Semi-Automated Abstract Screening for Comparative Effectiveness Reviews

描述（由申请人提供）：在这个为期三年的项目中，我们的目标是应用最先进的信息分析技术来协助系统评价和荟萃分析的产生，这些分析越来越多地被用作循证医学（EBM）和比较有效性评价的基础。我们计划开发一种人工引导的计算机摘要筛选工具，以大大减少手动筛选文献搜索生成的数千篇摘要的繁琐但至关重要的步骤，以便检索可能与进一步分析相关的一小部分。该工具将经过验证的机器学习技术与一种新的开源工具相结合，从而能够管理筛选过程。与目前的人工过程相比，这项新技术将使研究人员能够在很短的时间内筛选摘要。它将减少产生系统审查的时间和成本，提供过程的清晰文档，并可能更准确地执行任务。随着对循证医学的接受和对系统评价的需求的增加，非常需要工具来帮助生成新的系统评价并更新它们。这种需要再紧迫不过了。最近通过的《美国复苏和再投资法》以及为比较效力研究拨出的11亿美元使人们空前需要进行系统审查，并有机会改进其行为的方法和效率。

DESCRIPTION (provided by applicant): In this three-year project, we aim to apply state-of-the-art information analysis technologies to assist the production of systematic reviews and meta-analyses that are increasingly being used as a foundation for evidence-based medicine (EBM) and comparative effectiveness reviews. We plan to develop a human guided computerized abstract screening tool to greatly reduce the need to perform a tedious but crucial step of manually screening many thousands of abstracts generated by literature searches in order to retrieve a small fraction potentially relevant for further analysis. This tool will combine proven machine learning techniques with a new open source tool that enables management of the screening process. This new technology will enable investigators to screen abstracts in a small fraction of the time compared to the current manual process. It will reduce the time and cost of producing systematic reviews, provide clear documentation of the process and potentially perform the task more accurately. With the acceptance of EBM and increasing demands for systematic reviews, there is a great need for tools to assist in generating new systematic reviews and in updating them. This need cannot be more pressing. The recent passage of the American Recovery and Reinvestment Act and the $1.1 billion allocated for comparative effectiveness research have created an unprecedented need for systematic reviews and opportunities to improve the methodologies and efficiency of their conduct. We herein propose the development of novel, open-source software to help systematic reviewers better cope with these torrents of data. The research and development of this tool will be carried out by a highly experienced team of systematic review investigators with computer scientists at Tufts University who began to collaborate last year as a result of Tufts being awarded one of the NIH Clinical Translational Science Awards (CTSA). We will pursue dissemination of the new technology through numerous channels including, but not limited to publication, presentation at conferences, exploring interest in its adoption by the Agency for Healthcare Research and Quality (AHRQ) Evidence-based Practice Center (EPC) Program, Cochrane Collaboration, CTSA network, and other groups conducting systematic reviews, and production of tutorial material. Our aims are: 1. Conduct research to design and implement a semi-automated system using machine learning and information retrieval methods to identify relevant abstracts in order to improve the accuracy and efficiency of systematic reviews. 2. Develop Abstrackr, an open-source system with a Graphical User Interface (GUI) for screening abstracts, that applies the methods developed in Aim 1 to automatically exclude irrelevant abstracts/articles. 3. Evaluate the performance of the active learning model developed in Aim 1 and the functionality of Abstrackr developed in Aim 2 through application to a collection of manually screened datasets of biomedical abstracts that will subsequently be made publicly available for use as a repository to spur research in the machine learning and information retrieval communities.