Juliette Beni EdgcombAssistant Professor in Residence of Psychiatry and Biobehavioral Sciences at the David Geffen School of Medicine at UCLA: “EHR Phenotypes for Detection of Suicide Prevention Interventions Delivered to Children in Emergency Settings”
Although suicide is the second leading cause of death among children ages 10 to 14, tracking suicide prevention interventions using medical records remains difficult due to frequent documentation in unrelated fields or narrative text. standardized. Not knowing who, when and where suicide prevention interventions are taking place hinders progress toward targeted delivery to children most at risk.
This project aims to develop systematic search queries, or computable phenotypes, to detect suicide prevention interventions by applying natural language processing, artificial intelligence, and machine learning methods to discover data signals in electronic health records. Next, the project will examine whether the likelihood that individuals will benefit from an indicated intervention varies by race, ethnicity, age, gender, and neighborhood vulnerability.