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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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References

  1. Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nature Biomedical Engineering. 2018;2(10):719-31. https://doi.org/10.1038/s41551-018-0305-z.
  2. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama. 2016;316(22):2402-10. http://doi.org/10.1001/jama.2016.17216
  3. Jha S, Topol EJ. Adapting to artificial intelligence: radiologists and pathologists as information specialists. Jama.2016;316(22):2353-4. https://doi.org/10.1001/jama.2016.17438.
  4. Debnath S, Barnaby DP, Coppa K, Makhnevich A, Kim EJ, Chatterjee S, Tóth V, Levy TJ, Paradis MD, Cohen SL, Hirsch JS. Machine learning to assist clinical decision-making during the COVID-19 pandemic. Bioelectronic medicine. 2020;6(1):1-8. https://doi.org/10.1186/s42234-020-00050-8
  5. Alimadadi A, Aryal S, Manandhar I, Munroe PB, Joe B, Cheng X. Artificial intelligence and machine learning to fight COVID-19. Physiological Genomics. 2020;52(4):200-2. https://doi.org/10.1152/physiolgenomics.00029.2020
  6. Haleem A, Javaid M, Khan IH. Current status and applications of artificial intelligence (AI) in medical field: An overview. Current Medicine Research and Practice. 2019;9(6):231-37. https://doi.org/10.1016/j.cmrp.2019.11.005
  7. Alafif T, Tehame AM, Bajaba S, Barnawi A, Zia S. Machine and deep learning towards COVID-19 diagnosis and treatment: survey, challenges, and future directions. International Journal of Environmental Research and PublicHealth.2021;18(3):1117. https://doi.org/10.3390/ijerph18031117.
  8. Song J, Wang Y, Tang S, Zhang Y, Chen Z, Zhang Z, Zhang T, Wu F. Local–Global Memory Neural Network for Medication Prediction. IEEE Transactions on Neural Networks and Learning Systems. 2020;32(4):1723-36. https://doi.org/10.1109/TNNLS.2020.2989364
  9. OECD. OECD Glossary of Statistical Terms. 2008. p. 119. ISBN 978-92-64-025561.
  10. Fang L, Karakiulakis G, Roth M. Are patients with hypertension and diabetes mellitus at increased risk for COVID-19 infection?. The Lancet Respiratory Medicine. 2020;8(4):e21. https://doi.org/10.1016/S2213-2600(20)30116-8
  11. Dodge Y. The Oxford Dictionary of Statistical Terms, 2003. OUP. ISBN 0-19-920613-9 (entry for normalization of scores).
  12. Eijaz Allibhai (Medium, India). Building A Deep Learning Model using Keras. 2018. Available in: https://towardsdatascience.com/building-a-deep-learning-model-using-keras-1548ca149d37, (accessed 18 February 2022).
  13. Da K. A method for stochastic optimization. arXiv preprint. 2014; arXiv:1412.6980. https://doi.org/10.48550/arXiv.1412.6980
  14. Jamshidi M, Lalbakhsh A, Talla J, Peroutka Z, Hadjilooei F, Lalbakhsh P, Jamshidi M, La Spada L, Mirmozafari M, Dehghani M, Sabet A. Artificial intelligence and COVID-19: deep learning approaches for diagnosis and treatment. IEEE Access. 2020; 8:109581-95. https://doi.org/10.1109/ACCESS.2020.3001973
  15. Ge Y, Tian T, Huang S, Wan F, Li J, Li S, Yang H, Hong L, Wu N, Yuan E, Cheng L. A data-driven drug repositioning framework discovered a potential therapeutic agent targeting COVID-19. BioRxiv. 2020. https://doi.org/10.1101/2020.03.11.986836
  16. Zhavoronkov A, Zagribelnyy B, Zhebrak A, Aladinskiy V, Terentiev V, Vanhaelen Q, Bezrukov DS, Polykovskiy D, Shayakhmetov R, Filimonov A, Bishop M. Potential non-covalent SARS-CoV-2 3C-like protease inhibitors designed using generative deep learning approaches and reviewed by human medicinal chemist in virtual reality. Chemrxiv. 2020. https://doi.org/10.26434/chemrxiv.12301457.v1
  17. Ong E, Wong MU, Huffman A, He Y. COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning. Frontiers in Immunology. 2020;1581. https://doi.org/10.3389/fimmu.2020.01581
  18. Senior AW, Evans R, Jumper J, Kirkpatrick J, Sifre L, Green T, Qin C, Žídek A, Nelson AW, Bridgland A, Penedones H. Improved protein structure prediction using potentials from deep learning. Nature. 2020;577(7792):706-10. https://doi.org/10.1038/s41586-019-1923-7
  19. Murdoch TB, Detsky AS. The inevitable application of big data to health care. Jama. 2012;309(13):1351-52. https://doi.org/10.1001/jama.2013.393
  20. Fokas AS, Cuevas-Maraver J, Kevrekidis PG. A quantitative framework for exploring exit strategies from the COVID-19 lockdown. Chaos, Solitons & Fractals. 2020;140:110244. https://doi.org/10.1016/j.chaos.2020.110244
  21. Paul A, Chatterjee S, Bairagi N. Prediction on Covid-19 epidemic for different countries: Focusing on South Asia under various precautionary measures. Medrxiv. 2020. https://doi.org/10.1101/2020.04.08.20055095.
  22. Zhou X, Liang W, Kevin I, Wang K, Shimizu S. Multi-modality behavioral influence analysis for personalized recommendations in health social media environment. IEEE Transactions on Computational Social Systems. 2019;6(5):888-97. https://doi.org/10.1109/TCSS.2019.2918285
  23. Mei X, Lee HC, Diao KY, Huang M, Lin B, Liu C, Xie Z, Ma Y, Robson PM, Chung M, Bernheim A. Artificial intelligence–enabled rapid diagnosis of patients with COVID-19. Nature Medicine. 2020;26(8):1224-28. https://doi.org/10.1038/s41591-020-0931-3
  24. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H, Wang Y. Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology. 2017;2(4). https://dx.doi.org/10.1136/svn-2017-000101
  25. Rough K, Dai AM, Zhang K, Xue Y, Vardoulakis LM, Cui C, Butte AJ, Howell MD, Rajkomar A. Predicting inpatient medication orders from electronic health record data. Clinical Pharmacology & Therapeutics. 2020;108(1):145-54. https://doi.org/10.1002/cpt.1826
  26. Zhao, M., Hoti, K., Wang, H., Raghu, A., & Katabi, D. Assessment of medication self-administration using artificial intelligence. Nature Medicine. 2021;27(4):727-35. https://doi.org/10.1038/s41591-021-01273-1


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 2023 Feb. 9];5(4). Available from: https://jidhealth.com/index.php/jidhealth/article/view/259