Abstract:

Child maltreatment impacts a significant number of children per year and is typically not limited to one encounter with the system. Past records provide a wealth of information which may be used to supplement current maltreatment allegations. Machine learning algorithms in the form of Random Forests were applied to these data to predict risk of future child welfare outcomes, past and present factors.

Bio:

Dr. John Prindle is a research assistant professor with the Children’s Data Network at USC. His current work focuses on the impact of childhood maltreatment on downstream services such as education and medical services.