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ICU Mortality Prediction Using XGBoost-based Scoring Systems: A Study from a Developing Country.

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  • 1Department of Basic Medical Sciences, Faculty of Medicine, Yarmouk University, Irbid, Jordan.

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This study developed an XGBoost model for intensive care unit (ICU) mortality prediction in Jordan. The model achieved high accuracy, identifying hospital stay, albumin, and urea levels as key predictors.

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Area of Science:

  • Intensive Care Medicine
  • Machine Learning in Healthcare
  • Predictive Analytics

Background:

  • Accurate mortality prediction in intensive care units (ICUs) is crucial for patient outcomes and resource allocation.
  • Traditional scoring systems struggle with complex, high-dimensional ICU data.
  • This study addresses the need for a tailored mortality prediction model for the Jordanian population.

Purpose of the Study:

  • To develop and evaluate an efficacious machine learning model for ICU mortality prediction in Jordan.
  • To compare the performance of the XGBoost model against traditional methods.
  • To identify key factors associated with ICU mortality in the Jordanian context.

Main Methods:

  • A single-center, retrospective cohort study was conducted.
  • The XGBoost machine learning algorithm was employed to create a novel ICU mortality prediction model.
  • Model performance was assessed using clinical data and compared to logistic regression.

Main Results:

  • The XGBoost model demonstrated superior performance with 87.91% accuracy, 92.88% sensitivity, and a 94.29% AUC-ROC.
  • Key predictors of ICU mortality included length of hospital stay, albumin levels, and urea levels.
  • SHAP values indicated significant predictive power for these factors.

Conclusions:

  • A locally adapted ICU mortality prediction model using XGBoost was successfully developed.
  • Hospital stay duration, albumin, and urea levels are critical predictors of patient outcomes in Jordanian ICUs.
  • The XGBoost model offers a highly accurate and sensitive tool for mortality prediction in this setting.