  We present a personalized and reliable prediction model for healthcare, which
can provide individually tailored medical services such as diagnosis, disease
treatment, and prevention. Our proposed framework targets at making
personalized and reliable predictions from time-series data, such as Electronic
Health Records (EHR), by modeling two complementary components: i) a shared
component that captures global trend across diverse patients and ii) a
patient-specific component that models idiosyncratic variability for each
patient. To this end, we propose a composite model of a deep neural network to
learn complex global trends from the large number of patients, and Gaussian
Processes (GP) to probabilistically model individual time-series given
relatively small number of visits per patient. We evaluate our model on diverse
and heterogeneous tasks from EHR datasets and show practical advantages over
standard time-series deep models such as pure Recurrent Neural Network (RNN).
