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Forecasting daily emergency department arrivals using high-dimensional multivariate data: a feature selection

Jalmari Tuominen1, Francesco Lomio2, Niku Oksala3,4

  • 1Faculty of Medicine and Health Technology, Tampere University, Arvo Ylpön katu 34, 33520, Tampere, Finland. jalmari.tuominen@tuni.fi.

BMC Medical Informatics and Decision Making
|May 17, 2022
PubMed
Summary

Accurate emergency department (ED) arrival forecasts can be improved using extensive data and advanced feature selection. This study shows high-dimensional approaches enhance predictive accuracy for ED demand, aiding resource allocation.

Keywords:
CrowdingEmergency departmentFeature selectionMachine learningStatistical learningTime series forecasting

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Area of Science:

  • Healthcare Operations Research
  • Data Science
  • Public Health

Background:

  • Emergency Department (ED) overcrowding is a persistent global challenge linked to adverse patient outcomes.
  • Accurate demand forecasting is crucial for effective resource allocation and mitigating ED overcrowding.
  • Previous ED forecasting models have limited explanatory variables, primarily using calendar and weather data.

Purpose of the Study:

  • To enhance the predictive accuracy of next-day emergency department (ED) arrivals.
  • To investigate the impact of incorporating a high number of potentially relevant explanatory variables.
  • To document feature selection processes for identifying key predictors of ED arrivals.

Main Methods:

  • Collected daily ED arrival data from Tampere University Hospital (June 2015 - June 2019).
  • Utilized 158 potential explanatory variables including calendar, weather, public events, website traffic, hospital bed availability, and Google Trends.
  • Employed Simulated Annealing (SA) and Floating Search (FS) feature selection with Recursive Least Squares (RLS) and Least Mean Squares (LMS), comparing against ARIMA, ARIMAX, and Random Forest models.

Main Results:

  • Calendar variables, secondary care facility load, and local public events were identified as dominant predictive features.
  • RLS-SA and RLS-FA methods showed slightly improved accuracy compared to ARIMA.
  • ARIMAX demonstrated the highest accuracy, though differences between RLS-SA and RLS-FA were not statistically significant.

Conclusions:

  • High-dimensional feature selection significantly improves next-day ED arrival prediction accuracy compared to univariate or non-filtered approaches.
  • The study identifies key factors influencing ED demand, offering insights for operational management.
  • While ARIMAX remains a benchmark, further refinement of feature selection mechanisms is recommended for enhanced forecasting.