| [1] | Rasmy L., Xiang Y., Xie Z., et al. (2021). Med-BERT: Pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction. npj Digit. Med. 4:86. DOI:10.1038/s41746-021-00455-y |
| [2] | Shmatko A., Jung A.W., Gaurav K., et al. (2025). Learning the natural history of human disease with generative transformers. Nature 647:248−256. DOI:10.1038/s41586-025-09529-3 |
| [3] | Urbut S.M., Ding Y., Nakao T., et al. (2026). A Bayesian framework for longitudinal EHR and genetic discovery. Nature 656:924−935. DOI:10.1038/s41586-026-10780-5 |
| [4] | Blei D.M., Kucukelbir A. and McAuliffe J.D. (2017). Variational inference: A review for statisticians. J. Am. Stat. Assoc. 112:859−877. DOI:10.1080/01621459.2017.1285773 |
| [5] | Corral-Acero J., Margara F., Marciniak M., et al. (2020). The ‘digital twin’ to enable the vision of precision cardiology. Eur. Heart J. 41:4556−4564. DOI:10.1093/eurheartj/ehaa159 |
| Chen X., Li Z., Yang W., et al. (2026). Structured Bayesian modelling advances longitudinal EHR research. The Innovation Nutrition 1:100036. https://doi.org/10.59717/j.xinn-nutri.2026.100036 |
To request copyright permission to republish or share portions of our works, please visit Copyright Clearance Center's (CCC) Marketplace website at marketplace.copyright.com.
Complementary paradigms for longitudinal EHR modelling