Integrating ecological momentary and passive sensing data to improve depression severity prediction: insights from the WARN-D study
Year: 2025
Type: preprint
Abstract: Project hub for the associated manuscript containing relevant necessary information for replication. Scripts Scripts/code are available on Github. The scripts are divided into a notebook, and a folder containing the scripts used to run analyses on the ALICE HPC cluster at Leiden University. Note that the result section of the Github repo is empty, as the files are large. These files are included here (see below). Folder Contents Manuscript: Includes the manuscript file, figures, and the supplementary materials text. Data: Contains participant ID’s that were used in analyses. WARN-D data will be made publicly available once it has been fully deidentified in a central repository location. keras_models.zip: Final models generated by the training procedure across all folds. Model names consist of the base model_{day/week}_{ema/epa/all/phq}_fold{1-10}.keras training_test.zip: CSV files with the final results of the 10-fold CV process per fold, and the predictions yielded on the test set for each of the validated 10-fold models. Base naming results_{training/testing}_{day/week}_{ema/epa/all/PHQ}_fold{1-10}.csv shap_values.zip: SHAP values estimated in python, and saved for plotting in R because GGPLOT makes prettier plots. Base naming shap_values_{day/week}_{ema/epa/all}.csv
Source: OSF Preprints (OSF Preprints)
Authors: Tutunji, Rayyan, Proppert, Ricarda, Rieble, Carlotta, Jacobson, Nicholas C., Fried, Eiko
Institution: Leiden University
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7111469603
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