Journaling Data for Daily PHQ-2 Depression Prediction and Forecasting. (arXiv:2205.03391v1 [cs.LG])

Web: http://arxiv.org/abs/2205.03391

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, …

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