ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2022
A Multimodal Transformer: Fusing Clinical Notes with Structured EHR Data for Interpretable In-Hospital Mortality Prediction.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- AI-assisted post-mortem classification of vehicle trauma in free-roaming cats using transformer-based large language models.The veterinary quarterly · 2026Article
- From patient notes to prognostication: The revolutionary potential of event-based foundation models in orthopaedics.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026Article
- Less can be better: decomposing clinical data modalities in large language model-based healthcare applications.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Multimodal Prediction of Renal Tumor Malignancy From Radiology Reports and Structured Electronic Health Records: Retrospective Cohort Study.JMIR medical informatics · 2026Article
- Multimodal Wearable Biosensing Meets Multidomain AI: A Pathway to Decentralized Healthcare.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Machine Learning Model to Predict Postmastectomy Breast Reconstruction Complications.JAMA network open · 2026Article
- Cross-sectional stratification of fall-related vulnerability in Parkinson's disease through multimodal fusion of speech, gait, facial expression, and upper-limb movement.Frontiers in digital health · 2026Article
- ICU mortality prediction via multimodal learning on MIMIC-IV clinical data.Bioinformatics advances · 2026Article
- Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models: Quantitative Study Using Large Language Models.Journal of medical Internet research · 2025Article
- Review
- Learning to Route: Per-Sample Adaptive Routing for Multimodal Multitask Prediction.Advances in neural information processing systems · 2025Article
- [Research advances on the application of artificial intelligence technology in the diagnosis and treatment of sepsis patients].Zhonghua shao shang yu chuang mian xiu fu za zhi · 2025Review
- Combining Real and Synthetic Data to Overcome Limited Training Datasets in Multimodal Learning.medRxiv : the preprint server for health sciences · 2025Article
- The future of multimodal artificial intelligence models for integrating imaging and clinical metadata: a narrative reviewDiagnostic and interventional radiology (Ankara, Turkey) · 2025Review
- Emerging technologies for early risk stratification and precision management of diabetic kidney disease: a multimodal framework integrating digital phenotypes and clinical biomarkers.Frontiers in endocrinology · 2025Review
- Multi-Modal Fusion of Routine Care Electronic Health Records (EHR): A Scoping Review.Information (Basel) · 2025Article
- Bridging the Pathology Domain Gap: Efficiently Adapting CLIP for Pathology Image Analysis with Limited Labeled Data.Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision · 2025Article
- TriConvUNeXt: A Pure CNN-Based Lightweight Symmetrical Network for Biomedical Image Segmentation.Journal of imaging informatics in medicine · 2024Article
- Large language multimodal models for new-onset type 2 diabetes prediction using five-year cohort electronic health records.Scientific reports · 2024Article
- Natural Language Processing for Digital Health in the Era of Large Language Models.Yearbook of medical informatics · 2024Review
Corrections and comments
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Authors and funding
7 authors.
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Abstract
Deep-learning-based clinical decision support using structured electronic health records (EHR) has been an active research area for predicting risks of mortality and diseases. Meanwhile, large amounts of narrative clinical notes provide complementary information, but are often not integrated into predictive models. In this paper, we provide a novel multimodal transformer to fuse clinical notes and structured EHR data for better prediction of in-hospital mortality. To improve interpretability, we propose an integrated gradients (IG) method to select important words in clinical notes and discover the critical structured EHR features with Shapley values. These important words and clinical features are visualized to assist with interpretation of the prediction outcomes. We also investigate the significance of domain adaptive pretraining and task adaptive fine-tuning on the Clinical BERT, which is used to learn the representations of clinical notes. Experiments demonstrated that our model outperforms other methods (AUCPR: 0.538, AUCROC: 0.877, F1:0.490).
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37128451PMC10148371What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.