  Expert diagnostic support systems have been extensively studied. The
practical applications of these systems in real-world scenarios have been
somewhat limited due to well-understood shortcomings, such as lack of
extensibility. More recently, machine-learned models for medical diagnosis have
gained momentum, since they can learn and generalize patterns found in very
large datasets like electronic health records. These models also have
shortcomings - in particular, there is no easy way to incorporate prior
knowledge from existing literature or experts. In this paper, we present a
method to merge both approaches by using expert systems as generative models
that create simulated data on which models can be learned. We demonstrate that
such a learned model not only preserves the original properties of the expert
systems but also addresses some of their limitations. Furthermore, we show how
this approach can also be used as the starting point to combine expert
knowledge with knowledge extracted from other data sources, such as electronic
health records.
