Does BERT need domain adaptation for clinical negation detection?

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Apr 1, 2020

Abstract

INTRODUCTION: 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.

Authors

  • Chen Lin
    Faculty of Business and Economics, University of Hong Kong, Hong Kong SAR 999077, China.
  • Steven Bethard
    Department of Computer and Information Science, University of Alabama at Birmingham, Birmingham, Alabama, USA, 35294.
  • Dmitriy Dligach
    Department of Public Health Sciences, Stritch School of Medicine, Loyola University Chicago, Maywood, IL.
  • Farig Sadeque
    Computational Health Informatics Program, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
  • Guergana Savova
    Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
  • Timothy A Miller
    Boston Children's Hospital, 300 Longwood Avenue, Boston, MA 02115, United States; Harvard Medical School, 25 Shattuck St, Boston, MA 02115, United States.

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