Medicine and Healthnpj Digital Medicine
Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction
L. Rasmy, Y. Xiang, et al.
Discover how Med-BERT, an innovative contextualized embedding model tailored for electronic health records, revolutionizes disease prediction accuracy. Developed by Laila Rasmy, Yang Xiang, Ziqian Xie, Cui Tao, and Degui Zhi, this research showcases significant performance enhancements across clinical databases, paving the way for cost-efficient AI in healthcare.
Related Publications
Explore these studies to deepen your understanding
Adjacent work that informs or extends this paper's methodology and findings.
Medicine and Health
TransformEHR: transformer-based encoder-decoder generative model to enhance prediction of disease outcomes using electronic health records
Z. Yang, A. Mitra, et al.
Medicine and Health
Natural language processing of multi-hospital electronic health records for public health surveillance of suicidality
R. Bey, A. Cohen, et al.
Transportation
Calibrated confidence learning for large-scale real-time crash and severity prediction
M. R. Islam, D. Wang, et al.
Engineering and Technology
A tailored, electronic textile conformable suit for large-scale spatiotemporal physiological sensing *in vivo*
I. Wicaksono, C. I. Tucker, et al.
