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Accurate Forecasting of Emergency Department Arrivals With Internet Search Index and Machine Learning Models: Model

Bi Fan1, Jiaxuan Peng2, Hainan Guo1

  • 1College of Management, Institute of Business Analysis and Supply Chain Management, Shenzhen University, Shenzhen, China.

JMIR Medical Informatics
|July 20, 2022
PubMed
Summary

Forecasting emergency department (ED) patient arrivals using internet search data significantly improves prediction accuracy. Nonlinear models incorporating this real-time data offer the best performance for managing healthcare resources effectively.

Keywords:
emergency departmentinternet search indexmachine learningnonlinear modelpatient arrival forecasting

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Area of Science:

  • Health Informatics
  • Data Science
  • Epidemiology

Background:

  • Emergency department (ED) overcrowding is a global challenge exacerbated by unpredictable patient arrivals, particularly during pandemics.
  • Traditional forecasting models rely on historical data but often overlook real-time information sources.
  • Internet search data offers a novel, real-time surveillance tool for predicting patient flow.

Purpose of the Study:

  • To develop an intelligent forecasting system using machine learning and internet search index data for predicting ED patient arrivals.
  • To validate the effectiveness of internet search index data in improving forecasting accuracy.
  • To investigate the impact of nonlinear models in capturing the dynamic relationship between search trends and patient arrivals.

Main Methods:

  • Collected ED patient arrival data and traditional variables during the 2009 H1N1 pandemic.
  • Generated an internet search index from 268 Google search queries to reflect potential patient interest.
  • Employed linear and nonlinear machine learning models, validating relationships using correlation and causality tests.

Main Results:

  • Internet search index data proved to be a strong predictor of ED patient arrivals.
  • Nonlinear models incorporating the internet search index achieved superior accuracy, reducing Mean Absolute Percentage Error (MAPE) from 3.5% to 3% and Root Mean Square Error (RMSE) from 16.72 to 14.55.
  • The Extreme Learning Machine model with the internet search index demonstrated the best overall forecasting accuracy and robustness.

Conclusions:

  • The developed forecasting system provides accurate, real-time predictions of ED patient arrivals.
  • Internet search index data significantly enhances forecasting accuracy compared to traditional variables, capturing behavioral trends.
  • Nonlinear models are more effective than linear models in leveraging the dynamic relationship between internet search activity and patient influx, aiding healthcare management.