Expect the gap: A recommender approach to estimate the absenteeism of self-monitoring mHealth app users

Author:

Schleicher Miro1,Pryss Ruediger2,Schobel Johannes3,Schlee Winfried4,Spiliopoulou Myra1

Affiliation:

1. Otto-von-Guericke-University Magdeburg,Knowledge Management & Discovery Lab,Germany

2. University of Würzburg,Institute of Clinical Epidemiology and Biometry,Germany

3. Neu-Ulm University of Applied Sciences,Institute DigiHealth,Germany

4. University of Regensburg,Department of Psychiatry and Psychotherapy,Germany

Publisher

IEEE

Reference21 articles.

1. Temporal regularized matrix factorization for high-dimensional time series prediction;yu;Advances in neural information processing systems,2016

2. The data model concept in statistical mapping;jenks;International Yearbook of Cartography,1967

3. A Novel Approach Based on Matrix Factorization for Recovering Missing Time Series Sensor Data

4. Surprise: A Python library for recommender systems

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Predicting User Engagement in mHealth Apps with Neighborhood-based Approaches;2024 IEEE 37th International Symposium on Computer-Based Medical Systems (CBMS);2024-06-26

2. Prediction meets time series with gaps: User clusters with specific usage behavior patterns;Artificial Intelligence in Medicine;2023-08

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