Can we reliably automate clinical prognostic modelling? A retrospective cohort study for ICU triage prediction of in-hospital mortality of COVID-19 patients in the Netherlands

BACKGROUND: Building Machine Learning (ML) models in healthcare may suffer from time-consuming and potentially biased pre-selection of predictors by hand that can result in limited or trivial selection of suitable models. We aimed to assess the predictive performance of automating the process of bui...

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Detalles Bibliográficos
Autores principales: Vagliano, I., Brinkman, S., Abu-Hanna, A., Arbous, M.S, Dongelmans, D.A., Elbers, P.W.G., de Lange, D.W., van der Schaar, M., de Keizer, N.F., Schut, M.C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Author(s). Published by Elsevier B.V. 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8791240/
https://www.ncbi.nlm.nih.gov/pubmed/35114522
http://dx.doi.org/10.1016/j.ijmedinf.2022.104688

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8791240/
https://www.ncbi.nlm.nih.gov/pubmed/35114522
http://dx.doi.org/10.1016/j.ijmedinf.2022.104688

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