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Knowledge discovery from posts in online health communities using unified medical language system

Chen, D., Zhang, R., Liu, K. and Hou, L. (2018) Knowledge discovery from posts in online health communities using unified medical language system. International Journal of Environmental Research and Public Health, 15 (6). 1291. ISSN 1660-4601

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To link to this item DOI: 10.3390/ijerph15061291


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.

Item Type:Article
Divisions:Henley Business School > Business Informatics, Systems and Accounting
ID Code:78258
Publisher:MDPI Publishing


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