Research Area:  Machine Learning
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.
Author(s) Name:  Sicen Liu, Tao Li, Haoyang Ding, Buzhou Tang, Xiaolong Wang, Qingcai Chen, Jun Yan & Yi Zhou
Journal name:  International Journal of Machine Learning and Cybernetics
Publisher name:  Springer
Volume Information:  volume 11, pages 2849–2856 (2020)
Paper Link:   https://link.springer.com/article/10.1007/s13042-020-01155-x