  Understanding the latent processes from Electronic Medical Records could be a
game changer in modern healthcare. However, the processes are complex due to
the interaction between at least three dynamic components: the illness, the
care and the recording practice. Existing methods are inadequate in capturing
the dynamic structure of care. We propose an end-to-end model that reads
medical record and predicts future risk. The model adopts the algebraic view in
that discrete medical objects are embedded into continuous vectors lying in the
same space. The bag of disease and comorbidities recorded at each hospital
visit are modeled as function of sets. The same holds for the bag of
treatments. The interaction between diseases and treatments at a visit is
modeled as the residual of the diseases minus the treatments. Finally, the
health trajectory, which is a sequence of visits, is modeled using a recurrent
neural network. We report preliminary results on chronic diseases - diabetes
and mental health - for predicting unplanned readmission.
