TEPAPA: a novel in silico feature learning pipeline for mining prognostic and associative factors from text-based electronic medical records
Abstract 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 computat...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2017
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/beee489bea1846b2bde036d07648f143 |
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