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Related Concept Videos

Influenza01:27

Influenza

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Influenza is an acute, highly communicable viral disease that affects the respiratory tract and is responsible for seasonal epidemics worldwide. Influenza A is the most prevalent type associated with widespread outbreaks and is subtyped based on two surface glycoproteins: hemagglutinin (H) and neuraminidase (N), as in H1N1. These glycoproteins are essential for viral infectivity, transmission, and immune recognition. Transmission occurs primarily through respiratory droplets and contaminated...
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Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Influenza detection from emergency department reports using natural language processing and Bayesian network

Ye Ye1, Fuchiang Rich Tsui1, Michael Wagner1

  • 1Real-time Outbreak and Disease Surveillance Laboratory (RODS), Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA Intelligent Systems Program, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.

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Improving influenza detection involves enhancing natural language processing (NLP) accuracy and utilizing machine-parameterized Bayesian network (BN) classifiers with feature selection for better performance.

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Area of Science:

  • Medical Informatics
  • Computational Linguistics
  • Machine Learning

Background:

  • Accurate influenza detection is crucial for public health.
  • Natural Language Processing (NLP) and Bayesian Network (BN) classifiers offer potential for automated analysis of clinical data.
  • Evaluating the combined performance of these technologies is essential for optimizing diagnostic tools.

Purpose of the Study:

  • To assess the accuracy of NLP tools (Topaz, MedLEE) in extracting influenza-related findings.
  • To evaluate the discriminative ability of Bayesian Network (BN) classifiers with varying input features and parameterization.
  • To determine the impact of feature selection on classification performance for influenza detection.

Main Methods:

  • A dataset of 124 influenza and 87 non-influenza patients was curated.
  • NLP parsers (Topaz, MedLEE) were evaluated for accuracy, recall, and precision in extracting 31 influenza findings.
  • Bayesian Network (BN) classifiers, combined with expert-annotated and NLP-extracted findings, were compared using Area Under the Receiver Operating Characteristic (AUROC) curves.

Main Results:

  • Topaz demonstrated significantly higher accuracy than MedLEE in NLP extraction (0.78 vs 0.71).
  • BN classifiers using human-annotated findings outperformed those using NLP-extracted findings (AUROC 0.75 vs 0.68).
  • Machine-parameterized BN classifiers showed superior performance compared to human-parameterized ones (AUROC 0.73 vs 0.66), and feature selection improved accuracy.

Conclusions:

  • A three-component evaluation framework can elucidate the contributions of NLP, BN classifiers, and feature selection.
  • Improving NLP accuracy is vital for enhancing classification performance.
  • The study recommends using machine-parameterized classifiers and feature selection for more accurate influenza detection.