MuST: Multimodal Spatiotemporal Graph-Transformer for Hospital Readmission Prediction
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Hybrid spatiotemporal transformer models combine attention mechanisms with structural modeling capacity to capture both temporal evolution and relational dependencies.
“Hybrid spatiotemporal transformer models combine the strength of attention mechanisms with the structural modeling capacity to capture both temporal evolution and relational dependencies44,45.”
From K-STAMM: a knowledge-enhanced spatial - temporal attention model with multimodal fusion for pneumonia prediction (2026) · cites this paper as [44]
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Anbukkarasi, S., Hemalatha, S., Balakrishnan, A., Varadhaganapathy, S., & Easwaramoorthy, S. V. (2026) · Scientific reports · doi:10.1038/s41598-026-47146-w · Cited by 1 · see citing works
- Authors
- Miao, Y., & Yu, L.
- Affiliations
- University of Hong Kong (HK)
- Corresponding author
- Yu, Lequan
- ORCID
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2023
- Pages
- 276-285
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4391465165
- Cited by
- 1
- References
- 22
- Citations by year
- 2026: 1
- FWCI
- 0.18
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (62201483)
- 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 › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- hospital readmission prediction, multimodal fusion, graph convolutional networks, temporal transformer, electronic health records, chest radiograph, clinical notes, MIMIC-IV, medical prediction
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