Toward Transportable Acute Kidney Injury Prediction: An Explainable XGBoost Model with Temporal Validation Using MIMIC-IV
- 1Miva Open University
Abstract
BackgroundAcute kidney injury (AKI) is a frequent complication within intensive care units, with its sudden onset often missed. Existing machine learning and deep learning models have contributed to closing this gap, but their complexity, requiring hundreds to thousands of features, and lack of generalisation pose a limitation that prevents them from being integrated into clinical workflows across different electronic health-record ecosystems.
MethodsThis study presents a 37-feature XGBoost model trained on the MIMIC-IV dataset with 5.4% positive cases, with hyperparameters optimised via Optuna and probabilities calibrated using isotonic regression, designed for transportability across clinical settings. Validation was conducted internally using a temporal patient-level split simulating prospective deployment, training on 2008-2016 data and testing on 2017-2022 data. External validation was performed on the eICU Collaborative Research Database, a multi-centre dataset spanning 208 US hospitals, using the trained model without retraining. SHAP TreeExplainer was used to provide feature-level explainability for individual predictions.
ResultsInternal testing yielded an AUROC score of 0.794 for predicting AKI onset within a 12-24 hour window. External validation produced a 0.750 AUROC without retraining. Equitable discrimination was observed across gender, age, chronic kidney disease presence, race, and AKI stages on both datasets, with a 95% internal CI of 0.789-0.799 confirming the models estimate stability.
ConclusionThese results suggest that clinically useful prediction systems are achievable with substantially fewer features than current models require.
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BibTeX
@misc{okundaye2026toward,
title = {{Toward Transportable Acute Kidney Injury Prediction: An Explainable XGBoost Model with Temporal Validation Using MIMIC-IV}},
author = {D. O. Okundaye and C. C. Isiekwene},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.01.26360393},
url = {https://www.medrxiv.org/content/10.64898/2026.09.01.26360393v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Toward Transportable Acute Kidney Injury Prediction: An Explainable XGBoost Model with Temporal Validation Using MIMIC-IV
AU - D. O. Okundaye
AU - C. C. Isiekwene
PY - 2026
DA - 2026/09/01
PB - medRxiv
DO - 10.64898/2026.09.01.26360393
UR - https://www.medrxiv.org/content/10.64898/2026.09.01.26360393v1
AB - BackgroundAcute kidney injury (AKI) is a frequent complication within intensive care units, with its sudden onset often missed. Existing machine learning and deep learning models have contributed to closing this gap, but their complexity, requiring hundreds to thousands of features, and lack of generalisation pose a limitation that prevents them from being integrated into clinical workflows across different electronic health-record ecosystems. MethodsThis study presents a 37-feature XGBoost model trained on the MIMIC-IV dataset with 5.4% positive cases, with hyperparameters optimised via Optuna and probabilities calibrated using isotonic regression, designed for transportability across clinical settings. Validation was conducted internally using a temporal patient-level split simulating prospective deployment, training on 2008-2016 data and testing on 2017-2022 data. External validation was performed on the eICU Collaborative Research Database, a multi-centre dataset spanning 208 US hospitals, using the trained model without retraining. SHAP TreeExplainer was used to provide feature-level explainability for individual predictions. ResultsInternal testing yielded an AUROC score of 0.794 for predicting AKI onset within a 12-24 hour window. External validation produced a 0.750 AUROC without retraining. Equitable discrimination was observed across gender, age, chronic kidney disease presence, race, and AKI stages on both datasets, with a 95% internal CI of 0.789-0.799 confirming the models estimate stability. ConclusionThese results suggest that clinically useful prediction systems are achievable with substantially fewer features than current models require.
ER -