GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination
Open accessAbstract
Recent progress in deep learning is revolutionizing the healthcare domain including providing solutions to medication recommendations, especially recommending medication combination for patients with complex health conditions. Existing approaches either do not customize based on patient health history, or ignore existing knowledge on drug-drug interactions (DDI) that might lead to adverse outcomes. To fill this gap, we propose the Graph Augmented Memory Networks (GAMENet), which integrates the drug-drug interactions knowledge graph by a memory module implemented as a graph convolutional networks, and models longitudinal patient records as the query. It is trained end-to-end to provide safe and personalized recommendation of medication combination. We demonstrate the effectiveness and safety of GAMENet by comparing with several state-of-the-art methods on real EHR data. GAMENet outperformed all baselines in all effectiveness measures, and also achieved 3.60% DDI rate reduction from existing EHR data.
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Methods that embed medical entities including EHRs and medical ontologies leverage the inherently hierarchical structure in the medical concepts KG.
“Methods that embed medical entities, including EHRs and medical ontologies, leverage the inherently hierarchical structure in the medical concepts KG [201].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [201]
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References 24 · Cited by 345
Research works printed but not linked: 6
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- Authors
- Shang, J., Xiao, C., Ma, T., Li, H., & Sun, J.
- Affiliations
- Peking University (CN)
- IBM (United States) (US)
- Georgia Institute of Technology (US)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2019-07-17
- Volume
- 33
- Issue
- 01
- Pages
- 1126-1133
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2964068143
- Cited by
- 345
- References
- 24
- Citations by year
- 2026: 51 · 2025: 68 · 2024: 58 · 2023: 48 · 2022: 36 · 2021: 57 · 2020: 23 · 2019: 4
- FWCI
- 11.48
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- www.aaai.org/ (unspecified)
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1418511, 1533768)
- Peking University
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- drug-drug interactions, knowledge graphs, memory networks, graph convolutional networks, personalized recommendation, electronic health records, longitudinal patient records, drug safety
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