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Implementation and Assessment of Machine Learning Models for Forecasting Suspected Opioid Overdoses in Emergency

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Machine learning accurately forecasts suspected opioid overdoses in Kentucky using Emergency Medical Services (EMS) data. These predictions aid resource allocation for public health initiatives.

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Area of Science:

  • Data Science
  • Public Health Analytics
  • Epidemiological Forecasting

Background:

  • Opioid overdose is a critical public health issue requiring accurate forecasting for resource management.
  • Emergency Medical Services (EMS) data provides valuable real-time insights into overdose trends.

Purpose of the Study:

  • To develop and evaluate machine learning models for predicting suspected opioid overdoses.
  • To forecast overdose counts at county and district levels in Kentucky.
  • To identify key covariates impacting prediction accuracy.

Main Methods:

  • Utilized time series forecasting and machine learning techniques.
  • Aggregated suspected opioid overdose data from EMS at county and district levels.
  • Evaluated models of varying complexity and incorporated relevant public health covariates.

Main Results:

  • Accurate predictions of future suspected opioid overdose counts were achieved with minimal error.
  • Forecasting performance was robust across different regional levels.
  • Simple forecasting models with commonly available covariates demonstrated high performance.

Conclusions:

  • Machine learning models can effectively predict suspected opioid overdoses using EMS data.
  • Accurate forecasts support informed resource allocation for public health interventions.
  • The approach is adaptable for various regions and public health surveillance needs.