Forecasting adverse surgical events using self-supervised transfer learning for physiological signals

Hundreds of millions of surgical procedures take place annually across the world, which generate a prevalent type of electronic health record (EHR) data comprising time series physiological signals. Here, we present a transferable embedding method (i.e., a method to transform time series signals int...

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Detalles Bibliográficos
Autores principales: Chen, Hugh, Lundberg, Scott M., Erion, Gabriel, Kim, Jerry H., Lee, Su-In
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8654960/
https://www.ncbi.nlm.nih.gov/pubmed/34880410
http://dx.doi.org/10.1038/s41746-021-00536-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8654960/
https://www.ncbi.nlm.nih.gov/pubmed/34880410
http://dx.doi.org/10.1038/s41746-021-00536-y

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