Natural language processing systems for pathology parsing in limited data environments with uncertainty estimation

OBJECTIVE: Cancer is a leading cause of death, but much of the diagnostic information is stored as unstructured data in pathology reports. We aim to improve uncertainty estimates of machine learning-based pathology parsers and evaluate performance in low data settings. MATERIALS AND METHODS: Our dat...

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Detalles Bibliográficos
Autores principales: Odisho, Anobel Y, Park, Briton, Altieri, Nicholas, DeNero, John, Cooperberg, Matthew R, Carroll, Peter R, Yu, Bin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Research and Applications
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7751177/
https://www.ncbi.nlm.nih.gov/pubmed/33381748
http://dx.doi.org/10.1093/jamiaopen/ooaa029

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7751177/
https://www.ncbi.nlm.nih.gov/pubmed/33381748
http://dx.doi.org/10.1093/jamiaopen/ooaa029

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