Machine learning for real-time aggregated prediction of hospital admission for emergency patients.

King Z, Farrington J, Utley M, Kung E, Elkhodair S, Harris S, Sekula R, Gillham J, Li K, Crowe S

Open source

DOI
10.1038/s41746-022-00649-y
Published
2022 Jul 26
Container
NPJ digital medicine
Publisher
Not recorded
Open access
yes

Credibility signals

uncertain Score 53/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.

Show all credibility signals

Cite this work

BibTeX

@article{allodium:10.1038/s41746-022-00649-y,
  title = {Machine learning for real-time aggregated prediction of hospital admission for emergency patients.},
  author = {King Z and Farrington J and Utley M and Kung E and Elkhodair S and Harris S and Sekula R and Gillham J and Li K and Crowe S},
  year = {2022},
  journal = {NPJ digital medicine},
  doi = {10.1038/s41746-022-00649-y},
  url = {https://doi.org/10.1038/s41746-022-00649-y}
}

RIS

TY  - JOUR
TI  - Machine learning for real-time aggregated prediction of hospital admission for emergency patients.
AU  - King Z
AU  - Farrington J
AU  - Utley M
AU  - Kung E
AU  - Elkhodair S
AU  - Harris S
AU  - Sekula R
AU  - Gillham J
AU  - Li K
AU  - Crowe S
PY  - 2022
JO  - NPJ digital medicine
DO  - 10.1038/s41746-022-00649-y
UR  - https://doi.org/10.1038/s41746-022-00649-y
ER  -

APA

Z, K., J, F., M, U., E, K., S, E., S, H., R, S., J, G., K, L., & S, C. (2022). Machine learning for real-time aggregated prediction of hospital admission for emergency patients.. NPJ digital medicine. https://doi.org/10.1038/s41746-022-00649-y

Source records