Artificial intelligence for detecting electrolyte imbalance using electrocardiography

Artificial intelligence for detecting electrolyte imbalance using electrocardiography

Abstract Introduction The detection and monitoring of electrolyte imbalance is essential for appropriate management of many metabolic diseases; however, there is no tool that detects such imbalances reliably and noninvasively. In this study, we developed a deep learning model (DLM) using electrocard...

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Bibliographic Details
Main Authors: Joon‐myoung Kwon, Min‐Seung Jung, Kyung‐Hee Kim, Yong‐Yeon Jo, Jae‐Hyun Shin, Yong‐Hyeon Cho, Yoon‐Ji Lee, Jang‐Hyeon Ban, Ki‐Hyun Jeon, Soo Youn Lee, Jinsik Park, Byung‐Hee Oh
Format: Article
Language:English
Published: Wiley 2021-05-01
Series:Annals of Noninvasive Electrocardiology
Subjects:
artificial intelligence
deep learning
electrocardiography
electrolytes
Online Access:https://doi.org/10.1111/anec.12839

Internet

https://doi.org/10.1111/anec.12839

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