Text Mining Pipeline to Accelerate Systematic Reviews in Evidence-Based Medicine

描述(由申请人提供)：加速循证医学系统审查的文本挖掘管道将结合生物医学文本挖掘几个领域的重要研究，这些领域对于通过文本挖掘增强工作流程进行系统审查的过程实现亟需的改进是必要的。我们的联合体将承担支持这项工作的三个具体目标： 目的1.研究如何建立一个元搜索引擎和数据库，从重要的系统综述来源收集信息，对这些信息进行一致的索引，并提供一个健壮的、高查全率和精确度的信息检索系统，以访问这些扩展的文献库。 目的2.研究如何建立一个可为每个用户、系统评审组和系统评审主题定制和培训的文献分类和排名系统。该基于监督学习的分类和排名系统将对应于给定查询的检索到的文章的列表作为输入，并按照文章类型按预测的与撰写关于给定主题的系统评论的个人相关的概率的顺序输出它们。 目的3.研究如何创建一个研究聚合器，将涉及相同基础临床试验的文章收集在一起。这将节省审查员的工作和时间，因为他们现在可以自动帮助确定两篇文章是独立的数据源，还是从相同的原始数据得出证据。 综上所述，这些结果将有助于构建文本挖掘管道系统，减少系统审查员在文献收集和审查过程中的人工负担，并增加审查员用于合成证据和执行荟萃分析的时间比例。这一系统将在编制高质量证据报告的速度方面产生真正的差异。最终，循证医学在生物医学界的覆盖面、传播和接受度将增加，从而产生更好和更具成本效益的临床护理。

DESCRIPTION (provided by applicant): The Text Mining Pipeline to Accelerate Systematic Reviews in Evidence-Based Medicine will combine important research in several areas of biomedical text mining that are necessary to enable much-needed improvements in the process of conducting systematic reviews via a text mining enhanced workflow. Our consortium will undertake three specific aims to support this work: Aim 1. Study how to create a metasearch engine and database that collects information from important systematic review sources, indexes this information consistently, and provides a robust information retrieval system with high recall and precision for accessing this expanded literature collection. Aim 2. Study how to create a literature classification and ranking system that is customizable and trainable for each user, systematic review group, and systematic review topic. This supervised learning based classification and ranking system takes as input the list of retrieved articles corresponding to a given query, and outputs them grouped by article type, in order of predicted probability of relevance to an individual writing a systematic review on the given topic. Aim 3. Study how to create a study aggregator that collects together articles that refer to the same underlying clinical trial. This will save reviewers work and time as they will now have automated assistance in determining whether two articles are independent data sources, or derive their evidence from the same primary data. Taken together, these results will inform construction of a text mining pipeline system that will decrease the manual burden of systematic reviewers during the literature collection and review process, and increase the proportion of reviewer time spent synthesizing evidence and performing meta-analyses. The system will lead to a real difference in the rate that high-quality evidence reports can be compiled. Ultimately, the coverage, dissemination, and acceptance of evidence- based medicine in the biomedical community will increase, resulting in better and more cost- effective clinical care.