Capturing Emerging Experiential Knowledge for Vaccination Guidelines Through Natural Language Processing: Proof-of-Concept Study.

Journal: Journal of medical Internet research
PMID:

Abstract

BACKGROUND: Experience-based knowledge and value considerations of health professionals, citizens, and patients are essential to formulate public health and clinical guidelines that are relevant and applicable to medical practice. Conventional methods for incorporating such knowledge into guideline development often involve a limited number of representatives and are considered to be time-consuming. Including experiential knowledge can be crucial during rapid guidance production in response to a pandemic but it is difficult to accomplish.

Authors

  • Lea Lösch
    Athena Institute, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.
  • Teun Zuiderent-Jerak
    Athena Institute, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.
  • Florian Kunneman
    Radboud University, Erasmusplein 1, Nijmegen, 6525, HT, The Netherlands. f.a.kunneman@vu.nl.
  • Elena Syurina
    Athena Institute, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.
  • Marloes Bongers
    Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), Bilthoven, Netherlands.
  • Mart L Stein
    Centre for Infectious Disease Control (CIb), National Institute for Public Health and the Environment (RIVM), Bilthoven, Netherlands.
  • Michelle Chan
    Department of Computer Science, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.
  • Willemine Willems
    Athena Institute, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.
  • Aura Timen
    Athena Institute, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.