Drug-drug interactions is a frequently encountered issue in clinical practice, reduce effectiveness of treatment and lead to dangerous complications for the patients, possibly even death. The purpose of this study is to analyze contraindicated drug interactions when prescribing in some hospitals in Quang Ninh Province. Subjects and methods: List of concentrated contractor drugs applied Quang Ninh province and within the scope of payment of Health Insurance Fund in 2018. The result: the group of researchers developed the list of 112 pairs of the cotraindicated drug-drug interactions to serve as a reference to determine the appearing frequency of contraindicated drug-drug interactions over 519500 medical records and 1254099 prescriptions at 3 hospitals in Quang Ninh province. Of which, 26 contraindicated drug-drug interactions (0.006%) was found over 297 medical records and 789 prescriptions. 5 pairs with the most appearing frequency are: Amiodarone-Amitriptyline, Amiodarone-Moxifloxacin, Amiodarone-Colchicine, Levodopa -Sulpiride, Amiodarone-Haloperidol. Medical reccords with contraindicated drug-drug interactions were found in 13 different departments or interdisciplinary deparments from all 3 hospitals, of which the highest rates were found in Intensive Care Unit and Emergency Departments (24.2%), Endocrinology Departments (16.2%) and Cardiology Pediatric Departments (10.4%). The average age of patients with contraindicated drug-drug interactions in all 3 hospitals was greater than or equal to 67, most are inpatients. Conclusion: Based on retrospective data on drug prescribing at 3 hospitals in Quang Ninh Province, the study analyzed 26 pairs of common contraindicated drug interactions in clinical practice. Keywords Contraindicated drug-drug interactions, hospitals in Quang Ninh Province, medication review tools. References [1] D.C. Malone, J. Abarca, P.D. Hansten, A.J. Grizzle, E.P. Armstrong, R.C.V Bergen, B.S. Duncan-Edgar, S.L. Solomon, R.B. Lipton, Indentification of Serious Drug-Drug Interactions: Results of the Partnership to Prevent Drug-Drug Interactions, J. Am. Pharm. Assoc. 44 (2004) 142-151. https://doi.org/10.1331/154434504773062591.[2] P. Vonbachl, A. Dubied, S. Kra¨henbu¨hl, J.H.Beer, Evaluation of frequently used drug interaction screening programs, Pharm. World. Sci. 30 (2008) 367-374. https://doi.org/10.1007/s11096-008-9191-x.[3] N.T. Hanh, V.T.P. Thao, H.Q. Tuan, N.X. Bach, N. T. Hai, Developing a list of important drug-drug interactions in the clinical practice in the internal department – Kien An Hospital, Hai Phong City, VNU Journal of Science: Medical and Pharmaceutical Sciences, 35(2) (2019) 54-67. https://doi.org/10.25073/2588-1132/vnumps.4179.[4] L.H. Duong, Developing clinically signification drug-drug interactions at Hop Luc General Hospital (2017), Specialist Pharmacist Graduation thesis – 1st Class, Ha Noi University of Pharmacy (in Vietnamese).[5] L.T. Phuong, Managing clinically signification drug-drug interactions at the National Geriatric Hospital (2018), Pharmacist Graduation thesis, Ha Noi University of Pharmacy (in Vietnamese).[6] T.T. Hoai, Examining drug-drug interactions in the inpatient medical records at the Thai Nguyen Lung Hospital (2017), Specialist Pharmacist Graduation thesis – 1st Class, Ha Noi University of Pharmacy (in Vietnamese).[7] Ministry of Health, Vietnam Regulatory Affairs Society, Medical Publishing House one member Company Limited, Vietnam, 2015 (in Vietnamese).[8] A.P. Heverton, R.L.P Leonardo, M.V Carlos, F.F.C. Maria, Patient’s lack of understanding producing insulin drug-interactions in Southeast Brazilian primary care clinics, Diabetes & Metabolic Syndrome: Clinical Research & Reviews. 3(2) (2019) 1131-1136. https://doi.org/10.1016/j.dsx.2019.01.032
4Due to the limitations in self-protection and information processing capabilities at IoT (Internet of Things) nodes, these nodes are susceptible to attacks, turning them into malicious nodes that cause damage or danger to the system. Early detection of these threats is essential to make timely recommendations and limit severe consequences for individuals and organizations. The study proposes applying a machine learning model to detect malicious traffic and IoT devices, which can be deployed and applied on the Fog IoT platform. This solution helps detect and early warn threats from IoT data before they are sent to the cloud. The model is evaluated on the IoT-23 dataset and gives good results.
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