Liu Xiao, Chen Hsinchun
Department of Management Information Systems, The University of Arizona, Tucson, AZ, United States.
Department of Management Information Systems, The University of Arizona, Tucson, AZ, United States; Tsinghua National Laboratory for Info. Science and Technology, Tsinghua University, Beijing, China.
J Biomed Inform. 2015 Dec;58:268-279. doi: 10.1016/j.jbi.2015.10.011. Epub 2015 Oct 27.
Social media offer insights of patients' medical problems such as drug side effects and treatment failures. Patient reports of adverse drug events from social media have great potential to improve current practice of pharmacovigilance. However, extracting patient adverse drug event reports from social media continues to be an important challenge for health informatics research. In this study, we develop a research framework with advanced natural language processing techniques for integrated and high-performance patient reported adverse drug event extraction. The framework consists of medical entity extraction for recognizing patient discussions of drug and events, adverse drug event extraction with shortest dependency path kernel based statistical learning method and semantic filtering with information from medical knowledge bases, and report source classification to tease out noise. To evaluate the proposed framework, a series of experiments were conducted on a test bed encompassing about postings from major diabetes and heart disease forums in the United States. The results reveal that each component of the framework significantly contributes to its overall effectiveness. Our framework significantly outperforms prior work.
社交媒体提供了有关患者医疗问题的见解，如药物副作用和治疗失败情况。来自社交媒体的患者药物不良事件报告在改善当前药物警戒实践方面具有巨大潜力。然而，从社交媒体中提取患者药物不良事件报告仍然是健康信息学研究的一项重大挑战。在本研究中，我们开发了一个研究框架，运用先进的自然语言处理技术进行综合且高性能的患者报告药物不良事件提取。该框架包括用于识别患者对药物和事件讨论的医学实体提取、基于最短依存路径核的统计学习方法进行药物不良事件提取以及利用医学知识库信息进行语义过滤，还有报告源分类以剔除噪声。为评估所提出的框架，我们在美国主要糖尿病和心脏病论坛的约 个帖子组成的测试平台上进行了一系列实验。结果表明，该框架的每个组件都对其整体有效性有显著贡献。我们的框架显著优于先前的工作。
