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

Finding Algebraic Structure of Care in Time: A Deep Learning Approach