Predicting health risks from electronic health records (EHR) is a topic of
recent interest. Deep learning models have achieved success by modeling
temporal and feature interaction. However, these methods learn insufficient
representations and lead to poor performance when it comes to patients with few
visits or sparse records. Inspired by the fact that doctors may compare the
patient with typical patients and make decisions from similar cases, we propose
a Progressive Prototypical Network (PPN) to select typical patients as
prototypes and utilize their information to enhance the representation of the
given patient. In particular, a progressive prototype memory and two prototype
separation losses are proposed to update prototypes. Besides, a novel
integration is introduced for better fusing information from patients and
prototypes. Experiments on three real-world datasets demonstrate that our model
brings improvement on all metrics. To make our results better understood by
physicians, we developed an application at this http URL Our code is
released at this https URL

从电子健康记录（EHR）中预测健康风险是近期感兴趣的话题。深度学习模型通过建模时间和特征交互取得了成功，然而，这些方法在对少量就诊或稀疏记录患者的表现方面，学习到的表示存在不足，导致性能欠佳。受到医生可能将患者与典型患者进行比较并从相似病例中做决策的启发，我们提出了逐步原型网络（PPN），以选择典型患者作为原型，并利用他们的信息来增强给定患者的表示。特别地，我们提出了逐步原型记忆和两个原型分离损失来更新原型。此外，我们引入了一种新的集成方法，以更好地融合来自患者和原型的信息。三个真实数据集上的实验证明了我们模型在所有指标上的改进。为了使医生更好地理解我们的结果，我们开发了一个应用，网址为 http URL。我们的代码已经在此 https URL 上发布。