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Accurate and explainable health event predictions are becoming crucial forhealthcare providers to develop care plans for patients. The availability ofelectronic health records (EHR) has enabled machine learning advances inproviding these predictions. However, many deep learning based methods are notsatisfactory in solving several key challenges: 1) effectively utilizingdisease domain knowledge; 2) collaboratively learning representations ofpatients and diseases; and 3) incorporating unstructured text. To address theseissues, we propose a collaborative graph learning model to explorepatient-disease interactions and medical domain knowledge. Our solution is ableto capture structural features of both patients and diseases. The proposedmodel also utilizes unstructured text data by employing an attention regulationstrategy and then integrates attentive text features into a sequential learningprocess. We conduct extensive experiments on two important healthcare problemsto show the competitive prediction performance of the proposed method comparedwith various state-of-the-art models. We also confirm the effectiveness oflearned representations and model interpretability by a set of ablation andcase studies.

Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare