Importance Suicide is a leading cause of mortality, with suicide-related deaths increasing in
recent years. Automated methods for individualized risk prediction have great potential to …

Importance Numerous prognostic models of suicide risk have been published, but few have
been implemented outside of integrated managed care systems. Objective To evaluate …

Background Suicide is a leading cause of death worldwide and results in a large number of
person years of life lost. There is an opportunity to evaluate whether administrative health …

Suicide risk prediction models can identify individuals for targeted intervention. Discussions
of transparency, explainability, and transportability in machine learning presume complex …

Statistical models, including those based on electronic health records, can accurately
identify patients at high risk for a suicide attempt or death, leading to implementation of risk …

Objective The study sought to evaluate how availability of different types of health records
data affect the accuracy of machine learning models predicting suicidal behavior. Materials …

Importance Half of the people who die by suicide make a health care visit within 1 month of
their death. However, clinicians lack the tools to identify these patients. Objective To predict …

Importance Suicide prediction models have the potential to improve the identification of
patients at heightened suicide risk by using predictive algorithms on large-scale data …

Importance Understanding the differences and potential synergies between traditional
clinician assessment and automated machine learning might enable more accurate and …

Background To date, our ability to accurately identify patients at high risk from suicidal
behaviour, and thus to target interventions, has been fairly limited. This study examined a …