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. While opioid use results in multiple medical comorbidities, findings of the existing studies reported single comorbidities rather than a set, and furthermore, those findings are often conflicting because of the lack of controlling 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 lack specificity. Because medical comorbidities from opioid and cocaine use are very different, it is imperative to investigate medical comorbidities from opioids and cocaine in order to minimize negative effects from PDMPs. Therefore, this study attempts to discover sets of medical comorbidities from opioid and cocaine use by controlling for other substances in the analysis. Methods: A data mining technique, association rule mining algorithm, was employed to discover sets of medical comorbidities using electronic medical records. This method is ideal to discover co-occurring medical comorbidities. Findings: Opioid use was associated with a set of [high diastolic blood pressure (DBP), abnormal specific gravity], [high body mass index (BMI), low blood gas] among others. Cocaine use correlated with [high creatine 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 multidrug and reports sets of medical comorbidities from opioid and cocaine use
Comparing Medical Comorbidities Between Opioid and Cocaine Users: A Data Mining Approach
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