Optimizing ASReview simulations
Optimizing ASReview simulations with multiprocessing solutions for ‘light-data’ and ‘heavy-data’ users via a Kubernetes cluster.
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Optimizing ASReview simulations
The FORAS project
The FORAS project will replicate and extend an original review integrating advanced machine-learning techniques via the OpenAlex database.
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.
Seven ways to integrate ASReview in your systematic review workflow
Seven ways to integrate ASReview in your systematic review workflow Systematic reviewing using software implementing Active Learning (AL) is relatively new. Many users (and reviewers) have to get familiar with the many different ways how AL can be used in practice. In this blog post, we discuss seven ways meant to inspire users. Use ASReview…
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.
Active Learning Explained
Active Learning Explained The rapidly evolving field of artificial intelligence (AI) has allowed the development of AI-aided pipelines that assist in finding relevant texts for such search tasks[1]. A well-established approach to increasing the efficiency of screening large amounts of textual data is screening prioritization[2, 3] with active learning[4]. Screening prioritization re-arranges the records to…
the Zen of Elas
The Zen of Elas Elas is the Mascotte of ASReview and your Electronic Learning Assistant who will guide you through the interactive process of making decisions using Artificial Intelligence in ASReview. Elas comes with some essential principles: Humans are the Oracle The interaction between humans and machines will take us a significant leap forward. We…
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.
Paper introducing the ASReview project
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.
Simulation study replicating Smid et al. with active learning
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.
Simulation Study to Determine Defaults
To determine the defaults we performed a simulation study and Naive Bayes + TF-IDF model performed the best.
Researcher-in-the-loop for systematic reviewing
Combining human intelligence and machine learning into Researcher-in-the-loop machine learning. An effective technique for training models.