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Feature selection based multivariate time series forecasting: An application to antibiotic resistance outbreaks

Fernando Jiménez1, José Palma1, Gracia Sánchez1

  • 1Artificial Intelligence and Knowledge Engineering Group, Faculty of Computer Science, University of Murcia, Spain.

Artificial Intelligence in Medicine
|June 6, 2020
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Summary

Artificial Intelligence (AI) can predict Methicillin-resistant Staphylococcus aureus (MRSA) outbreaks using time series forecasting. This study introduces a novel AI method for early detection, aiding global health strategies against antimicrobial resistance.

Keywords:
Antibiotic resistance forecastingFeature selectionMulti-objective evolutionary algorithmsMultiple criteria decision makingMultivariate time series

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Area of Science:

  • Infectious Disease Epidemiology
  • Computational Biology
  • Health Informatics

Background:

  • Antimicrobial resistance is a critical global health challenge requiring proactive prevention strategies.
  • Predicting infectious disease outbreaks is essential for effective public health interventions and resource allocation.

Purpose of the Study:

  • To develop and evaluate an Artificial Intelligence (AI) based approach for forecasting Methicillin-resistant Staphylococcus aureus (MRSA) outbreaks.
  • To apply Time Series Forecasting techniques combined with feature selection for predicting MRSA incidence.

Main Methods:

  • Utilized multivariate time series data including Methicillin-sensitive and MRSA infections, influenza incidence, and antimicrobial therapy days (Levofloxacin, Oseltamivir) from 2009-2018.
  • Employed wrapper feature selection methods driven by multi-objective evolutionary algorithms (MOEA) and state-of-the-art regression algorithms.
  • Implemented a novel multi-criteria decision-making process integrating performance metrics (RMSE, MAE) and prediction slopes for model selection.

Main Results:

  • The proposed AI methodology successfully forecasted MRSA outbreaks with low Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values.
  • The best model achieved RMSE values of (0.1349, 0.1304, 0.1325) and MAE values of (0.1003, 0.096, 0.0987) for 1, 2, and 3 steps-ahead predictions, respectively.
  • Demonstrated the efficacy of feature selection and multi-criteria decision-making in developing accurate antibiotic resistance forecasting models.

Conclusions:

  • This study presents the first feature selection-based multivariate time series methodology for antibiotic resistance forecasting.
  • The developed AI approach offers a promising tool for the early prediction of MRSA outbreaks, contributing to antimicrobial resistance containment efforts.
  • The findings support the integration of advanced AI techniques into public health surveillance for infectious diseases.