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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...
Autores principales: | Lauritsen, Simon Meyer, Thiesson, Bo, Jørgensen, Marianne Johansson, Riis, Anders Hammerich, Espelund, Ulrick Skipper, Weile, Jesper Bo, Lange, Jeppe |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
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 |
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