School of Information and Library Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3360, USA.
Patient Educ Couns. 2012 May;87(2):250-7. doi: 10.1016/j.pec.2011.08.017. Epub 2011 Sep 17.
This study sought to characterize and compare online discussion forums for three conditions: breast cancer, type 1 diabetes and fibromyalgia. Though there has been considerable work examining online support groups, few studies have considered differences in discussion content between health conditions. In addition, in contrast to the extant literature, this study sought to employ a semi-automated approach to examine health-related online communities.
Online discussion content for the three conditions was compiled, pre-processed, and clustered at the thread level using the bisecting k-means algorithm.
Though the clusters for each condition differed, the clusters fell into a set of common categories: Generic, Support, Patient-Centered, Experiential Knowledge, Treatments/Procedures, Medications, and Condition Management.
The cluster analyses facilitate an increased understanding of various aspects of patient experience, including significant emotional and temporal aspects of the illness experience.
The clusters highlighted the changing nature of patients' information needs. Information provided to patients should be tailored to address their needs at various points during their illness. In addition, cluster analysis may be integrated into online support groups or other types of online interventions to assist patients in finding information.
本研究旨在描述和比较三种疾病（乳腺癌、1 型糖尿病和纤维肌痛）的在线讨论论坛。尽管已经有大量研究检查了在线支持小组，但很少有研究考虑过不同健康状况下的讨论内容差异。此外，与现有文献相比，本研究试图采用半自动方法来检查与健康相关的在线社区。
使用二分 k-均值算法在线编译、预处理和聚类三种条件的线程级别的讨论内容。
尽管每种情况的聚类不同，但聚类分为一组常见类别：通用、支持、以患者为中心、经验知识、治疗/程序、药物和疾病管理。
聚类分析有助于增加对患者体验各个方面的理解，包括疾病体验的重要情感和时间方面。
这些聚类突出了患者信息需求的变化性质。提供给患者的信息应根据其在疾病过程中的各个阶段的需求进行调整。此外，聚类分析可以集成到在线支持小组或其他类型的在线干预措施中，以帮助患者找到信息。
