Published on in Vol 12 (2024)

This is a member publication of University of Pittsburgh

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/55318, first published .
An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing: Algorithm Development and Validation Study

An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing: Algorithm Development and Validation Study

An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing: Algorithm Development and Validation Study

Sonish Sivarajkumar   1 , BS ;   Mark Kelley   2 , MS ;   Alyssa Samolyk-Mazzanti   2 , MS ;   Shyam Visweswaran   1, 3 , MD, PhD ;   Yanshan Wang   1, 2, 3 , PhD

1 Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, United States

2 Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA, United States

3 Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States

Corresponding Author:

  • Yanshan Wang, PhD
  • Department of Health Information Management
  • University of Pittsburgh
  • 6026 Forbes Tower
  • Pittsburgh, PA, 15260
  • United States
  • Phone: 1 4123832712
  • Email: yanshan.wang@pitt.edu