Yong-Mi Kim - Yong-Mi Kim, PhD Associate Professor School of Library and Information Studies University of Oklahoma Schusterman Center ۴۵۰۲ East ۴۱st Street Tulsa, OK ۷۴۱۳۵

Background: Prescription drug monitoring programs (PDMPs) are instrumental in controlling opioid misuse,but opioid users have increasingly shifted to cocaine, creating a different set of medical problems. Whileopioid use results in multiple medical comorbidities, findings of the existing studies reported singlecomorbidities rather than a set, and furthermore, those findings are often conflicting because of the lack ofcontrolling for other substances in the analysis when combined use of substance creates synergistic effects.On the other hand, the findings from cocaine use are mainly related to kidney and heart problems, which lackspecificity. Because medical comorbidities from opioid and cocaine use are very different, it is imperative toinvestigate medical comorbidities from opioids and cocaine in order to minimize negative effects fromPDMPs. Therefore, this study attempts to discover sets of medical comorbidities from opioid and cocaine useby controlling for other substances in the analysis.Methods: A data mining technique, association rule mining algorithm, was employed to discover sets ofmedical comorbidities using electronic medical records. This method is ideal to discover co-occurringmedical comorbidities.Findings: Opioid use was associated with a set of [high diastolic blood pressure (DBP), abnormal specificgravity], [high body mass index (BMI), low blood gas] among others. Cocaine use correlated with [highcreatine kinase (CK), high blood urea nitrogen (BUN)], [high CK, cardiopulmonary] among others.Conclusion: The findings of this study addresses some of the conflicting findings by eliminating multidrugand reports sets of medical comorbidities from opioid and cocaine use

Cocaine, Data mining, Electronic health records, Comorbidity, Opioids