ASReview against COVID-19¶
For many questions from medical doctors, journalists, policy makers the scientific literature on COVID-19 needs to be checked in a systematic way to avoid biased decision-making. For example, to develop evidence-based medical guidelines to transparently support medical doctors. Medical guidelines rely on comprehensive systematic reviews. Such reviews entail several explicit and reproducible steps, including identifying all likely relevant papers in a standardized way, extracting data from eligible studies, and synthesizing the results into medical guidelines. One might need to manually scan hundreds, or even thousands of COVID-19 related studies. This process is error prone and extremely time consuming; time we do not have right now!
The software relies on Active learning which denotes the scenario in which the reviewer is labeling data that are presented by a machine learning model. The machine learns from the reviewers’ decisions and uses this knowledge in selecting the reference that will be presented to the reviewer next. In this way, the COVID-19 related papers are presented in an orderly manner, that is from most to least relevant based on the input from the user. The goal of the software is to help scholars and practitioners to get an overview of the most relevant papers for their work as efficiently as possible, while being transparent in the process.
To help combat the COVID-19 crisis, the ASReview team developed an extension that provides two different datasets on COVID-19. These are automatically available in ASReview after installing the extension, so reviewers can start reviewing the latest scientific literature on COVID-19 as soon as possible!
The Cord19 database, found in full at CORD-19 dataset, is developed by the Allen Institute for AI. It contains all publications on COVID-19 and other coronavirus research (e.g. SARS, MERS, etc.) from PubMed Central, the WHO COVID-19 database of publications, the preprint servers bioRxiv and medRxiv and papers contributed by specific publishers.
In addition to the full dataset, there is a subset available of studies published after December 1st, 2019 to search for relevant papers published during the COVID-19 crisis.
The datasets are updated in ASReview extension shortly after a release by the Allen Institute for AI.
A separate dataset of COVID-19 related preprints. It contains metadata of preprints from over 15 preprints servers across disciplines, published since January 1, 2020. The preprint dataset is updated weekly by the maintainers (Nicholas Fraser and Bianca Kramer) and is subsequently updated in ASReview. As this dataset is not readily available to researchers through regular search engines (e.g. PubMed), its inclusion in ASReview provides added value to researchers interested in COVID-19 research, especially for those looking to screen preprints specifically.
Installation and usage¶
The COVID-19 extension requires ASReview 0.9.4 or higher. Install ASReview by following the instructions in Installation.
Install the extension with pip:
pip install asreview-covid19
The datasets are immediately available after starting ASReview.
The datasets are selectable in Step 2 of the project initialization. For more information on the usage of ASReview, have a look at the Oracle Mode.
The ASReview software and the extensions have an Apache 2.0 LICENSE. For the datasets, see the license of the CORD-19 dataset found at Semantic Scholar.