Akay Altug, Dragomir Andrei, Erlandsson Bjorn-Erik
IEEE J Biomed Health Inform. 2015 Jan;19(1):210-8. doi: 10.1109/JBHI.2014.2336251. Epub 2014 Jul 10.
Intelligently extracting knowledge from social media has recently attracted great interest from the Biomedical and Health Informatics community to simultaneously improve healthcare outcomes and reduce costs using consumer-generated opinion. We propose a two-step analysis framework that focuses on positive and negative sentiment, as well as the side effects of treatment, in users' forum posts, and identifies user communities (modules) and influential users for the purpose of ascertaining user opinion of cancer treatment. We used a self-organizing map to analyze word frequency data derived from users' forum posts. We then introduced a novel network-based approach for modeling users' forum interactions and employed a network partitioning method based on optimizing a stability quality measure. This allowed us to determine consumer opinion and identify influential users within the retrieved modules using information derived from both word-frequency data and network-based properties. Our approach can expand research into intelligently mining social media data for consumer opinion of various treatments to provide rapid, up-to-date information for the pharmaceutical industry, hospitals, and medical staff, on the effectiveness (or ineffectiveness) of future treatments.
从社交媒体中智能提取知识最近引起了生物医学与健康信息学界的极大兴趣，旨在利用消费者生成的意见同时改善医疗结果并降低成本。我们提出了一个两步分析框架，该框架关注用户论坛帖子中的积极和消极情绪以及治疗的副作用，并识别用户群体（模块）和有影响力的用户，以确定用户对癌症治疗的看法。我们使用自组织映射来分析从用户论坛帖子中得出的词频数据。然后，我们引入了一种新颖的基于网络的方法来对用户论坛互动进行建模，并采用了一种基于优化稳定性质量度量的网络划分方法。这使我们能够利用从词频数据和基于网络的属性中获得的信息，确定消费者的意见并识别检索到的模块内有影响力的用户。我们的方法可以扩展对社交媒体数据进行智能挖掘以获取各种治疗的消费者意见的研究，从而为制药行业、医院和医务人员提供有关未来治疗有效性（或无效性）的快速、最新信息。
