Characterizing Female Firearm Suicide Circumstances: A Natural Language Processing and Machine Learning Approach

Goldstein, Evan V.; Mooney, Stephen J.; Takagi-Stewart, Julian; Agnew, Brianna; Morgan, Erin; Haviland, Miriam J.; Zhou, Weipeng; Prater, Laura

doi:10.1016/j.amepre.2023.01.030

Cited by 7 publications

(7 citation statements)

References 26 publications

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“…We examined 1 continuous demographic variable (age) and 4 categorical demographic variables: sex, educational level, marital status, and veteran status. We examined these variables because previous studies have demonstrated that firearm suicide rates or firearm ownership tend to vary by age, 30 , 31 sex, 32 , 33 marital status, 34 , 35 and veteran status 35 , 36 ; however, little is known about these characteristics among Hispanic firearm suicide decedents specifically. We also examined the type of firearm involved in each death, organized into 5 categories (handgun, shotgun, rifle, submachine gun, and other), and birthplace (foreign- or US-born).…”

Section: Methodsmentioning

confidence: 99%

Critical gaps in understanding firearm suicide in Hispanic communities: demographics, mental health, and access to care

Goldstein

Brenes

Wilson

2023

Health Affairs Scholar

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Suicide rates increased by 26.7% among Hispanics from 2015-2020, driven at least in part by highly-lethal firearm suicide deaths. However, there are critical gaps in characterizing firearm suicide risks and prevention opportunities in Hispanic communities. We examined Hispanic adult firearm suicide decedents reported through the National Violent Death Reporting System from 2013-2019, focusing on demographic characteristics, firearm choices, suicidal thoughts/behaviors, mental health, and mental health treatment, compared to non-Hispanic adult firearm suicide decedents. Only 13.8% of Hispanic firearm suicide decedents were known to be undergoing treatment for a mental health or substance use problem prior to death, compared to 18.8% of non-Hispanic firearm suicide decedents. On average, Hispanic firearm suicide decedents were 19.0% less likely than non-Hispanic firearm suicide decedents known to have been treated for a mental health or substance use problem. These results may underscore the critical need for public health agencies and policymakers to promote initiatives integrating mental health screening into medical care, reducing mental health stigma among Hispanics, and expanding mental health treatment capacity in Hispanic communities.

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Section: Methodsmentioning

confidence: 99%

Critical gaps in understanding firearm suicide in Hispanic communities: demographics, mental health, and access to care

Goldstein

Brenes

Wilson

2023

Health Affairs Scholar

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“…We used the NVDRS Restricted Access Database of female firearm suicides from 2014 to 2018 [ 1 ]. The data set contained unstructured CME and LE narrative reports describing the circumstances leading up to the suicide deaths of 1462 females.…”

Section: Methodsmentioning

confidence: 99%

“…The reports were written in English. We manually coded 9 infrequent circumstances (ie, labels) preceding the firearm suicide deaths following the instructions specified by Goldstein et al [ 1 ]: sleep problems, abusive relationships, custody issues, sexual violence, isolation or loneliness, substance abuse, dementia, bullying, and caregiver issues. All infrequent labels occurred in <5% of the cases.…”

Section: Methodsmentioning

confidence: 99%

“…The model will yield an output of “Yes” if “bullying” is mentioned in the narrative report; if not, “No” will be the output. The question was adapted from the definition of each label developed through a previously reported manual review process with minimal changes [ 1 ]. A complete list of questions and definitions for each label is included in Table S2 in Multimedia Appendix 1 .…”

Section: Methodsmentioning

confidence: 99%

“…In a previous study [ 1 ], we found that conventional natural language processing (NLP) algorithms could successfully identify some relatively common circumstances preceding female firearm suicide, using coroners’ and medical examiners’ (CMEs’) and law enforcement (LE) narrative reports provided by the NVDRS. However, because reliably training a conventional NLP pipeline requires a sizeable training data set, the approach worked well only for the most common preceding circumstances.…”

Section: Introductionmentioning

confidence: 99%

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Identifying Rare Circumstances Preceding Female Firearm Suicides: Validating A Large Language Model Approach

Zhou,

Prater,

Goldstein

et al. 2023

JMIR Ment Health

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Background Firearm suicide has been more prevalent among males, but age-adjusted female firearm suicide rates increased by 20% from 2010 to 2020, outpacing the rate increase among males by about 8 percentage points, and female firearm suicide may have different contributing circumstances. In the United States, the National Violent Death Reporting System (NVDRS) is a comprehensive source of data on violent deaths and includes unstructured incident narrative reports from coroners or medical examiners and law enforcement. Conventional natural language processing approaches have been used to identify common circumstances preceding female firearm suicide deaths but failed to identify rarer circumstances due to insufficient training data. Objective This study aimed to leverage a large language model approach to identify infrequent circumstances preceding female firearm suicide in the unstructured coroners or medical examiners and law enforcement narrative reports available in the NVDRS. Methods We used the narrative reports of 1462 female firearm suicide decedents in the NVDRS from 2014 to 2018. The reports were written in English. We coded 9 infrequent circumstances preceding female firearm suicides. We experimented with predicting those circumstances by leveraging a large language model approach in a yes/no question-answer format. We measured the prediction accuracy with F1-score (ranging from 0 to 1). F1-score is the harmonic mean of precision (positive predictive value) and recall (true positive rate or sensitivity). Results Our large language model outperformed a conventional support vector machine–supervised machine learning approach by a wide margin. Compared to the support vector machine model, which had F1-scores less than 0.2 for most infrequent circumstances, our large language model approach achieved an F1-score of over 0.6 for 4 circumstances and 0.8 for 2 circumstances. Conclusions The use of a large language model approach shows promise. Researchers interested in using natural language processing to identify infrequent circumstances in narrative report data may benefit from large language models.

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Automatically extracting social determinants of health for suicide: a narrative literature review

Schoene,

Garverich,

Ibrahim

et al. 2024

npj Mental Health Res

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Suicide is a complex phenomenon that is often not preceded by a diagnosed mental health condition, therefore making it difficult to study and mitigate. Artificial Intelligence has increasingly been used to better understand Social Determinants of Health factors that influence suicide outcomes. In this review we find that many studies use limited SDoH information and minority groups are often underrepresented, thereby omitting important factors that could influence risk of suicide.

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Characterizing Female Firearm Suicide Circumstances: A Natural Language Processing and Machine Learning Approach

Cited by 7 publications

References 26 publications

Critical gaps in understanding firearm suicide in Hispanic communities: demographics, mental health, and access to care

Critical gaps in understanding firearm suicide in Hispanic communities: demographics, mental health, and access to care

Identifying Rare Circumstances Preceding Female Firearm Suicides: Validating A Large Language Model Approach

Automatically extracting social determinants of health for suicide: a narrative literature review

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