  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 Germany, for example, it is not possible to pool
routine data from different hospitals for research purposes without the consent
of the patients. The DataSHIELD software provides an infrastructure and a set
of statistical methods for joint analyses of distributed data. The contained
algorithms are reformulated to work with aggregated data from the participating
sites instead of the individual data. If a desired algorithm is not implemented
in DataSHIELD or cannot be reformulated in such a way, using artificial data is
an alternative. We present a methodology together with a software
implementation that builds on DataSHIELD to create artificial data that
preserve complex patterns from distributed individual patient data. Such data
sets of artificial patients, which are not linked to real patients, can then be
used for joint analyses. We use deep Boltzmann machines (DBMs) as generative
models for capturing the distribution of data. For the implementation, we
employ the package "BoltzmannMachines" from the Julia programming language and
wrap it for use with DataSHIELD, which is based on R. As an exemplary
application, we conduct a distributed analysis with DBMs on a synthetic data
set, which simulates genetic variant data. Patterns from the original data can
be recovered in the artificial data using hierarchical clustering of the
virtual patients, demonstrating the feasibility of the approach. Our
implementation adds to DataSHIELD the ability to generate artificial data that
can be used for various analyses, e. g. for pattern recognition with deep
learning. This also demonstrates more generally how DataSHIELD can be flexibly
extended with advanced algorithms from languages other than R.
