Optimizing ASReview simulations
Optimizing ASReview simulations with multiprocessing solutions for ‘light-data’ and ‘heavy-data’ users via a Kubernetes cluster.
Optimizing ASReview simulations with multiprocessing solutions for ‘light-data’ and ‘heavy-data’ users via a Kubernetes cluster.
The FORAS project will replicate and extend an original review integrating advanced machine-learning techniques via the OpenAlex database.
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.
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.