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，以协助生产系统性评价和荟萃分析，越来越多地被用作循证医学（EBM）和比较有效性审查的基础。我们计划开发一种人工引导的计算机化摘要筛选工具，以大大减少手动筛选文献检索生成的数千篇摘要的繁琐但关键的步骤，以便检索一小部分可能相关的摘要进行进一步分析。该工具将联合收割机结合成熟的机器学习技术和一个新的开源工具，使筛选过程的管理。这项新技术将使研究人员能够在一小部分时间内筛选摘要相比，目前的手动过程。它将减少进行系统审查的时间和费用，提供关于这一过程的明确文件，并有可能更准确地执行任务。随着对循证管理的接受和对系统评价的需求不断增加，非常需要工具来帮助产生新的系统评价和更新它们。这一需要非常紧迫。最近通过的《美国复苏和再投资法》和为比较有效性研究拨出的11亿美元，创造了前所未有的系统审查需求和机会，以改进其方法和效率。 在此，我们建议开发新颖的开源软件，以帮助系统评价者更好地 科普这些数据洪流。该工具的研究和开发将由一个经验丰富的系统综述研究人员团队与塔夫茨大学的计算机科学家进行，他们去年开始合作，因为塔夫茨大学被授予NIH临床转化科学奖（CTSA）之一。我们将通过多种渠道传播新技术，包括但不限于出版物、会议演示、探索医疗保健研究和质量局（AHRQ）循证实践中心（EPC）项目、科克伦合作组织、CTSA网络和其他进行系统评价的团体对其采用的兴趣，以及制作教程材料。我们的目标是： 1.进行研究，使用机器学习设计和实施半自动化系统， 信息检索方法，以确定相关摘要，以提高系统评价的准确性和效率。 2.开发Abstrackr，一个带有图形用户界面（GUI）的开源系统，用于筛选摘要，应用Aim 1中开发的方法自动排除不相关的摘要/文章。 3.评估目标1中开发的主动学习模型的性能以及 在Aim 2中开发的Abstrackr，通过应用于一系列手动筛选的数据集， 生物医学摘要，随后将公开提供作为一个知识库，以刺激 机器学习和信息检索社区的研究。

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.