Cargando…
Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography
BACKGROUND: In-hospital cardiac arrest is a major burden in health care. Although several track-and-trigger systems are used to predict cardiac arrest, they often have unsatisfactory performances. We hypothesized that a deep-learning-based artificial intelligence algorithm (DLA) could effectively pr...
Autores principales: | Kwon, Joon-myoung, Kim, Kyung-Hee, Jeon, Ki-Hyun, Lee, Soo Youn, Park, Jinsik, Oh, Byung-Hee |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7541213/ https://www.ncbi.nlm.nih.gov/pubmed/33023615 http://dx.doi.org/10.1186/s13049-020-00791-0 |
Ejemplares similares
-
Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography
por: Cho, Younghoon, et al.
Publicado: (2020) -
Artificial intelligence for detecting electrolyte imbalance using electrocardiography
por: Kwon, Joon‐myoung, et al.
Publicado: (2021) -
Deep Learning–Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography
por: Kwon, Joon‐Myoung, et al.
Publicado: (2020) -
Artificial intelligence to diagnose paroxysmal supraventricular tachycardia using electrocardiography during normal sinus rhythm
por: Jo, Yong-Yeon, et al.
Publicado: (2021) -
Artificial intelligence assessment for early detection and prediction of renal impairment using electrocardiography
por: Kwon, Joon-myoung, et al.
Publicado: (2022)