Disease Prediction via Graph Neural Networks
Abstract
With the increasingly available electronic medical records (EMRs), disease prediction has recently gained immense research attention, where an accurate classifier needs to be trained to map the input prediction signals (e.g., symptoms, patient demographics, etc.) to the estimated diseases for each patient. However, existing machine learning-based solutions heavily rely on abundant manually labeled EMR training data to ensure satisfactory prediction results, impeding their performance in the existence of rare diseases that are subject to severe data scarcity. For each rare disease, the limited EMR data can hardly offer sufficient information for a model to correctly distinguish its identity from other diseases with similar clinical symptoms. Furthermore, most existing disease prediction approaches are based on the sequential EMRs collected for every patient and are unable to handle new patients without historical EMRs, reducing their real-life practicality. In this paper, we introduce an innovative model based on Graph Neural Networks (GNNs) for disease prediction, which utilizes external knowledge bases to augment the insufficient EMR data, and learns highly representative node embeddings for patients, diseases and symptoms from the medical concept graph and patient record graph respectively constructed from the medical knowledge base and EMRs. By aggregating information from directly connected neighbor nodes, the proposed neural graph encoder can effectively generate embeddings that capture knowledge from both data sources, and is able to inductively infer the embeddings for a new patient based on the symptoms reported in her/his EMRs to allow for accurate prediction on both general diseases and rare diseases. Extensive experiments on a real-world EMR dataset have demonstrated the state-of-the-art performance of our proposed model.
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Cell morphology and tissue micro-architecture information can be aggregated from cell graphs to grade cancer histology images.
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Graph representation learning in biomedicine and healthcare
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- Authors
- Sun, Z., Yin, H., Chen, H., Chen, T., Cui, L., & Yang, F.
- Affiliations
- Shandong University (CN)
- The University of Queensland (AU)
- University of Technology Sydney (AU)
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2021-03
- Volume
- 25
- Issue
- 3
- Pages
- 818-826
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W3036619032
- Cited by
- 98
- References
- 36
- Citations by year
- 2026: 9 · 2025: 20 · 2024: 24 · 2023: 19 · 2022: 18 · 2021: 7 · 2019: 1
- FWCI
- 4.32
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Journal of Biomedical and Health Informatics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (91846205)
- National Key Research and Development Program of China Stem Cell and Translational Research (2017YFB1400100)
- Innovation Method Fund of China (2018IM020200)
- Shandong Key R&D Program (2018YFJH0506, 2019JZZY011007)
- Australian Research Council (DP190101985)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- disease prediction, graph neural networks, electronic health records, rare diseases, data scarcity, knowledge base augmentation, node embeddings, inductive learning, symptom-based diagnosis
- MeSH terms
- Machine Learning, Female, Humans, Neural Networks, Computer, Electronic Health Records
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