Optimizing ASReview simulations
Optimizing ASReview simulations with multiprocessing solutions for ‘light-data’ and ‘heavy-data’ users via a Kubernetes cluster.
The FORAS project
The FORAS project will replicate and extend an original review integrating advanced machine-learning techniques via the OpenAlex database.
The Noisy Label Filter procedure: a case study to address replication issues in systematic reviews
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.
Reproducibility and Data storage for Active Learning-Aided Systematic Reviews
This systematic review focused on synthesizing information on studies that evaluated the performance of Active Learning compared to human reading.
Simulation Study Switching between Models
This systematic review focused on synthesizing information on studies that evaluated the performance of Active Learning compared to human reading.
Simulation Study on Risk Analysis Documents
ASReview conducted a simulation study on risk analysis documents to evaluate the time-benefit for the Royal Dutch Pharmacists Association.
The Mega Meta project: Substance use, anxiety and depression
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.
Systematic Review on Studies Evaluating the Performance of Active Learning within Systematic Reviews
This systematic review focused on synthesizing information on studies that evaluated the performance of Active Learning compared to human reading.
Systematic Review on the Implementation of AI-aided Systematic Reviews in Clinical Guideline Development
The ASReview research team conducted a systematic review on the implementation of AI-aided Systematic Reviews within Clinical Guideline Development.
AI-aided literature screening in medical 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.