{Reference Type}: Journal Article
{Title}: Active learning-based systematic reviewing using switching classification models: the case of the onset, maintenance, and relapse of depressive disorders.
{Author}: Teijema JJ;Hofstee L;Brouwer M;de Bruin J;Ferdinands G;de Boer J;Vizan P;van den Brand S;Bockting C;van de Schoot R;Bagheri A;
{Journal}: Front Res Metr Anal
{Volume}: 8
{Issue}: 0
{Year}: 2023
暂无{DOI}: 10.3389/frma.2023.1178181
{Abstract}: <B>UNASSIGNED: </B>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.<BR><B>UNASSIGNED: </B>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.<BR><B>UNASSIGNED: </B>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.<BR><B>UNASSIGNED: </B>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.