Identification of patterns of fatal injuries in humans through big data
External 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 proble...
- Autores:
-
Silva, Jesus
Zilberman, Jack
Romero Marin, Ligia Cielo
Pineda, Omar
Herazo-Beltran, Yaneth
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7798
- Acceso en línea:
- https://hdl.handle.net/11323/7798
https://doi.org/10.1016/j.procs.2020.03.114
https://repositorio.cuc.edu.co/
- Palabra clave:
- Recognition of automated standards
mining
decision trees
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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| dc.title.spa.fl_str_mv |
Identification of patterns of fatal injuries in humans through big data |
| title |
Identification of patterns of fatal injuries in humans through big data |
| spellingShingle |
Identification of patterns of fatal injuries in humans through big data Recognition of automated standards mining decision trees |
| title_short |
Identification of patterns of fatal injuries in humans through big data |
| title_full |
Identification of patterns of fatal injuries in humans through big data |
| title_fullStr |
Identification of patterns of fatal injuries in humans through big data |
| title_full_unstemmed |
Identification of patterns of fatal injuries in humans through big data |
| title_sort |
Identification of patterns of fatal injuries in humans through big data |
| dc.creator.fl_str_mv |
Silva, Jesus Zilberman, Jack Romero Marin, Ligia Cielo Pineda, Omar Herazo-Beltran, Yaneth |
| dc.contributor.author.spa.fl_str_mv |
Silva, Jesus Zilberman, Jack Romero Marin, Ligia Cielo Pineda, Omar Herazo-Beltran, Yaneth |
| dc.subject.spa.fl_str_mv |
Recognition of automated standards mining decision trees |
| topic |
Recognition of automated standards mining decision trees |
| description |
External 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. |
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2020 |
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2020 |
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2021-01-29T14:17:34Z |
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2021-01-29T14:17:34Z |
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https://doi.org/10.1016/j.procs.2020.03.114 |
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eng |
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1 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. 165-276 2 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]. 3 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. 4 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. 5 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 8 6 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. 7 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 2008 8 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. 9 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]. 10 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. 1100-1107 11 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. 994-1007 12 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. 2190-2201 13 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. 87-94 14 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. 67-79 15 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. pdf 16 Villena J. CRISP-DM: La metodología para poner orden en los proyectos de Data Science. [Internet]. 2016. 17 Waikato. Weka 3: Data Mining Software in Java [Internet]. Nueva Zelanda: Machine Learning Group at the University of Waikato. 19 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. 182-225 20 World Health Organization The Global Burden of Disease 2004 update, WHO, Geneve (2008), p. 160 21 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. 1201-1206 22 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. 23 Viloria Amelec, et al. Integration of Data Mining Techniques to PostgreSQL Database Manager System Procedia Computer Science, 155 (2019), pp. 575-580 |
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Silva, Jesus659ae35f3326439474c6cca46ee77cb0Zilberman, Jack2569ad1a8bfaefc41b9464940807cada300Romero Marin, Ligia Cielo4300794b3051ef176ce3fed4897c5694Pineda, Omaraf4b322b3d3157067b1e466da357fb98Herazo-Beltran, Yanetha52f4c74b401da96e149b911e5fbf1143002021-01-29T14:17:34Z2021-01-29T14:17:34Z2020https://hdl.handle.net/11323/7798https://doi.org/10.1016/j.procs.2020.03.114Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/External 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.application/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Procedia Computer Sciencehttps://www.sciencedirect.com/science/article/pii/S1877050920305524#!Recognition of automated standardsminingdecision treesIdentification of patterns of fatal injuries in humans through big dataArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1 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. 165-2762 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].3 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.4 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.5 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 86 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.7 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 20088 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.9 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].10 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. 1100-110711 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. 994-100712 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. 2190-220113 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. 87-9414 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. 67-7915 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. pdf16 Villena J. CRISP-DM: La metodología para poner orden en los proyectos de Data Science. [Internet]. 2016.17 Waikato. Weka 3: Data Mining Software in Java [Internet]. Nueva Zelanda: Machine Learning Group at the University of Waikato.19 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. 182-22520 World Health Organization The Global Burden of Disease 2004 update, WHO, Geneve (2008), p. 16021 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. 1201-120622 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.23 Viloria Amelec, et al. 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