KENITO,el bot conversacional para la evaluación del manejo del dolor oncológico pediátrico - Fase 2

Un Bot conversacional corresponde a una aplicación de software que cuenta con capacidades de Procesamiento de Lenguaje Natural - PLN e Inteligencia Artificial – IA para entablar conversaciones habladas con seres humanos. A diferencia de los tradicionales bots textuales, en los cuales la interacción...

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Autores:
Niño Gómez, Juan Daniel
Tipo de recurso:
https://purl.org/coar/resource_type/c_7a1f
Fecha de publicación:
2024
Institución:
Universidad El Bosque
Repositorio:
Repositorio U. El Bosque
Idioma:
spa
OAI Identifier:
oai:repositorio.unbosque.edu.co:20.500.12495/13700
Acceso en línea:
https://hdl.handle.net/20.500.12495/13700
Palabra clave:
Modelo extenso de lenguaje
Bot conversacional
Dolor oncológico pediátrico
Transformadores
Inteligencia artificial
621.3
Large Language Model
Conversational Bot
Pediatric Oncologic Pain
Transformers
Artificial Intelligence
Rights
License
Atribución-NoComercial-CompartirIgual 4.0 Internacional
id UNBOSQUE2_347e319e6615863e9128183881ed1428
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network_acronym_str UNBOSQUE2
network_name_str Repositorio U. El Bosque
repository_id_str
dc.title.none.fl_str_mv KENITO,el bot conversacional para la evaluación del manejo del dolor oncológico pediátrico - Fase 2
dc.title.translated.none.fl_str_mv KENITO, the conversational bot for the evaluation of pediatric oncologic pain management - Phase 2
title KENITO,el bot conversacional para la evaluación del manejo del dolor oncológico pediátrico - Fase 2
spellingShingle KENITO,el bot conversacional para la evaluación del manejo del dolor oncológico pediátrico - Fase 2
Modelo extenso de lenguaje
Bot conversacional
Dolor oncológico pediátrico
Transformadores
Inteligencia artificial
621.3
Large Language Model
Conversational Bot
Pediatric Oncologic Pain
Transformers
Artificial Intelligence
title_short KENITO,el bot conversacional para la evaluación del manejo del dolor oncológico pediátrico - Fase 2
title_full KENITO,el bot conversacional para la evaluación del manejo del dolor oncológico pediátrico - Fase 2
title_fullStr KENITO,el bot conversacional para la evaluación del manejo del dolor oncológico pediátrico - Fase 2
title_full_unstemmed KENITO,el bot conversacional para la evaluación del manejo del dolor oncológico pediátrico - Fase 2
title_sort KENITO,el bot conversacional para la evaluación del manejo del dolor oncológico pediátrico - Fase 2
dc.creator.fl_str_mv Niño Gómez, Juan Daniel
dc.contributor.advisor.none.fl_str_mv Romero Alvarez, Fran Ernesto
dc.contributor.author.none.fl_str_mv Niño Gómez, Juan Daniel
dc.contributor.orcid.none.fl_str_mv Niño Gómez, Juan Daniel [0009-0004-7960-7645]
dc.subject.none.fl_str_mv Modelo extenso de lenguaje
Bot conversacional
Dolor oncológico pediátrico
Transformadores
Inteligencia artificial
topic Modelo extenso de lenguaje
Bot conversacional
Dolor oncológico pediátrico
Transformadores
Inteligencia artificial
621.3
Large Language Model
Conversational Bot
Pediatric Oncologic Pain
Transformers
Artificial Intelligence
dc.subject.ddc.none.fl_str_mv 621.3
dc.subject.keywords.none.fl_str_mv Large Language Model
Conversational Bot
Pediatric Oncologic Pain
Transformers
Artificial Intelligence
description Un Bot conversacional corresponde a una aplicación de software que cuenta con capacidades de Procesamiento de Lenguaje Natural - PLN e Inteligencia Artificial – IA para entablar conversaciones habladas con seres humanos. A diferencia de los tradicionales bots textuales, en los cuales la interacción se lleva a cabo mediante mensajes de texto, un Bot conversacional esta´ en capacidad de entender la voz humana y responder igualmente en lenguaje hablado. El Bot puede ser desplegado de diversas formas, desde sistemas de audio-respuesta sin ningún tipo de interfaz gráfica, hasta sofisticadas aplicaciones móviles que presentan al Bot como un personaje virtual animado con una personalidad y características bien definidas.
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2024-12-13T14:15:32Z
dc.date.available.none.fl_str_mv 2024-12-13T14:15:32Z
dc.date.issued.none.fl_str_mv 2024-05
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_7a1f
dc.type.local.spa.fl_str_mv Tesis/Trabajo de grado - Monografía - Pregrado
dc.type.coar.none.fl_str_mv https://purl.org/coar/resource_type/c_7a1f
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/bachelorThesis
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format https://purl.org/coar/resource_type/c_7a1f
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12495/13700
dc.identifier.instname.spa.fl_str_mv instname:Universidad El Bosque
dc.identifier.reponame.spa.fl_str_mv reponame:Repositorio Institucional Universidad El Bosque
dc.identifier.repourl.none.fl_str_mv repourl:https://repositorio.unbosque.edu.co
url https://hdl.handle.net/20.500.12495/13700
identifier_str_mv instname:Universidad El Bosque
reponame:Repositorio Institucional Universidad El Bosque
repourl:https://repositorio.unbosque.edu.co
dc.language.iso.fl_str_mv spa
language spa
dc.relation.references.none.fl_str_mv [1] S. A. Hadri y A. Bouramoul, “Towards a deep learning based contextual chat bot for preventing depression in young children with autistic spectrum disorder”, Smart Health, p. 100371, diciembre de 2022. Accedido el 4 de febrero de 2024. [En l ́ınea]. Disponible: https://doi.org/10.1016/j.smhl.2022.100371
[2] R. L. Weisenburger et al., “Conversational assessment using artificial intelligence is as clinically useful as depression scales and preferred by users”, J. Affect.Disorders, enero de 2024. Accedido el 6 de febrero de 2024. [En l ́ınea]. Disponible:https://doi.org/10.1016/j.jad.2024.01.212
[3]S. Thakur, D. Rastogi, y L. Singh, “MOODY: A Natural Language Processing-Based Chatbot for Mental Health Care”, en Lecture Notes in Electrical Engineering,Virtual, Online, 2022, pp. 899–908. [En l ́ınea]. Disponible en: http://dx.doi.org/10.1007/978-981-19-4364-5-64
[4]L. Marciano y S. Saboor, “Reinventing mental health care in youth through mobile approaches: Current status and future steps”, Front Psychol, vol. 14, 2023, doi: 10.3389/fpsyg.2023.1126015.
[5]P. Parmar, J. Ryu, S. Pandya, J. Sedoc, y S. Agarwal, “Health-focused conversational agents in person-centered care: a review of apps”, NPJ Digit Med, vol. 5,num. 1, 2022, doi: 10.1038/s41746-022-00560-6. ́
[6]H. Jahanshahi, S. Kazmi, y M. Cevik, “Auto Response Generation in Online Medical Chat Services”, J Healthc Inform Res, vol. 6, num. 3, pp. 344 – 374, 2022, ́doi: 10.1007/s41666-022-00118-x.
[7]B. M. Chaudhry y A. Islam, “Design Validation of a Workplace Stress Management Mobile App for Healthcare Workers During COVID-19 and Beyond”, en Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Virtual, Online, 2022, pp. 306–321. [En l ́ınea]. Disponible en: http://dx.doi.org/10.1007/978-3-030-94822-1-17
[8]M.-T. Ho, N.-T. B. Le, P. Mantello, M.-T. Ho, y N. Ghotbi, “Understanding the acceptance of emotional artificial intelligence in the Japanese healthcare system: Across-sectional survey of clinic visitors’ attitude”, Technol Soc, vol. 72, 2023, [En línea]. Disponible en: http://dx.doi.org/10.1016/j.techsoc.2022.102166
[9]Li, J., Wang, X., Wang, L., Kang, H. (2022). Effects of Artificial Intelligence and Virtual Reality in Martial Arts Sports on Students’ Physical and Mental Health. International Transactions on Electrical Energy Systems, 2022. http://dx.doi.org/10.1155/2022/1359243
[10]Joyce, D. W., Kormilitzin, A., Smith, K. A., Cipriani, A. (2023). Explainable artificial intelligence for mental health through transparency and interpretability for understandability. Npj Digital Medicine, 6(1). http://dx.doi.org/10.1038/s41746-023-00751-9
[11]Omarov, B., Narynov, S., Zhumanov, Z. (2023). Artificial Intelligence-Enabled Chatbots in Mental Health: A Systematic Review. Computers, Materials and Continua, 74(3), 5105–5122. http://dx.doi.org/10.32604/cmc.2023.034655
[12]Dham, V., Rai, K., Soni, U. (2021). Mental Stress Detection Using Artificial Intelligence Models. Journal of Physics: Conference Series, 1950(1). https://doi.org/10.1088/1742-6596/1950/1/012047
[13]Hilal, A. M., ISSAOUI, I., Obayya, M., Al-Wesabi, F. N., NEMRI, N., Hamza, M. A., al Duhayyim, M., Zamani, A. S. (2022). Modeling of explainable artificial intelligence for biomedical mental disorder diagnosis. Computers, Materials and Continua, 71(2), 3853–3867. http://dx.doi.org/10.32604/cmc.2022.022663
[14]The Tong Test: Evaluating Artificial General Intelligence Through Dynamic Embodied Physical and Social Interactions.https://doi.org/10.1016/j.eng.2023.07.006
[15] “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research,practice and policy https://doi.org/10.1016/j.ijinfomgt.2023.102642
[16] Can ChatGPT provide intelligent diagnoses? A comparative study between pre-dictive models and ChatGPT to define a new medical diagnostic bot https://doi.org/10.1016/j.eswa.2023.121186
[17] Generative artificial intelligence and the ecology of human development https://doi.org/10.1111/jcpp.13860
[18] A comparative study of retrieval-based and generative-based chatbots using Deep Learning and Machine Learning https://doi.org/10.1016/j.health.2023.100198
[19] Learning to Fake It: Limited Responses and Fabricated References Provided by ChatGPT for Medical Questions https://doi.org/10.1016/j.mcpdig.2023.05.004
[20] Using cognitive psychology to understand GPT-3 https://doi.org/10.1073/pnas
[21] Detecting Symptoms of Depression on Reddit https://doi.org/10.1145/3578503.3583621
[22] M. J. Page et al., “The PRISMA 2020 statement: an updated guideline for reporting systematic reviews,” BMJ, p. n71, Mar. 2021, doi: 10.1136/bmj.n71.
[23] “Ml-ops.org,” Jan. 22, 2024. https://ml-ops.org/content/crisp-ml
[24] Hilal, A. M., ISSAOUI, I., Obayya, M., Al-Wesabi, F. N., NEMRI, N., Hamza, M. A., al Duhayyim, M., Zamani, A. S. (2022). Modeling of explainable artificial intelligence for biomedical mental disorder diagnosis. Computers, Materials and Continua, 71(2), 3853–3867. http://dx.doi.org/10.32604/cmc.2022.022663
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dc.publisher.program.spa.fl_str_mv Ingeniería de Sistemas
dc.publisher.grantor.spa.fl_str_mv Universidad El Bosque
dc.publisher.faculty.spa.fl_str_mv Facultad de Ingeniería
institution Universidad El Bosque
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spelling Romero Alvarez, Fran ErnestoNiño Gómez, Juan DanielNiño Gómez, Juan Daniel [0009-0004-7960-7645]2024-12-13T14:15:32Z2024-12-13T14:15:32Z2024-05https://hdl.handle.net/20.500.12495/13700instname:Universidad El Bosquereponame:Repositorio Institucional Universidad El Bosquerepourl:https://repositorio.unbosque.edu.coUn Bot conversacional corresponde a una aplicación de software que cuenta con capacidades de Procesamiento de Lenguaje Natural - PLN e Inteligencia Artificial – IA para entablar conversaciones habladas con seres humanos. A diferencia de los tradicionales bots textuales, en los cuales la interacción se lleva a cabo mediante mensajes de texto, un Bot conversacional esta´ en capacidad de entender la voz humana y responder igualmente en lenguaje hablado. El Bot puede ser desplegado de diversas formas, desde sistemas de audio-respuesta sin ningún tipo de interfaz gráfica, hasta sofisticadas aplicaciones móviles que presentan al Bot como un personaje virtual animado con una personalidad y características bien definidas.Ingeniero de SistemasPregradoA conversational Bot is a software application that uses Natural Language Processing (NLP) and Artificial Intelligence (AI) capabilities to engage in spoken conversations with humans. - AI to engage in spoken conversations with human beings. Unlike traditional textual bots, in which the interaction is carried out through text messages, a conversational Bot is able to understand the human voice and respond in spoken language. The Bot can be deployed in a variety of ways, from audio-response systems without any graphical interface, to sophisticated mobile applications that present the Bot as an animated virtual character with a well-defined personality and characteristics.application/pdfAtribución-NoComercial-CompartirIgual 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-sa/4.0/Acceso abiertohttps://purl.org/coar/access_right/c_abf2http://purl.org/coar/access_right/c_abf2Modelo extenso de lenguajeBot conversacionalDolor oncológico pediátricoTransformadoresInteligencia artificial621.3Large Language ModelConversational BotPediatric Oncologic PainTransformersArtificial IntelligenceKENITO,el bot conversacional para la evaluación del manejo del dolor oncológico pediátrico - Fase 2KENITO, the conversational bot for the evaluation of pediatric oncologic pain management - Phase 2Ingeniería de SistemasUniversidad El BosqueFacultad de IngenieríaTesis/Trabajo de grado - Monografía - Pregradohttps://purl.org/coar/resource_type/c_7a1fhttp://purl.org/coar/resource_type/c_7a1finfo:eu-repo/semantics/bachelorThesishttps://purl.org/coar/version/c_ab4af688f83e57aa[1] S. A. Hadri y A. Bouramoul, “Towards a deep learning based contextual chat bot for preventing depression in young children with autistic spectrum disorder”, Smart Health, p. 100371, diciembre de 2022. Accedido el 4 de febrero de 2024. [En l ́ınea]. Disponible: https://doi.org/10.1016/j.smhl.2022.100371[2] R. L. Weisenburger et al., “Conversational assessment using artificial intelligence is as clinically useful as depression scales and preferred by users”, J. Affect.Disorders, enero de 2024. Accedido el 6 de febrero de 2024. [En l ́ınea]. Disponible:https://doi.org/10.1016/j.jad.2024.01.212[3]S. Thakur, D. Rastogi, y L. Singh, “MOODY: A Natural Language Processing-Based Chatbot for Mental Health Care”, en Lecture Notes in Electrical Engineering,Virtual, Online, 2022, pp. 899–908. [En l ́ınea]. Disponible en: http://dx.doi.org/10.1007/978-981-19-4364-5-64[4]L. Marciano y S. Saboor, “Reinventing mental health care in youth through mobile approaches: Current status and future steps”, Front Psychol, vol. 14, 2023, doi: 10.3389/fpsyg.2023.1126015.[5]P. Parmar, J. Ryu, S. Pandya, J. Sedoc, y S. Agarwal, “Health-focused conversational agents in person-centered care: a review of apps”, NPJ Digit Med, vol. 5,num. 1, 2022, doi: 10.1038/s41746-022-00560-6. ́[6]H. Jahanshahi, S. Kazmi, y M. Cevik, “Auto Response Generation in Online Medical Chat Services”, J Healthc Inform Res, vol. 6, num. 3, pp. 344 – 374, 2022, ́doi: 10.1007/s41666-022-00118-x.[7]B. M. Chaudhry y A. Islam, “Design Validation of a Workplace Stress Management Mobile App for Healthcare Workers During COVID-19 and Beyond”, en Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Virtual, Online, 2022, pp. 306–321. [En l ́ınea]. Disponible en: http://dx.doi.org/10.1007/978-3-030-94822-1-17[8]M.-T. Ho, N.-T. B. Le, P. Mantello, M.-T. Ho, y N. Ghotbi, “Understanding the acceptance of emotional artificial intelligence in the Japanese healthcare system: Across-sectional survey of clinic visitors’ attitude”, Technol Soc, vol. 72, 2023, [En línea]. Disponible en: http://dx.doi.org/10.1016/j.techsoc.2022.102166[9]Li, J., Wang, X., Wang, L., Kang, H. (2022). Effects of Artificial Intelligence and Virtual Reality in Martial Arts Sports on Students’ Physical and Mental Health. International Transactions on Electrical Energy Systems, 2022. http://dx.doi.org/10.1155/2022/1359243[10]Joyce, D. W., Kormilitzin, A., Smith, K. A., Cipriani, A. (2023). Explainable artificial intelligence for mental health through transparency and interpretability for understandability. Npj Digital Medicine, 6(1). http://dx.doi.org/10.1038/s41746-023-00751-9[11]Omarov, B., Narynov, S., Zhumanov, Z. (2023). Artificial Intelligence-Enabled Chatbots in Mental Health: A Systematic Review. Computers, Materials and Continua, 74(3), 5105–5122. http://dx.doi.org/10.32604/cmc.2023.034655[12]Dham, V., Rai, K., Soni, U. (2021). Mental Stress Detection Using Artificial Intelligence Models. Journal of Physics: Conference Series, 1950(1). https://doi.org/10.1088/1742-6596/1950/1/012047[13]Hilal, A. M., ISSAOUI, I., Obayya, M., Al-Wesabi, F. N., NEMRI, N., Hamza, M. A., al Duhayyim, M., Zamani, A. S. (2022). Modeling of explainable artificial intelligence for biomedical mental disorder diagnosis. Computers, Materials and Continua, 71(2), 3853–3867. http://dx.doi.org/10.32604/cmc.2022.022663[14]The Tong Test: Evaluating Artificial General Intelligence Through Dynamic Embodied Physical and Social Interactions.https://doi.org/10.1016/j.eng.2023.07.006[15] “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research,practice and policy https://doi.org/10.1016/j.ijinfomgt.2023.102642[16] Can ChatGPT provide intelligent diagnoses? A comparative study between pre-dictive models and ChatGPT to define a new medical diagnostic bot https://doi.org/10.1016/j.eswa.2023.121186[17] Generative artificial intelligence and the ecology of human development https://doi.org/10.1111/jcpp.13860[18] A comparative study of retrieval-based and generative-based chatbots using Deep Learning and Machine Learning https://doi.org/10.1016/j.health.2023.100198[19] Learning to Fake It: Limited Responses and Fabricated References Provided by ChatGPT for Medical Questions https://doi.org/10.1016/j.mcpdig.2023.05.004[20] Using cognitive psychology to understand GPT-3 https://doi.org/10.1073/pnas[21] Detecting Symptoms of Depression on Reddit https://doi.org/10.1145/3578503.3583621[22] M. J. Page et al., “The PRISMA 2020 statement: an updated guideline for reporting systematic reviews,” BMJ, p. n71, Mar. 2021, doi: 10.1136/bmj.n71.[23] “Ml-ops.org,” Jan. 22, 2024. https://ml-ops.org/content/crisp-ml[24] Hilal, A. M., ISSAOUI, I., Obayya, M., Al-Wesabi, F. N., NEMRI, N., Hamza, M. A., al Duhayyim, M., Zamani, A. S. (2022). Modeling of explainable artificial intelligence for biomedical mental disorder diagnosis. Computers, Materials and Continua, 71(2), 3853–3867. http://dx.doi.org/10.32604/cmc.2022.022663spaLICENSElicense.txtlicense.txttext/plain; charset=utf-82000https://repositorio.unbosque.edu.co/bitstreams/dfad9698-58f6-4723-9631-8341934c3c7d/download17cc15b951e7cc6b3728a574117320f9MD54Anexo 1. 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