  Due to potential applications in chronic disease management and personalized
healthcare, the EHRs data analysis has attracted much attention of both
researchers and practitioners. There are three main challenges in modeling
longitudinal and heterogeneous EHRs data: heterogeneity, irregular temporality
and interpretability. A series of deep learning methods have made remarkable
progress in resolving these challenges. Nevertheless, most of existing
attention models rely on capturing the 1-order temporal dependencies or 2-order
multimodal relationships among feature elements. In this paper, we propose a
time-guided high-order attention (TGHOA) model. The proposed method has three
major advantages. (1) It can model longitudinal heterogeneous EHRs data via
capturing the 3-order correlations of different modalities and the irregular
temporal impact of historical events. (2) It can be used to identify the
potential concerns of medical features to explain the reasoning process of the
healthcare model. (3) It can be easily expanded into cases with more modalities
and flexibly applied in different prediction tasks. We evaluate the proposed
method in two tasks of mortality prediction and disease ranking on two real
world EHRs datasets. Extensive experimental results show the effectiveness of
the proposed model.

Machine Learning