In The Canadian Journal of Psychiatry, DOI 10.1177/07067437221114094:

>Opioid use disorder (OUD) is a chronic relapsing disorder with a problematic pattern of opioid use, affecting nearly 27 million people worldwide. Machine learning (ML)-based prediction of OUD may lead to early detection and intervention.

>In the current study, we aimed to develop and prospectively validate an ML model that could predict individual OUD cases based on representative large-scale health data.

In the linked release written by Gillian Rutherford, 7 December 2022:

>Opioid use disorder is a treatable, chronic disease in which patients can't control their opioid use, leading to difficulties at work or home, and sometimes even overdose and death, according to the U.S. Centers for Disease Control and Prevention.

>People with opioid use disorder are originally exposed to the drugs either through prescriptions to manage pain or through the illicit drug market.

>“Most of those people have interacted with the health system before their diagnosis, and that provides us with data that could allow us to predict and potentially prevent some of the cases,” says principal investigator Bo Cao, Canada Research Chair in Computational Psychiatry and associate professor of psychiatry.

> 

>The machine learning model analyzed health data from nearly 700,000 Alberta patients who received prescriptions for opioids between 2014 and 2018, cross-referencing 62 factors such as the number of doctor and emergency room visits, diagnoses and sociodemographic information.

>The team found the top risk factors for opioid use disorder included frequency of opioid use, high dosage and a history of other substance use disorders, among others.

>They determined the model predicted high-risk patients with an accuracy of 86 per cent when it was validated against a new sample of 316,000 patients from 2019.

>“It’s important that the model’s prediction of whether someone will develop opioid use disorder is interpreted as a risk instead of a label,” says first author Yang Liu, post-doctoral fellow in psychiatry. “It is information to put into the hands of clinicians, who are actually making the diagnosis.”

University of Alberta