TEPAPA: a novel in silico feature learning pipeline for mining prognostic and associative factors from text-based electronic medical records

Vast amounts of clinically relevant text-based variables lie undiscovered and unexploited in electronic medical records (EMR). To exploit this untapped resource, and thus facilitate the discovery of informative covariates from unstructured clinical narratives, we have built a novel computational pip...

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Detalles Bibliográficos
Autores principales: Lin, Frank Po-Yen, Pokorny, Adrian, Teng, Christina, Epstein, Richard J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5537364/
https://www.ncbi.nlm.nih.gov/pubmed/28761061
http://dx.doi.org/10.1038/s41598-017-07111-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5537364/
https://www.ncbi.nlm.nih.gov/pubmed/28761061
http://dx.doi.org/10.1038/s41598-017-07111-0

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