Use of Deep Learning to Predict Acute Kidney Injury After Intravenous Contrast Media Administration: Prediction Model Development Study

BACKGROUND: Precise prediction of contrast media–induced acute kidney injury (CIAKI) is an important issue because of its relationship with poor outcomes. OBJECTIVE: Herein, we examined whether a deep learning algorithm could predict the risk of intravenous CIAKI better than other machine learning a...

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Detalles Bibliográficos
Autores principales: Yun, Donghwan, Cho, Semin, Kim, Yong Chul, Kim, Dong Ki, Oh, Kook-Hwan, Joo, Kwon Wook, Kim, Yon Su, Han, Seung Seok
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2021
Materias:
Original Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8520134/
https://www.ncbi.nlm.nih.gov/pubmed/34596574
http://dx.doi.org/10.2196/27177

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8520134/
https://www.ncbi.nlm.nih.gov/pubmed/34596574
http://dx.doi.org/10.2196/27177

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