Modelo computacional para evaluación de discapacidad intelectual usando datos de funcionamiento cognitivo

ilustraciones

Autores:: Leiva Ruiz, Nelson Fabian

Tipo de recurso:

Fecha de publicación:: 2021

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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dc.title.spa.fl_str_mv	Modelo computacional para evaluación de discapacidad intelectual usando datos de funcionamiento cognitivo
dc.title.translated.eng.fl_str_mv	Computational model for evaluation of intellectual disability using cognitive functioning data
title	Modelo computacional para evaluación de discapacidad intelectual usando datos de funcionamiento cognitivo
spellingShingle	Modelo computacional para evaluación de discapacidad intelectual usando datos de funcionamiento cognitivo 000 - Ciencias de la computación, información y obras generales Ciencia de datos Discapacidad intelectual Evaluación psicológica Inteligencia KDD Data science Intellectual disability Psychological assessment Intelligence Deficiencia mental Mental deficiency Procesamiento de datos Data processing
title_short	Modelo computacional para evaluación de discapacidad intelectual usando datos de funcionamiento cognitivo
title_full	Modelo computacional para evaluación de discapacidad intelectual usando datos de funcionamiento cognitivo
title_fullStr	Modelo computacional para evaluación de discapacidad intelectual usando datos de funcionamiento cognitivo
title_full_unstemmed	Modelo computacional para evaluación de discapacidad intelectual usando datos de funcionamiento cognitivo
title_sort	Modelo computacional para evaluación de discapacidad intelectual usando datos de funcionamiento cognitivo
dc.creator.fl_str_mv	Leiva Ruiz, Nelson Fabian
dc.contributor.advisor.none.fl_str_mv	Niño Vásquez, Luis Fernando Herrera Rojas, Aura Nidia
dc.contributor.author.none.fl_str_mv	Leiva Ruiz, Nelson Fabian
dc.contributor.researchgroup.spa.fl_str_mv	LABORATORIO DE INVESTIGACIÓN EN SISTEMAS INTELIGENTES - LISI
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales
topic	000 - Ciencias de la computación, información y obras generales Ciencia de datos Discapacidad intelectual Evaluación psicológica Inteligencia KDD Data science Intellectual disability Psychological assessment Intelligence Deficiencia mental Mental deficiency Procesamiento de datos Data processing
dc.subject.proposal.spa.fl_str_mv	Ciencia de datos Discapacidad intelectual Evaluación psicológica Inteligencia
dc.subject.proposal.eng.fl_str_mv	KDD Data science Intellectual disability Psychological assessment Intelligence
dc.subject.unesco.none.fl_str_mv	Deficiencia mental Mental deficiency Procesamiento de datos Data processing
description	ilustraciones
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-07-08T16:37:58Z
dc.date.available.none.fl_str_mv	2021-07-08T16:37:58Z
dc.date.issued.none.fl_str_mv	2021-07
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
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dc.type.content.spa.fl_str_mv	Text
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dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/79779
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/79779 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	[1] X. Ke and J. Liu, Discapacidad intelectual. Ginebra: Asociación Internacional de Psiquiatría del Niño y el Adolescente y Profesiones Afines: IACAPAP, 2017. [2] American Association on Intellectual and Developmental Disabilities (AAIDD), “Definition of Intellectual Disability.” https://aaidd.org/intellectual-disability/definition (accessed May 08, 2019). [3] O. P. de la salud OPS and O. M. de la S. OMS, “CIE-10 clasificación estadística internacional de enfermedades y problemas relacionados con la salud,” 554, vol. 3, no. 554, p. 758, 2008, Accessed: Feb. 01, 2021. [Online]. Available: http://ais.paho.org/classifications/Chapters/pdf/Volume3.pdf. [4] A. Srivastava, “A Vector Measure for the Intelligence of a Question-Answering (Q-A) System,” IEEE Trans. Syst. Man. Cybern., vol. 25, no. 5, pp. 814–823, 1995, doi: 10.1109/21.376494. [5] J. Hernández-Orallo and D. L. Dowe, “Measuring universal intelligence: Towards an anytime intelligence test,” Artif. Intell., vol. 174, pp. 1508–1539, 2010, doi: 10.1016/j.artint.2010.09.006. [6] C. L. Reeve, C. Scherbaum, and H. Goldstein, “Manifestations of intelligence: Expanding the measurement space to reconsider specific cognitive abilities,” Hum. Resour. Manag. Rev., vol. 25, pp. 28–37, 2015, doi: 10.1016/j.hrmr.2014.09.005. [7] W. J. Schneider and D. A. Newman, “Intelligence is multidimensional: Theoretical review and implications of specific cognitive abilities,” 2015, doi: 10.1016/j.hrmr.2014.09.004. [8] J. Hernández-Orallo, D. L. Dowe, and M. Victoria Hernández-Lloreda, “Universal psychometrics: Measuring cognitive abilities in the machine kingdom,” 2014, doi: 10.1016/j.cogsys.2013.06.001. [9] S. R. Vrana and D. T. Vrana, “Can a computer administer a Wechsler intelligence test?,” Prof. Psychol. Res. Pract., vol. 48, no. 3, pp. 191–198, 2017, doi: 10.1037/pro0000128. [10] J. Hernández-Orallo, F. Martínez-Plumed, U. Schmid, M. Siebers, and D. L. Dowe, “Computer models solving intelligence test problems: Progress and implications,” Artif. Intell., vol. 230, pp. 74–107, 2016, doi: 10.1016/j.artint.2015.09.011. [11] D. Wechsler, WISC-IV: escala Wechsler de inteligencia para niños-IV: manual técnico. México: Manual Moderno, 2005. [12] D. Flanagan and A. Kaufman, Claves para la evaluación con WISC-IV, 2nd ed. El Manual Moderno, 2012. [13] J. Stanton, VERSION 3: AN INTRODUCTION TO DATA SCIENCE, Third. iTunes Open Source eBook, 2012. [14] L. Cao, “Data science: A comprehensive overview,” ACM Comput. Surv, vol. 50, no. 43, 2017, doi: 10.1145/3076253. [15] M. Herman et al., The field guide to data science. Booz Allen Hamilton, 2013. [16] R. A. Pazminño-Maji, F. J. García-Peñalvo, and M. A. Conde-González, Statistical implicative analysis approximation to KDD and Data Mining: A systematic and mapping review in Knowledge Discovery Database framework, no. c. 2017. [17] U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “From Data Mining to Knowledge Discovery in Databases) (© AAAI),” Mar. 1996. doi: 10.1609/AIMAG.V17I3.1230. [18] “1.13. Feature selection — scikit-learn 0.24.1 documentation.” https://scikit-learn.org/stable/modules/feature_selection.html#rfe (accessed Feb. 24, 2021). [19] P. M. Granitto, C. Furlanello, F. Biasioli, and F. Gasperi, “Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products,” Chemom. Intell. Lab. Syst., vol. 83, no. 2, pp. 83–90, Sep. 2006, doi: 10.1016/j.chemolab.2006.01.007. [20] S. R. Das, Data Science: Theories, Models, Algorithms and Analytics, a web book. Das, Sanjiv Ranjan, 2013. [21] S. Kumar and H. Sharma, “A Survey on Decision Tree Algorithms of Classification in Data Mining,” 2016. Accessed: Jan. 27, 2021. [Online]. Available: www.ijsr.net. [22] S. Tangirala, “Evaluating the Impact of GINI Index and Information Gain on Classification using Decision Tree Classifier Algorithm,” Artic. Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 2, 2020, doi: 10.14569/IJACSA.2020.0110277. [23] “1.10. Decision Trees — scikit-learn 0.24.1 documentation.” https://scikit-learn.org/stable/modules/tree.html (accessed Jan. 27, 2021). [24] K. Fawagreh, M. Medhat Gaber, E. Elyan, and M. M. Gaber, “Random forests: from early developments to recent advancements,” Syst. Sci. Control Eng. An Open Access J., vol. 2, no. 1, pp. 602–609, 2014, doi: 10.1080/21642583.2014.956265. [25] M. Kohl, “Performance Measures in Binary Classification,” Int. J. Stat. Med. Res., vol. 1, no. 1, pp. 79–81, 2012, doi: 10.1016/j.ipm.2009.03.002. [26] A. M. McIntosh et al., “Data science for mental health: a UK perspective on a global challenge,” The Lancet Psychiatry, vol. 3, no. 10, pp. 993–998, 2016, doi: 10.1016/S2215-0366(16)30089-X. [27] R. Stewart and K. Davis, “‘Big data’ in mental health research: current status and emerging possibilities,” Soc. Psychiatry Psychiatr. Epidemiol., vol. 51, no. 8, pp. 1055–1072, 2016, doi: 10.1007/s00127-016-1266-8. [28] D. Hidalgo-Mazzei, A. Murru, M. Reinares, E. Vieta, and F. Colom, “Big Data in mental health: A challenging fragmented future,” World Psychiatry, vol. 15, no. 2, pp. 186–187, 2016, doi: 10.1002/wps.20307. [29] M. Kosinski and T. Behrend, “Editorial overview: Big data in the behavioral sciences,” COBEHA, vol. 18, pp. iv–vi, 2017, doi: 10.1016/j.cobeha.2017.11.007. [30] A. Markowetz, K. Błaszkiewicz, C. Montag, C. Switala, and T. E. Schlaepfer, “Psycho-Informatics: Big Data shaping modern psychometrics,” Med. Hypotheses, vol. 82, pp. 405–411, 2014, doi: 10.1016/j.mehy.2013.11.030. [31] D. D. Luxton, “An Introduction to Artificial Intelligence in Behavioral and Mental Health Care,” in Artificial Intelligence in Behavioral and Mental Health Care, Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA, United States, 2015, pp. 1–26. [32] C. C. Bennett and T. W. Doub, “Expert Systems in Mental Health Care: AI Applications in Decision-Making and Consultation,” in Artificial Intelligence in Behavioral and Mental Health Care, School of Informatics and Computing, Indiana University, Bloomington, IN, United States, 2015, pp. 27–51. [33] D. Becker, W. van Breda, B. Funk, M. Hoogendoorn, J. Ruwaard, and H. Riper, “Predictive modeling in e-mental health: A common language framework,” Internet Interv., vol. 12, pp. 57–67, 2018, doi: 10.1016/j.invent.2018.03.002. [34] S. G. Alonso et al., “Data Mining Algorithms and Techniques in Mental Health: A Systematic Review,” J. Med. Syst., vol. 42, no. 9, 2018, doi: 10.1007/s10916-018-1018-2. [35] A. G. Di Nuovo, V. Catania, S. Di Nuovo, and S. Buono, “Psychology with soft computing: An integrated approach and its applications,” Appl. Soft Comput. J., vol. 8, no. 1, pp. 829–837, 2007, doi: 10.1016/j.asoc.2007.03.001. [36] A. Di Nuovo, S. Di Nuovo, S. Buono, and V. Cutello, “Benefits of fuzzy logic in the assessment of intellectual disability,” in IEEE International Conference on Fuzzy Systems, 2014, pp. 1843–1850, doi: 10.1109/FUZZ-IEEE.2014.6891834. [37] S. Nor, W. Shamsuddin, N. Siti, F. Nik, W. Malini, and W. Isa, “Classification Techniques for Early Detection of Dyslexia Using Computer-Based Screening Test,” World Appl. Sci. J., vol. 35, no. 10, pp. 2108–2112, 2017, doi: 10.5829/idosi.wasj.2017.2108.2112. [38] H. M. Al-Barhamtoshy and D. M. Eldeen Motaweh, “Diagnosis of Dyslexia using Computing Analysis,” 2017. Accessed: May 10, 2019. [Online]. Available: http://www.joetsite.com/wp-content/uploads/2017/07/Vol.-62-37-2017.pdf. [39] H. Selvi and M. S. Saravanan, “A Study of dyslexia using different machine learning algorithm with data mining techniques,” International Journal of Engineering and Technology(UAE), vol. 7, no. 4, Research and Development Centre Science, Bharathiar University, India, pp. 3406–3411, 2018. [40] B.-M. Chen, X.-P. Fan, Z.-M. Zhou, and X.-R. Li, “Application of computer system based on artificial neural network and artificial intelligence in diagnosing child mental health disorders,” J. Clin. Rehabil. Tissue Eng. Res., vol. 15, no. 13, pp. 2467–2470, 2011, doi: 10.3969/j.issn.1673-8225.2011.13.044. [41] P. Dhaka and R. Johari, “Big data application: Study and archival of mental health data, using MongoDB,” in International Conference on Electrical, Electronics, and Optimization Techniques, ICEEOT 2016, 2016, pp. 3228–3232, doi: 10.1109/ICEEOT.2016.7755300. [42] D. A. Rosenthal, J. A. Dalton, and R. Gervey, “Analyzing vocational outcomes of individuals with psychiatric disabilities who received state vocational rehabilitation services: A data mining approach,” Int. J. Soc. Psychiatry, vol. 53, no. 4, pp. 357–368, 2007, doi: 10.1177/0020764006074555. [43] C. Yuan, “Data mining techniques with its application to the dataset of mental health of college students,” in Proceedings - 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications, WARTIA 2014, 2014, pp. 391–393, doi: 10.1109/WARTIA.2014.6976277. [44] D. Cheng, T. Li, and L. Niu, “A Study on the Application of the Decision Tree Algorithm in Psychological Information of Vocational College Students,” in MATEC Web of Conferences, 2015, vol. 22, doi: 10.1051/matecconf/20152201044. [45] J. QingHua, “Data mining and management system design and application for college student mental health,” in Proceedings - 2016 International Conference on Intelligent Transportation, Big Data and Smart City, ICITBS 2016, 2017, pp. 410–413, doi: 10.1109/ICITBS.2016.96. [46] S. V Tyulyupo, A. A. Andrakhanov, B. A. Dashieva, and A. V Tyryshkin, “Adolescents psychological well-being estimation based on a data mining algorithm,” in 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2018 - Proceedings, 2018, vol. 1, pp. 475–478, doi: 10.1109/STC-CSIT.2018.8526628. [47] A. Shrestha, S. Bergquist, E. Montz, and S. Rose, “Mental Health Risk Adjustment with Clinical Categories and Machine Learning,” Health Serv. Res., vol. 53, pp. 3189–3206, 2018, doi: 10.1111/1475-6773.12818. [48] M. Srividya, S. Mohanavalli, and N. Bhalaji, “Behavioral Modeling for Mental Health using Machine Learning Algorithms,” J. Med. Syst., vol. 42, no. 5, 2018, doi: 10.1007/s10916-018-0934-5. [49] S. Ohlsson, R. H. Sloan, G. Turán, and A. Urasky, “Measuring an artificial intelligence system’s performance on a Verbal IQ test for young children,” J. Exp. Theor. Artif. Intell., vol. 29, no. 4, pp. 679–693, 2017, doi: 10.1080/0952813X.2016.1213060. [50] S. Ohlsson, R. H. Sloan, G. Turán, D. Uber, and A. Urasky, “An approach to evaluate AI commonsense reasoning systems,” in Proceedings of the 25th International Florida Artificial Intelligence Research Society Conference, FLAIRS-25, 2012, pp. 371–374, [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84864991190&partnerID=40&md5=1f74a6af3d3d81c4a3bb261335f63ef2. [51] F. Martínez-Plumed, C. Ferri, J. Hernández-Orallo, and M. J. Ramírez-Quintana, “A computational analysis of general intelligence tests for evaluating cognitive development,” Cogn. Syst. Res., vol. 43, pp. 100–118, 2017, doi: 10.1016/j.cogsys.2017.01.006. [52] D. G. T. Barrett, F. Hill, A. Santoro, A. S. Morcos, and T. Lillicrap, “Measuring abstract reasoning in neural networks,” in 35th International Conference on Machine Learning, ICML 2018, 2018, vol. 10, pp. 7118–7127, [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057325020&partnerID=40&md5=e585112ce962e18dc3b0143e9dc7f36e.
dc.rights.spa.fl_str_mv	Derechos reservados al autor, 2021
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dc.rights.license.spa.fl_str_mv	Atribución-NoComercial-SinDerivadas 4.0 Internacional
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dc.rights.accessrights.spa.fl_str_mv	info:eu-repo/semantics/openAccess
rights_invalid_str_mv	Atribución-NoComercial-SinDerivadas 4.0 Internacional Derechos reservados al autor, 2021 http://creativecommons.org/licenses/by-nc-nd/4.0/ http://purl.org/coar/access_right/c_abf2
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dc.format.extent.spa.fl_str_mv	90 páginas
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y Computación
dc.publisher.department.spa.fl_str_mv	Departamento de Ingeniería de Sistemas e Industrial
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ingeniería
dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
institution	Universidad Nacional de Colombia
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spelling	Atribución-NoComercial-SinDerivadas 4.0 InternacionalDerechos reservados al autor, 2021http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Niño Vásquez, Luis Fernandobc784b82735e16fe53653c3f5c8f3bbeHerrera Rojas, Aura Nidia48aeef9036c36ebd2fbc4c038c57b531Leiva Ruiz, Nelson Fabianf53113a6a5c72fff2446c98f1242d37fLABORATORIO DE INVESTIGACIÓN EN SISTEMAS INTELIGENTES - LISI2021-07-08T16:37:58Z2021-07-08T16:37:58Z2021-07https://repositorio.unal.edu.co/handle/unal/79779Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustracionesA lo largo de esta investigación, se llevó a cabo un proceso de descubrimiento de conocimiento (KDD, por sus siglas en inglés), en el marco de ciencia de datos, aplicado a datos de evaluación de capacidad cognitiva aportados por el Servicio de Atención Psicológica (SAP) de la Universidad Nacional de Colombia. Se realizó el preprocesamiento y tratamiento de datos en concordancia con el objetivo de la investigación: desarrollar un modelo computacional que permita determinar las agrupaciones de variables, asociadas al Coeficiente intelectual, que predicen el diagnóstico de discapacidad intelectual. Se obtuvo un total de 18 variables cognitivas más informativas, con las cuales se implementaron Árbol de decisión y Regresión logística como modelos predictivos e interpretables. (Texto tomado de la fuente)Throughout this research, a knowledge discovery process (KDD) was carried out, within the framework of data science, applied to cognitive capacity assessment data, provided by the Psychological Attention Service (SAP) of the National University of Colombia. Data pre-processing and treatment was carried out in accordance with the objective of the research: to develop a computational model that allows determining the groupings of variables, associated with the IQ, that predict the diagnosis of intellectual disability. A total of 18 more informative cognitive variables were obtained, with which Decision Tree and Logistic Regression were implemented as predictive and interpretable models. (Text taken from source)MaestríaMagíster en Ingeniería - Ingeniería de Sistemas y ComputaciónCon el fin de llevar esta investigación a buen término, se implementó una metodología estructurada en cuatro etapas, las cuales dieron lugar a la consecución de los objetivos propuestos. Como el objetivo principal del proyecto fue desarrollar un modelo computacional analítico, se usaron métodos y herramientas en el marco de Ciencia de datos, vinculando procesos de KDD y técnicas de aprendizaje automático, los cuales no solo ayudaron a generar un modelo predictivo e interpretable, sino que dio lugar a nuevo conocimiento que ayudará al diagnóstico diferencial de discapacidad intelectual. A continuación, se describen las etapas seguidas en el desarrollo de la investigación: ▪ Etapa 1: Selección y preprocesamiento de datos En esta etapa se realizó la recopilación y preparación de los datos, los cuales fueron gestionados por el Servicio de Atención Psicológica (SAP) de la Universidad Nacional, y contienen tanto información socioeconómica de los evaluados, como de las puntuaciones obtenidas en la prueba de evaluación de funcionamiento WISC-IV. La preparación de los datos incluyó tratamiento de datos perdidos e imputación de valores, dados los errores de digitación con los que se encontraba la base inicialmente, así como eliminación de variables con alto porcentaje de datos perdidos. ▪ Etapa 2: Análisis Exploratorio de datos Una vez el conjunto de datos fue limpiado, se efectuó una exploración general del conjunto de datos usando estadística descriptiva, con el fin de describir la información socioeconómica que comprende el conjunto de datos y hallar patrones en la población. ▪ Etapa 3: Transformación y modelamiento de datos En esta etapa se filtró y dicotomizó la variable de diagnóstico final, de modo que solo tuviera en cuenta si los evaluados eran positivos o negativos para discapacidad intelectual. También se efectuó balanceo de clases, dada la diferencia de proporción entre las dos etiquetas del diagnóstico. Posteriormente se implementaron los métodos de selección de características y de aprendizaje automático necesarios. ▪ Etapa 4: Evaluación del modelo computacional En esta etapa se evaluó la calidad del modelo en términos de las métricas de rendimiento, y se analizan los resultados a la luz de los conocimientos previos sobre inteligencia y discapacidad intelectual.Sistemas Inteligentes90 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y ComputaciónDepartamento de Ingeniería de Sistemas e IndustrialFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generalesCiencia de datosDiscapacidad intelectualEvaluación psicológicaInteligenciaKDDData scienceIntellectual disabilityPsychological assessmentIntelligenceDeficiencia mentalMental deficiencyProcesamiento de datosData processingModelo computacional para evaluación de discapacidad intelectual usando datos de funcionamiento cognitivoComputational model for evaluation of intellectual disability using cognitive functioning dataTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMGeneral[1] X. Ke and J. Liu, Discapacidad intelectual. Ginebra: Asociación Internacional de Psiquiatría del Niño y el Adolescente y Profesiones Afines: IACAPAP, 2017.[2] American Association on Intellectual and Developmental Disabilities (AAIDD), “Definition of Intellectual Disability.” https://aaidd.org/intellectual-disability/definition (accessed May 08, 2019).[3] O. P. de la salud OPS and O. M. de la S. OMS, “CIE-10 clasificación estadística internacional de enfermedades y problemas relacionados con la salud,” 554, vol. 3, no. 554, p. 758, 2008, Accessed: Feb. 01, 2021. [Online]. Available: http://ais.paho.org/classifications/Chapters/pdf/Volume3.pdf.[4] A. Srivastava, “A Vector Measure for the Intelligence of a Question-Answering (Q-A) System,” IEEE Trans. Syst. Man. Cybern., vol. 25, no. 5, pp. 814–823, 1995, doi: 10.1109/21.376494.[5] J. Hernández-Orallo and D. L. Dowe, “Measuring universal intelligence: Towards an anytime intelligence test,” Artif. Intell., vol. 174, pp. 1508–1539, 2010, doi: 10.1016/j.artint.2010.09.006.[6] C. L. Reeve, C. Scherbaum, and H. Goldstein, “Manifestations of intelligence: Expanding the measurement space to reconsider specific cognitive abilities,” Hum. Resour. Manag. Rev., vol. 25, pp. 28–37, 2015, doi: 10.1016/j.hrmr.2014.09.005.[7] W. J. Schneider and D. A. Newman, “Intelligence is multidimensional: Theoretical review and implications of specific cognitive abilities,” 2015, doi: 10.1016/j.hrmr.2014.09.004.[8] J. Hernández-Orallo, D. L. Dowe, and M. Victoria Hernández-Lloreda, “Universal psychometrics: Measuring cognitive abilities in the machine kingdom,” 2014, doi: 10.1016/j.cogsys.2013.06.001.[9] S. R. Vrana and D. T. Vrana, “Can a computer administer a Wechsler intelligence test?,” Prof. Psychol. Res. Pract., vol. 48, no. 3, pp. 191–198, 2017, doi: 10.1037/pro0000128.[10] J. Hernández-Orallo, F. Martínez-Plumed, U. Schmid, M. Siebers, and D. L. 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