Literature review of machine-learning algorithms for pressure ulcer prevention: challenges and opportunities

Detalhes bibliográficos
Autor(a) principal: Ribeiro, Fernando Reinaldo
Data de Publicação: 2021
Outros Autores: Fidalgo, Filipe, Silva, Arlindo, Metrôlho, J.C.M.M., Santos, Osvaldo, Dionísio, Rogério
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.11/7734
Resumo: Pressure ulcers are associated with significant morbidity, resulting in a decreased quality of life for the patient, and contributing to healthcare professional burnout, as well as an increase of health service costs. Their prompt diagnosis and treatment are important, and several studies have proposed solutions to help healthcare professionals in this process. This work analyzes studies that use machine-learning algorithms for risk assessment and management of preventive treatments for pressure ulcers. More specifically, it focuses on the use of machine-learning algorithms that combine information from intrinsic and extrinsic pressure-ulcer predisposing factors to produce recommendations/alerts to healthcare professionals. The review includes articles published from January 2010 to June 2021. From 60 records screened, seven articles were analyzed in full-text form. The results show that most of the proposed algorithms do not use information related to both intrinsic and extrinsic predisposing factors and that many of the approaches separately address one of the following three components: data acquisition; data analysis, and production of complementary support to well-informed clinical decision-making. Additionally, only a few studies describe in detail the outputs of the algorithm, such as alerts and recommendations, without assessing their impacts on healthcare professionals’ activities
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spelling Literature review of machine-learning algorithms for pressure ulcer prevention: challenges and opportunitiesArtificial intelligenceBurnoutClinical decision supportLiterature reviewMachine learningPressure injury preventionPressure ulcers preventionQuality of healthcarePressure ulcers are associated with significant morbidity, resulting in a decreased quality of life for the patient, and contributing to healthcare professional burnout, as well as an increase of health service costs. Their prompt diagnosis and treatment are important, and several studies have proposed solutions to help healthcare professionals in this process. This work analyzes studies that use machine-learning algorithms for risk assessment and management of preventive treatments for pressure ulcers. More specifically, it focuses on the use of machine-learning algorithms that combine information from intrinsic and extrinsic pressure-ulcer predisposing factors to produce recommendations/alerts to healthcare professionals. The review includes articles published from January 2010 to June 2021. From 60 records screened, seven articles were analyzed in full-text form. The results show that most of the proposed algorithms do not use information related to both intrinsic and extrinsic predisposing factors and that many of the approaches separately address one of the following three components: data acquisition; data analysis, and production of complementary support to well-informed clinical decision-making. Additionally, only a few studies describe in detail the outputs of the algorithm, such as alerts and recommendations, without assessing their impacts on healthcare professionals’ activitiesMDPIRepositório Científico do Instituto Politécnico de Castelo BrancoRibeiro, Fernando ReinaldoFidalgo, FilipeSilva, ArlindoMetrôlho, J.C.M.M.Santos, OsvaldoDionísio, Rogério2021-12-03T16:56:45Z20212021-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.11/7734engRibeiro, Fernando, Filipe Fidalgo, Arlindo Silva, José Metrôlho, Osvaldo Santos, and Rogério Dionisio. 2021. “Literature Review of Machine-Learning Algorithms for Pressure Ulcer Prevention: Challenges and Opportunities.” Informatics 8(4).2227-9709https://doi.org/10.3390/informatics8040076info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-25T01:47:36Zoai:repositorio.ipcb.pt:10400.11/7734Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T16:38:13.905134Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Literature review of machine-learning algorithms for pressure ulcer prevention: challenges and opportunities
title Literature review of machine-learning algorithms for pressure ulcer prevention: challenges and opportunities
spellingShingle Literature review of machine-learning algorithms for pressure ulcer prevention: challenges and opportunities
Ribeiro, Fernando Reinaldo
Artificial intelligence
Burnout
Clinical decision support
Literature review
Machine learning
Pressure injury prevention
Pressure ulcers prevention
Quality of healthcare
title_short Literature review of machine-learning algorithms for pressure ulcer prevention: challenges and opportunities
title_full Literature review of machine-learning algorithms for pressure ulcer prevention: challenges and opportunities
title_fullStr Literature review of machine-learning algorithms for pressure ulcer prevention: challenges and opportunities
title_full_unstemmed Literature review of machine-learning algorithms for pressure ulcer prevention: challenges and opportunities
title_sort Literature review of machine-learning algorithms for pressure ulcer prevention: challenges and opportunities
author Ribeiro, Fernando Reinaldo
author_facet Ribeiro, Fernando Reinaldo
Fidalgo, Filipe
Silva, Arlindo
Metrôlho, J.C.M.M.
Santos, Osvaldo
Dionísio, Rogério
author_role author
author2 Fidalgo, Filipe
Silva, Arlindo
Metrôlho, J.C.M.M.
Santos, Osvaldo
Dionísio, Rogério
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Repositório Científico do Instituto Politécnico de Castelo Branco
dc.contributor.author.fl_str_mv Ribeiro, Fernando Reinaldo
Fidalgo, Filipe
Silva, Arlindo
Metrôlho, J.C.M.M.
Santos, Osvaldo
Dionísio, Rogério
dc.subject.por.fl_str_mv Artificial intelligence
Burnout
Clinical decision support
Literature review
Machine learning
Pressure injury prevention
Pressure ulcers prevention
Quality of healthcare
topic Artificial intelligence
Burnout
Clinical decision support
Literature review
Machine learning
Pressure injury prevention
Pressure ulcers prevention
Quality of healthcare
description Pressure ulcers are associated with significant morbidity, resulting in a decreased quality of life for the patient, and contributing to healthcare professional burnout, as well as an increase of health service costs. Their prompt diagnosis and treatment are important, and several studies have proposed solutions to help healthcare professionals in this process. This work analyzes studies that use machine-learning algorithms for risk assessment and management of preventive treatments for pressure ulcers. More specifically, it focuses on the use of machine-learning algorithms that combine information from intrinsic and extrinsic pressure-ulcer predisposing factors to produce recommendations/alerts to healthcare professionals. The review includes articles published from January 2010 to June 2021. From 60 records screened, seven articles were analyzed in full-text form. The results show that most of the proposed algorithms do not use information related to both intrinsic and extrinsic predisposing factors and that many of the approaches separately address one of the following three components: data acquisition; data analysis, and production of complementary support to well-informed clinical decision-making. Additionally, only a few studies describe in detail the outputs of the algorithm, such as alerts and recommendations, without assessing their impacts on healthcare professionals’ activities
publishDate 2021
dc.date.none.fl_str_mv 2021-12-03T16:56:45Z
2021
2021-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.11/7734
url http://hdl.handle.net/10400.11/7734
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Ribeiro, Fernando, Filipe Fidalgo, Arlindo Silva, José Metrôlho, Osvaldo Santos, and Rogério Dionisio. 2021. “Literature Review of Machine-Learning Algorithms for Pressure Ulcer Prevention: Challenges and Opportunities.” Informatics 8(4).
2227-9709
https://doi.org/10.3390/informatics8040076
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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