| Autor: |
Kathan, Alexander, Triantafyllopoulos, Andreas, He, Xiangheng, Milling, Manuel, Yan, Tianhao, Rajamani, Srividya Tirunellai, Küster, Ludwig, Harrer, Mathias, Heber, Elena, Grossmann, Inga, Ebert, David D., Schuller, Björn W. |
| Rok vydání: |
2022 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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