An In-Depth Examination of Drug-Drug Interaction Databases: Enhancing Patient Safety through Advanced Predictive Models and Artificial Intelligence Techniques
Sumayli, A., Daghriry, A., Sharahili, M., Alanazi, A., Alanazi, A., Alharbi, I., Alghamdi, F., Alqarni, K., Aljohani, N., Alfaqir, A., Shajiri, I., Albalawi, A., Alatwi, F., Alrashdi, M., Altaymani, K., Alsharari, M., Albalawi, A., Alanazi, S., Alanazi, A. (2024). An In-Depth Examination of Drug-Drug Interaction Databases: Enhancing Patient Safety through Advanced Predictive Models and Artificial Intelligence Techniques. EKB Journal Management System, 6(4), 553-565. doi: 10.21608/jmals.2024.410645
Abdulaziz Alkhallaf Sumayli; Abdulrahim Ibrahim Daghriry; Mohammed Ali Sharahili; Abdulhakim Awadh Alanazi; Adel Abdullah Alanazi; Ibrahim Fraih Alharbi; Fahad Nayesh Alghamdi; Kamal Ali Alqarni; Naser Mutiq Aljohani; Abdulmohsen Mohammad Alfaqir; Ibrahim Mohammed Shajiri; Abdulrahman Rashid Albalawi; Fahd Moter Alatwi; Munif Sadan Alrashdi; Khalid Abdullah Altaymani; Meshal Basheer Alsharari; Alnashmi Mahdi Albalawi; Sultan Radhi Alanazi; Abdullah Samah Alanazi. "An In-Depth Examination of Drug-Drug Interaction Databases: Enhancing Patient Safety through Advanced Predictive Models and Artificial Intelligence Techniques". EKB Journal Management System, 6, 4, 2024, 553-565. doi: 10.21608/jmals.2024.410645
Sumayli, A., Daghriry, A., Sharahili, M., Alanazi, A., Alanazi, A., Alharbi, I., Alghamdi, F., Alqarni, K., Aljohani, N., Alfaqir, A., Shajiri, I., Albalawi, A., Alatwi, F., Alrashdi, M., Altaymani, K., Alsharari, M., Albalawi, A., Alanazi, S., Alanazi, A. (2024). 'An In-Depth Examination of Drug-Drug Interaction Databases: Enhancing Patient Safety through Advanced Predictive Models and Artificial Intelligence Techniques', EKB Journal Management System, 6(4), pp. 553-565. doi: 10.21608/jmals.2024.410645
Sumayli, A., Daghriry, A., Sharahili, M., Alanazi, A., Alanazi, A., Alharbi, I., Alghamdi, F., Alqarni, K., Aljohani, N., Alfaqir, A., Shajiri, I., Albalawi, A., Alatwi, F., Alrashdi, M., Altaymani, K., Alsharari, M., Albalawi, A., Alanazi, S., Alanazi, A. An In-Depth Examination of Drug-Drug Interaction Databases: Enhancing Patient Safety through Advanced Predictive Models and Artificial Intelligence Techniques. EKB Journal Management System, 2024; 6(4): 553-565. doi: 10.21608/jmals.2024.410645

An In-Depth Examination of Drug-Drug Interaction Databases: Enhancing Patient Safety through Advanced Predictive Models and Artificial Intelligence Techniques

Journal of Medical and Life Science
Volume 6, Issue 4, December 2024, Page 553-565  XML PDF (470.12 K)
Document Type: Original Article
DOI: 10.21608/jmals.2024.410645
View on SCiNiTO View on SCiNiTO
Authors
Abdulaziz Alkhallaf Sumayli* ; Abdulrahim Ibrahim Daghriry; Mohammed Ali Sharahili; Abdulhakim Awadh Alanazi; Adel Abdullah Alanazi; Ibrahim Fraih Alharbi; Fahad Nayesh Alghamdi; Kamal Ali Alqarni; Naser Mutiq Aljohani; Abdulmohsen Mohammad Alfaqir; Ibrahim Mohammed Shajiri; Abdulrahman Rashid Albalawi; Fahd Moter Alatwi; Munif Sadan Alrashdi; Khalid Abdullah Altaymani; Meshal Basheer Alsharari; Alnashmi Mahdi Albalawi; Sultan Radhi Alanazi; Abdullah Samah Alanazi
KSAFH King Salman Armed Forces Hospital in Northwestern Region, Saudi Arabia
Abstract
Background: Drug-drug interactions (DDIs) pose a significant risk to patient safety, leading to adverse effects and increased hospitalizations. With the rise of polypharmacy, especially among elderly patients and those with chronic conditions, understanding and predicting DDIs has become imperative for effective clinical management. Methods: This review synthesizes current literature on drug-drug interaction databases and their role in predicting DDIs. A systematic search of relevant databases focused on studies utilizing artificial intelligence (AI) and machine learning techniques for DDI prediction. Key databases, such as DrugBank, PubChem, and the Pharmacogenomics Knowledgebase (PharmGKB), were analyzed for their contributions to DDI research. Results: The findings indicate that AI-driven methodologies significantly enhance the identification and prediction of DDIs. Various machine learning techniques, including conventional and unconventional methods, have been employed to assess drug interactions effectively. The review highlights real-world examples of critical DDIs, demonstrating the clinical implications of these interactions. Databases provide essential tools for healthcare providers to manage medications and prevent adverse events.
Conclusion: Integrating drug-drug interaction databases into clinical practice is crucial for improving patient safety and treatment efficacy. Future research should focus on enhancing the predictive capabilities of these models through continuous data integration and validation. By leveraging advanced computational techniques, healthcare systems can better anticipate and mitigate the risks associated with drug interactions.
Keywords
Drug-drug interactions; artificial intelligence; machine learning; patient safety; pharmacovigilance
Statistics
Article View: 235
PDF Download: 173