A Vanderbilt study has identified four separate profiles to help medical professionals better assess children at elevated risk for a self-harm event.

Natural language processing of notes in electronic health records can identify suicidal behavior and could be used to directly improve real-time risk prediction algorithms.

New Vanderbilt research is pointing to solutions for efficient clinical prediction of suicide attempt or suicidal thinking in adults.

New research from Vanderbilt identifies the first replicated genomic region contributing to risk of suicide attempt and confirms that genetic risk for the trait is not driven solely by risk for psychiatric disorders.

A machine learning algorithm that predicts suicide attempt recently underwent a prospective trial at the institution where it was developed, Vanderbilt University Medical Center.

Retrieval of clearer, more complete information from the EHR could go a long way toward improving predictive models of who will next be at risk of suicide, thereby improving care for patients with suicidal thoughts and behaviors.

Investigators will use computational methods to shed light on suicidal ideation and its relationship to attempted suicide, predict suicidal ideation and suicide attempt using routine electronic health records (EHRs) and explore the genetic underpinnings of both.