The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Uncertainty-Aware Dynamic Fusion for Multimodal Clinical Prediction Tasks
Abstract
Multimodal fusion offers significant potential for enhancing medical diagnosis, particularly in the Intensive Care Unit (ICU), where integrating diverse data sources is crucial. Traditional static fusion models often fail to account for sample-wise variations in modality importance, which can impact prediction accuracy. To address this issue, we propose a dynamic Uncertainty-Aware Weighting (UAW) strategy that adaptively adjusts the importance of different modalities based on their reliability. This strategy is coupled with an Expert Ensemble Fusion (EEF) module, which leverages self-attention mechanisms and modality-specific FeedForward Networks (FFNs) to preserve and integrate critical information from various modalities. The proposed method demonstrates its efficacy through extensive experiments on phenotype classification and mortality prediction tasks, showing improved accuracy and robustness in handling diverse clinical data.
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“In this review, only 6 studies addressed the issue of missing modalities [20,48,69,110–112].”
From Multimodal Artificial Intelligence for Precision Critical Care: A Scoping Review (2026) · cites this paper as [110]
Citations
Cites
Deep Residual Learning for Image Recognition
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Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 47,472
MIMIC-IV, a freely accessible electronic health record dataset
Johnson, A. E. W., Bulgarelli, L., Shen, L., Gayles, A., Shammout, A., Horng, S., Pollard, T. J., Hao, S., Moody, B., Gow, B., et al. (2023) · Scientific Data · doi:10.1038/s41597-022-01899-x · Cited by 3,401 · cited 1×
“MIMIC-IV contains clinical time-series EHR data from ICU wards [21], and MIMIC-CXR contains Chest X-ray images.”
MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports
Johnson, A. E. W., Pollard, T. J., Berkowitz, S. J., Greenbaum, N. R., Lungren, M. P., Deng, C.-Y., Mark, R. G., & Horng, S. (2019) · Scientific Data · doi:10.1038/s41597-019-0322-0 · Cited by 1,816
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Mobadersany, P., Yousefi, S., Amgad, M., Gutman, D. A., Barnholtz-Sloan, J. S., Velázquez Vega, J. E., Brat, D. J., & Cooper, L. A. D. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1717139115 · Cited by 1,084
Energy-based Out-of-distribution DetectionPreprint
Liu, W., Wang, X., Owens, J. D., & Li, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.03759 · Cited by 744
Zhou, H.-Y., Yu, Y., Wang, C., Zhang, S., Gao, Y., Pan, J., Shao, J., Lu, G., Zhang, K., & Li, W. (2023) · Nature Biomedical Engineering · doi:10.1038/s41551-023-01045-x · Cited by 343
MMTM: Multimodal Transfer Module for CNN Fusion
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Research works printed but not linked: 2
- Johnson, 2020, Mimic-iv, 49
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Sorted by citations
Multimodal Artificial Intelligence for Precision Critical Care: A Scoping Review
Yang, M., Shi, N., Chen, H., Po, E. Y. P., Ng, P. Y., Lai, P. C. K., Wu, Y.-C., & Fong, D. Y. T. (2026) · Health Data Science · doi:10.34133/hds.0356 · Cited by 3
“In this review, only 6 studies addressed the issue of missing modalities [ 20 , 48 , 69 , 110 – 112 ].”
Bakumenko, A., Hoelscher, J., Smith, D. H., Bakumenko, S., Masino, A. J., & Obeid, J. S. (2026) · Research Square · doi:10.21203/rs.3.rs-9217081/v1 · Cited by 1
Laboratory Test-Guided Medical Image Generation for Multi-Modal Disease Prediction
Xu, J., Lyu, F., Zhu, Y., & Yuen, P. C. (2026) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2026.3660978
Deep learning for multivariate time series analysis in electronic health records: a scoping review
Hernandez-Diaz, C. A., Vázquez, B., & Fuentes-Pineda, G. (2026) · Evolutionary Intelligence · doi:10.1007/s12065-026-01246-8
Metadata
- Authors
- Guo, J., Cheng, Y., He, W., Zhang, Y., Feng, R., & Zhang, X.
- Affiliations
- Fudan University (CN)
- Venue
- ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2025-04-06
- Pages
- 1-5
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W4408345869
- Cited by
- 3
- References
- 23
- Citations by year
- 2026: 3
- FWCI
- 2.84
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
- Funders
- National Natural Science Foundation of China
- Science and Technology Commission of Shanghai Municipality
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- multimodal fusion, self-attention, intensive care unit, clinical prediction, mortality prediction, phenotype classification
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