Cargando…
Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning
Accurate prediction of survival for cystic fibrosis (CF) patients is instrumental in establishing the optimal timing for referring patients with terminal respiratory failure for lung transplantation (LT). Current practice considers referring patients for LT evaluation once the forced expiratory volu...
Autores principales: | Alaa, Ahmed M., van der Schaar, Mihaela |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6062529/ https://www.ncbi.nlm.nih.gov/pubmed/30050169 http://dx.doi.org/10.1038/s41598-018-29523-2 |
Ejemplares similares
-
External validity of machine learning-based prognostic scores for cystic fibrosis: A retrospective study using the UK and Canadian registries
por: Qin, Yuchao, et al.
Publicado: (2023) -
AutoPrognosis 2.0: Democratizing diagnostic and prognostic modeling in healthcare with automated machine learning
por: Imrie, Fergus, et al.
Publicado: (2023) -
Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants
por: Alaa, Ahmed M., et al.
Publicado: (2019) -
Comparing COVID-19 risk factors in Brazil using machine learning: the importance of socioeconomic, demographic and structural factors
por: Baqui, Pedro, et al.
Publicado: (2021) -
CPAS: the UK’s national machine learning-based hospital capacity planning system for COVID-19
por: Qian, Zhaozhi, et al.
Publicado: (2020)