Predicting Ecological Momentary Assessments in an App for Tinnitus by Learning From Each User's Stream With a Contextual Multi-Armed Bandit.

Shahania, Saijal; Unnikrishnan, Vishnu; Pryss, Rüdiger; Kraft, Robin; Schobel, Johannes; Hannemann, Ronny; Schlee, Winny; Spiliopoulou, Myra

Shahania, Saijal; Unnikrishnan, Vishnu; Pryss, Rüdiger; Kraft, Robin; Schobel, Johannes; Hannemann, Ronny; Schlee, Winny; Spiliopoulou, Myra.

Affiliation

Shahania S; Knowledge Management and Discovery Lab, Otto-von-Guericke University Magdeburg, Magdeburg, Germany.
Unnikrishnan V; Knowledge Management and Discovery Lab, Otto-von-Guericke University Magdeburg, Magdeburg, Germany.
Pryss R; Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany.
Kraft R; Institute of Databases and Information Systems, Ulm University, Ulm, Germany.
Schobel J; DigiHealth Institute, Neu-Ulm University of Applied Sciences, Neu-Ulm, Germany.
Hannemann R; Sivantos GmbH - WS Audiology, Erlangen, Germany.
Schlee W; Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany.
Spiliopoulou M; Knowledge Management and Discovery Lab, Otto-von-Guericke University Magdeburg, Magdeburg, Germany.

Front Neurosci ; 16: 836834, 2022.

Article de En | MEDLINE | ID: mdl-35478848

Mots clés

EMA prediction; contextual multi-armed bandits; mHealth for tinnitus; prediction in mHealth data; prediction on sparse data; prediction on time series with gaps; similarity-based prediction

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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Type d'étude: Prognostic_studies / Risk_factors_studies Langue: En Journal: Front Neurosci Année: 2022 Type de document: Article Pays d'affiliation: Allemagne Pays de publication: Suisse

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