Construcción de un prototipo de registro de caídas basado en machine learning para mayores institucionalizados

dc.contributor.authorDinamarca Montecinos, José Luis
dc.contributor.authorDurán Novoa, Roberto Alejandro
dc.contributor.authorFlores Moraga, María Jesús
dc.contributor.authorBriede Westermeyer, Juan Carlos
dc.contributor.orcidDinamarca Montecinos, José Luis [0000-0002-0186-5992]spa
dc.contributor.orcidDurán Novoa, Roberto Alejandro [0000-0003-4073-9363]spa
dc.contributor.orcidFlores Moraga, María Jesús [0009-0007-8099-2220]spa
dc.contributor.orcidBriede Westermeyer, Juan Carlos [0000-0002-5746-0169]spa
dc.date.accessioned2025-11-04T22:32:46Z
dc.date.available2025-11-04T22:32:46Z
dc.date.issued2025-07-31
dc.description.abstractLas 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.spa
dc.description.abstractenglishFalls 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.eng
dc.description.abstractotherQuedas 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.por
dc.format.mimetypeapplication/pdfspa
dc.identifier.doihttps://doi.org/10.29375/01237047.5165
dc.identifier.instnameinstname:Universidad Autónoma de Bucaramanga UNABspa
dc.identifier.issni-ISSN 0123-7047spa
dc.identifier.issne-ISSN 2382-4603spa
dc.identifier.reponamereponame:Repositorio Institucional UNABspa
dc.identifier.repourlrepourl:https://repository.unab.edu.cospa
dc.identifier.urihttp://hdl.handle.net/20.500.12749/32103
dc.language.isospaspa
dc.publisher.facultyFacultad Ciencias de la Saludspa
dc.publisher.grantorUniversidad Autónoma de Bucaramanga UNABspa
dc.relationhttps://revistas.unab.edu.co/index.php/medunab/article/view/5165/4217spa
dc.relation.referencesWorld 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
dc.relation.referencesGutiérrez-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
dc.relation.referencesGhasemi 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
dc.relation.referencesWorld Health Organization. Falls [Internet]. Ginebra: WHO; 2021. Recuperado a partir de: https://www.who. int/news-room/fact-sheets/detail/falls
dc.relation.referencesShao 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
dc.relation.referencesStefanacci 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
dc.relation.referencesCampiño-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
dc.relation.referencesGillespie 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
dc.relation.referencesTiwari 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
dc.relation.referencesShao 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
dc.relation.referencesIslam 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
dc.relation.referencesStevens 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
dc.relation.referencesLin 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
dc.relation.referencesMarín-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
dc.relation.referencesLinnerud 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
dc.relation.referencesBorra 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.
dc.relation.referencesKadayat 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
dc.relation.referencesFragoso-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
dc.relation.referencesBeninho 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
dc.relation.referencesMajka 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
dc.relation.referencesWang 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
dc.relation.referencesArcia 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
dc.relation.referencesStevenson 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
dc.relation.referencesCook 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
dc.relation.referencesSolomon 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
dc.relation.referencesLeiva-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/
dc.relation.referencesFernandes 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
dc.relation.referencesZorzetti 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
dc.relation.referencesKamel-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
dc.relation.referencesCahoolessur 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
dc.relation.referencesKang 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
dc.relation.referencesJahandideh 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
dc.relation.urihttps://revistas.unab.edu.co/index.php/medunab/issue/view/305spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.creativecommonsAtribución-NoComercial-SinDerivadas 2.5 Colombia*
dc.rights.localAbierto (Texto Completo)spa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/co/*
dc.sourceVol. 28 Núm. 1 (2025): abril-julio 2025: Envejecimiento Saludable; Geriatría; Salud del Anciano; 154-169spa
dc.subjectCiencias médicasspa
dc.subjectCiencias de la vidaspa
dc.subjectCiencias de la saludspa
dc.subjectAnciano de 80 o más Añosspa
dc.subjectAprendizaje Automáticospa
dc.subjectAncianospa
dc.subjectInformática Médicaspa
dc.subjectPrevención de Accidentesspa
dc.subjectRegistros Electrónicos de Saludspa
dc.subjectInteligencia Artificialspa
dc.subjectHogares para Ancianosspa
dc.subject.keywordsMedical scienceseng
dc.subject.keywordsLife scienceseng
dc.subject.keywordsHealth scienceseng
dc.subject.keywordsCiências médicaspor
dc.subject.keywordsCiências da vidapor
dc.subject.keywordsCiências da saúdepor
dc.subject.keywordsAged, 80 and overeng
dc.subject.keywordsMachine Learningeng
dc.subject.keywordsAgedeng
dc.subject.keywordsMedical Informaticseng
dc.subject.keywordsAccident Preventioneng
dc.subject.keywordsElectronic Health Recordseng
dc.subject.keywordsArtificial Intelligenceeng
dc.subject.keywordsHomes for the Agedeng
dc.subject.keywordsIdoso de 80 Anos ou maispor
dc.subject.keywordsAprendizagem de Máquinapor
dc.subject.keywordsIdosopor
dc.subject.keywordsInformática Médicapor
dc.subject.keywordsPrevenção de Acidentespor
dc.subject.keywordsRegistros Eletrônicos de Saúdepor
dc.subject.keywordsInteligência Artificialpor
dc.subject.keywordsInstituição de Longa Permanência para Idosospor
dc.subject.lembCiencias médicasspa
dc.subject.lembCiencias de la vidaspa
dc.subject.proposalCiencias de la saludspa
dc.titleConstrucción de un prototipo de registro de caídas basado en machine learning para mayores institucionalizadosspa
dc.title.translatedDevelopment of a fall registration prototype based on machine learning for institutionalized older adultseng
dc.title.translatedConstrução de um protótipo de registro de quedas baseado em machine learning para idosos institucionalizadospor
dc.typeArticleeng
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.driverinfo:eu-repo/semantics/article
dc.type.hasversioninfo:eu-repo/semantics/publishedVersion
dc.type.localArtículospa
dc.type.redcolhttp://purl.org/redcol/resource_type/ART

Archivos

Bloque original

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
Articulo 11.pdf
Tamaño:
2.61 MB
Formato:
Adobe Portable Document Format
Descripción:
Artículo

Bloque de licencias

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
license.txt
Tamaño:
183 B
Formato:
Item-specific license agreed upon to submission
Descripción:

Colecciones