Large language multimodal models for new-onset type 2 diabetes prediction using five-year cohort electronic health records
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Abstract
Type 2 diabetes mellitus (T2DM) is a prevalent health challenge faced by countries worldwide. In this study, we propose a novel large language multimodal models (LLMMs) framework incorporating multimodal data from clinical notes and laboratory results for diabetes risk prediction. We collected five years of electronic health records (EHRs) dating from 2017 to 2021 from a Taiwan hospital database. This dataset included 1,420,596 clinical notes, 387,392 laboratory results, and more than 1505 laboratory test items. Our method combined a text embedding encoder and multi-head attention layer to learn laboratory values, and utilized a deep neural network (DNN) module to merge blood features with chronic disease semantics into a latent space. In our experiments, we observed that integrating clinical notes with predictions based on textual laboratory values significantly enhanced the predictive capability of the unimodal model in the early detection of T2DM. Moreover, we achieved an area greater than 0.70 under the receiver operating characteristic curve (AUC) for new-onset T2DM prediction, demonstrating the effectiveness of leveraging textual laboratory data for training and inference in LLMs and improving the accuracy of new-onset diabetes prediction.
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A study demonstrated that LLMs can predict diabetic neuropathy by analyzing a combination of EHR data, physician notes, and patient-reported symptoms.
“A study demonstrated the use of LLMs in predicting diabetic neuropathy by analyzing a combination of EHR data, physician notes, and patient-reported symptoms (88).”
From Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review (2025) · cites this paper as [88]
The Introduction and Conclusions identify the predicted outcome as new-onset type 2 diabetes, not diabetic neuropathy. The study does use EHR clinical notes and laboratory data, but its Data collection and study design section does not identify patient-reported symptoms as a separate input. (LLM, conf=0.89)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe LLM model was able to detect early signs of nerve damage before patients presented with severe symptoms.
“The model was able to detect early signs of nerve damage before patients presented with severe symptoms.”
From Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review (2025) · cites this paper as [88]
The Results section reports early prediction of new-onset type 2 diabetes, not detection of nerve damage. Searches for nerve damage, neuropathy, and symptoms found no passage saying the model detected nerve damage before severe symptoms appeared. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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- Authors
- Ding, J.-E., Thao, P. N. M., Peng, W.-C., Wang, J.-Z., Chug, C.-C., Hsieh, M.-C., Tseng, Y.-C., Chen, L., Luo, D., Wu, C., Wang, C.-T., Hsu, C.-H., Chen, Y.-T., Chen, P.-F., Liu, F., & Hung, F.-M.
- Affiliations
- Stevens Institute of Technology (US)
- National Yang Ming Chiao Tung University (TW)
- Florida International University (US)
- University of Michigan (US)
- Far Eastern Memorial Hospital (TW)
- National Taipei University of Nursing and Health Science (TW)
- Corresponding author
- Hung, Fang-Ming
- ORCID
- Venue
- Scientific Reports
- Published
- 2024-09-06
- Volume
- 14
- Issue
- 1
- Pages
- 20774-20774
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC11377777
- OpenAlex
- W4402324956
- Cited by
- 36
- References
- 46
- Citations by year
- 2026: 18 · 2025: 17 · 2024: 1
- FWCI
- 5.31
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Scientific Reports (publishedVersion, cc-by-nc-nd)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- type 2 diabetes, large language models, multimodal models, electronic health records, clinical notes, laboratory parameters, text embeddings, multi-head attention, early detection, chronic disease risk prediction
- MeSH terms
- Deep Learning, Diabetes Mellitus, Type 2, Female, Humans, Male, Middle Aged, ROC Curve, Taiwan, Cohort Studies, Databases, Factual, Neural Networks, Computer, Electronic Health Records
About these checks
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.
What counts as a claim ›How we measure confidence ›How we check this ›
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