Prediction of suicidal behaviors in hospitalized children and adolescents in middle-income countries: a case study of Brazil
Resumo
Suicide is the first leading cause of death among children and adolescents worldwide. Predictors of suicide-related behaviours might help in the task of intervening to avoid or monitor future suicide risks. In this paper, a sample of individuals who were taken to a Child Psychiatry Facility in Brazil was analyzed. Machine learning algorithms were used to generate models for predicting suicidal behaviour, and the features that better explain this complex behaviour were also analyzed. Results show a sensitivity of 0.83 and a specificity of 0.97.
Referências
Carroll, R., Metcalfe, C., and Gunnell, D. (2014). Hospital presenting self-harm and risk of fatal and non-fatal repetition: systematic review and metaanalysis. PLoS One, 9(2):e89944.
Harris, I., Beese, S., and Moore, D. (2019). Predicting future self-harm or suicide in adolescents: a systematic review of risk assessment scales/tools. BMJ open, 9.
Ji, S., Pan, S., Li, X., Cambria, E., Long, G., and Huang, Z. (2021). Suicidal ideation detection: A review of machine learning methods and applications. IEEE Transactions on Computational Social Systems, 8(1):214–226.
Knipe, D., Padmanathan, P., Newton-Howes, G., Chan, L. F., and Kapur, N. (2022). Suicide and self-harm. Lancet, 399(10338):1903–1916.
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I. (2020). From local explanations to global understanding with explainable ai for trees. Nature Machine Intelligence, 2(1):2522–5839.
Naghavi, M. and Collaborators (2019). Global, regional, and national burden of suicide mortality 1990 to 2016: systematic analysis for the global burden of disease study 2016. BMJ, 364:l94.
Navarro, M. C., Ouellet-Morin, I., Geoffroy, M.-C., Boivin, M., Tremblay, R. E., Côté, S. M., and Orri, M. (2021). Machine Learning Assessment of Early Life Factors Predicting Suicide Attempt in Adolescence or Young Adulthood. JAMA Network Open, 4(3):e211450–e211450.
Orri, M., Boivin, M., and Chen, C. e. a. (2021). Cohort profile: Quebec longitudinal study of child development (qlscd). Soc Psychiatry Psychiatr Epidemiol, 56.
Su, C., Aseltine, R., Doshi, R., Chen, K., Rogers, S. C., and Wang, F. (2020). Machine learning for suicide risk prediction in children and adolescents with electronic health records. Translational Psychiatry, 10(1):413.
van Mens, K., de Schepper, C., Wijnen, B., Koldijk, S. J., Schnack, H., de Looff, P., Lokkerbol, J., Wetherall, K., Cleare, S., C O’Connor, R., and de Beurs, D. (2020). Predicting future suicidal behaviour in young adults, with different machine learning techniques: A population-based longitudinal study. Journal of Affective Disorders, 271:169–177.