeddwyd: Lippincott Williams & Wilkins 2024
Pynciau:
Original Articles
Mynediad Ar-lein:https://ncbi.nlm.nih.gov/pmc/articles/PMC7616866/
https://ncbi.nlm.nih.gov/pubmed/39540287
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1161/CIRCOUTCOMES.123.010602
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