Electronic health records (EHRs) have been widely used to help physicians to make decisions by predicting medical events such as diseases, prescriptions, outcomes, and so on. How to represent patient longitudinal medical data is the key to making these predictions. Recurrent neural network (RNN) is a popular model for patient longitudinal medical data representation from the view of patient status sequences, but it cannot represent complex interactions among different types of medical information, i.e., temporal medical event graphs, which can be represented by graph neural network (GNN). In this paper, we propose a hybrid method of RNN and GNN, called RGNN, for next-period prescription prediction from two views, where RNN is used to represent patient status sequences, and GNN is used to represent temporal medical event graphs. Experiments conducted on the public MIMIC-III ICU data show that the proposed method is effective for next-period prescription prediction, and RNN and GNN are mutually complementary.

电子健康记录 (EHR) 已被广泛用于帮助医生通过预测疾病、处方、结果等医疗事件来做出决策。如何表示患者纵向医疗数据是做出这些预测的关键。循环神经网络（RNN）是从患者状态序列角度表示患者纵向医疗数据的流行模型，但它无法表示不同类型医疗信息之间的复杂交互，即时态医疗事件图，而可以用图来表示神经网络（GNN）。在本文中，我们提出了一种 RNN 和 GNN 的混合方法，称为 RGNN，用于从两个视图进行下一周期处方预测，其中 RNN 用于表示患者状态序列，GNN 用于表示时间医疗事件图。在公开的MIMIC-III ICU数据上进行的实验表明，该方法对于下一周期的处方预测是有效的，并且RNN和GNN是相互补充的。