In the area of Big Data one of the major obstacles for the progress of biomedical research is the existence of data "silos", because legal and ethical constraints often do not allow for sharing sensitive patient data from clinical studies across institutions. While federated machine learning now allows for building models from scattered data, there is still the need to investigate, mine and understand clinical data that cannot be accessed directly. Simulation of sufficiently realistic virtual patients could be a way to fill this gap. In this work we propose a new machine learning approach (VAMBN) to learn a generative model of longitudinal clinical study data. VAMBN considers typical key aspects of such data, namely limited sample size coupled with comparable many variables of different numerical scales and statistical properties, and many missing values. We show that with VAMBN we can simulate virtual patients in a sufficiently realistic manner while making theoretical guarantees on data privacy. In addition, VAMBN allows for simulating counterfactual scenarios. Hence, VAMBN could facilitate data sharing as well as design of clinical trials.
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