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首页> 外文期刊>JMIR Medical Informatics >Novel Approach to Cluster Patient-Generated Data Into Actionable Topics: Case Study of a Web-Based Breast Cancer Forum
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Novel Approach to Cluster Patient-Generated Data Into Actionable Topics: Case Study of a Web-Based Breast Cancer Forum

机译：将患者生成的数据聚类为可操作主题的新方法：基于网络的乳腺癌论坛的案例研究

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Background The increasing use of social media and mHealth apps has generated new opportunities for health care consumers to share information about their health and well-being. Information shared through social media contains not only medical information but also valuable information about how the survivors manage disease and recovery in the context of daily life. Objective The objective of this study was to determine the feasibility of acquiring and modeling the topics of a major online breast cancer support forum. Breast cancer patient support forums were selected to discover the hidden, less obvious aspects of disease management and recovery. Methods First, manual topic categorization was performed using qualitative content analysis (QCA) of each individual forum board. Second, we requested permission from the Breastcancer.org Community for a more in-depth analysis of the postings. Topic modeling was then performed using open source software Machine Learning Language Toolkit, followed by multiple linear regression (MLR) analysis to detect highly correlated topics among the different website forums. Results QCA of the forums resulted in 20 categories of user discussion. The final topic model organized 4 million postings into 30 manageable topics. Using qualitative analysis of the topic models and statistical analysis, we grouped these 30 topics into 4 distinct clusters with similarity scores of ≥0.80; these clusters were labeled Symptoms & Diagnosis, Treatment, Financial, and Family & Friends. A clinician review confirmed the clinical significance of the topic clusters, allowing for future detection of actionable items within social media postings. To identify the most significant topics across individual forums, MLR demonstrated that 6 topics—based on the Akaike information criterion values ranging from ?642.75 to ?412.32—were statistically significant. Conclusions The developed method provides an insight into the areas of interest and concern, including those not ascertainable in the clinic. Such topics included support from lay and professional caregivers and late side effects of therapy that consumers discuss in social media and may be of interest to clinicians. The developed methods and results indicate the potential of social media to inform the clinical workflow with regards to the impact of recovery on daily life.
机译：背景技术社交媒体和mHealth应用程序的日益普及为医疗保健消费者提供了分享其健康和福祉信息的新机会。通过社交媒体共享的信息不仅包含医学信息，还包含有关幸存者如何在日常生活中管理疾病和康复的宝贵信息。目的这项研究的目的是确定获取和建模主要在线乳腺癌支持论坛主题的可行性。选择了乳腺癌患者支持论坛，以发现疾病管理和恢复的隐蔽，较不明显的方面。方法首先，使用每个论坛板块的定性内容分析（QCA）对主题进行手动分类。其次，我们要求获得Breastcancer.org社区的许可，以便对帖子进行更深入的分析。然后使用开源软件Machine Learning Language Toolkit执行主题建模，然后进行多元线性回归（MLR）分析，以检测不同网站论坛之间的高度相关主题。结果论坛的QCA导致20个类别的用户讨论。最终的主题模型将超过400万个帖子组织到30个可管理的主题中。使用主题模型的定性分析和统计分析，我们将这30个主题分为4个相似得分≥0.8的不同聚类。这些类别分别标记为“症状与诊断”，“治疗”，“财务”和“家人和朋友”。临床医生的评论确认了主题簇的临床意义，从而允许将来在社交媒体发布中检测可操作的项目。为了确定各个论坛中最重要的主题，MLR演示了6个主题（基于Akaike信息标准值从642.75到412.32）在统计上是重要的。结论所开发的方法可洞悉感兴趣和关注的领域，包括在临床上无法确定的领域。这些主题包括来自非专业护理人员和专业护理人员的支持以及消费者在社交媒体上讨论的治疗的后期副作用，这可能是临床医生感兴趣的。所开发的方法和结果表明社交媒体有可能就恢复对日常生活的影响告知临床工作流程。

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