Construcción de un prototipo de registro de caídas basado en machine learning para mayores institucionalizados
| dc.contributor.author | Dinamarca Montecinos, José Luis | |
| dc.contributor.author | Durán Novoa, Roberto Alejandro | |
| dc.contributor.author | Flores Moraga, María Jesús | |
| dc.contributor.author | Briede Westermeyer, Juan Carlos | |
| dc.contributor.orcid | Dinamarca Montecinos, José Luis [0000-0002-0186-5992] | spa |
| dc.contributor.orcid | Durán Novoa, Roberto Alejandro [0000-0003-4073-9363] | spa |
| dc.contributor.orcid | Flores Moraga, María Jesús [0009-0007-8099-2220] | spa |
| dc.contributor.orcid | Briede Westermeyer, Juan Carlos [0000-0002-5746-0169] | spa |
| dc.date.accessioned | 2025-11-04T22:32:46Z | |
| dc.date.available | 2025-11-04T22:32:46Z | |
| dc.date.issued | 2025-07-31 | |
| dc.description.abstract | 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. | spa |
| dc.description.abstractenglish | 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. | eng |
| dc.description.abstractother | 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. | por |
| dc.format.mimetype | application/pdf | spa |
| dc.identifier.doi | https://doi.org/10.29375/01237047.5165 | |
| dc.identifier.instname | instname:Universidad Autónoma de Bucaramanga UNAB | spa |
| dc.identifier.issn | i-ISSN 0123-7047 | spa |
| dc.identifier.issn | e-ISSN 2382-4603 | spa |
| dc.identifier.reponame | reponame:Repositorio Institucional UNAB | spa |
| dc.identifier.repourl | repourl:https://repository.unab.edu.co | spa |
| dc.identifier.uri | http://hdl.handle.net/20.500.12749/32103 | |
| dc.language.iso | spa | spa |
| dc.publisher.faculty | Facultad Ciencias de la Salud | spa |
| dc.publisher.grantor | Universidad Autónoma de Bucaramanga UNAB | spa |
| dc.relation | https://revistas.unab.edu.co/index.php/medunab/article/view/5165/4217 | spa |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | World Health Organization. Falls [Internet]. Ginebra: WHO; 2021. Recuperado a partir de: https://www.who. int/news-room/fact-sheets/detail/falls | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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. | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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/ | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.references | 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 | |
| dc.relation.uri | https://revistas.unab.edu.co/index.php/medunab/issue/view/305 | spa |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
| dc.rights.creativecommons | Atribución-NoComercial-SinDerivadas 2.5 Colombia | * |
| dc.rights.local | Abierto (Texto Completo) | spa |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/2.5/co/ | * |
| dc.source | Vol. 28 Núm. 1 (2025): abril-julio 2025: Envejecimiento Saludable; Geriatría; Salud del Anciano; 154-169 | spa |
| dc.subject | Ciencias médicas | spa |
| dc.subject | Ciencias de la vida | spa |
| dc.subject | Ciencias de la salud | spa |
| dc.subject | Anciano de 80 o más Años | spa |
| dc.subject | Aprendizaje Automático | spa |
| dc.subject | Anciano | spa |
| dc.subject | Informática Médica | spa |
| dc.subject | Prevención de Accidentes | spa |
| dc.subject | Registros Electrónicos de Salud | spa |
| dc.subject | Inteligencia Artificial | spa |
| dc.subject | Hogares para Ancianos | spa |
| dc.subject.keywords | Medical sciences | eng |
| dc.subject.keywords | Life sciences | eng |
| dc.subject.keywords | Health sciences | eng |
| dc.subject.keywords | Ciências médicas | por |
| dc.subject.keywords | Ciências da vida | por |
| dc.subject.keywords | Ciências da saúde | por |
| dc.subject.keywords | Aged, 80 and over | eng |
| dc.subject.keywords | Machine Learning | eng |
| dc.subject.keywords | Aged | eng |
| dc.subject.keywords | Medical Informatics | eng |
| dc.subject.keywords | Accident Prevention | eng |
| dc.subject.keywords | Electronic Health Records | eng |
| dc.subject.keywords | Artificial Intelligence | eng |
| dc.subject.keywords | Homes for the Aged | eng |
| dc.subject.keywords | Idoso de 80 Anos ou mais | por |
| dc.subject.keywords | Aprendizagem de Máquina | por |
| dc.subject.keywords | Idoso | por |
| dc.subject.keywords | Informática Médica | por |
| dc.subject.keywords | Prevenção de Acidentes | por |
| dc.subject.keywords | Registros Eletrônicos de Saúde | por |
| dc.subject.keywords | Inteligência Artificial | por |
| dc.subject.keywords | Instituição de Longa Permanência para Idosos | por |
| dc.subject.lemb | Ciencias médicas | spa |
| dc.subject.lemb | Ciencias de la vida | spa |
| dc.subject.proposal | Ciencias de la salud | spa |
| dc.title | Construcción de un prototipo de registro de caídas basado en machine learning para mayores institucionalizados | spa |
| dc.title.translated | Development of a fall registration prototype based on machine learning for institutionalized older adults | eng |
| dc.title.translated | Construção de um protótipo de registro de quedas baseado em machine learning para idosos institucionalizados | por |
| dc.type | Article | eng |
| dc.type.coar | http://purl.org/coar/resource_type/c_2df8fbb1 | |
| dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | spa |
| dc.type.driver | info:eu-repo/semantics/article | |
| dc.type.hasversion | info:eu-repo/semantics/publishedVersion | |
| dc.type.local | Artículo | spa |
| dc.type.redcol | http://purl.org/redcol/resource_type/ART |
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