A real-time machine learning framework for improved intraoperative risk predictions in cardiac surgery patients.

Zhu D, Xue B, Lu C, Abraham J

DOI: 10.1093/jamia/ocag142
Published: 2026 Sep 24
Container: Journal of the American Medical Informatics Association : JAMIA
Publisher: Not recorded
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1093/jamia/ocag142,
  title = {A real-time machine learning framework for improved intraoperative risk predictions in cardiac surgery patients.},
  author = {Zhu D and Xue B and Lu C and Abraham J},
  year = {2026},
  journal = {Journal of the American Medical Informatics Association : JAMIA},
  doi = {10.1093/jamia/ocag142},
  url = {https://doi.org/10.1093/jamia/ocag142}
}

RIS

TY  - JOUR
TI  - A real-time machine learning framework for improved intraoperative risk predictions in cardiac surgery patients.
AU  - Zhu D
AU  - Xue B
AU  - Lu C
AU  - Abraham J
PY  - 2026
JO  - Journal of the American Medical Informatics Association : JAMIA
DO  - 10.1093/jamia/ocag142
UR  - https://doi.org/10.1093/jamia/ocag142
ER  -

APA

D, Z., B, X., C, L., & J, A. (2026). A real-time machine learning framework for improved intraoperative risk predictions in cardiac surgery patients.. Journal of the American Medical Informatics Association : JAMIA. https://doi.org/10.1093/jamia/ocag142

Source records

pubmed · retrieved 2026-09-25T13:41:37.001Z