A machine-learning prediction model to identify risk of firearm injury using electronic health records data.

ABSTRACT:

Importance

Firearm injuries constitute a public health crisis. At the healthcare encounter level, they are, however, rare events.

Objective

To develop a predictive model to identify healthcare encounters of adult patients at increased risk of firearm injury to target screening and prevention efforts.

Materials and methods

Electronic health records data from Kaiser Permanente Southern California (KPSC) were used to identify healthcare encounters of patients with fatal and non-fatal firearm injuries, as well as healthcare visits of a sample of matched controls during 2010-2018. More than 170 predictors, including diagnoses, healthcare utilization, and neighborhood characteristics were identified. Extreme gradient boosting (XGBoost) and a split sample design were used to

SUBMITTER: Zhou H 

PROVIDER: S-EPMC11413429 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature