Identification of Suicide Attempt Risk Factors in a National US Survey Using Machine Learning.

ABSTRACT:

Importance

Because more than one-third of people making nonfatal suicide attempts do not receive mental health treatment, it is essential to extend suicide attempt risk factors beyond high-risk clinical populations to the general adult population.

Objective

To identify future suicide attempt risk factors in the general population using a data-driven machine learning approach including more than 2500 questions from a large, nationally representative survey of US adults.

Design, setting, and participants

Data came from wave 1 (2001 to 2002) and wave 2 (2004 to 2005) of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). NESARC is a face-to-face longitudinal survey conducted with a national representative sample of noninstitutionalized civilian

SUBMITTER: Garcia de la Garza A 

PROVIDER: S-EPMC7788508 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature