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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Simplified Pediatric Index of Mortality 3 Score by Explainable Machine Learning Algorithm.

Orkun Baloglu1,2, Matthew Nagy3, Chidiebere Ezetendu1

  • 1Pediatric Critical Care Medicine, Cleveland Clinic Children's, Cleveland, OH.

Critical Care Explorations
|October 25, 2021
PubMed
Summary
This summary is machine-generated.

Machine learning can simplify the Pediatric Index of Mortality 3 (a tool for assessing mortality risk in PICU patients) by reducing data entry. This approach maintains similar risk predictions with fewer variables.

Keywords:
Pediatric Index of Mortalitycritical caredata sciencemachine learningmortalitypediatrics

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Area of Science:

  • Pediatric critical care medicine
  • Health informatics
  • Machine learning applications in healthcare

Background:

  • The Pediatric Index of Mortality 3 (PIM3) is an 11-variable tool used to assess mortality risk in pediatric intensive care unit (PICU) patients.
  • Current data collection and entry for PIM3 can be time-consuming and labor-intensive.
  • Advances in explainable machine learning offer potential for simplifying complex scoring systems.

Purpose of the Study:

  • To assess the feasibility of using explainable machine learning models to simplify the Pediatric Index of Mortality 3 (PIM3) scoring system.
  • To reduce the time and labor associated with PIM3 data collection and entry.
  • To maintain the accuracy of mortality risk prediction while simplifying the PIM3 tool.

Main Methods:

  • A retrospective cohort study was conducted using data from 5,068 patients admitted to a quaternary children's hospital PICU between 2008 and 2019.
  • Light Gradient Boosting Machine Regressor was employed to build machine learning models predicting PIM3 mortality risk.
  • SHapley Additive exPlanations (SHAP) were used to analyze variable importance.

Main Results:

  • Machine learning models maintained predictive accuracy comparable to the original PIM3 model even when the number of input variables was reduced to four.
  • A model utilizing five key variables (mechanical ventilation in the first hour, very-high-risk diagnosis, surgical recovery from a noncardiac procedure, low-risk diagnosis, and base excess) demonstrated the lowest mean root mean squared error (1.49) and highest R-squared (0.73).
  • These findings suggest that a simplified PIM3 model is achievable with minimal loss of predictive power.

Conclusions:

  • Explainable machine learning methods are feasible for simplifying the Pediatric Index of Mortality 3 (PIM3) scoring system.
  • Simplified PIM3 models achieved similar mortality risk predictions compared to the original 11-variable model in a single-center dataset.
  • This simplification has the potential to streamline data collection and improve the efficiency of mortality risk assessment in PICU settings.