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A Coupled Experiment-finite Element Modeling Methodology for Assessing High Strain Rate Mechanical Response of Soft Biomaterials
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Identifying work related injuries: comparison of methods for interrogating text fields.

Kirsten McKenzie1, Margaret A Campbell, Deborah A Scott

  • 1National Centre for Health Information Research and Training, Queensland University of Technology, Victoria Park Road, Kelvin Grove, Queensland, 4059, Australia. k.mckenzie@qut.edu.au

BMC Medical Informatics and Decision Making
|April 9, 2010
PubMed
Summary

Text mining emergency department data can improve work-related injury surveillance. Content analytic text mining identified 77% of work-related injuries, outperforming keyword and index searches.

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Area of Science:

  • Occupational Health
  • Public Health Surveillance
  • Emergency Medicine Data Analysis

Background:

  • Work-related injuries in Australia incur significant annual costs ($57.5 billion).
  • Current surveillance data for work-related injuries is insufficient for evidence-based public health responses.
  • Emergency departments (EDs) are a potential data source, but lack specific coding for work-related injuries.

Purpose of the Study:

  • To compare different methods for identifying work-related injuries in emergency department free-text fields.
  • To inform the development of improved surveillance strategies for occupational injuries.
  • To assess the effectiveness of text interrogation techniques for injury surveillance.

Main Methods:

  • Three text interrogation approaches were evaluated: keyword search, index search, and content analytic text mining.
  • Sensitivity and specificity of each method were determined by comparing results to existing 'Activity Code' classifications.
  • Techniques within each approach were adjusted to optimize performance.

Main Results:

  • Content analytic text mining achieved the highest sensitivity (77%) with good specificity (0.95).
  • Index search identified 62% of cases (Specificity 0.87), while basic keyword search detected 58% (Specificity 0.99).
  • Adjusting classification techniques improved the performance of each method.

Conclusions:

  • Text searching methods show promise for extracting valuable injury information from routine ED data.
  • Continued development of these methods is crucial for enhancing comprehensive injury surveillance.
  • Utilizing ED free-text fields can significantly improve the capacity for monitoring work-related injuries.