Vaghela Uddhav, Rabinowicz Simon, Bratsos Paris, Martin Guy, Fritzilas Epameinondas, Markar Sheraz, Purkayastha Sanjay, Stringer Karl, Singh Harshdeep, Llewellyn Charlie, Dutta Debabrata, Clarke Jonathan M, Howard Matthew, Serban Ovidiu, Kinross James
PanSurg Collaborative, Department of Surgery and Cancer, Imperial College London, London, United Kingdom.
Amazon Web Services UK Limited, London, United Kingdom.
J Med Internet Res. 2021 May 6;23(5):e25714. doi: 10.2196/25714.
The scale and quality of the global scientific response to the COVID-19 pandemic have unquestionably saved lives. However, the COVID-19 pandemic has also triggered an unprecedented "infodemic"; the velocity and volume of data production have overwhelmed many key stakeholders such as clinicians and policy makers, as they have been unable to process structured and unstructured data for evidence-based decision making. Solutions that aim to alleviate this data synthesis-related challenge are unable to capture heterogeneous web data in real time for the production of concomitant answers and are not based on the high-quality information in responses to a free-text query.
The main objective of this project is to build a generic, real-time, continuously updating curation platform that can support the data synthesis and analysis of a scientific literature framework. Our secondary objective is to validate this platform and the curation methodology for COVID-19-related medical literature by expanding the COVID-19 Open Research Dataset via the addition of new, unstructured data.
To create an infrastructure that addresses our objectives, the PanSurg Collaborative at Imperial College London has developed a unique data pipeline based on a web crawler extraction methodology. This data pipeline uses a novel curation methodology that adopts a human-in-the-loop approach for the characterization of quality, relevance, and key evidence across a range of scientific literature sources.
REDASA (Realtime Data Synthesis and Analysis) is now one of the world's largest and most up-to-date sources of COVID-19-related evidence; it consists of 104,000 documents. By capturing curators' critical appraisal methodologies through the discrete labeling and rating of information, REDASA rapidly developed a foundational, pooled, data science data set of over 1400 articles in under 2 weeks. These articles provide COVID-19-related information and represent around 10% of all papers about COVID-19.
This data set can act as ground truth for the future implementation of a live, automated systematic review. The three benefits of REDASA's design are as follows: (1) it adopts a user-friendly, human-in-the-loop methodology by embedding an efficient, user-friendly curation platform into a natural language processing search engine; (2) it provides a curated data set in the JavaScript Object Notation format for experienced academic reviewers' critical appraisal choices and decision-making methodologies; and (3) due to the wide scope and depth of its web crawling method, REDASA has already captured one of the world's largest COVID-19-related data corpora for searches and curation.
全球科学界对新冠疫情的应对规模和质量无疑挽救了许多生命。然而，新冠疫情也引发了一场前所未有的“信息疫情”；数据产生的速度和数量让许多关键利益相关者不堪重负，比如临床医生和政策制定者，因为他们无法处理结构化和非结构化数据以进行基于证据的决策。旨在缓解与数据综合相关挑战的解决方案无法实时捕捉异构网络数据以生成相应答案，且并非基于对自由文本查询的高质量回复信息。
本项目的主要目标是构建一个通用的、实时的、持续更新的管理平台，该平台能够支持科学文献框架的数据综合与分析。我们的次要目标是通过添加新的非结构化数据来扩展新冠开放研究数据集，从而验证该平台以及针对新冠相关医学文献的管理方法。
为创建一个能实现我们目标的基础设施，伦敦帝国理工学院的泛外科协作组基于网络爬虫提取方法开发了一种独特的数据管道。此数据管道采用一种新颖的管理方法，该方法采用人工参与的方式来表征一系列科学文献来源中的质量、相关性和关键证据。
REDASA（实时数据综合与分析）现已成为全球最大且最新的新冠相关证据来源之一；它包含104,000份文档。通过对信息进行离散标记和评级来捕捉管理人员的关键评估方法，REDASA在不到两周的时间内迅速开发出了一个基础的、汇总的、包含1400多篇文章的数据科学数据集。这些文章提供了与新冠相关的信息，约占所有关于新冠论文的10%。
该数据集可作为未来实施实时自动系统综述的依据。REDASA设计的三个优点如下：（1）它通过将高效、用户友好的管理平台嵌入自然语言处理搜索引擎，采用了用户友好的人工参与方法；（2）它以JavaScript对象表示法格式提供了一个经过整理的数据集，用于展示经验丰富的学术评审人员的关键评估选择和决策方法；（3）由于其网络爬虫方法的广泛范围和深度，REDASA已经捕获了全球最大的新冠相关数据语料库之一用于搜索和管理。
