| Creators: | Shahania, Saijal and Unnikrishnan, Vishnu and Pryss, Rüdiger and Kraft, Robin and Schobel, Johannes and Hannemann, Ronny and Schlee, Winny and Spiliopoulou, Myra |
|---|---|
| Title: | User-centric vs whole-stream learning for EMA prediction |
| Item Type: | Conference or Workshop Item |
| Event Title: | (Proceedings of the) IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS) |
| Event Location: | Aveiro, Portugal (online) |
| Event Dates: | June, 7-9, 2021 |
| Projects: | DigiHealth |
| Page Range: | pp. 307-312 |
| Date: | 2021 |
| Divisions: | Gesundheitsmanagement |
| Abstract (ENG): | A stream of users' interactions with an mHealth app can be seen as the result of a stochastic process that can be captured by an algorithm that learns over the whole stream. But is it only one process? We investigate to what extend learning for each user separately delivers better predictions than learning one model over the whole stream. Our application scenario is the prediction of Ecological Momentary Assessments (EMA) for an mHealth app (TinnitusTipps) on tinnitus. The data were recorded as part of a pilot study, in which one group of users received non-personalized suggestions (tips) throughout the study, while the other group received tips only during the second half of the study. Our method encompasses user-centric and global stream learning for EMA prediction, combined under a Contextual Multi-Armed Bandit (CMAB) that captures the context of each user group and incorporates the prediction quality of each learner into the reward function. We show that user-centric learning is beneficial for users who contribute many EMA, while a learner over the whole stream is better for users with few EMA. |
| Forthcoming: | No |
| Language: | English |
| Uncontrolled Keywords: | Tinnitus; Ecological Momentary Assessments; mHealth; Hoeffding Adaptive Tree Regressors; Contextual Multi-Armed Bandit |
| Citation: | Shahania, Saijal and Unnikrishnan, Vishnu and Pryss, Rüdiger and Kraft, Robin and Schobel, Johannes and Hannemann, Ronny and Schlee, Winny and Spiliopoulou, Myra (2021) User-centric vs whole-stream learning for EMA prediction. In: (Proceedings of the) IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), June, 7-9, 2021, Aveiro, Portugal (online), pp. 307-312. ISBN 9781665441216 |
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