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

Donghua Chen, Runtong Zhang, Kecheng Liu and Lei Hou
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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

IJERPH, 2018, vol. 15, issue 6, 1-16

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.

Keywords: online posts; online health communities; knowledge discovery; Unified Medical Language System; text mining (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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