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Knowledge Discovery from Posts in Online Health Communities Using Unified Medical Language System

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  • Donghua Chen

    (Department of Information Management, School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China)

  • Runtong Zhang

    (Department of Information Management, School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China)

  • Kecheng Liu

    (Henley Business School, University of Reading, Reading RG6 6UD, UK)

  • Lei Hou

    (Henley Business School, University of Reading, Reading RG6 6UD, UK)

Abstract

Patient-reported posts in Online Health Communities (OHCs) contain various valuable information that can help establish knowledge-based online support for online patients. However, utilizing these reports to improve online patient services in the absence of appropriate medical and healthcare expert knowledge is difficult. Thus, we propose a comprehensive knowledge discovery method that is based on the Unified Medical Language System for the analysis of narrative posts in OHCs. First, we propose a domain-knowledge support framework for OHCs to provide a basis for post analysis. Second, we develop a Knowledge-Involved Topic Modeling (KI-TM) method to extract and expand explicit knowledge within the text. We propose four metrics, namely, explicit knowledge rate, latent knowledge rate, knowledge correlation rate, and perplexity, for the evaluation of the KI-TM method. Our experimental results indicate that our proposed method outperforms existing methods in terms of providing knowledge support. Our method enhances knowledge support for online patients and can help develop intelligent OHCs in the future.

Suggested Citation

  • Donghua Chen & Runtong Zhang & Kecheng Liu & Lei Hou, 2018. "Knowledge Discovery from Posts in Online Health Communities Using Unified Medical Language System," IJERPH, MDPI, vol. 15(6), pages 1-16, June.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:6:p:1291-:d:153320
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    References listed on IDEAS

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    1. Courtney D. Corley & Diane J. Cook & Armin R. Mikler & Karan P. Singh, 2010. "Text and Structural Data Mining of Influenza Mentions in Web and Social Media," IJERPH, MDPI, vol. 7(2), pages 1-20, February.
    2. Foster, Drew, 2016. "‘Keep complaining til someone listens’: Exchanges of tacit healthcare knowledge in online illness communities," Social Science & Medicine, Elsevier, vol. 166(C), pages 25-32.
    3. Jannis Kallinikos & Niccolò Tempini, 2014. "Patient Data as Medical Facts: Social Media Practices as a Foundation for Medical Knowledge Creation," Information Systems Research, INFORMS, vol. 25(4), pages 817-833, December.
    4. Marco Palomino & Tim Taylor & Ayse Göker & John Isaacs & Sara Warber, 2016. "The Online Dissemination of Nature–Health Concepts: Lessons from Sentiment Analysis of Social Media Relating to “Nature-Deficit Disorder”," IJERPH, MDPI, vol. 13(1), pages 1-23, January.
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    Cited by:

    1. Pi-Chun Hsu & I-Hsiung Chang & Ru-Si Chen, 2019. "Online Learning Communities and Mental Health Literacy for Preschool Teachers: The Moderating Role of Enthusiasm for Engagement," IJERPH, MDPI, vol. 16(22), pages 1-9, November.
    2. Chaoran Li & E. Zhang & Jingti Han, 2020. "Exploring the Effect of Market Conditions on Price Premiums in the Online Health Community," IJERPH, MDPI, vol. 17(4), pages 1-14, February.
    3. Lifeng He & Dongmei Han & Xiaohang Zhou & Zheng Qu, 2020. "The Voice of Drug Consumers: Online Textual Review Analysis Using Structural Topic Model," IJERPH, MDPI, vol. 17(10), pages 1-18, May.

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