EHR-KnowGen: Knowledge-enhanced multimodal learning for disease diagnosis generation
Open accessAbstract
Electronic health records (EHRs) contain diverse patient information, including medical notes, clinical events, and laboratory test results. Integrating this multimodal data can improve disease diagnoses using deep learning models. However, effectively combining different modalities for diagnosis remains challenging. Previous approaches, such as attention mechanisms and contrastive learning, have attempted to address this but do not fully integrate the modalities into a unified feature space. This paper presents EHR-KnowGen, a multimodal learning model enhanced with external domain knowledge, for improved disease diagnosis generation from diverse patient information in EHRs. Unlike previous approaches, our model integrates different modalities into a unified feature space with soft prompts learning and leverages large language models (LLMs) to generate disease diagnoses. By incorporating external domain knowledge from different levels of granularity, we enhance the extraction and fusion of multimodal information, resulting in more accurate diagnosis generation. Experimental results on real-world EHR datasets demonstrate the superiority of our generative model over comparative methods, providing explainable evidence to enhance the understanding of diagnosis results.
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Every study conducting mechanistic or internal analysis necessarily used open-source models.
“Notably, every study conducting mechanistic or internal analysis: influence functions [129], attention visualization [113,116, 128], activation patching [114], necessarily used open-source models.”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [128]
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- Authors
- Niu, S., Ma, J., Bai, L., Wang, Z., Guo, L., & Yang, X.
- Affiliations
- Hong Kong Baptist University (HK)
- Shanxi University (CN)
- Zhejiang University (CN)
- Manchester Metropolitan University (GB)
- University of Manchester (GB)
- Corresponding author
- Yang, Xian
- Venue
- Information Fusion
- Publisher
- Elsevier BV
- Published
- 2024-02
- Volume
- 102
- Pages
- 102069
- ISSN
- 1566-2535 (ISSN-L)
- OpenAlex
- W4387627515
- Cited by
- 59
- References
- 50
- Citations by year
- 2026: 17 · 2025: 30 · 2024: 12
- FWCI
- 5.56
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Information Fusion (publishedVersion, cc-by)
- e-space (Manchester Metropolitan University) (submittedVersion)
- Research Explorer (The University of Manchester) (submittedVersion, cc-by)
- Funders
- Key Technologies Research and Development Program
- National Key Research and Development Program of China (2021ZD0113303)
- National Natural Science Foundation of China (62022052, 62276159)
- 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 › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- electronic health records, multimodal learning, soft prompts, large language models, clinical notes, clinical events, laboratory findings, explainable diagnosis
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