Artificial intelligence for detecting electrolyte imbalance using electrocardiography-Reference-Cited by-同舟云学术

Artificial intelligence for detecting electrolyte imbalance using electrocardiography

Published:2021-03-15 Issue:3 Volume:26 Page:
ISSN:1082-720X
Container-title:Annals of Noninvasive Electrocardiology
language:en
Short-container-title:Ann Noninvasive Electrocardiol

Author:

Kwon Joon‐myoung¹²³⁴^ORCID,Jung Min‐Seung¹,Kim Kyung‐Hee²⁵,Jo Yong‐Yeon¹,Shin Jae‐Hyun¹,Cho Yong‐Hyeon¹,Lee Yoon‐Ji¹,Ban Jang‐Hyeon⁴,Jeon Ki‐Hyun²⁵,Lee Soo Youn²⁵,Park Jinsik⁵,Oh Byung‐Hee⁵

Affiliation:

1. Medical Research Team Medical AI Co. Ltd. Seoul South Korea

2. Artificial Intelligence and Big Data Research Center Sejong Medical Research Institute Bucheon South Korea

3. Department of Critical Care and Emergency Medicine Mediplex Sejong Hospital Incheon South Korea

4. Medical R&D Center Bodyfriend Co. Ltd. Seoul South Korea

5. Division of Cardiology Cardiovascular Center Mediplex Sejong Hospital Incheon South Korea

Funder

National Research Foundation of Korea

Publisher

Wiley

Subject

Physiology (medical),Cardiology and Cardiovascular Medicine,General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/anec.12839

Reference33 articles.

1. Impact of diuretic therapy-associated electrolyte disorders present on admission to the emergency department: a cross-sectional analysis

2. Age and Sex Estimation Using Artificial Intelligence From Standard 12-Lead ECGs

3. Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram

4. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction

5. General characteristics of patients with electrolyte imbalance admitted to emergency department

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