Few-shot learning for medical text: A review of advances, trends, and opportunities.

Journal: Journal of biomedical informatics
Published Date: Aug 1, 2023

Abstract

BACKGROUND: Few-shot learning (FSL) is a class of machine learning methods that require small numbers of labeled instances for training. With many medical topics having limited annotated text-based data in practical settings, FSL-based natural language processing (NLP) holds substantial promise. We aimed to conduct a review to explore the current state of FSL methods for medical NLP.

Authors

  • Yao Ge
    James Watt School of Engineering, University of Glasgow, Glasgow, G12 8QQ, UK.
  • Yuting Guo
    State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China.
  • Sudeshna Das
    Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
  • Mohammed Ali Al-Garadi
    Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia.
  • Abeed Sarker
    Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, United States.

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