The ASReview Research Team consistently explores and investigates new ways to improve ASReview through simulation studies, use cases, and more. These endeavors often involve collaborations with various organizations, research groups, and developers. The outcomes of these investigations are published in peer-reviewed journals like Nature Machine Intelligence, ensuring transparency and credibility through the publication of data, scripts, and underlying code.
The ASReview project was initiated in 2018 when Prof. dr Rens van de Schoot, dr. Daniel Oberski and Prof. dr. Lars Tummers launched it at Utrecht University. The award-winning project has since evolved into a comprehensive multidisciplinary research team that closely intertwines with the ASReview community.
The team works according to the Open Science principles and invests in an inclusive community contributing to the project. In short, research is conducted according to the following fundamental principles:
Utrecht University has established specific regulations governing conduct for its employees. These are based on the key principles of professional and quality academic conduct and ethically-responsible research. Members of the team employed by Utrecht University, commit themselves to these regulations in all their conduct, including all work related to ASReview. Adherence to similar key principles is expected of all researchers involved in all facets of the ASReview project.
In this study, we addressed the issue of the lack of replicability of systematic reviews datasets. We used a case study format and developed a procedure to optimize and finalize the by rule imperfect reconstructed dataset.
This systematic review focused on synthesizing information on studies that evaluated the performance of Active Learning compared to human reading.
This systematic review focused on synthesizing information on studies that evaluated the performance of Active Learning compared to human reading.
ASReview conducted a simulation study on risk analysis documents to evaluate the time-benefit for the Royal Dutch Pharmacists Association.
The MegaMeta project is a large scale project to review factors that contribute to substance use, anxiety and depressive disorders. Read more information on the search and screening protocol, hyperparameter tuning and post-processing used in this post.
This systematic review focused on synthesizing information on studies that evaluated the performance of Active Learning compared to human reading.
The ASReview research team conducted a systematic review on the implementation of AI-aided Systematic Reviews within Clinical Guideline Development.
In a time of exponential growth of new evidence supporting clinical decision making, combined with a labor-intensive process of selecting this evidence, there is a need for methods to speed up current processes in order to keep medical guidelines up-to-date.
This dataset contains an overview of 117 systematic reviews published by corresponding authors affiliated to Utrecht University (UU) or UMC Utrecht in 2020.
ASReview-wordcloud is a supplemental package for ASReview. Wordclouds can help you to get a visual impression of the contents of datasets.
We show that by using active learning, ASReview can lead to far more efficient reviewing than manual reviewing, while exhibiting adequate quality. Furthermore, the presented software is fully transparent and open source.
Explore the systematic review dataset that was used for the publication “Psychological theories of depressive relapse and recurrence” from Brouwer et al., 2019. From pre-processing to the final dataset, a look into the complete systematic review process behind this publication.
Recreate the simulation study on the systematic review of Smid et al.. From pre-processing to the final dataset, dive into the complete process behind this publication.
After manually screening 5050 studies, Smid et al included only 27 studies for their review. In the current study, both the Bayesian and logistic regression models found more than 80% of relevant publications after screening only 10% of all publications.
To determine the defaults we performed a simulation study and Naive Bayes + TF-IDF model performed the best.
Combining human intelligence and machine learning into Researcher-in-the-loop machine learning. An effective technique for training models.
The CORD-19 database is available in ASReview and can be used to search for relevant Corona-related publication using active learning.
ASReview LAB is user-friendly software for exploring the future of AI in systematic reviews. The software implements an Oracle Mode, an Exploration Mode, and a Simulation Mode.
All the documentation surrounding ASReview, from API to the Zen of ELAS, can be found within the Read the Docs. If you want to know more about ASReview, this is the place to be! Main topics include: An introduction, ASReview LAB, Features, Extensions & API
