Development and external validation of a pretrained deep learning model for the prediction of non-accidental trauma

Non-accidental trauma (NAT) is deadly and difficult to predict. Transformer models pretrained on large datasets have recently produced state of the art performance on diverse prediction tasks, but the optimal pretraining strategies for diagnostic predictions are not known. Here we report the develop...

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Detalles Bibliográficos
Autores principales: Huang, David, Cogill, Steven, Hsia, Renee Y., Yang, Samuel, Kim, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10356774/
https://www.ncbi.nlm.nih.gov/pubmed/37468526
http://dx.doi.org/10.1038/s41746-023-00875-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10356774/
https://www.ncbi.nlm.nih.gov/pubmed/37468526
http://dx.doi.org/10.1038/s41746-023-00875-y

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