Multi-Modal Graph Learning for Disease Prediction
Abstract
Benefiting from the powerful expressive capability of graphs, graph-based approaches have been popularly applied to handle multi-modal medical data and achieved impressive performance in various biomedical applications. For disease prediction tasks, most existing graph-based methods tend to define the graph manually based on specified modality (e.g., demographic information), and then integrated other modalities to obtain the patient representation by Graph Representation Learning (GRL). However, constructing an appropriate graph in advance is not a simple matter for these methods. Meanwhile, the complex correlation between modalities is ignored. These factors inevitably yield the inadequacy of providing sufficient information about the patient's condition for a reliable diagnosis. To this end, we propose an end-to-end Multi-modal Graph Learning framework (MMGL) for disease prediction with multi-modality. To effectively exploit the rich information across multi-modality associated with the disease, modality-aware representation learning is proposed to aggregate the features of each modality by leveraging the correlation and complementarity between the modalities. Furthermore, instead of defining the graph manually, the latent graph structure is captured through an effective way of adaptive graph learning. It could be jointly optimized with the prediction model, thus revealing the intrinsic connections among samples. Our model is also applicable to the scenario of inductive learning for those unseen data. An extensive group of experiments on two disease prediction tasks demonstrates that the proposed MMGL achieves more favorable performance. The code of MMGL is available at https://github.com/SsGood/MMGL.
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Nontextual data types play a crucial role in diagnosis, effective treatment planning, research, and patient care.
“Moreover, nontextual data types play a crucial role in diagnosis, effective treatment planning, research, and patient care [9-11].”
From Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook. (2024) · cites this paper as [11]
Zheng et al. introduced the MMGL framework that applied modal-aware representation learning to extract intra- and inter-modality representations and then used adaptive graph learning (AGL) to identify associations between patients in disease prediction.
“Zheng et al. introduced the MMGL framework that applied modal-aware representation learning to extract the intra- and inter-modality representations and then used adaptive graph learning (AGL) to identify associations between patients in disease prediction [68].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [68]
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- Authors
- Zheng, S., Zhu, Z., Liu, Z., Guo, Z., Liu, Y., Yang, Y., & Zhao, Y.
- Affiliations
- Beijing Jiaotong University (CN)
- Johns Hopkins University (US)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2022-09
- Volume
- 41
- Issue
- 9
- Pages
- 2207-2216
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W4221022534
- Cited by
- 196
- References
- 49
- Citations by year
- 2026: 48 · 2025: 64 · 2024: 45 · 2023: 30 · 2022: 9
- FWCI
- 12.18
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Science and Technology Innovation 2030—“New Generation Artificial Intelligence” (2018AAA0102100)
- National Natural Science Foundation of China (61976018)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- disease prediction, graph representation learning, adaptive graph learning, inductive learning, end-to-end learning, patient representation
- MeSH terms
- Machine Learning, Humans
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