A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction
Open accessAbstract
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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Li, Y., Xie, Z., Wang, M., Xin, X., Huang, J., Chen, T., Lan, W., Fei, X., & Chen, H. (2026) · doi:10.2196/preprints.107521
Sharma, S., Nagarajana, K., Bhardwaj, S., Tripathi, A. K., Jain, A., Khatter, H., & Goel, R. (2026) · Current Proteomics · doi:10.1016/j.curpro.2026.100150
A Comprehensive Study of Graph Neural Networks: Types and Application
Dubey, R., Kaur, R., & Ahuja, N. (2026) · Expert Systems · doi:10.1111/exsy.70429
- Authors
- Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., & Zhou, Y.
- Affiliations
- Harbin Institute of Technology (CN)
- Sun Yat-sen University (CN)
- Corresponding author
- Tang, Buzhou
- Zhou, Yi
- Venue
- International Journal of Machine Learning and Cybernetics
- Published
- 2020-06-23
- Volume
- 11
- Issue
- 12
- Pages
- 2849-2856
- ISSN
- 1868-8071 (ISSN-L)
- 1868-808X
- PMC
- PMC7308113
- OpenAlex
- W3036836886
- Cited by
- 64
- References
- 38
- Citations by year
- 2026: 11 · 2025: 11 · 2024: 7 · 2023: 17 · 2022: 13 · 2021: 3 · 2020: 1
- FWCI
- 3.34
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- International Journal of Machine Learning and Cybernetics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (U1813215, 61876052, 61573118)
- Special Foundation for Technology Research Program of Guangdong Province (2015B010131010)
- National Natural Science Foundations of Guangdong, China (2019A1515011158)
- Strategic Emerging Industry Development Special Funds of Shenzhen (JCYJ20180306172232154, JCYJ20170307150528934)
- Innovation Fund of Harbin Institute of Technology (HIT.NSRIF.2017052)
- Medical Science and Technology Foundation of Guangdong Province (2020KZDZX1222)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Keywords
- recurrent neural networks, graph neural networks, electronic health records
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