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Articles
Published: 2022-12-22

Artificial intelligence-enabled rapid and symptom-based medication recommendation system (COV-MED) for the COVID-19 patients

Department of Electrical and Electronic Engineering, Sylhet Engineering College, Sylhet-3100, Bangladesh
Directorate General of Health Services, Ministry of Health and Family Welfare, Bangladesh
Department of Farm Power and Machinery, Faculty of Agricultural Engineering and Technology, Sylhet Agricultural University, Sylhet-3100, Bangladesh
Department of Agricultural Chemistry, Bangladesh Agricultural University, Mymensingh-2202, Bangladesh
Department of Agricultural Chemistry, Sher-E-Bangla Agricultural University, Dhaka-1207, Bangladesh
Department of Food Engineering and Technology, Faculty of Agricultural Engineering and Technology, Sylhet Agricultural University, Sylhet-3100, Bangladesh
Department of Farm Power and Machinery, Faculty of Agricultural Engineering and Technology, Sylhet Agricultural University, Sylhet-3100, Bangladesh
Department of Farm Power and Machinery, Faculty of Agricultural Engineering and Technology, Sylhet Agricultural University, Sylhet-3100, Bangladesh
Directorate General of Health Services, Ministry of Health and Family Welfare, Bangladesh
Directorate General of Health Services, Ministry of Health and Family Welfare, Bangladesh
Department of Clinical Tropical Medicine, Cox’s Bazar Medical College and Hospital, Cox’s Bazar-4700, Bangladesh
Department of Medicine, Cox’s Bazar Medical College and Hospital, Cox’s Bazar-4700, Bangladesh
Medical Officer, BRAC, Bangladesh
Directorate General of Health Services, Ministry of Health and Family Welfare, Bangladesh
Department of Food Engineering and Technology, Faculty of Agricultural Engineering and Technology, Sylhet Agricultural University, Sylhet-3100, Bangladesh

Abstract

In a general COVID-19 population in Cox’s Bazar, Bangladesh, we developed a medication recommendation system based on clinical information from the electronic medical record (EMR). Our goal was also to enable deep learning (DL) strategies to quickly assist physicians and COVID-19 patients by recommending necessary medications. The general demographic data, clinical symptoms, basic clinical tests, and drug information of 8953 patients were used to create a dataset. The learning model in this COVID-MED model was created using Keras (an open-source artificial neural network library) to solve regression problems. In this study, a sequential model was adopted. In order to improve the prediction capability and achieve global minima quickly and smoothly, the COVID-MED model incorporates an adaptive optimizer dubbed Adam. The model calculated a mean absolute error of 0.0037, a mean squared error of 0.000035, and a root mean squared error of 0.0059. The model predicts the output medications, such as injections or other oral medications, with around 99% accuracy. These findings show that medication can be predicted using information from the EMR. Similar models allow for patient-specific decision support to help prevent medication errors in diseases other than COVID-19.



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How to Cite

1.
Surja AS, Limon MTI, Soeb MJA, Arifin MS, Islam MM, Kayshar MS, Islam MA, Rahman MM, Malek MA, Mohsin FM, Jahan MS, Barua A, Tofaz TB, Sultana I, Jubayer MF. Artificial intelligence-enabled rapid and symptom-based medication recommendation system (COV-MED) for the COVID-19 patients. jidhealth [Internet]. 2022 Dec. 22 [cited 2024 Nov. 14];5(4). Available from: https://jidhealth.com/index.php/jidhealth/article/view/259