Artificial intelligence for detecting electrolyte imbalance using electrocardiography

Autor: 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

Publikováno v: Annals of Noninvasive Electrocardiology, Vol 26, Iss 3, Pp n/a-n/a (2021)

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 devel

Externí odkaz: https://doaj.org/article/386a234b95b248d8a2334ef8cb560a9b

Artificial intelligence for detecting electrolyte imbalance using electrocardiography

Autor: Ki-Hyun Jeon, Soo-Youn Lee, Jinsik Park, Joon-myoung Kwon, Min Seung Jung, Jang Hyeon Ban, Jae-Hyun Shin, Yong Hyeon Cho, Yong-Yeon Jo, Kyung-Hee Kim, Byung Hee Oh, Yoon Ji Lee

Publikováno v: Annals of Noninvasive Electrocardiology, Vol 26, Iss 3, Pp n/a-n/a (2021)
Annals of Noninvasive Electrocardiology

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 de

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8b64131353f2e4e855f706972c6128e0
https://doi.org/10.1111/anec.12839

Deep-learning model for screening sepsis using electrocardiography

Autor: Yong-Hyeon Cho, Jang-Hyeon Ban, Kyung-Hee Kim, Da-Young Kang, Jae-Hyun Shin, Min-Seung Jung, Yong-Yeon Jo, Joon-myoung Kwon, Ye Rang Lee, Soo-Youn Lee, Yoon-Ji Lee

Publikováno v: Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine
Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, Vol 29, Iss 1, Pp 1-12 (2021)

Background Sepsis is a life-threatening organ dysfunction and a major healthcare burden worldwide. Although sepsis is a medical emergency that requires immediate management, screening for the occurrence of sepsis is difficult. Herein, we propose a de

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cdc8fef1bb3de6bbf684559aff1c19b3
https://pubmed.ncbi.nlm.nih.gov/34602084

Detection and classification of arrhythmia using an explainable deep learning model

Autor: Joon-myoung Kwon, Yoon Ji Lee, Ki-Hyun Jeon, Kyung-Hee Kim, Byung Hee Oh, Yong-Yeon Jo, Jang Hyeon Ban, Soo-Youn Lee, Jinsik Park, Yong Hyeon Cho, Jae-Hyun Shin, Min Seung Jung

Publikováno v: Journal of electrocardiology. 67

Background Early detection and intervention is the cornerstone for appropriate treatment of arrhythmia and prevention of complications and mortality. Although diverse deep learning models have been developed to detect arrhythmia, they have been criti

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0048184421d4e9539b249e62f2ed4efa
https://pubmed.ncbi.nlm.nih.gov/34225095

Deep-learning model for screening sepsis using electrocardiography.

Autor: Kwon, Joon-myoung, Lee, Ye Rang, Jung, Min-Seung, Lee, Yoon-Ji, Jo, Yong-Yeon, Kang, Da-Young, Lee, Soo Youn, Cho, Yong-Hyeon, Shin, Jae-Hyun, Ban, Jang-Hyeon, Kim, Kyung-Hee

Publikováno v: Scandinavian Journal of Trauma, Resuscitation & Emergency Medicine; 10/3/2021, Vol. 29 Issue 1, p1-12, 12p