  Accurate and explainable health event predictions are becoming crucial for
healthcare providers to develop care plans for patients. The availability of
electronic health records (EHR) has enabled machine learning advances in
providing these predictions. However, many deep learning based methods are not
satisfactory in solving several key challenges: 1) effectively utilizing
disease domain knowledge; 2) collaboratively learning representations of
patients and diseases; and 3) incorporating unstructured text. To address these
issues, we propose a collaborative graph learning model to explore
patient-disease interactions and medical domain knowledge. Our solution is able
to capture structural features of both patients and diseases. The proposed
model also utilizes unstructured text data by employing an attention regulation
strategy and then integrates attentive text features into a sequential learning
process. We conduct extensive experiments on two important healthcare problems
to show the competitive prediction performance of the proposed method compared
with various state-of-the-art models. We also confirm the effectiveness of
learned representations and model interpretability by a set of ablation and
case studies.
