A heterogeneous data fusion mortality prediction model based on time-aware self-attention mechanism
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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 [47]
Cites
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 · cited 1× · see citing works
“Rajkumar et al. [14] combined three deep learning models to develop an integrated model for predicting readmission probability and length of stay.”
Self-citationBEHRT: 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
Min, X., Yu, B., & Wang, F. (2019) · Scientific Reports · doi:10.1038/s41598-019-39071-y · Cited by 170 · see citing works
Liu, S., Li, T., Ding, H., Tang, B., Wang, X., Chen, Q., Yan, J., & Zhou, Y. (2020) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-020-01155-x · Cited by 64 · 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
“…on PubMed and MIMIC-III database [ 81 ]), BioBERT [ 82 – 85 ] and BioMedBERT [ 37 ] (trained on PubMed database), ClinicalBERT [ 40 , 41 , 45 , 47 , 49 , 86 – 88 ] (trained on MIMIC-III database [ 81 ]), and SapBERT [ 87 ] (trained on the Unified Medical Language System database [ 89 ]), have…”
- Authors
- Chen, S., Wang, D., Che, C., Wei, Z., & Zhong, Z.
- Affiliations
- Dalian University (CN)
- Chinese Academy of Sciences (CN)
- Corresponding author
- Wei, Ziqi
- Zhong, Zhaoqian
- Venue
- International Journal of Machine Learning and Cybernetics
- Published
- 2025-04-19
- Volume
- 16
- Issue
- 9
- Pages
- 6381-6397
- ISSN
- 1868-8071 (ISSN-L)
- 1868-808X
- OpenAlex
- W4409589768
- Cited by
- 1
- References
- 4
- Citations by year
- 2026: 1
- FWCI
- 0.40
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Journal of Machine Learning and Cybernetics (publishedVersion)
- Funders
- National Natural Science Foundation of China (62076045, 62102058)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Nursing › Issues, ethics and legal aspects › Nursing Diagnosis and Documentation
- Keywords
- mortality prediction, in-hospital mortality, heterogeneous data fusion, clinical notes, unstructured medical data, electronic health records, patient representation learning, intensive care unit patients, MIMIC-III
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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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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