  The widespread availability of electronic health records (EHRs) promises to
usher in the era of personalized medicine. However, the problem of extracting
useful clinical representations from longitudinal EHR data remains challenging.
In this paper, we explore deep neural network models with learned medical
feature embedding to deal with the problems of high dimensionality and
temporality. Specifically, we use a multi-layer convolutional neural network
(CNN) to parameterize the model and is thus able to capture complex non-linear
longitudinal evolution of EHRs. Our model can effectively capture local/short
temporal dependency in EHRs, which is beneficial for risk prediction. To
account for high dimensionality, we use the embedding medical features in the
CNN model which hold the natural medical concepts. Our initial experiments
produce promising results and demonstrate the effectiveness of both the medical
feature embedding and the proposed convolutional neural network in risk
prediction on cohorts of congestive heart failure and diabetes patients
compared with several strong baselines.
