COVID-19 … What are drugs and strategies now?
Keywords:
COVID-19, MACHINE LEARNING, ARTIFICIAL INTELLIGENCE, DRUGSAbstract
From February 2019 the World faces the Covid19 pandemic. The data in our possession are still insufficient to effectively combat this pathology.
The gold standard for diagnosis remains molecular testing, while clinical and instrumental and serological diagnostics are highly nonspecific leading to a slowdown in the battle against covid19.[3]
Can Artificial Intelligence (AI) and Machine Learning (ML) help us? The use of large databases to cross-reference data to stratify the diagnostic scores, to quickly differentiate a critical Covid-19 patient from a non-critical one is the challenge of the future. All to achieve better management of resources in the field and a more effective therapeutic approach.[2]
References
Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time [published correction appears in Lancet Infect Dis. 2020 Jun 12;:]. Lancet Infect Dis. 2020;20(5):533-534. doi:10.1016/S1473-3099(20)30120-1
Alimadadi A, Aryal S, Manandhar I, Munroe PB, Joe B, Cheng X. Artificial intelligence and machine learning to fight COVID-19. Physiol Genomics. 2020;52(4):200-202. doi:10.1152/physiolgenomics.00029.202
Wiersinga WJ, Rhodes A, Cheng AC, Peacock SJ, Prescott HC. Pathophysiology, Transmission, Diagnosis, and Treatment of Coronavirus Disease 2019 (COVID-19): A Review [published online ahead of print, 2020 Jul 10]. JAMA. 2020;10.1001/jama.2020.12839. doi:10.1001/jama.2020.12839
Nouvenne A, Ticinesi A, Parise A, Prati B, Esposito M, Cocchi V, Crisafulli E, Volpi A, Rossi S, Bignami EG, Baciarello M, Brianti E, Fabi M, Meschi T. Point-of-Care Chest Ultrasonography as a Diagnostic Resource for COVID-19 Outbreak in Nursing Homes. J Am Med Dir Assoc. 2020 Jul;21(7):919-923. doi: 10.1016/j.jamda.2020.05.050. Epub 2020 May 25. PMID: 32571651; PMCID: PMC7247494.
Potì F, Pozzoli C, Adami M, Poli E, Costa LG. Treatments for COVID-19: emerging drugs against the coronavirus. Acta Biomed. 2020;91(2):118-136. Published 2020 May 11. doi:10.23750/abm.v91i2.9639.
Assaf D, Gutman Y, Neuman Y, Segal G, Amit S, Gefen-Halevi S, Shilo N, Epstein A, Mor-Cohen R, Biber A, Rahav G, Levy I, Tirosh A. Utilization of machine-learning models to accurately predict the risk for critical COVID-19. Intern Emerg Med. 2020 Aug 18:1–9. doi: 10.1007/s11739-020-02475-0. Epub ahead of print.
Downloads
Published
Issue
Section
License
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Transfer of Copyright and Permission to Reproduce Parts of Published Papers.
Authors retain the copyright for their published work. No formal permission will be required to reproduce parts (tables or illustrations) of published papers, provided the source is quoted appropriately and reproduction has no commercial intent. Reproductions with commercial intent will require written permission and payment of royalties.