Investigation of Drug Interactions through Analysis of Prescribed Medications Association Rules Using the FP -growth Algorithm

Introduction: The identification of hidden patterns in pharmaceutical data can enhance the performance of hospital pharmacies. One of the applications of advanced data analysis techniques is the identification of drug interactions. Method: This study utilized data mining techniques using the FP -growth algorithm within the RapidMiner Studio® 10.1 environment to extract association rules and frequent pharmaceutical patterns. Data preprocessing and modeling were conducted based on the CRISP -DM model. The type and level of drug interactions were determined based on the algorithm's results and by referencing the database at www.drugs.com . Results: The results included 17 association rules and 126 prescribing patterns, ranging from single -drug to four -drug combinations. Of the 64 two -drug prescribing patterns, 56 had no interactions, 6 had moderate interactions, 1 had a minor interaction, and 1 had a major interaction. Additionally, among the 19 three -drug patterns, 18 had no interactions, and only 1 had a moderate interaction. No interactions were observed in the four -drug prescribing pattern . Conclusion: The results of this study can help stakeholders to improve the pharmaceutical supply chain, optimize prescriptions, reduce drug interactions, and lower costs. The identified patterns may also be integrated into a clinical decision support system. Although no significant drug interactions were observed, the identification of even a single major interaction highlights its importance and further underscores the practical role of computer applications in medicine .