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A machine learning model for predicting short-term outcomes after rapid response system activation.

Takaki Naito1,2, Micheal Li1, Shigeki Fujitani2

  • 1Enterprise Analytics Thomas Jefferson University Hospital Philadelphia Pennsylvania USA.

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|August 13, 2025
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Summary
This summary is machine-generated.

Machine learning models can predict short-term outcomes after rapid response system (RRS) activation. The eXtreme Gradient Boosted Tree Classifier (XGB) model demonstrated superior predictive performance for patient prognosis.

Keywords:
early warning scoremachine learningmedical emergency teamrapid response system

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Area of Science:

  • Medical Informatics
  • Clinical Decision Support
  • Machine Learning in Healthcare

Background:

  • Maintaining the quality of rapid response team (RRT) interventions is challenging.
  • Predictive models for short-term prognosis following rapid response system (RRS) activation are limited.
  • RRS activation is crucial for patient safety, necessitating better prognostic tools.

Purpose of the Study:

  • To develop and evaluate machine learning models for predicting short-term outcomes after RRS activation.
  • To compare the performance of machine learning models against established scoring systems like NEWS and MEWS.
  • To identify key predictors of adverse outcomes in patients receiving RRS support.

Main Methods:

  • A retrospective cohort study utilizing data from the In-Hospital Emergency Registry in Japan.
  • Development of logistic regression (LR), Random Forest (RF), and eXtreme Gradient Boosted Tree Classifier (XGB) models.
  • Comparison of model performance using receiver-operating area under the curve (AUC), with benchmarking against NEWS and MEWS.

Main Results:

  • The study included 5414 cases, with an outcome event rate of 28.4%.
  • The XGB model achieved the highest AUC (0.798), outperforming RF (0.796), LR (0.785), NEWS (0.696), and MEWS (0.660).
  • Key predictors identified by the XGB model included doctor activation, hypotension, and oxygen usage.

Conclusions:

  • The developed XGB model represents the first machine learning approach for predicting short-term prognosis post-RRS activation.
  • This model shows potential to significantly aid RRT decision-making and improve patient care.
  • The findings highlight the utility of machine learning in enhancing RRS effectiveness.