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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...
| Autores principales: | , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2023
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| Materias: | |
| 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 |