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Updated: Jun 9, 2025

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Generative AI-based knowledge graphs for the illustration and development of mHealth self-management content.

Marc Blanchard1, Vincenzo Venerito2, Pedro Ming Azevedo1

  • 1Department of Rheumatology, University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland.

Frontiers in Digital Health
|October 22, 2024
PubMed
Summary

Generative AI and knowledge graphs effectively structured digital therapeutics content for fibromyalgia, enhancing program transparency and identifying content gaps. This approach improves self-management program development and patient feedback integration.

Keywords:
ChatGPTartificial intelligencechronic musculoskeletal pain syndromesfibromyalgiaknowledge graphlarge language modelmHealth (mobile health)

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Area of Science:

  • Digital health
  • Artificial intelligence
  • Medical informatics

Background:

  • Digital therapeutics (DTx), particularly mobile health (mHealth) self-management programs, show promise in reducing disease activity for conditions like fibromyalgia.
  • The heterogeneity of content in online self-management programs hinders effective comparison and development.

Purpose of the Study:

  • To utilize generative AI-based knowledge graphs and network analysis for categorizing and structuring mHealth content.
  • To apply this methodology to a fibromyalgia self-management program as a case study.

Main Methods:

  • Developed a multimodal mHealth program for fibromyalgia, customizing content based on patient clustering.
  • Employed generative AI tools (ChatGPT-4, Infranodus) for knowledge graph creation and text network analysis.
  • Conducted sentiment analysis on patient feedback to evaluate program content relevance.

Main Results:

  • AI-generated knowledge graphs visually organized DTx content, highlighting key themes like "Pain management" and "Mental health challenges".
  • Network analysis revealed "pain" as a central concept, connected to "sleep" and "stress".
  • Gap analysis identified missing links, e.g., between "negative behavior" and "systemic inflammation", and patient feedback indicated a lack of focus on social contacts.

Conclusions:

  • Generative AI and text network analysis offer effective tools for structuring and visualizing digital therapeutics content.
  • Knowledge graphs enhance transparency, facilitate new conceptual framework development, and enable the integration of patient feedback into self-management programs.