Machine Learning, Electronic Health Records, and Suicide: Can We Predict and Prevent?
In this course, David Brent, MD discusses promising studies that use machine learning and natural language processing to potentially predict suicide risk. Given that this technology is still merely demonstrating proof of concept, Dr. Brent acknowledges some of the barriers that exist to utilization, including concerns for patients and providers.
(Presented on January 7, 2016)
Presenter: David Brent, MD, Endowed Chair in Suicide Studies, Academic Chief in Child and Adolescent Psychiatry, Department of Psychiatry, University of Pittsburgh School of Medicine; Director, Services for Teens at Risk (STAR) Center
Course Length: 45 minutes
Course Learning Objectives:
- Understand the utility of machine learning and natural language processing for the prediction of suicide.
- Review findings from some of the main studies using machine learning and natural language processing (NLP) to predict suicide and suicidal behavior
- Recognize some of the limitations of this approach, as well as ethical and clinical considerations
Course Online Evaluation: You will be prompted with a link to an online survey at the end of the course.
Course Achievement: No certificate will be available for this course. Completion for this course will, however, show up on the user’s transcript with any other courses they completed on the website.
Course Supplemental Material:
Services for Teens at Risk (STAR) Center – https://www.starcenter.pitt.edu/