AUTHOR=Teijema Jelle Jasper , Hofstee Laura , Brouwer Marlies , de Bruin Jonathan , Ferdinands Gerbrich , de Boer Jan , Vizan Pablo , van den Brand Sofie , Bockting Claudi , van de Schoot Rens , Bagheri Ayoub 

TITLE=Active learning-based systematic reviewing using switching classification models: the case of the onset, maintenance, and relapse of depressive disorders

JOURNAL=Frontiers in Research Metrics and Analytics

VOLUME=Volume 8 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/research-metrics-and-analytics/articles/10.3389/frma.2023.1178181

DOI=10.3389/frma.2023.1178181

ISSN=2504-0537

ABSTRACT=Systematic reviews and meta-analyses are top of the bill in research. However, the screening
phase requires an enormous effort in reading and labeling thousands of papers identified via
systematic search. Active learning-aided systematic reviewing offers a solution by combining
machine learning algorithms with user input to reduce screening load. This study explores the
performance of these algorithms and different ways to apply them. This study is divided into four
studies evaluating and improving this active learning pipeline. First, the performance and
stability of the active learning pipeline were assessed via simulations and re-analysis of the
outcome. Secondly, a convolutional neural network was developed to improve upon available
machine learning algorithms. Thirdly, the performance of different algorithm combinations was
tested and compared. Finally, algorithm-switching models were built for increased performance.
The study concludes with proposals for improving active learning-aided systematic reviews
based on combinations of the four studies. It was found that switching models can outperform
the currently used models.