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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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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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Infectious Diseases and Their Occurrence01:28

Infectious Diseases and Their Occurrence

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Infectious diseases appear in populations through various transmission patterns, influenced by pathogen characteristics, population immunity, environmental conditions, and social behavior. Understanding these patterns is essential for effective public health surveillance and intervention. These categories—sporadic, outbreak, epidemic, pandemic, and endemic—help frame the nature and scope of disease events.Sporadic diseases occur irregularly and infrequently, without a predictable...
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Investigation of Disease Outbreaks01:23

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Multistate foodborne outbreaks pose significant public health risks and require meticulous investigation to identify sources and implement control measures. The Centers for Disease Control and Prevention (CDC) utilizes a dynamic seven-step process for these investigations, integrating data from laboratories, interviews, and environmental assessments to protect public health.Outbreak Detection: The detection of multistate outbreaks typically begins with PulseNet, the CDC's national laboratory...
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Adapting Semantic Natural Language Processing Technology to Address Information Overload in Influenza Epidemic

Alla Keselman1, Graciela Rosemblat, Halil Kilicoglu

  • 1National Library of Medicine, Bethesda, MD.

Journal of the American Society for Information Science and Technology : JASIST
|December 7, 2013
PubMed
Summary

Semantic natural language processing (NLP) can manage disaster health information overload. Adapting NLP summarizers to influenza pandemics shows feasibility for improved information access and visualization in disaster response.

Keywords:
Disaster Information ManagementNatural Language ProcessingOnline Information Extraction ApplicationSemantic PredicationsSemi-Automated Ontology Development

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Area of Science:

  • Public Health
  • Informatics
  • Natural Language Processing

Background:

  • Disaster response professionals face information overload due to the rapid increase in disaster health data.
  • Existing information retrieval methods struggle to efficiently process and synthesize large volumes of specialized health information.

Purpose of the Study:

  • To assess the feasibility of using semantic natural language processing (NLP) to alleviate information overload in disaster health.
  • To characterize key concepts and relationships in influenza pandemic documents for NLP adaptation.

Main Methods:

  • Human review and semantic NLP analysis of disaster health documents focusing on influenza pandemics.
  • Adaptation of a semantic summarizer and a pilot test with information specialists for information seeking tasks.

Main Results:

  • An ontology for influenza epidemic management was described using a limited set of semantic types and relationships.
  • Pilot users successfully utilized the adapted application for information retrieval, demonstrating practical utility.

Conclusions:

  • Semantic NLP technology is adaptable for summarizing and visualizing disaster health information, particularly for influenza epidemics.
  • Further research is required in terminology development, NLP refinement, and user interface design for optimal application.