Interpretable Machine Learning for In-Hospital Mortality Prediction in ICU Patients Using First-24-Hour Routine Vital Signs: A SHAP-Based MIMIC-IV Study
- DOI
- 10.3390/diagnostics16182982
- Published
- 2026-09-15
- Container
- Diagnostics
- Publisher
- MDPI AG
- Open access
- unknown
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Cite this work
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
Source records
- crossref · retrieved 2026-09-26T18:39:22.107Z