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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Pubblicato in:Circ Cardiovasc Qual Outcomes
Autori principali: 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
Natura: Artigo
Lingua:Inglês
Pubblicazione: Lippincott Williams & Wilkins 2024
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Original Articles
Accesso online: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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