Discontinuous named entities in clinical text: A systematic literature review.

Journal: Journal of biomedical informatics
Published Date:

Abstract

OBJECTIVE: Extracting named entities from clinical free-text presents unique challenges, particularly when dealing with discontinuous entities-mentions that are separated by unrelated words. Traditional NER methods often struggle to accurately identify these entities, prompting the development of specialised computational solutions. This paper systematically reviews and presents the methodologies developed for Discontinuous Named Entity Recognition in clinical texts, highlighting their effectiveness and the challenges they face.

Authors

  • Areej Alhassan
    University of Manchester, United Kingdom; King Saud University, Saudi Arabia. Electronic address: aralhassan@ksu.edu.sa.
  • Viktor Schlegel
    University of Manchester, United Kingdom; Imperial Global, Singapore.
  • Monira Aloud
    King Saud University, Saudi Arabia.
  • Riza Batista-Navarro
  • Goran Nenadic
    School of Computer Science, University of Manchester, Manchester, UK.