Cargando…
A deep learning model for real-time mortality prediction in critically ill children
BACKGROUND: The rapid development in big data analytics and the data-rich environment of intensive care units together provide unprecedented opportunities for medical breakthroughs in the field of critical care. We developed and validated a machine learning-based model, the Pediatric Risk of Mortali...
Autores principales: | Kim, Soo Yeon, Kim, Saehoon, Cho, Joongbum, Kim, Young Suh, Sol, In Suk, Sung, Youngchul, Cho, Inhyeok, Park, Minseop, Jang, Haerin, Kim, Yoon Hee, Kim, Kyung Won, Sohn, Myung Hyun |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6694497/ https://www.ncbi.nlm.nih.gov/pubmed/31412949 http://dx.doi.org/10.1186/s13054-019-2561-z |
Ejemplares similares
-
Pediatric Home Mechanical Ventilation in Korea: the Present Situation and Future Strategy
por: Park, Mireu, et al.
Publicado: (2019) -
Mitochondrial and Nuclear Mitochondrial Variants in Allergic Diseases
por: Jang, Haerin, et al.
Publicado: (2020) -
Predicting allergic diseases in children using genome-wide association study (GWAS) data and family history()
por: Park, Jaehyun, et al.
Publicado: (2021) -
Clinical usefulness of capnographic monitoring when inserting a feeding tube in critically ill patients: retrospective cohort study
por: Ryu, Jeong-Am, et al.
Publicado: (2016) -
Effects of continuous ketamine infusion on hemodynamics and mortality in critically ill children
por: Park, Sojin, et al.
Publicado: (2019)