ClusteringProposal Supportfor theCOVID-19 Making Decision Process in a Data Demanding Scenario

dc.contributorGiBiome
dc.creatorOrjuela Canon, Alvaro David
dc.creatorPerdomo Charry, Oscar Julian
dc.date.accessioned2021-05-11
dc.date.accessioned2021-10-01T17:16:48Z
dc.date.accessioned2022-09-29T14:33:47Z
dc.date.available2021-05-11
dc.date.available2021-10-01T17:16:48Z
dc.date.available2022-09-29T14:33:47Z
dc.date.created2021-05-11
dc.date.created2021-10-01T17:16:48Z
dc.date.issued2021
dc.identifier1548-0992
dc.identifierhttps://repositorio.escuelaing.edu.co/handle/001/1422
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3775199
dc.description.abstractThe COVID-19 disease surprised the world in the last monthsdue to the number of infections and deaths have been increased in an exponential way.Since the pandemic was established by the World Health Organization, different strategies have been proposedfordealingdiverse problems in cities that the coronavirus affected. This work presents a method to decision making support processes, specificallyin environment with few data and variables to be considered. Thus, artificial neural networks architectures were employed to cluster the informationavailable intheBogota city, and provide a tool that allows generatingadditional findings in a simultaneous mode, andexpressed as a visualmap. The present proposal reachedsensitivity measures around 75%, obtaining100% for thebest cases.
dc.description.abstractLa enfermedad COVID-19 ha sorprendido al mundo en los últimos meses debido a que el número de contagios y muertes se ha incrementado de forma exponencial.Desde que se estableció la pandemia por parte de la Organización Mundial de la Salud, se han propuesto diferentes estrategias para hacer frente a los diversos problemas en las ciudades que el coronavirus afectó. Este trabajo presenta un método de apoyo a los procesos de toma de decisiones, concretamente en entornos con pocos datos y variables a considerar. Así, se emplearon arquitecturas de redes neuronales artificiales para agrupar la información disponible en la ciudad de Bogotá, y proporcionar una herramienta que permite generar hallazgos adicionales de manera simultánea, y expresados como un mapa visual. La presente propuesta alcanzó medidas de sensibilidad en torno al 75%, obteniendo un 100% para los mejores casos.
dc.languageeng
dc.publisherSociedad de Informática IEEE
dc.publisherEstados Unidos
dc.relationVol. 19 No. 6 (2021): Número especial sobre la lucha contra COVID-19
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dc.relation1041
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dc.relationN/A
dc.relationIEEE Latin America Transactio
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Comput., pp. 1–11, 2019.[48]S. Khanmohammadi, N. Adibeig, and S. Shanehbandy, “An improved overlappingk-means clustering method for medical applications,” Expert Syst. Appl., vol. 67, pp. 12–18, 2017.[49]G. Cherry et al., “Loss of smell and taste: a new marker of COVID-19? Tracking reduced sense of smell during the coronavirus pandemic using search trends,” Expert Rev. Anti. Infect. Ther., vol. 18, no. 11, pp. 1165–1170, 2020Alvaro D. Orjuela-Cañón(StM’ 00-M’06–SM’17) nació en Bogotá D.C., Colombia en1981. Recibió su grado de ingeniería electrónicade la Universidad Distrital Francisco José de Caldas in Bogotá D.C., en el año 2006. Realizó su maestría y doctorado en la Universidade Federal do Rio de Janeiro, RJ, Brasil en 2009 y 2015, respectivamente.Actualmente hace parte del programa de ingeniería biomédica de la Escuela de Medicina y Ciencias de la Salud de la Universidad del Rosario en la misma ciudad.Tiene intereses en áreas como el procesamiento digital de señalesbiomédicas, inteligencia computacional en salud, así como energías alternativas. Dr. Orjuela-Cañón es miembro de IEEE en los últimos 18 años.Participando activamente en el capítulo profesional de inteligencia computacional IEEE-CIS.
dc.relationJ. A. Hartigan, Clustering algorithms. 1975.
dc.relationD. Xu and Y. Tian, “A comprehensive survey of clustering algorithms,” Ann. Data Sci., vol. 2, no. 2, pp. 165–193, 2015
dc.relationE. Elveren and N. Yumuvak, “Tuberculosis disease diagnosis using artificial neural network trained with genetic algorithm,” J. Med. Syst., vol. 35, no. 3, pp. 329–332, 2011.
dc.relationP. Venkatesan and M. Mullai, “Clustering of Disease Data base using Self Organizing Maps and Logical Inferences,” Indian J. Autom. Artif. Intell., vol. 1, no. 1, pp. 2–6, 2013.
dc.relationS.-L. Shieh and I.-E. Liao, “A new approach for data clustering and visualization using self-organizing maps,” Expert Syst. Appl., vol. 39, no. 15, pp. 11924–11933, 2012.
dc.relationF. S. Aguiar, R. C. Torres, J. V. F. Pinto, A. L. Kritski, J. M. Seixas, and F. C. Q. Mello, “Development of two artificial neural network models to support the diagnosis of pulmonary tuberculosis in hospitalized patients in Rio de Janeiro, Brazil,” Med. Biol. Eng. Comput., vol. 54, no. 11, pp. 1751–1759, 2016.
dc.relationG. A. Carpenter, S. Grossberg, and D. B. Rosen, “Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system,” Neural networks, vol. 4, no. 6, pp. 759–771, 1991.
dc.relationA. D. Orjuela-Cañón, J. E. C. Mendoza, C. E. A. García, and E. P. V. Vela, “Tuberculosis diagnosis support analysis for precarious health information systems,” Comput. Methods Programs Biomed., 2018.
dc.relationA. D. Orjuela-Cañón and J. de Seixas, “Fuzzy-ART neural networks for triage in pleural tuberculosis,” in Health Care Exchanges (PAHCE), 2013 Pan American, 2013, pp. 1–4
dc.relationA. D. Orjuela-Cañón, J. M. de Seixas, and A. Trajman, “SOM Neural Networks as a Tool in Pleural Tuberculosis Diagnostic,” in Annals of the 11th Brazilian Congress on Computational Intelligence, 2013, pp. 1–5.
dc.relationY. Mohamadou, A. Halidou, and P. T. Kapen, “A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19,” Appl. Intell., pp. 1–13, 2020.
dc.relationA. Kumar, P. K. Gupta, and A. Srivastava, “A review of modern technologies for tackling COVID-19 pandemic,” Diabetes Metab. Syndr. Clin. Res. Rev., vol. 14, no. 4, pp. 569–573, 2020.
dc.relationS. Debnath et al., “Machine learning to assist clinical decision-making during the COVID-19 pandemic,” Bioelectron. Med., vol. 6, no. 1, pp. 1–8, 2020.
dc.relationL. Wynants et al., “Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal,” bmj, vol. 369, 2020.
dc.relationM. Nemati, J. Ansary, and N. Nemati, “Machine Learning Approaches in COVID-19 Survival Analysis and Discharge Time Likelihood Prediction using Clinical Data,” Patterns, p. 100074, 2020
dc.relationR. Chen et al., “Risk factors of fatal outcome in hospitalized subjects with coronavirus disease 2019 from a nationwide analysis in China,” Chest, 2020
dc.relationM. R. Desjardins, A. Hohl, and E. M. Delmelle, “Rapid surveillance of COVID-19 in the United States using a prospective space-time scan statistic: Detecting and evaluating emerging clusters,” Appl. Geogr., p. 102202, 2020.
dc.relationS. E. F. Yong et al., “Connecting clusters of COVID-19: an epidemiological and serological investigation,” Lancet Infect. Dis., 2020
dc.relationM. A. Rahman, “Data-driven dynamic clustering framework for mitigating the adverse economic impact of Covid-19 lockdown practices,” Sustain. Cities Soc., p. 102372, 2020.
dc.relationT. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman and A. Y. Wu, “An efficient k-means clustering algorithm: Analysis and implementation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 881–892, 2002.
dc.relationS. Haykin, Neural Networks and Learning Machines, 3ra ed. Pearson, 2009.
dc.relationC. Budayan, I. Dikmen, and M. T. Birgonul, “Comparing the performance of traditional cluster analysis, self-organizing maps and fuzzy C-means method for strategic grouping,” Expert Syst. Appl., vol. 36, no. 9, pp. 11772–11781, 2009
dc.relationJ. Huang, M. Georgiopoulos, and G. L. Heileman, “Fuzzy ART properties,” Neural Networks, vol. 8, no. 2, pp. 203–213, 1995.
dc.relationT. Kohonen, “Self-organizing maps, ser,” Inf. Sci. Berlin Springer, vol. 30, 2001
dc.relationM. Zribi, Y. Boujelbene, I. Abdelkafi, and R. Feki, “The self-organizing maps of Kohonen in the medical classification,” in Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), 2012 6th International Conference on, 2012, pp. 852–856.
dc.relationD. L. Davies and D. W. Bouldin, “A cluster separation measure,”IEEE Trans. Pattern Anal. Mach. Intell., no. 2, pp. 224–227, 1979.
dc.relationP. J. Rousseeuw, “Silhouettes: a graphical aid to the interpretation and validation of cluster analysis,” J. Comput. Appl. Math., vol. 20, pp. 53–65, 1987.
dc.relationL. Kaufman and P. J. Rousseeuw, Finding groups in data: an introduction to cluster analysis, vol. 344. John Wiley & Sons, 2009.
dc.relationA. Agresti, An introduction to categorical data analysis, vol. 135. Wiley New York, 1996.
dc.relationA. Ahmad and L. Dey, “A k-mean clustering algorithm for mixed numeric and categorical data,” Data Knowl. Eng., vol. 63, no. 2, pp. 503–527, 2007.
dc.relationT.-H. T. Nguyen, D.-T. Dinh, S. Sriboonchitta, and V.-N. Huynh, “A method for k-means-like clustering of categorical data,” J. Ambient Intell. Humaniz. Comput., pp. 1–11, 2019.
dc.relationS. Khanmohammadi, N. Adibeig, and S. Shanehbandy, “An improved overlappingk-means clustering method for medical applications,” Expert Syst. Appl., vol. 67, pp. 12–18, 2017.
dc.relationG. Cherry et al., “Loss of smell and taste: a new marker of COVID-19? Tracking reduced sense of smell during the coronavirus pandemic using search trends,” Expert Rev. Anti. Infect. Ther., vol. 18, no. 11, pp. 1165–1170, 2020
dc.relationMinisterio de Tecnologías de la Información y las Comunicaciones, “Guía para el uso y aprovechamiento de Datos Abiertos en Colombia.” 2016.
dc.relationAlcaldía Mayor de Bogotá, “COVID-19 en Bogotá.” 2020
dc.rightsinfo:eu-repo/semantics/openAccess
dc.sourcehttps://latamt.ieeer9.org/index.php/transactions/article/view/4403
dc.titleClusteringProposal Supportfor theCOVID-19 Making Decision Process in a Data Demanding Scenario
dc.typeArtículo de revista


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