@文章{info:doi/10.2196/30634，作者=“李明达史金河陈毅”，标题=“识别在线健康社区患者决策过程中的影响:数据科学方法”，期刊=“J医学互联网研究”，年=“2022”，月=“8”，日=“31”，卷=“24”，数=“8”，页=“e30634”，关键词=“影响关系;决策的线程;在线健康社区;病人参与;深度学习;背景:近年来，越来越多的用户加入在线健康社区(OHCs)以获取信息和寻求支持。病人经常寻求信息和建议来支持他们的医疗保健决策。重要的是要了解患者的决策过程并确定患者从OHCs中受到的影响。目的:我们旨在确定讨论线程中对寻求帮助的用户决策有影响的帖子。方法:提出讨论区帖子影响关系的定义。 We then developed a framework and a deep learning model for identifying influence relationships. We leveraged the state-of-the-art text relevance measurement methods to generate sparse feature vectors to present text relevance. We modeled the probability of question and action presence in a post as dense features. We then used deep learning techniques to combine the sparse and dense features to learn the influence relationships. Results: We evaluated the proposed techniques on discussion threads from a popular cancer survivor OHC. The empirical evaluation demonstrated the effectiveness of our approach. Conclusions: It is feasible to identify influence relationships in OHCs. Using the proposed techniques, a significant number of discussions on an OHC were identified to have had influence. Such discussions are more likely to affect user decision-making processes and engage users' participation in OHCs. Studies on those discussions can help improve information quality, user engagement, and user experience. ", issn="1438-8871", doi="10.2196/30634", url="//www.mybigtv.com/2022/8/e30634", url="https://doi.org/10.2196/30634", url="http://www.ncbi.nlm.nih.gov/pubmed/36044266" }