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Machine learning based clinical decision tool to predict acute kidney injury and survival in therapeutic hypothermia treated neonates
Published 2025-05-01“…The XGBoost model achieved an AUC of 95% and an accuracy of 75.08% in predicting AKI and survival, surpassing other ML classifiers that demonstrated accuracy levels ranging from 54% to 65%. …”
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