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Machine learning to refine decision making within a syndromic surveillance service.

I R Lake1,2, F J Colón-González3,4, G C Barker4

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Summary

Machine learning can aid syndromic surveillance by improving the assessment of public health alarms. A naïve Bayes classifier showed promise in classifying important health alerts, but human expertise remains crucial.

Keywords:
Artificial intelligenceBayes’ theoremDecision makingMachine learningPublic healthSyndromic surveillance

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Area of Science:

  • Public Health
  • Epidemiology
  • Machine Learning

Background:

  • Syndromic surveillance systems generate numerous statistical alarms daily for public health threat detection.
  • Current risk assessment processes are time-consuming and require specialized expertise, with only 0.1% of alarms being of public health importance.

Purpose of the Study:

  • To evaluate machine learning as a tool for computer-assisted decision-making in assessing statistical alarms within syndromic surveillance.
  • To determine if machine learning can enhance the automation, robustness, and rigor of risk assessment processes.

Main Methods:

  • Utilized a dataset of 67,505 statistical alarms and their corresponding risk assessment outcomes from Public Health England (August 2013 - October 2015).
  • Employed three Bayesian classifiers (naïve Bayes, tree-augmented naïve Bayes, Multinets) to analyze alarm characteristics and predict epidemiologist decisions ('Alert', 'Monitor', 'No-action').
  • Implemented additional classifications to address the high proportion of 'No-action' outcomes.

Main Results:

  • The naïve Bayes classifier achieved the highest performance, correctly classifying 51.5% of 'Alert' outcomes.
  • Combining 'Alert' and 'Monitor' classifications improved performance to 82.6% accuracy.
  • Demonstrated the potential for operationalizing a decision support system using a naïve Bayes classifier within syndromic surveillance.

Conclusions:

  • Machine learning techniques offer significant potential to automate and improve the risk assessment of statistical alarms in syndromic surveillance.
  • Specialist human input remains essential for the effective interpretation and validation of public health alarm assessments.