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Identification of patterns of fatal injuries in humans through big data

dc.contributor.authorSilva, Jesusspa
dc.contributor.authorZilberman, Jackspa
dc.contributor.authorRomero Marin, Ligia Cielospa
dc.contributor.authorPineda, Omarspa
dc.contributor.authorHerazo-Beltran, Yanethspa
dc.date.accessioned2021-01-29T14:17:34Z
dc.date.available2021-01-29T14:17:34Z
dc.date.issued2020
dc.identifier.urihttps://hdl.handle.net/11323/7798spa
dc.description.abstractExternal cause injuries are defined as intentionally or unintentionally harm or injury to a person, which may be caused by trauma, poisoning, assault, accidents, etc., being fatal (fatal injury) or not leading to death (non-fatal injury). External injuries have been considered a global health problem for two decades. This work aims to determine criminal patterns using data mining techniques to a sample of patients from Mumbai city in India.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoeng
dc.publisherCorporación Universidad de la Costaspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/spa
dc.sourceProcedia Computer Sciencespa
dc.subjectRecognition of automated standardsspa
dc.subjectminingspa
dc.subjectdecision treesspa
dc.titleIdentification of patterns of fatal injuries in humans through big dataspa
dc.typeArtículo de revistaspa
dc.source.urlhttps://www.sciencedirect.com/science/article/pii/S1877050920305524#!spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doihttps://doi.org/10.1016/j.procs.2020.03.114spa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
dc.relation.references1 Chen H, Chung W, Qin Y, Chau M, Xu JJ, Wang G, et al. Crime Data Mining: An Overview and Case Studies Commun ACM, 2 (2002), pp. 165-276spa
dc.relation.references2 Daylight Chemical Information Systems, «4. SMARTS - A Language for Describing Molecular Patterns, » 2008. [En línea]. Available: http://www.daylight.com/dayhtml/doc/theory/theory.smarts.html. [Último acceso: 26 Julio 2018].spa
dc.relation.references3 Bucci, N., Luna, M., Viloria, A., García, J.H., Parody, A., Varela, N., & López, L.A.B. (2018, June). Factor analysis of the psychosocial risk assessment instrument. In International Conference on Data Mining and Big Data (pp. 149-158). Springer, Cham.spa
dc.relation.references4 Gamero, W.M., Ramírez, M.C., Parody, A., Viloria, A., López, M.H.A., & Kamatkar, S.J. (2018, June). Concentrations and size distributions of fungal bioaerosols in a municipal landfill. In International Conference on Data Mining and Big Data (pp. 244-253). Springer, Cham.spa
dc.relation.references5 Valenga F, Fernández E, Merlino H, Procopio C, Britos P, Garcia-Martinez R Minería de Datos Aplicada a la Detección de Patrones Delictivos en Argentina En: VI Jornadas Iberoamericanas de Ingeniería de Software e Ingeniería del Conocimiento; Guayaquil, Escuela Superior Politécnica del Litoral Facultad de Ingeniería Eléctrica y Computación Área de Ingeniería en Software VLIR -ESPOL Componente, Guayaquil (2008), p. 427 8spa
dc.relation.references6 Instituto CISALVA. Sistematización de Experiencias sobre Sistemas de Vigilancia, Observatorios o Sistemas de Información de Violencia en América Latina. Cali,: Centro Editorial CATORSE SCS; 2009. 62 p.spa
dc.relation.references7 M. Linderman, J. Sorenson, L. Lee, G. Nolan Computational solutions to large-scale data management and analysis Nature Reviews Genetics, 11 (2010), pp. 647-657 2008spa
dc.relation.references8 Viloria, A., Bucci, N., Luna, M., Lis-Gutiérrez, J.P., Parody, A., Bent, D.E.S., & López, L.A.B. (2018, June). Determination of dimensionality of the psychosocial risk assessment of internal, individual, double presence and external factors in work environments. In International Conference on Data Mining and Big Data (pp. 304-313). Springer, Cham.spa
dc.relation.references9 X. Su, «Introduction to Big Data,» 29 Agosto 2017. [En línea]. Available: https://www.ntnu.no/iie/fag/big/lessons/lesson2.pdf. [Último acceso: 16 enero 2018].spa
dc.relation.references10 A. Gaulton, L. Bellis, P. Bento, J. Chambers, M. Davies, A. Hersey, Y. Light, S. McGlinchey, D. Michalovich, B. Al-Lazikani, J. Overington ChEMBL: a large-scale bioactivity database for drug discovery Nucleic Acids Research, 40 (1) (2012), pp. 1100-1107spa
dc.relation.references11 M. Cruz Monteagudo, E. Tejera, Y. Pérez, J. Medina Fronco, A. Sánchez Rodríguez, F. Borges Systemic QSAR and phenotypic virtual screening: chasing butterflies in drug discovery Drug Discovery Today, 22 (7) (2017), pp. 994-1007spa
dc.relation.references12 N. Wale, G. Karypis Target Fishing for Chemical Compounds Using Target-Ligand Activity Data and Ranking Based Methods Journal of Chemical Information and Modeling, 49 (10) (2009), pp. 2190-2201spa
dc.relation.references13 Timaran R, Baron A, Hernàndez G, Arsenio H, Betancourth C SIGEODEP: Un primer paso para la Detección de Patrones Delictivos con Técnicas de Minería de Datos Pow-Sang JA, Melgar A (Eds.), IX Jornadas Iberoamericanas de Ingeniería de Software e Ingeniería del Conocimiento, Pontificia Universidad Católica del Perú, Lima, Perú (2012), pp. 87-94spa
dc.relation.references14 Timaran R, Calderón A, Hidalgo A, Baron A, Hernández G Construcción de un mercado de datos para el almacenamiento de lesiones de causa externa Vent Inform., 30 (2014), pp. 67-79spa
dc.relation.references15 Gallardo J. Metodología para el Desarrollo de Proyectos en Minería de Datos CRISP-DM. [Internet]. 2009. Disponible en: http://www.oldemarrodriguez.com/yahoo_site_admin/assets/docs/Documento_CRISP-DM.2385037. pdfspa
dc.relation.references16 Villena J. CRISP-DM: La metodología para poner orden en los proyectos de Data Science. [Internet]. 2016.spa
dc.relation.references17 Waikato. Weka 3: Data Mining Software in Java [Internet]. Nueva Zelanda: Machine Learning Group at the University of Waikato.spa
dc.relation.references19 Ministerio de Salud de la Nación Lesiones por causa externa Informe de resultados Segunda Encuesta Nacional de Factores de Riesgo, Ministerio de Salud, Buenos Aires (2009), pp. 182-225spa
dc.relation.references20 World Health Organization The Global Burden of Disease 2004 update, WHO, Geneve (2008), p. 160spa
dc.relation.references21 Viloria Amelec, Lezama Omar Bonerge Pineda Improvements for Determining the Number of Clusters in k-Means for Innovation Databases in SMEs Procedia Computer Science, 151 (2019), pp. 1201-1206spa
dc.relation.references22 Kamatkar, S.J., Kamble, A., Viloria, A., Hernández-Fernández, L., & Cali, E.G. (2018, June). Database performance tuning and query optimization. In International Conference on Data Mining and Big Data (pp. 3-11). Springer, Cham.spa
dc.relation.references23 Viloria Amelec, et al. Integration of Data Mining Techniques to PostgreSQL Database Manager System Procedia Computer Science, 155 (2019), pp. 575-580spa
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