Health-ATM: A Deep Architecture for Multifaceted Patient Health Record Representation and Risk Prediction

Leveraging massive electronic health records (EHR) brings tremendous promises to advance clinical and precision medicine informatics research. However, it is very challenging to directly work with multifaceted patient information encoded in their EHR data. Deriving effective representations of patient EHRs is a crucial step to bridge raw EHR information and the endpoint analytical tasks, such as risk prediction or disease subtyping. In this paper, we propose Health-ATM, a novel and integrated deep architecture to uncover patients’ comprehensive health information from their noisy, longitudinal, heterogeneous and irregular EHR data. Health-ATM extracts comprehensive multifaceted patient information patterns with attentive and time-aware mod-ulars (ATM) and a hybrid network structure composed of both Recurrent Neural Network (RNN) and Convolu-tional Neural Network (CNN). The learned features are finally fed into a prediction layer to conduct the risk prediction task. We evaluated the Health-ATM on both ar-tificial and real world EHR corpus and demonstrated its promising utility and efficacy on representation learning and disease onset predictions.