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Area of Science:

  • Public Health
  • Veterans Affairs
  • Machine Learning Applications

Background:

  • United States (US) Veterans have higher suicide rates than civilians.
  • Machine learning (ML) models aid in identifying high-risk individuals for suicide prevention.
  • Firearms are the primary method in most Veteran suicides, necessitating targeted interventions.

Purpose of the Study:

  • To develop and compare firearm-specific ML models against a method-agnostic model.
  • To predict firearm suicides among transitioning US Army Veterans up to 10 years post-discharge.
  • To evaluate model performance and fairness across different intervention thresholds.

Main Methods:

  • Utilized data from US Army Veterans (2010-2019, N=800,579).
  • Developed and compared the predictive accuracy of firearm-specific ML models versus a method-agnostic model.
  • Assessed model performance using AU-ROC and ICI metrics, considering fairness across sex and race/ethnicity.

Main Results:

  • Both firearm-specific and method-agnostic models demonstrated comparable overall performance (AU-ROC=0.710-0.708).
  • The method-agnostic model excelled at predicting firearm suicides at higher intervention thresholds.
  • The firearm-specific model performed better at lower thresholds and demonstrated superior fairness across demographic groups.

Conclusions:

  • Model selection for Veteran suicide prevention depends on balancing predictive accuracy and fairness.
  • Optimal thresholds may differ for coordinated firearm-specific and method-agnostic suicide prevention interventions.
  • Firearm-specific models offer advantages in fairness, crucial for equitable suicide risk assessment in Veterans.