Hierarchical attention networks for information extraction from cancer pathology reports

OBJECTIVE: We explored how a deep learning (DL) approach based on hierarchical attention networks (HANs) can improve model performance for multiple information extraction tasks from unstructured cancer pathology reports compared to conventional methods that do not sufficiently capture syntactic and s...

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Detalles Bibliográficos
Autores principales: Gao, Shang, Young, Michael T, Qiu, John X, Yoon, Hong-Jun, Christian, James B, Fearn, Paul A, Tourassi, Georgia D, Ramanthan, Arvind
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Research and Applications
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7282502/
https://www.ncbi.nlm.nih.gov/pubmed/29155996
http://dx.doi.org/10.1093/jamia/ocx131

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7282502/
https://www.ncbi.nlm.nih.gov/pubmed/29155996
http://dx.doi.org/10.1093/jamia/ocx131

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