Inteligencia Artificial y Big Data como soluciones frente al COVID-19

Jairo Eduardo Márquez Díaz

doi:10.1344/rbd2020.50.31643

Autors/ores

Jairo Eduardo Márquez Díaz Universidad de Cundinamarca

DOI:

https://doi.org/10.1344/rbd2020.50.31643

Paraules clau:

algoritmos de aprendizaje, analítica avanzada, aprendizaje automático, aprendizaje profundo, ciencia de datos, pandemia, representación de datos, COVID-19

Resum

La inteligencia artificial y el Big Data se articulan para poder lidiar con diferentes problemas relacionados con el análisis de datos masivos, en particular información de la COVID-19. En el presente artículo se muestran algunos proyectos de investigación relacionados con el aprendizaje profundo, el aprendizaje automático, el Big Data y la ciencia de datos, tendientes a dar soluciones plausibles bien en el monitoreo, detección, diagnóstico y tratamiento de las enfermedades asociadas con el virus. Con esto en mente, se muestra la correspondencia entre las tecnologías disruptivas y la información crítica, creando sinergias que permiten elaborar sistemas más avanzados de estudio y análisis facilitando la obtención de datos relevantes para la toma de decisiones sanitarias.

Biografia de l'autor/a

Jairo Eduardo Márquez Díaz, Universidad de Cundinamarca

Doctor en Educación. Ingeniero de Sistemas, Licenciado en Matemáticas y Física. Master en Bioética, Master en seguridad de la información empresarial. Especialista en docencia Universitaria y Especialista en Bioética, Especialista en Actuaria, Especialista en Ciberseguridad.

Referències

Abdessater, M., Rouprêt, M., Misrai, V., Matillon, X., Tellier, B. G., Freton, L., … Pradere, B. (2020) COVID19 pandemic impact on anxiety of French urologists in training: outcomes from a national survey. Progrès en Urologie. 1-12. http://doi.org/10.1016/j.purol.2020.04.015.

Ahuja, A. S., Reddy, V. P., & Marques, O. (2020). Artificial Intelligence and COVID-19: A Multidisciplinary Approach. Integrative Medicine Research 100434. Advance online publication. https://doi.org/10.1016/j.imr.2020.100434.

Banerjee, A., Kulcsar, K., Misra, V., Frieman, M., & Mossman, K. (2019). Bats and Coronaviruses. Viruses, 11(1), 41. http://doi.org/10.3390/v11010041.

Chahal, H., Jyoti, J. & Wirtz, J. (2019). Business Analytics: Concepts and Applications. In Understanding the Role of Business Analytics; Springer: London, UK, 1–8.

Ciliberto, G., & Cardone, L. (2020). Boosting the arsenal against COVID-19 through computational drug repurposing. Drug discovery today, S1359-6446(20)30152-5. Advance online publication. https://doi.org/10.1016/j.drudis.2020.04.005.

De Lecuona, I. (2018). Evaluación de los aspectos metodológicos, éticos, legales y sociales de proyectos de investigación en salud con datos masivos (big data). Gaceta Sanitaria 1-3. http://doi/org/10.1016/j.gaceta.2018.02.007.

De Lecuona, I., & Villalobos, Q. M. (2018). European perspectives on big data applied to health: The case of biobanks and human databases. Developing World Bioethics. 1-8. http://doi.org/10.1111/dewb.12208.

Fraile, G. V. et al. (2020). Revisión narrativa de la ecografía en el manejo del paciente crítico con infección por SARS-CoV-2 (COVID-19): aplicaciones clínicas en Medicina Intensiva-una revisión narrativa. Medicina Intensiva. 1-15. http://doi.org/10.1016/j.medin.2020.04.016.

Freedman, D. H. (2019). Hunting for New Drugs with AI. Nature, 576, 7787, S49–S53. doi:10.1038/d41586-019-03846-0.

Gené, B. J., Gallo, de P. P., & de Lecuona, I. (2018). Big data y seguridad de la información. Atención Primaria, 50, 1, 3–5. http://doi.org/10.1016/j.aprim.2017.10.004.

Guan WJ, Ni ZY, Hu Y, Liang WH, Ou CQ, He JX, Liu L, Shan H, Lei CL, Hui DSC, Du B, Li LJ, Zeng G, et al. (2020) Características clínicas de la enfermedad por coronavirus 2019 en China. The New England Journal of Medicine. 1-13. http://doi.org/10.1056/NEJMoa2002032.

Ishwarappa K. & Anuradha, J. (2015). A Brief Introduction on Big Data 5Vs Characteristics and Hadoop Technology. Procedia Computer Science 48, 319-324. 10.1016/j.procs.2015.04.188.

Ivankov, D. I. & Finkelstein, A. V. (2020). Solution of Levinthal’s Paradox and a Physical Theory of Protein Folding Times. Biomolecules, 10, 2, 250. doi:10.3390/biom10020250.

Jiang, X., Coffe, M., Bari, A., Wang, J., Jiang, X., Huang, J., et al. (2020). Towards an Artificial Intelligence Framework for Data-Driven Prediction of Coronavirus Clinical Severity. Computers, Materials & Continua (CMC). 63, 1, 537-551. http://doi.org/ 10.32604/cmc.2020.010691.

Kampf, G. (2020) Potential role of inanimate surfaces for the spread of coronaviruses and their inactivation with disinfectant agents. Elsevier. 2, 2, 1-2. https://doi.org/10.1016/j.infpip.2020.100044.

Kampf, G., Todt, D., Pfaender, S., & Steinmann, E. (2020). Persistence of coronaviruses on inanimate surfaces and its inactivation with biocidal agents. Journal of Hospital Infection. 1-13. http://doi.org/10.1016/j.jhin.2020.01.022.

Law, S., Leung, A. W., & Xu, C. (2020). Severe Acute Respiratory Syndrome (SARS) and Coronavirus disease-2019 (COVID-19): From Causes to Preventions in Hong Kong. International Journal of Infectious Diseases. http://doi.org/10.1016/j.ijid.2020.03.059.

Li, L. & Ayscue, P. (2020). Using viral genomics to estimate undetected infections and extent of superspreading events for COVID-19. medRxiv, 1-17. https://doi.org/10.1101/2020.05.05.20092098.

López, B. M. (2019). Las narrativas de la inteligencia artificial. Revista de Bioética y Derecho, 46, 5-28. https://doi.org/10.1344/rbd2019.0.27280.

Márquez, D. J. E. (2019). Early identification of non-melanoma cancer and actinal keratosis through artificial vision. Revista Compusoft. An international journal of advanced computer technology, 8(3), 3079-3087. http://dx.doi.org/10.6084/ijact.v8i3.786.

Mei, X., Lee, H., Diao, K. et al. (2020) Artificial intelligence–enabled rapid diagnosis of patients with COVID-19. Nat Med. 1-11. https://doi.org/10.1038/s41591-020-0931-3.

Mohamed, A., Nahafabadi, M.K., Wah, Y.B., Zaman, E.A.K. & Maskat, R. (2019). The state of the art and taxonomy of big data analytics: View from the new big data framework. Artif. Intell. Rev. 1–49. https://doi.org/10.1007/s10462-019-09685-9.

Mohebi, A.; Aghabozorgi, S.; Wah, T.Y.; Herawan, T.; Yayapour, R. (2016). Iterative big data clustering algorithms: A review. Softw. Pract. Exp. 46, 107–129. https://doi.org/10.1002/spe.2341.

Öner, O. (2020). Coronavirus Disease 2019 (COVID-19): Diagnosis and Management (Narrative Review). Erciyes Med J. 42(3): 1-6.

Ozturk, T., Talo, M., Yildirim, E. A., Baloglu, U. B., Yildirim, O., & Rajendra, A. U. (2020). Automated detection of COVID-19 cases using deep neural networks with X-ray images. Computers in Biology and Medicine, 103792. Advance online publication. https://doi.org/10.1016/j.compbiomed.2020.103792.

Pang, J, Wang MX, Ang IYH, Tan SHX, Lewis RF, Chen JI-P, et al. (2020) Potential Rapid Diagnostics, Vaccine and Therapeutics for 2019 Novel Coronavirus (2019-nCoV). A Systematic Review. J Clin Med. 9, 3,623.

Raoult, D., Zumla, A., Locatelli, F., Ippolito, G., & Kroemer, G. (2020). Coronavirus infections: Epidemiological, clinical and immunological features and hypotheses. Cell Stress, 1-10. https://doi.org/10.15698/cst2020.04.216.

Réda, C. Kaufmann, E. & Delahaye, D. A. (2020). Machine learning applications in drug development. Computational and Structural Biotechnology Journal, 18, 241-252. https://doi.org/10.1016/j.csbj.2019.12.006.

Sánchez, O. R., Torres, N. J., & Martínez, S. G. (2020). Radiological findings for diagnosis of SARS-CoV-2 pneumonia (COVID-19). La radiología en el diagnóstico de la neumonía por SARS-CoV-2 (COVID-19). Medicina clínica, S0025-7753(20) 30185-8. Advance online publication. https://doi.org/10.1016/j.medcli.2020.03.004.

Smith, M. & Smith, J. C. (2020). Repurposing Therapeutics for COVID-19: Supercomputer-Based Docking to the SARS-CoV-2 Viral Spike Protein and Viral Spike Protein-Human ACE2 Interface. ChemRxiv. 1-28. https://doi.org/10.26434/chemrxiv.11871402.v4.

Sohrabi, C, Alsafi, Z, O'Neill, N, et al. (2020). World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19). Int J Surg. 76:71‐76. http://doi.org/10.1016/j.ijsu.2020.02.034.

Tang, Y., Tang, Y., Peng, Y. et al. (2020). Automated abnormality classification of chest radiographs using deep convolutional neural networks. npj Digit. Med. 3, 70. https://doi.org/10.1038/s41746-020-0273-z.

Wang, L., y Wong, A. (2020). COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images, 1-12.

Wang, Y. Zhang, D., Du, G., Zhao, J., Jin, Y., Fu, S. et al. (2020) Remdesivir in adults with severe COVID-19: a randomised, double-blind, placebo-controlled, multicentre trial. The Lancet, 1-10 https://doi.org/10.1016/S0140-6736(20)31022-9.

Yang, C. y Wang, J. (2020) A mathematical model for the novel coronavirus epidemic in Wuhan, China. Mathematical Biosciences and Engineering, 17, 3, 2708-2724. http://doi.org/10.3934/mbe.2020148.

Yang, S., Fu, C., Lian, X., Dong, X., & Zhang, Z. (2019). Understanding Human-Virus Protein-Protein Interactions Using a Human Protein Complex-Based Analysis Framework. mSystems, 4(2), e00303-18. https://doi.org/10.1128/mSystems.00303-18.

Zhou, X., Park, B., Choi, D., & Han, K. (2018). A generalized approach to predicting protein-protein interactions between virus and host. BMC Genomics, 19(S6). http://doi.org/10.1186/s12864-018-4924-2.