Journaling Data for Daily PHQ-2 Depression Prediction and Forecasting
Alexander Kathan, Andreas Triantafyllopoulos, Xiangheng He, Manuel, Milling, Tianhao Yan, Srividya Tirunellai Rajamani, Ludwig K\"uster, Mathias, Harrer, Elena Heber, Inga Grossmann, David D. Ebert, Bj\"orn W. Schuller

TL;DR
This paper investigates using actively-collected data to predict and forecast daily PHQ-2 depression scores, demonstrating improved accuracy in a longitudinal dataset for mental health monitoring.
Contribution
It introduces a novel approach for depression score prediction using actively-collected data and provides empirical results showing its effectiveness.
Findings
Best MAE of 1.417 for daily prediction
Best MAE of 1.914 for forecasting up to 7 days
Active data improves depression monitoring accuracy
Abstract
Digital health applications are becoming increasingly important for assessing and monitoring the wellbeing of people suffering from mental health conditions like depression. A common target of said applications is to predict the results of self-assessed Patient-Health-Questionnaires (PHQ), indicating current symptom severity of depressive individuals. In this work, we explore the potential of using actively-collected data to predict and forecast daily PHQ-2 scores on a newly-collected longitudinal dataset. We obtain a best MAE of 1.417 for daily prediction of PHQ-2 scores, which specifically in the used dataset have a range of 0 to 12, using leave-one-subject-out cross-validation, as well as a best MAE of 1.914 for forecasting PHQ-2 scores using data from up to the last 7 days. This illustrates the additive value that can be obtained by incorporating actively-collected data in a…
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Taxonomy
TopicsMental Health Research Topics · Digital Mental Health Interventions · Mental Health Treatment and Access
MethodsMasked autoencoder
