A new study is proving that artificial intelligence can significantly improve the way emergency 911 call data is categorized and used by public safety agencies. Researchers applied machine learning and natural language processing techniques to thousands of real incident descriptions, achieving over 90% accuracy in automated classification. The findings mark a major step forward in modernizing emergency response systems and ensuring more reliable, actionable data for fire departments and first responders across the country.
Highlights
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Inconsistent reporting can compromise the quality and utility of incident data.
- Natural Language Processing alongside Machine Learning models can help standardize and categorize incident descriptions.
- Unigram models achieved 93% accuracy, showing how data systems may support emergency response strategies.
- Mis-categorizations were primarily observed within “Emergency Medical Services (EMS)”, indicating where further refinement may be needed to improve accuracy.
Click here to read the full study.