Identification of Subphenotypes of Opioid Use Disorder Using Unsupervised Machine Learning.

This paper aimed to detect the latent clusters of patients with opioid use disorder and to identify the risk factors affecting drug misuse using unsupervised machine learning. The cluster with the highest proportion of successful treatment outcomes was characterized by the highest percentage of employment rate at admission and discharge, the highest percentage of patients who also recovered from alcohol and other drug co-use, and the highest proportion of patients who recovered from untreated health issues. Longer participation in opioid treatment programs was associated with the highest proportion of treatment success.