Engineering biomarker representations of vital signs data enhances deep learning mortality prediction

Behrooz Mamandipoor, Isabella Shen, Chun-Nan Hsu, Rodney A Gabriel

Open source

DOI: 10.1093/jamia/ocag066
Published: 2026-05-02
Container: Journal of the American Medical Informatics Association
Publisher: Oxford University Press (OUP)
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1093/jamia/ocag066,
  title = {Engineering biomarker representations of vital signs data enhances deep learning mortality prediction},
  author = {Behrooz Mamandipoor and Isabella Shen and Chun-Nan Hsu and Rodney A Gabriel},
  year = {2026},
  journal = {Journal of the American Medical Informatics Association},
  doi = {10.1093/jamia/ocag066},
  url = {https://doi.org/10.1093/jamia/ocag066}
}

RIS

TY  - JOUR
TI  - Engineering biomarker representations of vital signs data enhances deep learning mortality prediction
AU  - Behrooz Mamandipoor
AU  - Isabella Shen
AU  - Chun-Nan Hsu
AU  - Rodney A Gabriel
PY  - 2026
JO  - Journal of the American Medical Informatics Association
DO  - 10.1093/jamia/ocag066
UR  - https://doi.org/10.1093/jamia/ocag066
ER  -

APA

Mamandipoor, B., Shen, I., Hsu, C., & Gabriel, R. A. (2026). Engineering biomarker representations of vital signs data enhances deep learning mortality prediction. Journal of the American Medical Informatics Association. https://doi.org/10.1093/jamia/ocag066

Source records

crossref · retrieved 2026-09-27T13:06:01.159Z