Using Technologies to Advance the Understanding, Prediction, and Prevention of Suicidal Behavior

Suicide is a devastating and perplexing behavior and continues to be the second leading cause of death among those ages 15-34 years old. Fortunately, recent research has made significant advances in our understanding of suicide and our ability to predict and prevent it. This presentation will review what is currently known about why people try to kill themselves, how to identify those at risk for suicidal thoughts and behaviors, and what treatments are most effective at preventing these concerning outcomes.

Matthew K. Nock, Ph.D. is the Edgar Pierce Professor of Psychology in the Department of Psychology at Harvard University. He received his Ph.D. in psychology from Yale University, and completed his clinical internship at Bellevue Hospital and the New York University’s Child Study Center. Prof. Nock’s research is aimed at advancing the understanding why people behave in ways that are harmful to themselves, with an emphasis on suicide and other forms of self-harm. His research is multi-disciplinary in nature and uses a range of methodological approaches (e.g., epidemiologic surveys, laboratory-based experiments, clinic- based studies, digital monitoring via smartphones and biosensors, and web- and social-media-based experiments) to better understand how these behaviors develop, how to predict them, and how to prevent their occurrence. This work is funded by grants from the US National Institutes of Health, US Department of Defense, US Army, and private foundations and has been published in over 250 scientific papers. Prof. Nock’s work has been recognized through the receipt of career awards from the American Psychological Association, the Association for Behavioral and Cognitive Therapies, and the American Association of Suicidology; and in 2011 he received a MacArthur Fellowship.

Prof. Nock’s Grand Rounds talk will describe how recent advances in technology and computing are advancing the understanding, prediction, and prevention of suicidal behavior. From the use of machine learning applied to patient medical records to new smartphone-based treatments, these advances are providing clinicians with new tools to fight this perplexing public health problem.