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=8 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=Introduction

This 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.

Methods

Comprising 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.

Results

Lighter 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.

Discussion

The 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.