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Automated healthcare-associated infection surveillance using an artificial intelligence algorithm.

R P Dos Santos1, D Silva1, A Menezes1

  • 1Qualis Soluções em Infectologia, Brazil.

Infection Prevention in Practice
|September 2, 2021
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) accurately detected most healthcare-associated infections (HAIs) in a study. This AI surveillance method shows promise for improving hospital infection prevention and saving time.

Keywords:
Artificial intelligenceHealthcare-associated infectionInfection surveillance

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Infection Control

Background:

  • Healthcare-associated infections (HAIs) are a significant cause of adverse events in hospital settings.
  • Effective surveillance is crucial for preventing and controlling HAIs.

Purpose of the Study:

  • To evaluate the efficacy of artificial intelligence (AI) algorithms for detecting healthcare-associated infections (HAIs).
  • To assess the performance of a multilayer perceptron neural network for HAI surveillance.

Main Methods:

  • A cohort study was conducted using AI algorithms for infection surveillance.
  • A multilayer perceptron neural network model was developed and evaluated.
  • Performance metrics included area under the receiver operating curve (AUROC), specificity, and sensitivity.

Main Results:

  • The AI model correctly identified 67 out of 73 patients with HAIs.
  • The final model achieved an AUROC of 90.27%, specificity of 78.86%, and sensitivity of 88.57%.
  • Respiratory infections demonstrated superior detection rates with AUROC values of 93.47% or higher.

Conclusions:

  • AI algorithms are a feasible and effective method for HAI surveillance.
  • AI has the potential to enhance the accuracy and efficiency of hospital-wide infection surveillance.
  • Implementing AI can improve overall infection prevention performance and reduce adverse events.