Literature review of machine-learning algorithms for pressure ulcer prevention: challenges and opportunities
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , |
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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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 |
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application/pdf |
dc.publisher.none.fl_str_mv |
MDPI |
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MDPI |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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