Interpretable Machine Learning for In-Hospital Mortality Prediction in ICU Patients Using First-24-Hour Routine Vital Signs: A SHAP-Based MIMIC-IV Study

Abdul Karim, Jinwon Kim, In cheol Jeong

Open source

DOI
10.3390/diagnostics16182982
Published
2026-09-15
Container
Diagnostics
Publisher
MDPI AG
Open access
unknown

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BibTeX

@article{allodium:10.3390/diagnostics16182982,
  title = {Interpretable Machine Learning for In-Hospital Mortality Prediction in ICU Patients Using First-24-Hour Routine Vital Signs: A SHAP-Based MIMIC-IV Study},
  author = {Abdul Karim and Jinwon Kim and In cheol Jeong},
  year = {2026},
  journal = {Diagnostics},
  doi = {10.3390/diagnostics16182982},
  url = {https://doi.org/10.3390/diagnostics16182982}
}

RIS

TY  - JOUR
TI  - Interpretable Machine Learning for In-Hospital Mortality Prediction in ICU Patients Using First-24-Hour Routine Vital Signs: A SHAP-Based MIMIC-IV Study
AU  - Abdul Karim
AU  - Jinwon Kim
AU  - In cheol Jeong
PY  - 2026
JO  - Diagnostics
DO  - 10.3390/diagnostics16182982
UR  - https://doi.org/10.3390/diagnostics16182982
ER  - 

APA

Karim, A., Kim, J., & Jeong, I. C. (2026). Interpretable Machine Learning for In-Hospital Mortality Prediction in ICU Patients Using First-24-Hour Routine Vital Signs: A SHAP-Based MIMIC-IV Study. Diagnostics. https://doi.org/10.3390/diagnostics16182982

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