Machine Learning Interpretability to Assess the Association Between Time in Tight Range and Mortality in Cardiogenic Shock.

Jiang Y, Li QL, Huang YL, Ning YL, Chen LH, Zhang Y, Xu XH.

DOI: 10.1111/nicc.70469
Published: 2026-05-01
Container: Nurs Crit Care
Publisher: Not recorded
Open access: no

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Cite this work

BibTeX

@article{allodium:10.1111/nicc.70469,
  title = {Machine Learning Interpretability to Assess the Association Between Time in Tight Range and Mortality in Cardiogenic Shock.},
  author = {Jiang Y and  Li QL and  Huang YL and  Ning YL and  Chen LH and  Zhang Y and  Xu XH.},
  year = {2026},
  journal = {Nurs Crit Care},
  doi = {10.1111/nicc.70469},
  url = {https://doi.org/10.1111/nicc.70469}
}

RIS

TY  - JOUR
TI  - Machine Learning Interpretability to Assess the Association Between Time in Tight Range and Mortality in Cardiogenic Shock.
AU  - Jiang Y
AU  -  Li QL
AU  -  Huang YL
AU  -  Ning YL
AU  -  Chen LH
AU  -  Zhang Y
AU  -  Xu XH.
PY  - 2026
JO  - Nurs Crit Care
DO  - 10.1111/nicc.70469
UR  - https://doi.org/10.1111/nicc.70469
ER  -

APA

Y, J., QL, L., YL, H., YL, N., LH, C., Y, Z., & XH., X. (2026). Machine Learning Interpretability to Assess the Association Between Time in Tight Range and Mortality in Cardiogenic Shock.. Nurs Crit Care. https://doi.org/10.1111/nicc.70469

Source records

europe-pmc · retrieved 2026-09-26T06:23:01.967Z