Using iterative random forest to find geospatial environmental and Sociodemographic predictors of suicide attempts.

ABSTRACT:

Introduction

Despite a recent global decrease in suicide rates, death by suicide has increased in the United States. It is therefore imperative to identify the risk factors associated with suicide attempts to combat this growing epidemic. In this study, we aim to identify potential risk factors of suicide attempt using geospatial features in an Artificial intelligence framework.

Methods

We use iterative Random Forest, an explainable artificial intelligence method, to predict suicide attempts using data from the Million Veteran Program. This cohort incorporated 405,540 patients with 391,409 controls and 14,131 attempts. Our predictive model incorporates multiple climatic features at ZIP-code-level geospatial resolution. We additionally consider demographic features from the A

SUBMITTER: Pavicic M 

PROVIDER: S-EPMC10433206 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature