Generating actionable insights to support point-of-care suicide risk decision-making in a safety-net healthcare system: a machine learning approach to predicting dynamic risk of intentional self-harm.

ABSTRACT:

Background

Suicide rates have increased over the last couple of decades globally, particularly in the United States and among populations with lower economic status who present at safety-net healthcare systems. Recently, predictive models for suicide risk have shown promise; however, a model for this specific population does not exist.

Objective

To develop a predictive risk model of suicide and intentional self-harm (ISH) for patients presenting at the psychiatric emergency department (ED) of JPS Health Network, a safety net medical and mental healthcare system in Texas.

Methods

The study used structured and unstructured electronic medical record (EMR) data (2015-2019) and local medical examiner data (2015-2020) to create predictors and outcome variables. All psychiat

SUBMITTER: Sarkar J 

PROVIDER: S-EPMC12863338 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature