Integration of large-scale community-developed causal loop diagrams: a Natural Language Processing approach to merging factors based on semantic similarity.

Journal: BMC public health
PMID: 40055777

Abstract

BACKGROUND: Complex public health problems have been addressed in communities through systems thinking and participatory methods like Group Model Building (GMB) and Causal Loop Diagrams (CLDs) albeit with some challenges. This study aimed to explore the feasibility of Natural Language Processing (NLP) in simplifying and enhancing CLD merging processes, avoiding manual merging of factors, utilizing different semantic textual similarity models.

Authors

  • Melissa Valdivia Cabrera
    Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, Geelong, VIC, Australia. melissa.v@research.deakin.edu.au.
  • Michael Johnstone
    Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, Geelong, VIC, Australia.
  • Joshua Hayward
    Global Centre for Preventive Health and Nutrition, Institute for Health Transformation, Deakin University, Geelong, VIC, Australia.
  • Kristy A Bolton
    Global Centre for Preventive Health and Nutrition, Institute for Health Transformation, Deakin University, Geelong, VIC, Australia.
  • Douglas Creighton
    Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, Geelong, VIC, Australia.

Keywords

External Resources

Popular Topics
Recent Journals