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International Journal of Machine Learning and Cybernetics

Erschienen in:

23.06.2020 | Original Article

A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction

verfasst von: Sicen Liu, Tao Li, Haoyang Ding, Buzhou Tang, Xiaolong Wang, Qingcai Chen, Jun Yan, Yi Zhou

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 12/2020

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Abstract

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.

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Titel: A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction
verfasst von: Sicen Liu
Tao Li
Haoyang Ding
Buzhou Tang
Xiaolong Wang
Qingcai Chen
Jun Yan
Yi Zhou
Publikationsdatum: 23.06.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: International Journal of Machine Learning and Cybernetics / Ausgabe 12/2020
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI: https://doi.org/10.1007/s13042-020-01155-x

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