Application of convex hull analysis for the evaluation of data heterogeneity between patient populations of different origin and implications of hospital bias in downstream machine-learning-based data processing: A comparison of 4 critical-care patient datasets

Machine learning (ML) models are developed on a learning dataset covering only a small part of the data of interest. If model predictions are accurate for the learning dataset but fail for unseen data then generalization error is considered high. This problem manifests itself within all major sub-fi...

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Detalles Bibliográficos
Autores principales: Sharafutdinov, Konstantin, Bhat, Jayesh S., Fritsch, Sebastian Johannes, Nikulina, Kateryna, E. Samadi, Moein, Polzin, Richard, Mayer, Hannah, Marx, Gernot, Bickenbach, Johannes, Schuppert, Andreas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Big Data
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9659720/
https://www.ncbi.nlm.nih.gov/pubmed/36387013
http://dx.doi.org/10.3389/fdata.2022.603429

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9659720/
https://www.ncbi.nlm.nih.gov/pubmed/36387013
http://dx.doi.org/10.3389/fdata.2022.603429

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