A Data Mining Approach on Polypharmacy and Drug-drug Interactions of Common Diabetes Medications.

Background: When managing diabetes, polypharmacy the use of several drugs simultaneously to obtain the best possible glucose control is typical. Drug-drug interactions (DDIs), which can result in side effects and reduced treatment efficacy, have increased.

Objective: This study evaluated the data mining approach of polypharmacy-based drug-drug interactions for common diabetes medication.

Methods: To identify publications that met the inclusion criteria, several scientific reviews and research papers were searched, including Scopus, Web of Science, Google Scholar, PubMed, Science Direct, Springer Link, and NCBI, using keywords such as diabetes, drug-drug interaction, polypharmacy, data mining, and herbal interaction.

Results: Many important drug-drug interactions among popular anti-diabetic drugs have been identified using data mining. Using iodinated contrast media and metformin together increased the risk of lactic acidosis, and using NSAIDs and sulfonylureas simultaneously increased the risk of hypoglycemia. A higher incidence of DDIs was found in an analysis of elderly individuals and those with several comorbidities. Predictive models have demonstrated high sensitivity and accuracy in detecting possible DDIs from patient and drug data.

Conclusion: Finding and evaluating DDIs in polypharmacy related to diabetes care are made possible through data mining. These results could potentially improve patient safety by influenc-ing more individualized and cautious prescription techniques. The improvement of these methods and their application in standard clinical practice should be the main goal of future studies.