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Improving natural language information extraction from cancer pathology reports using transfer learning and zero-shot string similarity
OBJECTIVE: We develop natural language processing (NLP) methods capable of accurately classifying tumor attributes from pathology reports given minimal labeled examples. Our hierarchical cancer to cancer transfer (HCTC) and zero-shot string similarity (ZSS) methods are designed to exploit shared inf...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Oxford University Press
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8484934/ https://www.ncbi.nlm.nih.gov/pubmed/34604711 http://dx.doi.org/10.1093/jamiaopen/ooab085 |