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

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

IntroductionAccurately 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, wh...

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Bibliographic Details
Main Authors: Andreas Skov Millarch, Alexander Bonde, Mikkel Bonde, Kiril Vadomovic Klein, Fredrik Folke, Søren Steemann Rudolph, Martin Sillesen
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-11-01
Series:Frontiers in Digital Health
Subjects:
artificial intelligence
surgery
trauma
healthcare system
prediction model
transfer learning
Online Access:https://www.frontiersin.org/articles/10.3389/fdgth.2023.1249258/full

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