Journal article
Characterizing Female Firearm Suicide Circumstances: A Natural Language Processing and Machine Learning Approach
American journal of preventive medicine, Vol.65(2), pp.278-285
01 Aug 2023
PMID: 36931986
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Introduction: Since 2005, female firearm suicide rates increased by 34%, outpacing the rise in male firearm suicide rates over the same period. The objective of this study was to develop and evaluate a natural language processing pipeline to identify a select set of common and important circumstances preceding female firearm suicide from coroner/medical examiner and law enforcement narratives.
Methods: Unstructured information from coroner/medical examiner and law enforcement narratives were manually coded for 1,462 randomly selected cases from the National Violent Death Reporting System. Decedents were included from 40 states and Puerto Rico from 2014 to 2018. Naive Bayes, Random Forest, Support Vector Machine, and Gradient Boosting classifier models were tuned using 5-fold cross-validation. Model performance was assessed using sensitivity, specificity, positive predictive value, F1, and other metrics. Analyses were conducted from February to November 2022.
Results: The natural language processing pipeline performed well in identifying recent interpersonal disputes, problems with intimate partners, acute/chronic pain, and intimate partners and immediate family at the scene. For example, the Support Vector Machine model had a mean of 98.1% specificity and 90.5% positive predictive value in classifying a recent interpersonal dispute before suicide. The Gradient Boosting model had a mean of 98.7% specificity and 93.2% positive predictive value in classifying a recent interpersonal dispute before suicide.
Conclusions: This study developed a natural language processing pipeline to classify 5 female fire-arm suicide antecedents using narrative reports from the National Violent Death Reporting System, which may improve the examination of these circumstances. Practitioners and researchers should weigh the efficiency of natural language processing pipeline development against conventional text mining and manual review.
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Details
- Title
- Characterizing Female Firearm Suicide Circumstances: A Natural Language Processing and Machine Learning Approach
- Creators
- Evan V. Goldstein - University of UtahStephen J. Mooney - University of WashingtonJulian Takagi-Stewart - Drexel UniversityBrianna F. Agnew - University of San FranciscoErin R. Morgan - Univ Washing ton, Sch Publ Hlth, Dept Epidemiol, Seattle, WA USAMiriam J. Haviland - Harborview Injury Prevention and Research CenterWeipeng Zhou - University of Washington Medical CenterLaura C. Prater - Harborview Injury Prevention and Research Center
- Publication Details
- American journal of preventive medicine, Vol.65(2), pp.278-285
- Publisher
- Elsevier
- Number of pages
- 8
- Grant note
- National Collaborative on Gun Violence Research
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- College of Medicine
- Web of Science ID
- WOS:001045896200001
- Scopus ID
- 2-s2.0-85150287481
- Other Identifier
- 991021861173604721
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- Collaboration types
- Industry collaboration
- Domestic collaboration
- Web of Science research areas
- Public, Environmental & Occupational Health