Using a Secure, Continually Updating, Web Source Processing Pipeline to Support the Real-Time Data Synthesis and Analysis of Scientific Literature: Development and Validation Study.
Uddhav VaghelaSimon RabinowiczSosipatros BratsosGuy MartinEpameinondas FritzilasSheraz Rehan MarkarSanjay PurkayasthaKarl StringerHarshdeep SinghCharlie LlewellynDebabrata DuttaJonathan M ClarkeMatthew Howardnull nullOvidiu SerbanJames Macalister KinrossPublished in: Journal of medical Internet research (2021)
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.
Keyphrases
- systematic review
- electronic health record
- big data
- decision making
- coronavirus disease
- endothelial cells
- healthcare
- working memory
- machine learning
- high throughput
- deep learning
- randomized controlled trial
- transcription factor
- artificial intelligence
- optical coherence tomography
- current status
- low cost
- medical students