Swift.ai: research and development of an integrated platform for machine-assisted research synthesis

项目摘要(30行文本) 1系统审查和证据绘图，这两种研究综合形式，都是正式的、顺序的过程。 2用于识别、评估和整合主要科学文献。这些方法已经 循证医学的3个基石，最近在其他几个国家获得了显著的人气 4个学科，包括环境、农业和公共卫生研究，并越来越多地被用于 5.政府组织在知情的情况下作出决策。据估计，超过25,000人 每年进行和发表系统评价，选择纳入的研究是最多的 7针对任何系统审查或证据图的资源密集型步骤。在我们研究计划的第一阶段，我们有 8开发了一个基于Web的协作系统审查Web应用程序，名为Swift-Active Screener，以及 9创新的文档筛选工具，允许用户在筛选后识别大多数相关文章 10只占摘要总数的一小部分。我们当前提案的目标是进行额外的 11使SWIFT-Active筛选器获得商业成功所需的研发，同时还 12构建并利用我们以前构建的方法和软件，以解决 13系统评审流水线。因此，我们正在进行的研究和开发的主要目标之一是 14通过将Active Screener应用程序扩展为一个集成的研究平台来满足这一需求 15通过将其与我们的其他几个相关软件产品相结合来进行合成。由此产生的平台，我们称之为 16“Swift.ai”在“Aim 1-软件工程创建统一的研究平台”中有详细描述 17合成.“在《Aim 2-Active Screener2.0的改进统计方法》中，我们对方法进行了扩展 18在SBIR第一阶段完成的研究，以进一步发展和改进我们的方法。具体来说，我们 19研究在深度学习中整合最先进方法的新方法，以及更好地利用 为了改进我们的模型，从我们的用户那里收集了大量的筛选数据。最后，在《目标3-- 21张由Active Screener 2.0提供支持的活体证据地图，我们探索使用机器的新方法 22学习促进证据测绘。

Project Abstract (30 lines of text) 1 Systematic review and evidence mapping, both forms of research synthesis, are formal, sequential processes 2 for identifying, assessing, and integrating the primary scientific literature. These approaches, already 3 cornerstones of evidence-based medicine, have recently gained significant popularity in several other 4 disciplines including environmental, agricultural, and public health research and are increasingly utilized for 5 informed decision making by governmental organizations. It has been estimated that more than 25,000 6 systematic reviews are conducted and published annually and selecting studies for inclusion is one of the most 7 resource intensive steps for any systematic review or evidence map. In Phase I of our research plan, we have 8 developed a web-based, collaborative systematic review web application called SWIFT-Active Screener, an 9 innovative document screening tool that allows users to identify the majority of relevant articles after screening 10 only a fraction of the total number of abstracts. Our goal for the current proposal is to conduct additional 11 research and development required to make SWIFT-Active Screener a commercial success, while also 12 building on and leveraging methods and software we have previously built to address other stages in the 13 systematic review pipeline. Therefore, one of the primary aims of our ongoing research and development is to 14 address this need by expanding the Active Screener application into an integrated platform for research 15 synthesis by uniting it with several of our other related software products. The resulting platform, which we call 16 “swift.ai,” is described in detail in “Aim 1 – Software engineering to create a unified platform for research 17 synthesis.” In “Aim 2 – Improved statistical methods for Active Screener 2.0”, we expand on the methodological 18 research completed during Phase I of this SBIR, to further develop and refine our methods. Specifically, we 19 investigate new ways to integrate state-of-the art methods in deep learning and new ways to better utilize the 20 large amounts of screening data collected from our users in order to improve our models. Finally, in “Aim 3 – 21 Living evidence maps powered by Active Screener 2.0,” we explore new approaches for using machine 22 learning to facilitate evidence mapping.