Mathematical and Computational Modeling of Suicidal Thoughts and Behaviors
Suicide is a leading cause of death worldwide, claiming over 800,000 lives each year. Unfortunately, whereas scientific advances have led to declines in other leading causes of death over time, the current suicide rate is nearly identical to what it was 100 years ago. In order to make meaningful progress, new methods and approaches are needed that can capture and model the immense complexity of suicide and other forms of self-harm. In the first half of this talk, I will present a series of studies harnessing machine learning and real-time monitoring to predict whois at risk for suicide and whenrisk is highest. In the second half of this talk, I will take a complementary theory-driven computational approach to investigate whyand howsuicide and self-harm arise by building formal mathematical models of these phenomena as complex dynamical systems. Together, this work may help us advance the understanding, prediction, and prevention of suicide and self-harm.
