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Area of Science:

  • Health Informatics
  • Machine Learning in Healthcare
  • Paediatric Emergency Medicine

Background:

  • Emergency Department (ED) overcrowding is a significant issue, leading to increased waiting times and patients leaving without treatment.
  • Early identification of potential admissions can improve patient flow and bed management within hospitals.
  • Existing predictive models may require extensive data, limiting their applicability in resource-constrained settings.

Purpose of the Study:

  • To develop a low-dimensional predictive model for early identification of paediatric admissions from the Emergency Department.
  • To create a decision support tool for clinicians and a planning aid for bed managers.
  • To leverage routinely collected data for accessible predictive modeling.

Main Methods:

  • Utilized the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology.
  • Developed 15 models using logistic regression, naïve Bayes, and gradient boosting machine algorithms, combined with 5 sampling techniques to address class imbalance.
  • Identified key predictors from demographics, registration, triage, hospital usage, and medical history data.

Main Results:

  • The study identified key variables for predicting paediatric admissions from routinely collected ED data.
  • The optimal model, selected by Area Under the Curve, provided a basis for a deployable, low-dimensional prediction tool.
  • The research explored novel applications of sampling techniques to enhance model performance with imbalanced outcome data.

Conclusions:

  • A low-dimensional model using post-triage data can effectively predict paediatric admissions, benefiting hospitals with limited data infrastructure.
  • The study's findings from the Republic of Ireland contribute to the field of predictive analytics in paediatric emergency care.
  • The developed model offers a practical solution for improving ED patient flow and resource allocation.