376 citations · 472 across the 11 of their papers we have counts for
6 papers · 1 filter
Deep learning and differential equations for modeling changes in individual-level latent dynamics between observation periods
Göran Köber, Raffael Kalisch, Lara Puhlmann +3
When modeling longitudinal biomedical data, often dimensionality reduction as well as dynamic modeling in the resulting latent representation is needed. This can be achieved by art…
Adapting deep generative approaches for getting synthetic data with realistic marginal distributions
Kiana Farhadyar, Federico Bonofiglio, Daniela Zoeller +1
Synthetic data generation is of great interest in diverse applications, such as for privacy protection. Deep generative models, such as variational autoencoders (VAEs), are a popul…
Deep dynamic modeling with just two time points: Can we still allow for individual trajectories?
Maren Hackenberg, Philipp Harms, Michelle Pfaffenlehner +4
Longitudinal biomedical data are often characterized by a sparse time grid and individual-specific development patterns. Specifically, in epidemiological cohort studies and clinica…
Deep generative models in DataSHIELD
Stefan Lenz, Harald Binder
The best way to calculate statistics from medical data is to use the data of individual patients. In some settings, this data is difficult to obtain due to privacy restrictions. In…
Distributed Multivariate Regression Modeling For Selecting Biomarkers Under Data Protection Constraints
Daniela Zöller, Harald Binder
The discovery of clinical biomarkers requires large patient cohorts and is aided by a pooled data approach across institutions. In many countries, data protection constraints, espe…
Modeling Activity Tracker Data Using Deep Boltzmann Machines
Martin Treppner, Stefan Lenz, Harald Binder +1
Commercial activity trackers are set to become an essential tool in health research, due to increasing availability in the general population. The corresponding vast amounts of mos…