EHR foundation models improve robustness in the presence of temporal distribution shift

Temporal distribution shift negatively impacts the performance of clinical prediction models over time. Pretraining foundation models using self-supervised learning on electronic health records (EHR) may be effective in acquiring informative global patterns that can improve the robustness of task-sp...

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Detalles Bibliográficos
Autores principales: Guo, Lin Lawrence, Steinberg, Ethan, Fleming, Scott Lanyon, Posada, Jose, Lemmon, Joshua, Pfohl, Stephen R., Shah, Nigam, Fries, Jason, Sung, Lillian
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/PMC9992466/
https://www.ncbi.nlm.nih.gov/pubmed/36882576
http://dx.doi.org/10.1038/s41598-023-30820-8

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9992466/
https://www.ncbi.nlm.nih.gov/pubmed/36882576
http://dx.doi.org/10.1038/s41598-023-30820-8

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