Article
Prognostic Significance and Associations of Neural Network-Derived Electrocardiographic Features.
Circulation. Cardiovascular quality and outcomes - 1 Dec 2024
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
Abstract excerpt
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-derived ECG features could be used to predict future cardiovascular disease and mortality and have phenotypic and...
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