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Identifying Influences in Patient Decision-making Processes in Online Health Communities: Data Science Approach.

Mingda Li1, Jinhe Shi1, Yi Chen2

  • 1Ying Wu College of Computing, New Jersey Institute of Technology, Newark, NJ, United States.

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Summary
This summary is machine-generated.

Researchers developed a deep learning model to identify influential posts in online health communities (OHCs). This approach helps understand how online discussions impact patient decision-making and engagement.

Keywords:
decision-making threadsdeep learninginfluence relationshiponline health communitiespatient engagementtext relevance measurement

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Area of Science:

  • Health Informatics
  • Computational Social Science
  • Artificial Intelligence

Background:

  • Online health communities (OHCs) are increasingly used by patients for information and support.
  • Understanding patient decision-making processes and influences within OHCs is crucial.

Purpose of the Study:

  • To identify posts within OHC discussion threads that influence user decision-making.
  • To analyze the impact of specific online discussions on patient choices.

Main Methods:

  • Defined influence relationships for posts in discussion threads.
  • Developed a deep learning framework combining text relevance and post features (question/action probability).
  • Utilized state-of-the-art text relevance measurement for feature generation.

Main Results:

  • Evaluated the model on a cancer survivor OHC discussion dataset.
  • Empirical results demonstrated the effectiveness of the proposed influence identification techniques.
  • Identified a significant number of influential discussions within the OHC.

Conclusions:

  • Feasible to identify influence relationships in OHCs using computational methods.
  • Influential discussions can significantly affect user decision-making and engagement.
  • Findings can enhance information quality, user engagement, and experience in OHCs.