@article {1524006,
	title = {Getting to the root of the problem: A decision-tree analysis for suicide risk among young people experiencing homelessness},
	journal = {Journal of the Society for Social Work and Research},
	volume = {(to appear)},
	year = {2020},
	abstract = {Objective : The assessment and prediction of suicide risk among young people experiencinghomelessness (YEH) has proven difficult. Although a large number of suicide risk factors havebeen identified, there is limited guidance about their relative importance and the combinations offactors (i.e., profiles) that heighten risk. Method : Using survey and social network methods, wegathered information about 940 YEH and their relationships. We then used a machine learningapproach to construct Classification and Regression Tree models to predict suicidal ideation andsuicide attempts. Results : Thirteen variables were important correlates in the decision treemodels. This included prominent individual risk factors (e.g., trauma, depression), but over halfof them were social network factors (e.g., hard drug use). For suicidal ideation, the model had anarea under the receiver operating characteristic curve (AUC) value of 0.79, with Accuracy of68\%, Sensitivity of 48\%, and Specificity of 73\%. For suicide attempt, the model had an AUCvalue of 0.86, with Accuracy of 71\%, Sensitivity of 68\%, and Specificity of 72\%. Conclusions :Effective suicide prevention programming should target the syndemic that threatens YEH (i.e.,co-occurrence of trauma-depression-substance use-violence), including social norms in theirenvironments. With refinement, our decision trees may be useful aids for suicide risk screeningand guiding targeted intervention.},
	author = {Anthony Fulgianti and Avi Segal and Jennifer Wilson and Chyna Hill and Tambe, Milind and Carl Castro and Eric Rice}
}
