  Overdose related to prescription opioids have reached an epidemic level in
the US, creating an unprecedented national crisis. This has been exacerbated
partly due to the lack of tools for physicians to help predict the risk of
whether a patient will develop opioid use disorder. Little is known about how
machine learning can be applied to a big-data platform to ensure an informed,
sustained and judicious prescribing of opioids, in particular for commercially
insured population. This study explores Massachusetts All Payer Claims Data, a
de-identified healthcare dataset, and proposes a machine learning framework to
examine how na\"ive users develop opioid use disorder. We perform several
feature selections techniques to identify influential demographic and clinical
features associated with opioid use disorder from a class imbalanced analytic
sample. We then compare the predictive power of four well-known machine
learning algorithms: Logistic Regression, Random Forest, Decision Tree, and
Gradient Boosting to predict the risk of opioid use disorder. The study results
show that the Random Forest model outperforms the other three algorithms while
determining the features, some of which are consistent with prior clinical
findings. Moreover, alongside the higher predictive accuracy, the proposed
framework is capable of extracting some risk factors that will add significant
knowledge to what is already known in the extant literature. We anticipate that
this study will help healthcare practitioners improve the current prescribing
practice of opioids and contribute to curb the increasing rate of opioid
addiction and overdose.
