Multitask Learning for Mental Health: Depression, Anxiety, Stress (DAS) Using Wearables

Berrenur Saylam*, Özlem Durmaz İncel*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
13 Downloads (Pure)

Abstract

This study investigates the prediction of mental well-being factors—depression, stress, and anxiety—using the NetHealth dataset from college students. The research addresses four key questions, exploring the impact of digital biomarkers on these factors, their alignment with conventional psychology literature, the time-based performance of applied methods, and potential enhancements through multitask learning. The findings reveal modality rankings aligned with psychology literature, validated against paper-based studies. Improved predictions are noted with temporal considerations, and further enhanced by multitasking. Mental health multitask prediction results show aligned baseline and multitask performances, with notable enhancements using temporal aspects, particularly with the random forest (RF) classifier. Multitask learning improves outcomes for depression and stress but not anxiety using RF and XGBoost.

Original languageEnglish
Article number501
Number of pages18
JournalDiagnostics
Volume14
Issue number5
Early online date26 Feb 2024
DOIs
Publication statusPublished - Mar 2024
Externally publishedYes

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