Juliet Beni Edgcomb

Dr. Juliet Beni Edgcomb is an Assistant Professor-in-residence in the Department of Psychiatry and Biobehavioral Sciences at the University of California, Los Angeles (UCLA) School of Medicine. Her research program focuses on applying data science and clinical informatics to improve child mental health care delivery and suicide prevention.

Dr. Edgcomb's research interests center on critical challenges in pediatric mental health:

• Development of EHR phenotyping algorithms for suicide risk detection in children
• Application of machine learning to predict self-harm behaviors in vulnerable populations
• Analysis of health equity issues in mental healthcare access and quality
• Impact of public health emergencies on child mental health service utilization
• Quality improvement initiatives in pediatric psychiatric care

Her recent work demonstrates innovative approaches to bridging data science with clinical practice. Dr. Edgcomb has developed computable phenotyping methods to identify children at risk of suicide-related emergencies in emergency department settings, with several publications in high-impact journals including JAMA Network Open and the Journal of the American Academy of Child & Adolescent Psychiatry. Her research on the impact of the COVID-19 pandemic on children's mental health services has provided valuable insights for healthcare systems navigating public health crises.

Dr. Edgcomb's publications have received significant attention, with multiple papers being widely cited and covered by news outlets. Her work on suicide prevention in pediatric populations has particular relevance to current public health challenges.

She maintains productive collaborations with UCLA colleagues including Dr. Bonnie Zima, Dr. John Brooks, and Dr. Alexandra Klomhaus, forming a research team focused on leveraging informatics to address critical gaps in child mental health services. Her conceptual framework spans diverse areas including self-injurious behavior, suicide prevention, bipolar disorder, diplomacy, and machine learning applications in healthcare.