MedGCN: Medication recommendation and lab test imputation via graph convolutional networks
Open accessAbstract
Laboratory testing and medication prescription are two of the most important routines in daily clinical practice. Developing an artificial intelligence system that can automatically make lab test imputations and medication recommendations can save costs on potentially redundant lab tests and inform physicians of a more effective prescription. We present an intelligent medical system (named MedGCN) that can automatically recommend the patients' medications based on their incomplete lab tests, and can even accurately estimate the lab values that have not been taken. In our system, we integrate the complex relations between multiple types of medical entities with their inherent features in a heterogeneous graph. Then we model the graph to learn a distributed representation for each entity in the graph based on graph convolutional networks (GCN). By the propagation of graph convolutional networks, the entity representations can incorporate multiple types of medical information that can benefit multiple medical tasks. Moreover, we introduce a cross regularization strategy to reduce overfitting for multi-task training by the interaction between the multiple tasks. In this study, we construct a graph to associate 4 types of medical entities, i.e., patients, encounters, lab tests, and medications, and applied a graph neural network to learn node embeddings for medication recommendation and lab test imputation. we validate our MedGCN model on two real-world datasets: NMEDW and MIMIC-III. The experimental results on both datasets demonstrate that our model can outperform the state-of-the-art in both tasks. We believe that our innovative system can provide a promising and reliable way to assist physicians to make medication prescriptions and to save costs on potentially redundant lab tests.
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A cell-to-tissue graph can capture cell morphology and interactions, tissue morphology and spatial distribution, cell-to-tissue hierarchies, and spatial distribution of cells with respect to tissues, and a hierarchical GNN can learn representations from these modalities.
“Further, cell morphology and interactions, tissue morphology and spatial distribution, cell-to-tissue hierarchies, and spatial distribution of cells with respect to tissues can be captured in a cell-to-tissue graph, upon which a hierarchical GNN can learn representations using these different data modalities [189].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [189]
Deep graph learning has been shown to detect subtle signs of disease progression in medical images while integrating other modalities such as tissue localization and genomic features.
“Deep graph learning has been shown to detect subtle signs of disease progression in the images while integrating other modalities (e.g., tissue localization [189] and genomic features [8]) to improve medical image processing.”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [189]
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Graph representation learning in biomedicine and healthcare
Li, M. M., Huang, K., & Zitnik, M. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00942-x · Cited by 294 · see citing works
“For example, low-dimensional embed-dings of EHR data can be generated by separately considering medical services, doctors and patients in shallow network embeddings and GNNs 188,189 .”
- Authors
- Mao, C., Yao, L., & Luo, Y.
- Affiliations
- Northwestern University (US)
- Corresponding author
- Luo, Yuan
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2022-03
- Volume
- 127
- Pages
- 104000
- ISSN
- 1532-0464 (ISSN-L)
- PMC
- PMC8901567
- OpenAlex
- W4210277067
- Cited by
- 70
- References
- 56
- Citations by year
- 2026: 14 · 2025: 18 · 2024: 14 · 2023: 13 · 2022: 11
- FWCI
- 4.61
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 365 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Journal of Biomedical Informatics (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Institutes of Health (5UL1TR001422, N1R21LM012618)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Keywords
- drug recommendation, graph convolutional networks, heterogeneous graphs, multi-task learning, node embeddings, clinical decision support
- MeSH terms
- Artificial Intelligence, Humans, Learning, Neural Networks, Computer
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