  Bayesian networks are well-suited for clinical reasoning on tabular data, but
are less compatible with natural language data, for which neural networks
provide a successful framework. This paper compares and discusses strategies to
augment Bayesian networks with neural text representations, both in a
generative and discriminative manner. This is illustrated with simulation
results for a primary care use case (diagnosis of pneumonia) and discussed in a
broader clinical context.
