  Data-driven Clinical Decision Support Systems (CDSS) have the potential to
improve and standardise care with personalised probabilistic guidance. However,
the size of data required necessitates collaborative learning from analogous
CDSS's, which are often unsharable or imperfectly interoperable (IIO), meaning
their feature sets are not perfectly overlapping. We propose Modular Clinical
Decision Support Networks (MoDN) which allow flexible, privacy-preserving
learning across IIO datasets, while providing interpretable, continuous
predictive feedback to the clinician.
  MoDN is a novel decision tree composed of feature-specific neural network
modules. It creates dynamic personalised representations of patients, and can
make multiple predictions of diagnoses, updatable at each step of a
consultation. The modular design allows it to compartmentalise training updates
to specific features and collaboratively learn between IIO datasets without
sharing any data.
