Assessing optimal methods for transferring machine learning models to low-volume and imbalanced clinical datasets: experiences from predicting outcomes of Danish trauma patients

INTRODUCTION: Accurately predicting patient outcomes is crucial for improving healthcare delivery, but large-scale risk prediction models are often developed and tested on specific datasets where clinical parameters and outcomes may not fully reflect local clinical settings. Where this is the case,...

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Detalles Bibliográficos
Autores principales: Millarch, Andreas Skov, Bonde, Alexander, Bonde, Mikkel, Klein, Kiril Vadomovic, Folke, Fredrik, Rudolph, Søren Steemann, Sillesen, Martin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Digital Health
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10656776/
https://www.ncbi.nlm.nih.gov/pubmed/38026835
http://dx.doi.org/10.3389/fdgth.2023.1249258

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10656776/
https://www.ncbi.nlm.nih.gov/pubmed/38026835
http://dx.doi.org/10.3389/fdgth.2023.1249258

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