Late-life suicide: machine learning predictors from a large European longitudinal cohort
Prospective clinical and social information can predict death from suicide with good accuracy in late adulthood. Most of the variables that surfaced as risk factors can be attributed to the construct of social connectedness, which has been shown to play a decisive role in suicide in late life.
Authors
- Fabio Sambataro (ORCID: https://orcid.org/0000-0003-2102-416X)
- Nicola Meda (ORCID: https://orcid.org/0000-0003-1114-3632)
- Diego De Leo (ORCID: https://orcid.org/0000-0001-8255-6480)
- Josephine Zammarrelli (ORCID: https://orcid.org/0000-0003-1760-5324)
Institutions
- Griffith University (AU)
- University of Primorska (SI)
- University of Padua (IT)
- Italian Association for Cancer Research (IT)
- Lundbeck (Italy) (IT)
Publication Details
- Journal
- Frontiers in Psychiatry
- Published
- 2024-09-17
- DOI
- https://doi.org/10.3389/fpsyt.2024.1455247
- Citations
- 5
- Primary Topic
- Suicide and Self-Harm Studies
- Type
- article
- Field-Weighted Citation Impact
- 3.20
Funders
- Università degli Studi di Padova