Predicting the patient's clinical outcome from the historical electronic
medical records (EMR) is a fundamental research problem in medical informatics.
Most deep learning-based solutions for EMR analysis concentrate on learning the
clinical visit embedding and exploring the relations between visits. Although
those works have shown superior performances in healthcare prediction, they
fail to explore the personal characteristics during the clinical visits
thoroughly. Moreover, existing works usually assume that the more recent record
weights more in the prediction, but this assumption is not suitable for all
conditions. In this paper, we propose ConCare to handle the irregular EMR data
and extract feature interrelationship to perform individualized healthcare
prediction. Our solution can embed the feature sequences separately by modeling
the time-aware distribution. ConCare further improves the multi-head
self-attention via the cross-head decorrelation, so that the inter-dependencies
among dynamic features and static baseline information can be effectively
captured to form the personal health context. Experimental results on two
real-world EMR datasets demonstrate the effectiveness of ConCare. The medical
findings extracted by ConCare are also empirically confirmed by human experts
and medical literature.

本文提出了 ConCare 方法来处理不规则的电子病历数据并提取特征之间的关系以进行个性化医疗预测。该方法模拟了时间感知分布，进一步通过跨头部去关联来改善多头自我关注，以有效捕获动态特征和静态基线信息之间的相互依赖关系以形成个人健康背景。实验结果表明 ConCare 的有效性，并且由医学专家和医学文献经验证实了 ConCare 提取的医学发现