A cross-modal clinical prediction system for intensive care unit patient outcome
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Attention mechanisms such as gated attention, cross-attention, or transformer encoders are increasingly adopted in multimodal AI.
“attention mechanisms such as gated attention [40,41], cross-attention [42–45], or transformer encoders [20,36,46–49] are increasingly adopted.”
From Multimodal Artificial Intelligence for Precision Critical Care: A Scoping Review (2026) · cites this paper as [43]
Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works
Scalable and accurate deep learning with electronic health records
Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., et al. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0029-1 · Cited by 2,648 · see citing works
Self-citationGraph convolutional networks: a comprehensive review
Zhang, S., Tong, H., Xu, J., & Maciejewski, R. (2019) · Computational Social Networks · doi:10.1186/s40649-019-0069-y · Cited by 1,888 · see citing works
Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016) · Scientific Reports · doi:10.1038/srep26094 · Cited by 1,710 · see citing works
BEHRT: Transformer for Electronic Health Records
Li, Y., Rao, S., Solares, J. R. A., Hassaine, A., Ramakrishnan, R., Canoy, D., Zhu, Y., Rahimi, K., & Salimi-Khorshidi, G. (2020) · Scientific Reports · doi:10.1038/s41598-020-62922-y · Cited by 595 · see citing works
Artificial intelligence-based methods for fusion of electronic health records and imaging data
Mohsen, F., Ali, H., El Hajj, N., & Shah, Z. (2022) · Scientific Reports · doi:10.1038/s41598-022-22514-4 · Cited by 250 · see citing works
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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 · see citing works
“Specifically for unstructured text data, several pre-trained models, such as BlueBERT [ 21 , 78 ] and BioClinicalBERT [ 42 , 43 , 79 , 80 ] (trained on PubMed and MIMIC-III database [ 81 ]), BioBERT [ 82 – 85 ] and BioMedBERT [ 37 ] (trained on PubMed database), ClinicalBERT [ 40 , 41 , 45 , 47 ,…”
- Authors
- Sun, M., Yang, X., Niu, J., Gu, Y., Wang, C., & Zhang, W.
- Affiliations
- Chinese Academy of Sciences (CN)
- Corresponding author
- Yang, Xuebing
- Zhang, Wensheng
- Venue
- Knowledge-Based Systems
- Publisher
- Elsevier BV
- Published
- 2024-01
- Volume
- 283
- Pages
- 111160
- ISSN
- 0950-7051 (ISSN-L)
- OpenAlex
- W4388451021
- Cited by
- 18
- References
- 6
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 3
- FWCI
- 1.59
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Knowledge-Based Systems (publishedVersion)
- Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China (2021ZD0111005, 61976212, 61976213, 62006139, 62203437)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Keywords
- cross-modal contrastive learning, electronic health records, clinical notes, clinical codes, ICU mortality prediction, eICU-CRD, data heterogeneity, data quality
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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