Prediction of Adolescent Suicide Attempt by Integrating Clinical, Neurocognitive and Geocoded Neighborhood Environment Data
Results suggest that data collected at a single timepoint during preadolescence can inform suicide attempt prediction during mid-late adolescence, in different clinical settings. Findings encourage incorporation of multiple data types including neurocognitive and geocoded data, alongside clinical data, in machine learning suicide attempt prediction pipelines.
Authors
- Fuchiang Tsui (ORCID: https://orcid.org/0000-0002-6383-8471)
- Tyler M. Moore (ORCID: https://orcid.org/0000-0002-1384-0151)
- Vı́ctor Ruiz (ORCID: https://orcid.org/0000-0003-0440-2093)
- Elina Visoki
- Monica E. Calkins (ORCID: https://orcid.org/0000-0002-0546-0263)
- Tami D. Benton (ORCID: https://orcid.org/0000-0003-0185-5839)
- Ran Barzilay (ORCID: https://orcid.org/0000-0002-3247-2331)
- Joel A. Fein (ORCID: https://orcid.org/0000-0003-1650-3051)
- Ruben C Gur
- Raquel E. Gur
Institutions
- Children's Hospital of Philadelphia (US)
- Penn Center for AIDS Research (US)
- Lifespan (US)
- University of Pennsylvania (US)
Publication Details
- Journal
- Schizophrenia Bulletin
- Published
- 2025-04-18
- DOI
- https://doi.org/10.1093/schbul/sbaf064
- Citations
- 3
- Primary Topic
- Suicide and Self-Harm Studies
- Type
- article
- Field-Weighted Citation Impact
- 6.92
Funders
- National Institute of Mental Health