@Article{信息:doi 10.2196 / / medinform。9162，作者=“Jones, Josette and Pradhan, Meeta and Hosseini, Masoud and Kulanthaivel, Anand and Hosseini, Mahmood”，标题=“将患者生成的数据聚类为可操作的主题的新方法:基于web的乳腺癌论坛的案例研究”，期刊=“JMIR Med Inform”，年=“2018”，月=“11月”，日=“29”，卷=“6”，数=“4”，页=“e45”，关键词=“数据解释;自然语言处理;我们相信信息;社交媒体;统计分析;背景:越来越多地使用社交媒体和移动健康应用程序为医疗保健消费者提供了分享他们的健康和福祉信息的新机会。通过社交媒体分享的信息不仅包括医疗信息，还包括幸存者在日常生活中如何管理疾病和恢复的宝贵信息。目的:本研究的目的是确定获取一个主要的乳腺癌在线支持论坛的主题和建模的可行性。我们选择了乳腺癌患者支持论坛，以发现疾病管理和康复中隐藏的、不太明显的方面。 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. ", issn="2291-9694", doi="10.2196/medinform.9162", url="http://medinform.www.mybigtv.com/2018/4/e45/", url="https://doi.org/10.2196/medinform.9162", url="http://www.ncbi.nlm.nih.gov/pubmed/30497991" }