How to leverage the multimodal EHR data for better medical prediction?
Open accessAbstract
Healthcare is becoming a more and more important research topic recently.With the growing data in the healthcare domain, it offers a great opportunity for deep learning to improve the quality of medical service.However, the complexity of electronic health records (EHR) data is a challenge for the application of deep learning.Specifically, the data produced in the hospital admissions are monitored by the EHR system, which includes structured data like daily body temperature, and unstructured data like free text and laboratory measurements.Although there are some preprocessing frameworks proposed for specific EHR data, the clinical notes that contain significant clinical value are beyond the realm of their consideration.Besides, whether these different data from various views are all beneficial to the medical tasks and how to best utilize these data remain unclear.Therefore, in this paper, we first extract the accompanying clinical notes from EHR and propose a method to integrate these data, we also comprehensively study the different models and the data leverage methods for better medical task prediction.The results on two medical prediction tasks show that our fused model with different data outperforms the state-of-the-art method that without clinical notes, which illustrates the importance of our fusion method and the value of clinical note features.Our code is available at https: //github.com/emnlp-mimic/mimic.
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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 [45]
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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
“While simple concatenation of deeply extracted features remains the most straightforward approach, attention mechanisms such as gated attention [ 40 , 41 ], cross-attention [ 42 – 45 ], or trans-former encoders [ 20 , 36 , 46 – 49 ] are increasingly adopted.”
- Authors
- Yang, B., & Wu, L.
- Affiliations
- Microsoft Research Asia (China) (CN)
- Corresponding author
- Wu, Lijun
- Venue
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Published
- 2021
- Pages
- 4029-4038
- OpenAlex
- W4205464244
- Cited by
- 26
- References
- 5
- Citations by year
- 2026: 2 · 2025: 10 · 2024: 8 · 2023: 3 · 2022: 3
- FWCI
- 1.62
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- Funders
- National Natural Science Foundation of China (U1711263)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- electronic health records, multimodal fusion, clinical notes, clinical text extraction, medical prediction, hospital admission data, structured and unstructured data, MIMIC dataset
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