Machine learning strategies for predicting pediatric suicidal behaviors in a Brazilian emergency setting
Machine learning, specifically Random Forest models together with oversampling and SHAP, demonstrates strong potential for identifying suicidal risk in pediatric emergency settings. By integrating clinical data with social determinants, these models provide a transparent and scalable strategy for early identification in regions with limited specialized psychiatric resources.
Authors
- Gisele L. Pappa (ORCID: https://orcid.org/0000-0002-0349-4494)
- Wagner Meira (ORCID: https://orcid.org/0000-0002-2614-2723)
- Anísio Lacerda (ORCID: https://orcid.org/0000-0002-2483-7572)
- Romano-Silva Marco Aurelio
- Ana Paula Couto da Silva (ORCID: https://orcid.org/0000-0001-5951-3562)
- Danilo Bastos Bispo Ferreira (ORCID: https://orcid.org/0000-0003-2997-6616)
- Débora Marques de Miranda (ORCID: https://orcid.org/0000-0002-7081-8401)
- Maria Carolina Lobato Machado
- Isis F. Carvalho
- Lys Malloy Diniz
- Alexandre Luiz de Oliveira Serpa
Institutions
- Shanghai Mental Health Center (CN)
- Faculdade de Desenvolvimento do Rio Grande do Sul (BR)
- Centro de Tecnologia Mineral (BR)
- Fundação Faculdade de Medicina (BR)
Publication Details
- Journal
- Frontiers in Artificial Intelligence
- Published
- 2026-02-18
- DOI
- https://doi.org/10.3389/frai.2026.1662264
- Primary Topic
- Suicide and Self-Harm Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Fundação de Amparo à Pesquisa do Estado de São Paulo
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
- Conselho Nacional de Desenvolvimento Científico e Tecnológico
- Fundação de Amparo à Pesquisa do Estado de Minas Gerais