The Framing of machine learning risk prediction models illustrated by evaluation of sepsis in general wards

Problem framing is critical to developing risk prediction models because all subsequent development work and evaluation takes place within the context of how a problem has been framed and explicit documentation of framing choices makes it easier to compare evaluation metrics between published studie...

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Detalles Bibliográficos
Autores principales: Lauritsen, Simon Meyer, Thiesson, Bo, Jørgensen, Marianne Johansson, Riis, Anders Hammerich, Espelund, Ulrick Skipper, Weile, Jesper Bo, Lange, Jeppe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8593052/
https://www.ncbi.nlm.nih.gov/pubmed/34782696
http://dx.doi.org/10.1038/s41746-021-00529-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8593052/
https://www.ncbi.nlm.nih.gov/pubmed/34782696
http://dx.doi.org/10.1038/s41746-021-00529-x

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