ArticleInternational journal of environmental research and public health2018
Knowledge Discovery from Posts in Online Health Communities Using Unified Medical Language System.
Article in International journal of environmental research and public health, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The trial behind it
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Who cites it
8 citing papers in PubMed.
- Named Entity Recognition of Diabetes Online Health Community Data Using Multiple Machine Learning Models.Bioengineering (Basel, Switzerland) · 2023Article
- Exploring public attitudes toward live-streaming fitness in China: A sentiment and content analysis of China's social media Weibo.Frontiers in public health · 2022Article
- Application of natural language processing techniques to identify off-label drug usage from various online health communities.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- UMLS users and uses: a current overview.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- The Voice of Drug Consumers: Online Textual Review Analysis Using Structural Topic Model.International journal of environmental research and public health · 2020Article
- Exploring the Effect of Market Conditions on Price Premiums in the Online Health Community.International journal of environmental research and public health · 2020Article
- Coping With Stress and Burnout Associated With Telecommunication and Online Learning.Frontiers in public health · 2020Review
- Online Learning Communities and Mental Health Literacy for Preschool Teachers: The Moderating Role of Enthusiasm for Engagement.International journal of environmental research and public health · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.