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Data-driven Clinical Decision Support Systems (CDSS) have the potential toimprove and standardise care with personalised probabilistic guidance. However,the size of data required necessitates collaborative learning from analogousCDSS's, which are often unsharable or imperfectly interoperable (IIO), meaningtheir feature sets are not perfectly overlapping. We propose Modular ClinicalDecision Support Networks (MoDN) which allow flexible, privacy-preservinglearning across IIO datasets, while providing interpretable, continuouspredictive feedback to the clinician.  MoDN is a novel decision tree composed of feature-specific neural networkmodules. It creates dynamic personalised representations of patients, and canmake multiple predictions of diagnoses, updatable at each step of aconsultation. The modular design allows it to compartmentalise training updatesto specific features and collaboratively learn between IIO datasets withoutsharing any data.

Modular Clinical Decision Support Networks (MoDN) -- Updatable, Interpretable, and Portable Predictions for Evolving Clinical Environments