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

Swift.ai：机器辅助研究合成综合平台的研发

• 批准号: 10259172
• 负责人: Brian Howard
• 金额: $ 82.03万
• 依托单位: SCIOME, LLC
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-30 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10259172
• 关键词: Active Learning; Address; Adoption; Agriculture; Artificial Intelligence; Classification; Collaborations; Collection; Communities; Complex; Computer software; Consensus; Country; Data; Data Set; Decision Making; Development; Discipline; Elements; Endocrine disruption; Environmental Health; Evidence Based Medicine; Feedback; Focus Groups; Goals; Government; Health; Influentials; Internet; Learning; Letters; Literature; Machine Learning; Maps; Medical; Methodology; Methods; Mission; Modeling; National Institute of Environmental Health Sciences; Natural Language Processing; Online Systems; Phase; Policies; Positioning Attribute; Problem Formulations; Procedures; Process; Publishing; Reporting; Research; Research Methodology; Research Personnel; Resources; Review Literature; Sampling; Scientist; Screening procedure; Small Business Innovation Research Grant; Software Engineering; Speed; Statistical Methods; System; Testing; Text; Toxicology; Uncertainty; Update; active method; cost; data modeling; data sharing; deep learning; evidence base; improved; innovation; novel strategies; public health research; research and development; response; screening; simulation; success; systematic review; user-friendly; web app

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.

项目摘要（30行文本） 1系统综述和证据图，这两种形式的研究综合，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着的普及， 4个学科，包括环境，农业和公共卫生研究，并越来越多地用于 5、政府机构的决策。据估计，超过25，000 每年进行并发表6项系统性综述，选择纳入的研究是最多的研究之一。 任何系统性综述或证据图的7个资源密集型步骤。在我们研究计划的第一阶段， 8开发了一个基于网络的协作式系统评价网络应用程序，称为SWIFT-Active Screener， 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只是摘要总数的一小部分。我们目前提案的目标是进行额外的 使SWIFT-Active Screener取得商业成功所需的11项研发，同时 12建立和利用我们以前建立的方法和软件来解决 13系统性综述管道。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应用扩展为一个集成的研究平台来满足这一需求 15合成通过将它与我们的其他几个相关的软件产品。由此产生的平台，我们称之为 16“swift.ai”，在“目标1 -创建统一研究平台的软件工程”中详细描述 17合成”。在“目标2 -Active Screener 2.0的改进统计方法”中，我们扩展了方法论， 在本SBIR的第一阶段完成了18项研究，以进一步发展和完善我们的方法。我们特别 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 20从我们的用户收集的大量筛选数据，以改善我们的模型。最后，在“目标3 - 21由Active Screener 2.0提供支持的活证据地图，“我们探索使用机器的新方法， 22学习促进证据制图。