Late-life suicide: machine learning predictors from a large European longitudinal cohort.

ABSTRACT:

Background

People in late adulthood die by suicide at the highest rate worldwide. However, there are still no tools to help predict the risk of death from suicide in old age. Here, we leveraged the Survey of Health, Ageing, and Retirement in Europe (SHARE) prospective dataset to train and test a machine learning model to identify predictors for suicide in late life.

Methods

Of more than 16,000 deaths recorded, 74 were suicides. We matched 73 individuals who died by suicide with people who died by accident, according to sex (28.8% female in the total sample), age at death (67 ± 16.4 years), suicidal ideation (measured with the EURO-D scale), and the number of chronic illnesses. A random forest algorithm was trained on demographic data, physical health, depression, and cogniti

SUBMITTER: Meda N 

PROVIDER: S-EPMC11442232 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature