Open AccessDoes BERT need domain adaptation for clinical negation detection?
Author(s) -
Chen Lin,
Steven Bethard,
Dmitriy Dligach,
Farig Sadeque,
Guergana Savova,
Timothy A. Miller
Publication year - 2020
Publication title -
journal of the american medical informatics association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.614
H-Index - 150
eISSN - 1527-974X
pISSN - 1067-5027
DOI - 10.1093/jamia/ocaa001
Subject(s) - computer science , negation , artificial intelligence , transformer , domain adaptation , overfitting , adaptation (eye) , classifier (uml) , machine learning , natural language processing , task (project management) , language model , transfer of learning , artificial neural network , psychology , economics , quantum mechanics , programming language , voltage , physics , management , neuroscience
Classifying whether concepts in an unstructured clinical text are negated is an important unsolved task. New domain adaptation and transfer learning methods can potentially address this issue.
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