Construcción de un prototipo de registro de caídas basado en machine learning para mayores institucionalizados
Las caídas en personas mayores institucionalizadas representan un problema de salud pública subestimado, asociado a discapacidad, dependencia y mortalidad. En Chile, la ausencia de registros estandarizados en establecimientos de larga estadía para adultos mayores (ELEAM) limita la prevención efectiv...
- Autores:
-
Dinamarca Montecinos, José Luis
Durán Novoa, Roberto Alejandro
Flores Moraga, María Jesús
Briede Westermeyer, Juan Carlos
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2025
- Institución:
- Universidad Autónoma de Bucaramanga - UNAB
- Repositorio:
- Repositorio UNAB
- Idioma:
- spa
- OAI Identifier:
- oai:repository.unab.edu.co:20.500.12749/32103
- Palabra clave:
- Ciencias médicas
Ciencias de la vida
Ciencias de la salud
Anciano de 80 o más Años
Aprendizaje Automático
Anciano
Informática Médica
Prevención de Accidentes
Registros Electrónicos de Salud
Inteligencia Artificial
Hogares para Ancianos
Medical sciences
Life sciences
Health sciences
Aged, 80 and over
Machine Learning
Aged
Medical Informatics
Accident Prevention
Electronic Health Records
Artificial Intelligence
Homes for the Aged
Ciências médicas
Ciências da vida
Ciências da saúde
Idoso de 80 Anos ou mais
Aprendizagem de Máquina
Idoso
Informática Médica
Prevenção de Acidentes
Registros Eletrônicos de Saúde
Inteligência Artificial
Instituição de Longa Permanência para Idosos
Ciencias médicas
Ciencias de la vida
Ciencias de la salud
- Rights
- License
- http://creativecommons.org/licenses/by-nc-nd/2.5/co/
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Construcción de un prototipo de registro de caídas basado en machine learning para mayores institucionalizados |
| dc.title.translated.eng.fl_str_mv |
Development of a fall registration prototype based on machine learning for institutionalized older adults |
| dc.title.translated.por.fl_str_mv |
Construção de um protótipo de registro de quedas baseado em machine learning para idosos institucionalizados |
| title |
Construcción de un prototipo de registro de caídas basado en machine learning para mayores institucionalizados |
| spellingShingle |
Construcción de un prototipo de registro de caídas basado en machine learning para mayores institucionalizados Ciencias médicas Ciencias de la vida Ciencias de la salud Anciano de 80 o más Años Aprendizaje Automático Anciano Informática Médica Prevención de Accidentes Registros Electrónicos de Salud Inteligencia Artificial Hogares para Ancianos Medical sciences Life sciences Health sciences Aged, 80 and over Machine Learning Aged Medical Informatics Accident Prevention Electronic Health Records Artificial Intelligence Homes for the Aged Ciências médicas Ciências da vida Ciências da saúde Idoso de 80 Anos ou mais Aprendizagem de Máquina Idoso Informática Médica Prevenção de Acidentes Registros Eletrônicos de Saúde Inteligência Artificial Instituição de Longa Permanência para Idosos Ciencias médicas Ciencias de la vida Ciencias de la salud |
| title_short |
Construcción de un prototipo de registro de caídas basado en machine learning para mayores institucionalizados |
| title_full |
Construcción de un prototipo de registro de caídas basado en machine learning para mayores institucionalizados |
| title_fullStr |
Construcción de un prototipo de registro de caídas basado en machine learning para mayores institucionalizados |
| title_full_unstemmed |
Construcción de un prototipo de registro de caídas basado en machine learning para mayores institucionalizados |
| title_sort |
Construcción de un prototipo de registro de caídas basado en machine learning para mayores institucionalizados |
| dc.creator.fl_str_mv |
Dinamarca Montecinos, José Luis Durán Novoa, Roberto Alejandro Flores Moraga, María Jesús Briede Westermeyer, Juan Carlos |
| dc.contributor.author.none.fl_str_mv |
Dinamarca Montecinos, José Luis Durán Novoa, Roberto Alejandro Flores Moraga, María Jesús Briede Westermeyer, Juan Carlos |
| dc.contributor.orcid.spa.fl_str_mv |
Dinamarca Montecinos, José Luis [0000-0002-0186-5992] Durán Novoa, Roberto Alejandro [0000-0003-4073-9363] Flores Moraga, María Jesús [0009-0007-8099-2220] Briede Westermeyer, Juan Carlos [0000-0002-5746-0169] |
| dc.subject.spa.fl_str_mv |
Ciencias médicas Ciencias de la vida Ciencias de la salud Anciano de 80 o más Años Aprendizaje Automático Anciano Informática Médica Prevención de Accidentes Registros Electrónicos de Salud Inteligencia Artificial Hogares para Ancianos |
| topic |
Ciencias médicas Ciencias de la vida Ciencias de la salud Anciano de 80 o más Años Aprendizaje Automático Anciano Informática Médica Prevención de Accidentes Registros Electrónicos de Salud Inteligencia Artificial Hogares para Ancianos Medical sciences Life sciences Health sciences Aged, 80 and over Machine Learning Aged Medical Informatics Accident Prevention Electronic Health Records Artificial Intelligence Homes for the Aged Ciências médicas Ciências da vida Ciências da saúde Idoso de 80 Anos ou mais Aprendizagem de Máquina Idoso Informática Médica Prevenção de Acidentes Registros Eletrônicos de Saúde Inteligência Artificial Instituição de Longa Permanência para Idosos Ciencias médicas Ciencias de la vida Ciencias de la salud |
| dc.subject.keywords.eng.fl_str_mv |
Medical sciences Life sciences Health sciences Aged, 80 and over Machine Learning Aged Medical Informatics Accident Prevention Electronic Health Records Artificial Intelligence Homes for the Aged |
| dc.subject.keywords.por.fl_str_mv |
Ciências médicas Ciências da vida Ciências da saúde Idoso de 80 Anos ou mais Aprendizagem de Máquina Idoso Informática Médica Prevenção de Acidentes Registros Eletrônicos de Saúde Inteligência Artificial Instituição de Longa Permanência para Idosos |
| dc.subject.lemb.spa.fl_str_mv |
Ciencias médicas Ciencias de la vida |
| dc.subject.proposal.spa.fl_str_mv |
Ciencias de la salud |
| description |
Las caídas en personas mayores institucionalizadas representan un problema de salud pública subestimado, asociado a discapacidad, dependencia y mortalidad. En Chile, la ausencia de registros estandarizados en establecimientos de larga estadía para adultos mayores (ELEAM) limita la prevención efectiva. Este estudio tuvo como objetivo diseñar un prototipo de sistema digital de registro de caídas basado en aprendizaje automático, o machine learning (ML), para su implementación en ELEAM. |
| publishDate |
2025 |
| dc.date.accessioned.none.fl_str_mv |
2025-11-04T22:32:46Z |
| dc.date.available.none.fl_str_mv |
2025-11-04T22:32:46Z |
| dc.date.issued.none.fl_str_mv |
2025-07-31 |
| dc.type.eng.fl_str_mv |
Article |
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http://purl.org/coar/resource_type/c_6501 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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info:eu-repo/semantics/article |
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Artículo |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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http://purl.org/redcol/resource_type/ART |
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i-ISSN 0123-7047 e-ISSN 2382-4603 |
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http://hdl.handle.net/20.500.12749/32103 |
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instname:Universidad Autónoma de Bucaramanga UNAB |
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reponame:Repositorio Institucional UNAB |
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repourl:https://repository.unab.edu.co |
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https://doi.org/10.29375/01237047.5165 |
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i-ISSN 0123-7047 e-ISSN 2382-4603 instname:Universidad Autónoma de Bucaramanga UNAB reponame:Repositorio Institucional UNAB repourl:https://repository.unab.edu.co |
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http://hdl.handle.net/20.500.12749/32103 https://doi.org/10.29375/01237047.5165 |
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spa |
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spa |
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https://revistas.unab.edu.co/index.php/medunab/article/view/5165/4217 |
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https://revistas.unab.edu.co/index.php/medunab/issue/view/305 |
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World Health Organization. Ageing and health [Internet]. Ginebra: WHO; 2022. Recuperado a partir de: https://www.who.int/news-room/fact-sheets/detail/ ageing-and-health Gutiérrez-Murillo RS. Population aging in Latin America: a salutogenic understanding is needed. Eur J Environ Public Health [Internet]. 2022;6(2):em0121. doi: https://doi.org/10.21601/ejeph/12322 Ghasemi H, Kharaghani MA, Golestani A, Najafi M, Khosravi S, Malekpour MR, et al. The national and subnational burden of falls and its attributable risk factors among older adults in Iran from 1990 to 2021: findings from the global burden of disease study. BMC Geriatr [Internet]. 2025;25(1):253. doi: https://doi. org/10.1186/s12877-025-05909-6 World Health Organization. Falls [Internet]. Ginebra: WHO; 2021. Recuperado a partir de: https://www.who. int/news-room/fact-sheets/detail/falls Shao L, Shi Y, Xie XY, Wang Z, Wang ZA, Zhang JE. Incidence and risk factors of falls among older people in nursing homes: systematic review and meta-analysis. J Am Med Dir Assoc [Internet]. 2023;24(11):1708-1717. doi: https://doi.org/10.1016/j.jamda.2023.06.002 Stefanacci RG, Wilkinson JR. Falls in older adults. MSD Manual Professional Edition. [Internet]. 2023. Recuperado a partir de: https://www.msdmanuals.com/ professional/geriatrics/falls-in-older-adults/falls-inolder- adults Campiño-Valderrama SM, Serna-Zuluaga AS, Ayala IC. Riesgo de caídas y su relación con la capacidad física y cognitiva en una residencia de adultos mayores de Santiago de Chile. Cultura del Cuidado [Internet]. 2020;17(2):61-74. doi: https://doi.org/10.18041/1794- 5232/cultrua.2020v17n2.7658 Gillespie LD, Robertson MC, Gillespie WJ, Sherrington C, Gates S, Clemson LM, et al. Interventions for preventing falls in older people living in the community. Cochrane Database Syst Rev [Internet]. 2012;9:CD007146. doi: https://doi.org/10.1002/14651858.cd007146.pub3 Tiwari P, Colborn KL, Smith DE, Xing F, Ghosh D, Rosenberg MA. Assessment of a machine learning model applied to harmonized electronic health record data for the prediction of incident atrial fibrillation. JAMA Netw Open [Internet]. 2020;3(1):e1919396. doi: https://doi.org/10.1001/jamanetworkopen.2019.19396 Shao L, Wang Z, Xie X, Xiao L, Shi Y, Wang ZA, et al. Development and external validation of a machine learning–based fall prediction model for nursing home residents: a prospective cohort study. J Am Med Dir Assoc [Internet]. 2024;25(9):105169. doi: https://doi. org/10.1016/j.jamda.2024.105169 Islam M, Tayan O, Islam R, Islam S, Nooruddin S, Nomani-Kabir M, et al. Deep learning based systems developed for fall detection: a review. IEEE Access [Internet]. 2020;8:166117-37. doi: https://doi. org/10.1109/ACCESS.2020.3021943 Stevens JA, Phelan EA. Development of STEADI: a fall prevention resource for health care providers. Health Promot Pract [Internet]. 2013;14(5):706-14. doi: https://doi.org/10.1177/1524839912463576 Lin CC, Meardon S, O’Brien K. The predictive validity and clinical application of Stopping Elderly Accidents, Deaths & Injuries (STEADI) for fall risk screening. Adv Geriatr Med Res [Internet]. 2022;4(3):e220008. doi: https://doi.org/10.20900/agmr20220008 Marín-Úbeda J, Berna-Martínez J (dir). Diseño y desarrollo de una app de monitorización geoespacial para la supervisión de personas dependientes [tesis en Internet]. 2024 [Valencia]: Universidad de Alicante; 2023. Recuperado a partir de: https://rua.ua.es/entities/ publication/1cd22bf1-46bc-4d98-945a-957e5a34feb7 Linnerud S, Hartford-Kvael LA, Graverholt B, Idland G, Taraldsen K, Brovold T. Stakeholder development of an implementation strategy for fall prevention in Norwegian home care: a qualitative cocreation approach. BMC Health Serv Res [Internet]. 2023;23(1):1390. doi: https://doi.org/10.1186/s12913-023-10394-x Borra P. The Transformative Role of Microsoft Azure AI in Healthcare. Int J Emerg Trends Eng Res [Internet]. 2024;12(7):108-13. doi: https://doi.org/10.30534/ ijeter/2024/021272024. Kadayat Y, Sharma S, Agarwal P, Mohan S. Internetof- Things Enabled Smart Health Monitoring System Using AutoAI: A Graphical Tool of IBM Watson Studio. Communication Technologies and Security Challenges in IoT [Internet]. 2024:427-45. doi: https:// doi.org/10.1007/978-981-97-0052-3_21 Fragoso-Mendoza MI, Dávila-Mendoza R, López- Ortiz G. Importancia y uso de guías para reportar los principales tipos de estudio en investigación médica. Cir Cir [Internet]. 2023;91(2):277-283. doi: https://doi. org/10.24875/CIRU.22000122 Beninho SG, Rosales Plaza F (dir). Análisis de la arquitectura institucional del servicio nacional de adultos mayores (SENAMA): una mirada hacia protección social de la vejez en Chile [tesis en Internet]. 2024 [Santiago de Chile]: Universidad de Chile; 2017. Recuperado a partir de: https://repositorio.uchile.cl/handle/2250/150604 Majka M. Mastering product development with the double diamond framework. ResearchGate [Internet]. 2024. Recuperado a partir de: https://www.researchgate. net/publication/384691492_Mastering_Product_ Development_with_the_Double_Diamond_Framework Wang X, Huang Z, Xu T, Li Y, Qin X. Exploring the future design approach to ageing based on the double diamond model. Systems [Internet]. 2023;11(8):404. doi: https://doi.org/10.3390/systems11080404 Arcia A, Stonbraker S, Mangal S, Lor M. A practical guide to participatory design sessions for the development of information visualizations: tutorial. J Particip Med [Internet]. 2024;16:e64508. doi: https://doi. org/10.2196/64508 Stevenson R, Burnell D, Fisher G. The minimum viable product (MVP): theory and practice. J Manag [Internet]. 2024;50(8):3202-31. doi: https://doi. org/10.1177/01492063241227154 Cook DA, Bikkani A, Poterucha-Carter MJ. Evaluating education innovations rapidly with build-measure-learn: applying lean startup to health professions education. Med Teach [Internet]. 2023;45(2):167-78. doi: https:// doi.org/10.1080/0142159X.2022.2118038 Solomon DH, Rudin RS. Digital health technologies: opportunities and challenges inrheumatology. Nat Rev Rheumatol [Internet]. 2020;16:525-35. doi: https://doi. org/10.1038/s41584-020-0461-x Leiva-Caro JA, Salazar González BC (dir). Relación entre competencia, usabilidad, ambiente y caídas en el adulto mayor [Tesis de grado]. Nuevo León: Universidad Autónoma de Nuevo León; 2013. Recuperado a partir de: http://eprints.uanl.mx/3525/ Fernandes C, Miles S, Pereira-Lucena CJ. Detecting false alarms by analyzing alarm-context information: algorithm development and validation. JMIR Med Inform [Internet]. 2020;8(5):e15407. doi: https://doi. org/10.2196/15407 Zorzetti M, Signoretti I, Salerno L, Marczak S, Bastos R. Improving agile software development using user-centered design and lean startup. Inf Softw Technol [Internet]. 2022;141:106718. doi: https://doi. org/10.1016/j.infsof.2021.106718 Kamel-Rahimi A, Pienaar O, Ghadimi M, Canfell OJ, Pole JD, Shrapnel S, et al. Implementing AI in hospitals to achieve a learning health system: systematic review of current enablers and barriers. J Med Internet Res [Internet]. 2024;26:e49655. doi: https://doi.org/10.2196/49655 Cahoolessur DK, Rajkumarsingh B. Fall detection system using XGBoost and IoT. R&D Journal [Internet]. 2020;36:8-18. doi: https://doi.org/10.17159/2309- 8988/2020/v36a2 Kang CW, Yan ZK, Tian JL, Pu XB, Wu LX. Constructing a fall risk prediction model for hospitalized patients using machine learning. BMC Public Health [Internet]. 2025;25(1):242. doi: https://doi.org/10.1186/s12889- 025-21284-8 Jahandideh S, Hutchinson AF, Bucknall TK, Considine J, Driscoll A, Manias E, et al. Using machine learning models to predict falls in hospitalised adults. Int J Med Inform [Internet]. 2024;187:105436. doi: https://doi. org/10.1016/j.ijmedinf.2024.105436 |
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Dinamarca Montecinos, José Luis67409477-8b7d-4aa7-980a-8b85f6340058Durán Novoa, Roberto Alejandrob78a195d-b271-4f2a-96fb-417d6758fea2Flores Moraga, María Jesúsbb78ca1e-ce47-4959-a5ba-4ac9b0098b31Briede Westermeyer, Juan Carlos60945137-7fcc-4c4c-ab40-4063eb4520a1Dinamarca Montecinos, José Luis [0000-0002-0186-5992]Durán Novoa, Roberto Alejandro [0000-0003-4073-9363]Flores Moraga, María Jesús [0009-0007-8099-2220]Briede Westermeyer, Juan Carlos [0000-0002-5746-0169]2025-11-04T22:32:46Z2025-11-04T22:32:46Z2025-07-31i-ISSN 0123-7047e-ISSN 2382-4603http://hdl.handle.net/20.500.12749/32103instname:Universidad Autónoma de Bucaramanga UNABreponame:Repositorio Institucional UNABrepourl:https://repository.unab.edu.cohttps://doi.org/10.29375/01237047.5165Las caídas en personas mayores institucionalizadas representan un problema de salud pública subestimado, asociado a discapacidad, dependencia y mortalidad. En Chile, la ausencia de registros estandarizados en establecimientos de larga estadía para adultos mayores (ELEAM) limita la prevención efectiva. Este estudio tuvo como objetivo diseñar un prototipo de sistema digital de registro de caídas basado en aprendizaje automático, o machine learning (ML), para su implementación en ELEAM.Falls in institutionalized older adults represent an underestimated public health problem associated with disability, dependence and mortality. In Chile, the absence of standardized records in long-term care facilities (LTCF) for older adults limits effective prevention. The objective of this study was to design a prototype of a digital fall registration system based on machine learning (ML) for its implementation in LTCF.Quedas em idosos institucionalizados representam um problema de saúde pública subestimado, associado à deficiência, dependência e mortalidade. No Chile, a falta de registros padronizados em instituições de longa permanência para idosos (ILPI) limita a prevenção eficaz. Este estudo teve como objetivo projetar um protótipo de sistema digital de registro de quedas baseado em aprendizado de máquina, ou machine learning (ML), para sua implementação em ILPI.application/pdfspahttps://revistas.unab.edu.co/index.php/medunab/article/view/5165/4217https://revistas.unab.edu.co/index.php/medunab/issue/view/305World Health Organization. Ageing and health [Internet]. Ginebra: WHO; 2022. Recuperado a partir de: https://www.who.int/news-room/fact-sheets/detail/ ageing-and-healthGutiérrez-Murillo RS. Population aging in Latin America: a salutogenic understanding is needed. Eur J Environ Public Health [Internet]. 2022;6(2):em0121. doi: https://doi.org/10.21601/ejeph/12322Ghasemi H, Kharaghani MA, Golestani A, Najafi M, Khosravi S, Malekpour MR, et al. The national and subnational burden of falls and its attributable risk factors among older adults in Iran from 1990 to 2021: findings from the global burden of disease study. BMC Geriatr [Internet]. 2025;25(1):253. doi: https://doi. org/10.1186/s12877-025-05909-6World Health Organization. Falls [Internet]. Ginebra: WHO; 2021. Recuperado a partir de: https://www.who. int/news-room/fact-sheets/detail/fallsShao L, Shi Y, Xie XY, Wang Z, Wang ZA, Zhang JE. Incidence and risk factors of falls among older people in nursing homes: systematic review and meta-analysis. J Am Med Dir Assoc [Internet]. 2023;24(11):1708-1717. doi: https://doi.org/10.1016/j.jamda.2023.06.002Stefanacci RG, Wilkinson JR. Falls in older adults. MSD Manual Professional Edition. [Internet]. 2023. Recuperado a partir de: https://www.msdmanuals.com/ professional/geriatrics/falls-in-older-adults/falls-inolder- adultsCampiño-Valderrama SM, Serna-Zuluaga AS, Ayala IC. Riesgo de caídas y su relación con la capacidad física y cognitiva en una residencia de adultos mayores de Santiago de Chile. Cultura del Cuidado [Internet]. 2020;17(2):61-74. doi: https://doi.org/10.18041/1794- 5232/cultrua.2020v17n2.7658Gillespie LD, Robertson MC, Gillespie WJ, Sherrington C, Gates S, Clemson LM, et al. Interventions for preventing falls in older people living in the community. Cochrane Database Syst Rev [Internet]. 2012;9:CD007146. doi: https://doi.org/10.1002/14651858.cd007146.pub3Tiwari P, Colborn KL, Smith DE, Xing F, Ghosh D, Rosenberg MA. Assessment of a machine learning model applied to harmonized electronic health record data for the prediction of incident atrial fibrillation. JAMA Netw Open [Internet]. 2020;3(1):e1919396. doi: https://doi.org/10.1001/jamanetworkopen.2019.19396Shao L, Wang Z, Xie X, Xiao L, Shi Y, Wang ZA, et al. Development and external validation of a machine learning–based fall prediction model for nursing home residents: a prospective cohort study. J Am Med Dir Assoc [Internet]. 2024;25(9):105169. doi: https://doi. org/10.1016/j.jamda.2024.105169Islam M, Tayan O, Islam R, Islam S, Nooruddin S, Nomani-Kabir M, et al. Deep learning based systems developed for fall detection: a review. IEEE Access [Internet]. 2020;8:166117-37. doi: https://doi. org/10.1109/ACCESS.2020.3021943Stevens JA, Phelan EA. Development of STEADI: a fall prevention resource for health care providers. Health Promot Pract [Internet]. 2013;14(5):706-14. doi: https://doi.org/10.1177/1524839912463576Lin CC, Meardon S, O’Brien K. The predictive validity and clinical application of Stopping Elderly Accidents, Deaths & Injuries (STEADI) for fall risk screening. Adv Geriatr Med Res [Internet]. 2022;4(3):e220008. doi: https://doi.org/10.20900/agmr20220008Marín-Úbeda J, Berna-Martínez J (dir). 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Int J Med Inform [Internet]. 2024;187:105436. doi: https://doi. org/10.1016/j.ijmedinf.2024.105436http://creativecommons.org/licenses/by-nc-nd/2.5/co/Abierto (Texto Completo)Atribución-NoComercial-SinDerivadas 2.5 Colombiahttp://purl.org/coar/access_right/c_abf2Vol. 28 Núm. 1 (2025): abril-julio 2025: Envejecimiento Saludable; Geriatría; Salud del Anciano; 154-169Ciencias médicasCiencias de la vidaCiencias de la saludAnciano de 80 o más AñosAprendizaje AutomáticoAncianoInformática MédicaPrevención de AccidentesRegistros Electrónicos de SaludInteligencia ArtificialHogares para AncianosMedical sciencesLife sciencesHealth sciencesAged, 80 and overMachine LearningAgedMedical InformaticsAccident PreventionElectronic Health RecordsArtificial IntelligenceHomes for the AgedCiências médicasCiências da vidaCiências da saúdeIdoso de 80 Anos ou maisAprendizagem de MáquinaIdosoInformática MédicaPrevenção de AcidentesRegistros Eletrônicos de SaúdeInteligência ArtificialInstituição de Longa Permanência para IdososCiencias médicasCiencias de la vidaCiencias de la saludConstrucción de un prototipo de registro de caídas basado en machine learning para mayores institucionalizadosDevelopment of a fall registration prototype based on machine learning for institutionalized older adultsConstrução de um protótipo de registro de quedas baseado em machine learning para idosos institucionalizadosArticleinfo:eu-repo/semantics/articleArtículohttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/resource_type/c_6501http://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85Universidad Autónoma de Bucaramanga UNABFacultad Ciencias de la SaludORIGINALArticulo 11.pdfArticulo 11.pdfArtículoapplication/pdf2735501https://repository.unab.edu.co/bitstream/20.500.12749/32103/1/Articulo%2011.pdf2cd8e407d117a1c4efac6db84d96b29bMD51open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-8183https://repository.unab.edu.co/bitstream/20.500.12749/32103/2/license.txt737346e09d47a3db691f1370de49426aMD52open accessTHUMBNAILArticulo 11.pdf.jpgArticulo 11.pdf.jpgIM Thumbnailimage/jpeg12907https://repository.unab.edu.co/bitstream/20.500.12749/32103/3/Articulo%2011.pdf.jpge04855709e40b1b7a44755c6a4ae03d7MD53open access20.500.12749/32103oai:repository.unab.edu.co:20.500.12749/321032025-11-06 08:45:15.641open accessRepositorio Institucional | Universidad Autónoma de Bucaramanga - UNABrepositorio@unab.edu.coTGFzIHB1YmxpY2FjaW9uZXMgZGUgbGEgcmV2aXN0YSBNZWRVTkFCIGVzdMOhbiBiYWpvIHVuYSBMaWNlbmNpYSBkZSBBdHJpYnVjacOzbiBkZSBCaWVuZXMgQ29tdW5lcyBDcmVhdGl2b3MgKENyZWF0aXZlIENvbW1vbnMsIENDKSB0aXBvIDQuMCwgY29uIGRlcmVjaG9zIGRlIGF0cmlidWNpw7NuIHkgbm8gY29tZXJjaWFs |
