Journaling Data for Daily PHQ-2 Depression Prediction and Forecasting

• URL: http://arxiv.org/abs/2205.03391v1
• Date: Fri, 6 May 2022 17:47:05 GMT
• Title: Journaling Data for Daily PHQ-2 Depression Prediction and Forecasting
• Authors: 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
• Abstract summary: 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. This illustrates the additive value that can be obtained by incorporating actively-collected data in a depression monitoring application.
• Score: 47.93070579578704
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 depression monitoring application.

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