Using a secure continually updating web-source processing pipeline supporting a Real-time data synthesis and analysis of scientific literature: development and validation study
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Background: 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. Objective: 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. Methods: 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. Results: 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. Conclusions: 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.
Date Issued
2021-05-06
Date Acceptance
2021-04-03
ISSN
1438-8871
Publisher
JMIR Publications
Journal / Book Title
Journal of Medical Internet Research
Volume
23
Issue
5
Copyright Statement
©Uddhav Vaghela, Simon Rabinowicz, Paris Bratsos, Guy Martin, Epameinondas Fritzilas, Sheraz Markar, Sanjay Purkayastha, Karl Stringer, Harshdeep Singh, Charlie Llewellyn, Debabrata Dutta, Jonathan M Clarke, Matthew Howard, PanSurg REDASA Curators, Ovidiu Serban, James Kinross. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 06.05.2021.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
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Subjects
Science & Technology
Life Sciences & Biomedicine
Health Care Sciences & Services
Medical Informatics
structured data synthesis
data science
critical analysis
web crawl data
pipeline
database
literature
research
COVID-19
infodemic
decision making
data
data synthesis
misinformation
infrastructure
methodology
COVID-19
critical analysis
data
data science
data synthesis
database
decision making
infodemic
infrastructure
literature
methodology
misinformation
pipeline
research
structured data synthesis
web crawl data
COVID-19
Data Interpretation, Statistical
Datasets as Topic
Humans
Internet
Longitudinal Studies
Natural Language Processing
SARS-CoV-2
Search Engine
PanSurg REDASA Curators
Humans
Data Interpretation, Statistical
Longitudinal Studies
Natural Language Processing
Internet
Search Engine
Datasets as Topic
COVID-19
SARS-CoV-2
Medical Informatics
08 Information and Computing Sciences
11 Medical and Health Sciences
17 Psychology and Cognitive Sciences
Publication Status
Published
Article Number
e25714