%0 Journal Article %1 Schleicher2023AdhPred %A Schleicher, Miro %A Unnikrishnan, Vishnu %A Pryss, Rüdiger %A Schobel, Johannes %A Schlee, Winfried %A Spiliopoulou, Myra %D 2023 %I Elsevier BV %J Artificial Intelligence in Medicine %K Adherence Chronic_diseases Law_of_attrition Time_series_with_gaps mHealth %P 102575 %R 10.1016/j.artmed.2023.102575 %T Prediction meets time series with gaps: User clusters with specific usage behavior patterns %U https://doi.org/10.1016%2Fj.artmed.2023.102575

@article{Schleicher2023AdhPred, added-at = {2023-05-03T10:55:10.000+0200}, author = {Schleicher, Miro and Unnikrishnan, Vishnu and Pryss, Rüdiger and Schobel, Johannes and Schlee, Winfried and Spiliopoulou, Myra}, biburl = {https://www.bibsonomy.org/bibtex/2fbff8528916f9627abfa57636e42d542/kmd-ovgu}, doi = {10.1016/j.artmed.2023.102575}, interhash = {83b19069a2262344e05d36830a983e52}, intrahash = {fbff8528916f9627abfa57636e42d542}, journal = {Artificial Intelligence in Medicine}, keywords = {Adherence Chronic_diseases Law_of_attrition Time_series_with_gaps mHealth}, month = may, pages = 102575, publisher = {Elsevier {BV}}, timestamp = {2023-05-03T10:55:10.000+0200}, title = {Prediction meets time series with gaps: User clusters with specific usage behavior patterns}, url = {https://doi.org/10.1016%2Fj.artmed.2023.102575}, year = 2023 }