Explainable Machine Learning for Prediction of Future High-Severity States Using Longitudinal APACHE-II Trajectories: Internal Validation and Cross-Cohort Portability Assessment.

Akpolat İ, Çelik F

Open source

DOI
10.3390/diagnostics16193118
Published
2026 Sep 25
Container
Diagnostics (Basel, Switzerland)
Publisher
Not recorded
Open access
yes

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BibTeX

@article{allodium:10.3390/diagnostics16193118,
  title = {Explainable Machine Learning for Prediction of Future High-Severity States Using Longitudinal APACHE-II Trajectories: Internal Validation and Cross-Cohort Portability Assessment.},
  author = {Akpolat İ and Çelik F},
  year = {2026},
  journal = {Diagnostics (Basel, Switzerland)},
  doi = {10.3390/diagnostics16193118},
  url = {https://doi.org/10.3390/diagnostics16193118}
}

RIS

TY  - JOUR
TI  - Explainable Machine Learning for Prediction of Future High-Severity States Using Longitudinal APACHE-II Trajectories: Internal Validation and Cross-Cohort Portability Assessment.
AU  - Akpolat İ
AU  - Çelik F
PY  - 2026
JO  - Diagnostics (Basel, Switzerland)
DO  - 10.3390/diagnostics16193118
UR  - https://doi.org/10.3390/diagnostics16193118
ER  -

APA

İ, A., & F, Ç. (2026). Explainable Machine Learning for Prediction of Future High-Severity States Using Longitudinal APACHE-II Trajectories: Internal Validation and Cross-Cohort Portability Assessment.. Diagnostics (Basel, Switzerland). https://doi.org/10.3390/diagnostics16193118

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