UF study uses machine learning to predict opioid use disorder treatment interruptions
The tool could help doctors keep patients on track with their treatment. (Matt Splett)
University of Florida researchers have developed a system designed to identify patients at high risk of discontinuing buprenorphine treatment for opioid use disorder.
An FDA-approved prescription drug, buprenorphine is one of three commercially available treatments for opioid use disorder proven to be effective in treating both pain and addiction.
In a study published in the journal Computers in Biology and Medicine, Md Mahmudul Hasan, Ph.D., and his research team found that roughly 15% of patients did not complete the clinically recommended yearlong buprenorphine treatment, while about 46% of patients stopped treatment within the first three months. With the help of artificial intelligence, or AI, the team also identified high-risk patients and several factors associated with treatment discontinuation.
Hasan, an assistant professor in the UF College of Pharmacy department of pharmaceutical outcomes and policy with a joint appointment in the UF Warrington College of Business department of information systems and operations management, said the retrospective study, which included insured individuals aged 18 to 64 who were prescribed buprenorphine to treat opioid use disorder, offers new insights to use in the fight against the national public health epidemic that claimed more than 80,000 lives in the United States in 2021.