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Estimating Nonfatal Gunshot Injury Locations With Natural Language Processing and Machine Learning Models.

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Nonfatal gunshot injuries are most common on streets or highways, not homes. Natural language processing (NLP) and machine learning models can accurately classify injury locations to aid prevention efforts.

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Area of Science:

  • Public Health
  • Injury Epidemiology
  • Computational Medicine

Background:

  • Nonfatal gunshot injuries are a significant public health concern in the US.
  • Accurate data on the locations of these injuries is limited, hindering prevention strategies.
  • Natural language processing (NLP) offers a potential solution to extract location data from unstructured medical records.

Purpose of the Study:

  • To evaluate the effectiveness of NLP and machine learning (ML) models in predicting nonfatal gunshot injury locations.
  • To generate updated national estimates of where these injuries occur.
  • To inform targeted firearm injury prevention efforts.

Main Methods:

  • A cross-sectional study utilized data from the National Electronic Injury Surveillance System Firearm Injury Surveillance Study (1993-2015).
  • NLP techniques were employed to generate predictors for injury location from medical text narratives.
  • Four ML models (multinomial support vector machines, lasso regression, XgBoost, feed-forward neural networks) were trained and validated to classify injury locations.

Main Results:

  • The analysis included over 59,000 unweighted gunshot injury records, weighted to approximate national estimates.
  • Existing data suggested homes as the most common injury location, followed by streets/highways.
  • NLP-enhanced analysis revealed streets/highways as the most frequent location (46.1%), followed by homes (37.7%).

Conclusions:

  • NLP and ML models demonstrate utility in classifying nonfatal gunshot injury locations.
  • The study indicates a higher prevalence of injuries occurring on streets/highways compared to previous estimates.
  • These findings provide crucial data for developing more effective firearm injury prevention strategies.