Machine Learning Side Effect Trend Predictions and the SIDER Database
Stephen Onorato JrDepartment of Computer Science, West Chester University steveonoratojr@gmail.com0009-0007-8786-0538
Md AmiruzzamanDepartment of Computer Science, West Chester University mamiruzzaman@wcupa.edu0000-0002-2292-5798
Rizal Mohd NorDepartment of Computer Science, Kulliyyah of Information and Communication Technology rizalmohdnor@iium.edu.my0000-0002-8994-2234
Md. Rajibul IslamDepartment of Computer Science and Engineering, Bangladesh University of Business and Technology md.rajibul.islam@gmail.com0000-0003-0565-6917
Dr. Ilsun YouDepartment of Information Security, Cryptology, and Mathematics, Kookmin University ilsunu@gmail.com0000-0002-0604-3445
Keywords: Machine Learning, Side Effect Prediction, Pharmaceutical Industry, SIDER Database, Medication Therapy, Clinical Trials, Drug Safety, Supervised Learning
Abstract
In the Pharmaceutical and Healthcare industries, understanding medications is key in the treatment of patients. Worldwide, there are hundreds of thousands of medications available, classified in categories related to medication therapy and the remediation that they provide. With so many different types of medication, medical doctors and pharmacists need to determine what kinds of drugs to provide to patients with specific medical needs. New medication studies necessitate careful analysis of available medication data during clinical trials, prior to production of new medications, and through the course of prescribed medication therapy. he use of medication therapy is not justified if the number of side effects outweighs the remedial benefits. Therefore, not all medications are deemed medically safe for all patients. Supervised machine learning techniques assist scientists with predicting side effects of medications that are under development. Prediction techniques aid future development of medications based on the properties of current medication data models.