Walton, BettyHong, SaahoonKwon, HyejeanKim, Hea-WonMoynihan, Stephanie2024-09-252024-09-252024-09Walton, B., Hong, S., Kwon, H., Kim, H., & Moynihan, S. (2024). Improving treatment completion for young adults with substance use disorder: Machine learning-based prediction algorithm (Research Brief No. 5). Indiana University School of Social Work.https://hdl.handle.net/1805/43603Substance Use Disorder treatment completion has been associated with positive outcomes, such as reduced relapse rates and longer periods of abstinence. A study identified factors influencing SUD treatment completion among young adults (aged 18–25) receiving publicly funded outpatient services. This research brief describes how a machine learning decision tree model explored interactions between functional behavioral health needs and strengths, criminal justice system involvement, and completing treatment. A machine learning approach made it possible to identify complex relationships among many factors, improving our understanding on where to focus treatment.en-USYoung AdultsSubstance Use DisorderService CompletionMachine Learning