This paper is in the following e-collection/theme issue:

Artificial Intelligence (2132) Clinical Informatics (1325) Clinical Information and Decision Making (1916) Electronic Health Records (1319) Machine Learning (2077) Decision Support for Health Professionals (1540) Secondary Use of Clinical Data for Research and Surveillance (474) Cardiology (224)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/54363, first published .
Improving the Prognostic Evaluation Precision of Hospital Outcomes for Heart Failure Using Admission Notes and Clinical Tabular Data: Multimodal Deep Learning Model

Improving the Prognostic Evaluation Precision of Hospital Outcomes for Heart Failure Using Admission Notes and Clinical Tabular Data: Multimodal Deep Learning Model

Improving the Prognostic Evaluation Precision of Hospital Outcomes for Heart Failure Using Admission Notes and Clinical Tabular Data: Multimodal Deep Learning Model

Authors of this article:

Zhenyue Gao1 Author Orcid Image ;   Xiaoli Liu2 Author Orcid Image ;   Yu Kang3 Author Orcid Image ;   Pan Hu2 Author Orcid Image ;   Xiu Zhang3 Author Orcid Image ;   Wei Yan2 Author Orcid Image ;   Muyang Yan2 Author Orcid Image ;   Pengming Yu3 Author Orcid Image ;   Qing Zhang3 Author Orcid Image ;   Wendong Xiao1 Author Orcid Image ;   Zhengbo Zhang2 Author Orcid Image
Article Authors Cited by (17) Tweetations (3) Metrics

Journals

  1. Guo Y, Yu F, Jiang F, Yin S, Jiang M, Li Y, Yang H, Chen L, Cai W, He G. Development and validation of novel interpretable survival prediction models based on drug exposures for severe heart failure during vulnerable period. Journal of Translational Medicine 2024;22(1) View
  2. Yang X, Li Z, Lei L, Shi X, Zhang D, Zhou F, Li W, Xu T, Liu X, Wang S, Yuan Q, Yang J, Wang X, Zhong Y, Yu L. Noninvasive Oral Hyperspectral Imaging–Driven Digital Diagnosis of Heart Failure With Preserved Ejection Fraction: Model Development and Validation Study. Journal of Medical Internet Research 2025;27:e67256 View
  3. Li D, Xing W, Zhao J, Shi C, Wang F. Multimodal deep learning for predicting in-hospital mortality in heart failure patients using longitudinal chest X-rays and electronic health records. The International Journal of Cardiovascular Imaging 2025;41(3):427 View
  4. Teles A, de Moura I, Silva F, Roberts A, Stahl D. EHR-based prediction modelling meets multimodal deep learning: A systematic review of structured and textual data fusion methods. Information Fusion 2025;118:102981 View
  5. Xu Y, Liu S, Tian Q, Kou Z, Li W, Xie X, Wu X. Deep learning-based multimodal integration of imaging and clinical data for predicting surgical approach in percutaneous transforaminal endoscopic discectomy. European Spine Journal 2025;34(9):3683 View
  6. Timilsina M, Buosi S, Razzaq M, Haque R, Judge C, Curry E. Harmonizing foundation models in healthcare: A comprehensive survey of their roles, relationships, and impact in artificial intelligence’s advancing terrain. Computers in Biology and Medicine 2025;189:109925 View
  7. Wang J, Zhu J, Li H, Wu S, Li S, Yao Z, Zhu T, Tang B, Tang S, Liu J. Multimodal Visualization and Explainable Machine Learning–Driven Markers Enable Early Identification and Prognosis Prediction for Symptomatic Aortic Stenosis and Heart Failure With Preserved Ejection Fraction After Transcatheter Aortic Valve Replacement: Multicenter Cohort Study. Journal of Medical Internet Research 2025;27:e70587 View
  8. Guo C, Gao B, Han X, Zhang T, Tao T, Xia J, Liu H. Interpretable artificial intelligence model for predicting heart failure severity after acute myocardial infarction. BMC Cardiovascular Disorders 2025;25(1) View
  9. Kotula C, Martin J, Carey K, Edelson D, Dligach D, Mayampurath A, Afshar M, Churpek M. Comparison of Multimodal Deep Learning Approaches for Predicting Clinical Deterioration in Ward Patients: Observational Cohort Study. Journal of Medical Internet Research 2025;27:e75340 View
  10. Su C, Miao K, Zhang L, Yu X, Guo Z, Li D, Xu M, Zhang Q, Dong X. Multimodal Deep Learning Based on Ultrasound Images and Clinical Data for Better Ovarian Cancer Diagnosis. Journal of Imaging Informatics in Medicine 2025 View
  11. Yang X, Li Y, Wang J, Jia Y, Yi Z, Chen M. Utilizing multimodal artificial intelligence to advance cardiovascular diseases. Precision Clinical Medicine 2025;8(3) View
  12. Zhang C, Yuan-Lu , Tang F, Zhang S, Cai H. Modal interaction and contrastive learning for heart failure diagnosis combining chest X-rays and clinical text. Biomedical Signal Processing and Control 2026;112:108436 View
  13. Xu Q, Yu R, Cai X, Chen G, Zheng Y, Xu C, Sun J. Machine learning-based risk factor analysis and prediction model construction for mortality in chronic heart failure. Journal of Global Health 2025;15 View
  14. Wang T, Zhou X, Du L, Zhang C, Fu L, Pan G. AdaVIT: an Adaptive Visual-Tabular Fusion Multi-modal for road surface snow detection. Canadian Journal of Civil Engineering 2025;52(12):2191 View
  15. Demrozi F, Farmanbar M, Engan K. Multimodal AI (MMAI) for next-generation healthcare: data domains, algorithms, challenges, and future perspectives. Current Opinion in Biomedical Engineering 2026;37:100632 View

Books/Policy Documents

  1. Giaj Levra A. The First Steps of Artificial Intelligence in Cardiology. View
  2. Sharma K, Jaiswal A, Sachdeva N. Proceedings of Sixth Doctoral Symposium on Computational Intelligence. View