Using machine learning to identify features associated with different types of self-injurious behaviors in autistic youth
Specific SIB types have unique sets of associations. Future work can develop clinical likelihood scores for specific SIB types in inpatient settings, which can be tested with large community samples. Current approaches for SIB focus on the behavior functions, but there is an opportunity to further develop interventions by considering the specific SIB type in assessment and treatment. Identifying factors associated with specific SIB types may aid with screening, prevention, and treatment of these often-impairing behaviors.
Authors
- Helmet T. Karim (ORCID: https://orcid.org/0000-0002-9286-0694)
- Matthew Siegel (ORCID: https://orcid.org/0000-0003-3457-8532)
- Ligia Antezana (ORCID: https://orcid.org/0000-0003-0603-9371)
- Carla A. Mazefsky (ORCID: https://orcid.org/0000-0001-7467-0902)
- Caitlin M. Conner (ORCID: https://orcid.org/0000-0002-6224-2086)
- Safaa M. Eldeeb (ORCID: https://orcid.org/0000-0002-5704-5408)
- Samuel C. Turecki
Institutions
- Tufts University (US)
- Cincinnati Children's Hospital Medical Center (US)
- University of Pittsburgh (US)
Publication Details
- Journal
- Psychological Medicine
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1017/s0033291725000637
- Citations
- 3
- Primary Topic
- Autism Spectrum Disorder Research
- Type
- article
- Field-Weighted Citation Impact
- 4.29
Funders
- Nancy Lurie Marks Family Foundation
- Eunice Kennedy Shriver National Institute of Child Health and Human Development