Frontiers in Research Metrics and Analytics (May 2023)

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

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

DOI
https://doi.org/10.3389/frma.2023.1178181
Journal volume & issue
Vol. 8

Abstract

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IntroductionThis study examines the performance of active learning-aided systematic reviews using a deep learning-based model compared to traditional machine learning approaches, and explores the potential benefits of model-switching strategies.MethodsComprising four parts, the study: 1) analyzes the performance and stability of active learning-aided systematic review; 2) implements a convolutional neural network classifier; 3) compares classifier and feature extractor performance; and 4) investigates the impact of model-switching strategies on review performance.ResultsLighter models perform well in early simulation stages, while other models show increased performance in later stages. Model-switching strategies generally improve performance compared to using the default classification model alone.DiscussionThe study's findings support the use of model-switching strategies in active learning-based systematic review workflows. It is advised to begin the review with a light model, such as Naïve Bayes or logistic regression, and switch to a heavier classification model based on a heuristic rule when needed.

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