  In healthcare, applying deep learning models to electronic health records
(EHRs) has drawn considerable attention. EHR data consist of a sequence of
medical visits, i.e. a multivariate time series of diagnosis, medications,
physical examinations, lab tests, etc. This sequential nature makes EHR well
matching the power of Recurrent Neural Network (RNN). In this paper, we propose
"Deep Diabetologist" - using RNNs for EHR sequential data modelling, to provide
the personalized hyperglycemia medication prediction for diabetic patients.
Particularly, we develop a hierarchical RNN to capture the heterogeneous
sequential information in the EHR data. Our experimental results demonstrate
the improved performance, compared with a baseline classifier using logistic
regression. Moreover, hierarchical RNN models outperform basic ones, providing
deeper data insights for clinical decision support.
