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.

我们提出了一个基于协作图学习的模型，结合疾病领域知识，协同学习患者和疾病的表示，同时使用了注意力调节策略来整合有用的文本特征，解决了利用深度学习方法在卫生数据中存在的几个关键挑战，证实该模型在两个重要医疗问题中预测表现优于现有最先进模型，具有良好的解释性。