Mega-Meta-paper-simulations

A code repository for:

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

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 subdivided into four separate 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.

Data

The data used for this paper can be found at https://osf.io/r45yz/. The file is called Brouwer_2019_deduplicated.xlsx (Version: 1).

Contents

Folders: - plots - A folder containing generated images. - simulation_states - A folder containing the simulation states for different simulation runs. - results - Contains files with statistics from the simulations.

Files: - state_file_processor.ipynb - A file with different modules used for processing state files. - simulations.sh - A shell script containing commands all ran simulations

Simulation results

The simulation results from this paper are stored at a OSF location; they're available at https://osf.io/3h2tw/ for NB simulations files, and https://osf.io/zngmy/ for LR simulations files. These files were used for the fourth study of the paper.

Licence

The content in this repository is published under the MIT license.

Contact

For any questions or remarks, please contact the corresponding author:

Rens van de Schoot: Department of Methods and Statistics, Utrecht University, P.O. Box 80.140, 3508TC, Utrecht, The Netherlands; Tel.: +31 302534468; E-mail address: a.g.j.vandeschoot@uu.nl.

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