Explainable artificial intelligence models for predicting risk of suicide using health administrative data in Quebec

This study demonstrated the useful potential of explainable AI models as tools for decision-making and population-level suicide p revention actions. The ML models included individual, programmatic, systemic, and community levels variables available routinely to decision makers and planners in a public managed care system. Caution shall be exercised in the interpretation of variables associated in a predictive model since they are not causal, and other designs are required to establish the value of individual treatments. The next steps are to produce an intuitive user interface for decision makers, planners and other stakeholders like clinicians or representatives of families and people with live experience of suicidal be haviors or death by suicide. For example, how variations in the quality of local area primary care programs for depression or substance use disorders or increased in regional mental health and addiction budgets would lower suicide rates.
Source: PLoS One - Category: Biomedical Science Authors: Source Type: research