Cargando…
Embracing cohort heterogeneity in clinical machine learning development: a step toward generalizable models
This study is a simple illustration of the benefit of averaging over cohorts, rather than developing a prediction model from a single cohort. We show that models trained on data from multiple cohorts can perform significantly better in new settings than models based on the same amount of training da...
Autores principales: | Schinkel, Michiel, Bennis, Frank C., Boerman, Anneroos W., Wiersinga, W. Joost, Nanayakkara, Prabath W. B. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10209202/ https://www.ncbi.nlm.nih.gov/pubmed/37225751 http://dx.doi.org/10.1038/s41598-023-35557-y |
Ejemplares similares
-
Detecting changes in the performance of a clinical machine learning tool over time
por: Schinkel, Michiel, et al.
Publicado: (2023) -
Diagnostic stewardship for blood cultures in the emergency department: A multicenter validation and prospective evaluation of a machine learning prediction tool
por: Schinkel, Michiel, et al.
Publicado: (2022) -
Sepsis Performance Improvement Programs: From Evidence Toward Clinical Implementation
por: Schinkel, Michiel, et al.
Publicado: (2022) -
Using machine learning to predict blood culture outcomes in the emergency department: a single-centre, retrospective, observational study
por: Boerman, Anneroos W, et al.
Publicado: (2022) -
What Sepsis Researchers Can Learn from COVID-19
por: Schinkel, Michiel, et al.
Publicado: (2021)