Alnashwan Rana, O'Riordan Adrian, Sorensen Humphrey
Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
School of Computer Science and Information Technology, University College Cork, T12 K8AF Cork, Ireland.
Healthcare (Basel). 2023 Oct 12;11(20):2723. doi: 10.3390/healthcare11202723.
The growth of online health communities and socially generated health-related content has the potential to provide considerable value for patients and healthcare providers alike. For example, members of the public can acquire medical knowledge and interact with others online. However, the volume of information-and the consequent 'noise' associated with large data volumes-can create difficulties for users. In this paper, we present a data-driven approach to better understand these data from multiple stakeholder perspectives. We utilise three techniques-sentiment analysis, content analysis, and topic analysis-to analyse user-generated medical content related to Lyme disease. We use a supervised feature-based model to identify sentiments, content analysis to identify concepts that predominate, and latent Dirichlet allocation strategy as an unsupervised generative model to identify topics represented in the discourse. We validate that applying three different analytic methods highlights differing aspects of the information different stakeholders will be interested in based on the goals of different stakeholders, expert opinion, and comparison with patient information leaflets.
在线健康社区的发展以及社会生成的健康相关内容，有可能为患者和医疗服务提供者带来巨大价值。例如，公众可以在线获取医学知识并与他人互动。然而，信息量以及随之而来的与大量数据相关的“噪音”，可能给用户带来困难。在本文中，我们提出一种数据驱动的方法，从多个利益相关者的角度更好地理解这些数据。我们运用三种技术——情感分析、内容分析和主题分析——来分析与莱姆病相关的用户生成的医学内容。我们使用基于监督特征的模型来识别情感，通过内容分析来识别占主导地位的概念，并采用潜在狄利克雷分配策略作为无监督生成模型来识别话语中所呈现的主题。我们验证了应用三种不同的分析方法，基于不同利益相关者的目标、专家意见以及与患者信息手册的比较，突出了不同利益相关者感兴趣的信息的不同方面。
