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…
This systematic review focused on synthesizing information on studies that evaluated the performance of Active Learning compared to human reading.
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 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…
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.
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.
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.
Interview in Fortune In a time where a lot of research needs to be done as fast as possible, ASReview enables researchers to do exactly that. With the launch of the the covid19-extension, ASReview grasped international attention. Its impact was also discussed in an interview in Fortune magazine. See below for some excerpts of the…
The CORD-19 database is available in ASReview and can be used to search for relevant Corona-related publication using active learning.
ASReview receives NWO grant The call ‘Corona: fast-track data’ is developed by the NWO to provide funding for projects that collect data that can only be conducted now, during the COVID-19 crisis, and specifically research into issues that arise in society during the crisis. The ASReview project was one of the awardees of these NWO…
