5-year incidence of suicide-risk in youth: A gradient tree boosting and SHAP study

Autor(es)

Ballester, Pedro L. ^[1] ; Cardoso, Taiane de A. ^{[2, 3, 4]} ; Moreira, Fernanda Pedrotti ^{[2, 3]} ; Silva, Ricardo A. da ^{[2, 3]} ; Mondin, Thaise Campos ^[5] ; Araujo, Ricardo M. ^[6] ; Kapczinski, Flavio ^{[7, 2, 4, 8]} ; Frey, Benicio N. ^{[4, 9]} ; Jansen, Karen ^{[2, 3]} ; Souza, Luciano D. de Mattos ^{[2, 3, 10]}

Afiliação do(s) autor(es)

^[1] McMaster Univ, Neurosci Grad Program, Hamilton, ONTARIO - Canada

^[2] Inst Nacl Ciencia & Tecnol Translac Med INCT TM, Porto Alegre, RS - Brazil

^[3] Univ Catolica Pelotas, Dept Hlth & Behav, Pelotas, RS - Brazil

^[4] McMaster Univ, Dept Psychiat & Behav Neurosci, Mood Disorders Program, Hamilton, ONTARIO - Canada

^[5] Univ Fed Pelotas, Dept Student Affairs, Pelotas, RS - Brazil

^[6] Univ Fed Pelotas, Ctr Technol Dev, Pelotas, RS - Brazil

^[7] Univ Fed Rio Grande do Sul, Dept Psychiat, Porto Alegre, RS - Brazil

^[8] Hosp Clin Porto Alegre, Bipolar Disorder Program, Lab Mol Psychiat, Inst Nacl Ciencia & Tecnol Translac Med, Porto Alegre, RS - Brazil

^[9] Res Inst St Joes Hamilton, Hamilton, ONTARIO - Canada

^[10] Univ Catolica Pelotas, Hlth & Behav Dept, 373 Goncalves Chaves, Off C424, Centro, BR-96015560 Pelotas, RS - Brazil

• Tipo de documento: Artigo Científico
• Fonte: Journal of Affective Disorders; v. 297, p. 1049-1056, JAN 15 2022.
• Citações Web of Science: 0

Resumo

Background: Machine learning methods for suicidal behavior so far have failed to be implemented as a prediction tool. In order to use the capabilities of machine learning to model complex phenomenon, we assessed the predictors of suicide risk using state-of-the-art model explanation methods. Methods: Prospective cohort study including a community sample of 1,560 young adults aged between 18 and 24. The first wave took place between 2007 and 2009, and the second wave took place between 2012 and 2014. Sociodemographic and clinical characteristics were assessed at baseline. Incidence of suicide risk at five-years of follow-up was the main outcome. The outcome was assessed using the Mini Neuropsychiatric Interview (MINI) at both waves. Results: The risk factors for the incidence of suicide risk at follow-up were: female sex, lower socioeconomic status, older age, not studying, presence of common mental disorder symptoms, and poor quality of life. The interaction between overall health and socioeconomic status in relation to suicide risk was also captured and shows a shift from protection to risk by socioeconomic status as overall health increases. Limitations: Proximal factors associated with the incidence of suicide risk were not assessed. Conclusions: Our findings indicate that factors related to poor quality of life, not studying, and common mental disorder symptoms of young adults are already in place prior to suicide risk. Most factors present critical nonlinear patterns that were identified. These findings are clinically relevant because they can help clinicians to early detect suicide risk. (AU)

• Processo FAPESP: 14/50891-1 - INCT 2014: Translacional em Medicina
• Beneficiário: Jaime Eduardo Cecilio Hallak
• Modalidade de apoio: Auxílio à Pesquisa - Temático