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

• DOI: 10.3390/ijerph15061291
• Publication Date: 2018-06-19T15:20:36Z
• Authors: Donghua Chen; Runtong Zhang; Kecheng Liu; Lei Hou
• Keywords: Perplexity; Medical knowledge

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