Evaluating shallow and deep learning strategies for the 2018 n2c2 shared task on clinical text classification

OBJECTIVE: Automated clinical phenotyping is challenging because word-based features quickly turn it into a high-dimensional problem, in which the small, privacy-restricted, training datasets might lead to overfitting. Pretrained embeddings might solve this issue by reusing input representation sche...

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Detalles Bibliográficos
Autores principales: Oleynik, Michel, Kugic, Amila, Kasáč, Zdenko, Kreuzthaler, Markus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Research and Applications
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6798565/
https://www.ncbi.nlm.nih.gov/pubmed/31512729
http://dx.doi.org/10.1093/jamia/ocz149

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6798565/
https://www.ncbi.nlm.nih.gov/pubmed/31512729
http://dx.doi.org/10.1093/jamia/ocz149

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