Addiction and overdose related to prescription opioids have reached an epidemic level in the U.S. , creating an unprecedented national crisis. This has been exacerbated partly due to the lack of tools for physicians to help predict whether or not a patient will develop opioid use disorder. Prior research lacks the investigation of how machine learning can be applied to a big-data platform to ensure an informed  and judicious prescribing of opioids . In this study , we explore the Massachusetts All Payer Claim Data(MA APCD) , a de-identified healthcare claim dataset, and propose a novel framework to examine how na\"ive users develop opioid use disorder. We perform several feature engineering techniques to identify the influential demographic and clinical features associated with opioid use disorder from a class imbalanced analytic sample. We then use and 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 such dependency. Results showed that the random forest model outperforms the other three algorithms while determining the features, some of which are consistent with prior clinical findings.  We anticipate that this research has the potential for healthcare practitioners to improve the current prescribing practice of opioids , thereby curbing the increasing rate of opioid addiction  .
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 .
R
Addiction and overdose
Overdose
R
U.S.
US
R
whether or not
the risk of whether
R
Prior research lacks the investigation of
Little is known about
A
, sustained
R
. In this study , we explore the
, in particular for commercially insured population. This study explores
R
Claim Data(MA APCD)
Claims Data
D
claim
R
propose a novel
proposes a machine learning
R
engineering
selections
D
the
D
use and
R
logistic regression, random forest, decision tree, and gradient boosting,
Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting
R
such dependency. Results showed that the random forest
opioid use disorder. The study results show that the Random Forest
A
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.
R
research has the potential for healthcare practitioners to
study will help healthcare practitioners
R
, thereby curbing
and contribute to curb
A
and overdose