Prediction of Sex-Specific Suicide Risk Using Machine Learning and Single-Payer Health Care Registry Data From Denmark.

ABSTRACT:

Importance

Suicide is a public health problem, with multiple causes that are poorly understood. The increased focus on combining health care data with machine-learning approaches in psychiatry may help advance the understanding of suicide risk.

Objective

To examine sex-specific risk profiles for death from suicide using machine-learning methods and data from the population of Denmark.

Design, setting, and participants

A case-cohort study nested within 8 national Danish health and social registries was conducted from January 1, 1995, through December 31, 2015. The source population was all persons born or residing in Denmark as of January 1, 1995. Data were analyzed from November 5, 2018, through May 13, 2019.

Exposures

Exposures included 1339 variables spanning

SUBMITTER: Gradus JL 

PROVIDER: S-EPMC6813578 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature