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

2021Journal article Open access
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dc.contributor.authorRibeiro, Fernando Reinaldo
dc.contributor.authorFidalgo, Filipe
dc.contributor.authorSilva, Arlindo F.
dc.contributor.authorMetrôlho, J.C.M.M.
dc.contributor.authorSantos, Osvaldo
dc.contributor.authorDionísio, Rogério
dc.date.accessioned2021-12-03T16:56:45Z
dc.date.available2021-12-03T16:56:45Z
dc.date.issued2021
dc.description.abstractPressure 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’ activitiespt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationRibeiro, 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).pt_PT
dc.identifier.doihttps://doi.org/10.3390/informatics8040076pt_PT
dc.identifier.issn2227-9709
dc.identifier.urihttp://hdl.handle.net/10400.11/7734
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationCENTRO-01-0247-FEDER-070107pt_PT
dc.relation.publisherversionhttps://www.mdpi.com/2227-9709/8/4/76pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectArtificial intelligencept_PT
dc.subjectBurnoutpt_PT
dc.subjectClinical decision supportpt_PT
dc.subjectLiterature reviewpt_PT
dc.subjectMachine learningpt_PT
dc.subjectPressure injury preventionpt_PT
dc.subjectPressure ulcers preventionpt_PT
dc.subjectQuality of healthcarept_PT
dc.titleLiterature review of machine-learning algorithms for pressure ulcer prevention: challenges and opportunitiespt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue4pt_PT
oaire.citation.titleInformaticspt_PT
oaire.citation.volume8pt_PT
person.familyNameReinaldo Silva Garcia Ribeiro
person.familyNameFidalgo
person.familyNameSilva
person.familyNameMetrôlho
person.familyNameSantos
person.familyNamePAIS DIONÍSIO
person.givenNameFernando
person.givenNameFilipe
person.givenNameArlindo
person.givenNameJosé Carlos
person.givenNameOsvaldo
person.givenNameROGÉRIO
person.identifier2374225
person.identifier1688084
person.identifier30skvuAAAAAJ
person.identifier.ciencia-id7B1C-D761-291D
person.identifier.ciencia-idBC11-DFB7-A451
person.identifier.ciencia-id541A-41FB-4DC3
person.identifier.ciencia-id4B17-3AF4-7DD4
person.identifier.ciencia-id121D-8892-F723
person.identifier.ciencia-id2F1A-414F-368B
person.identifier.orcid0000-0002-1225-3844
person.identifier.orcid0000-0001-7326-9957
person.identifier.orcid0000-0002-0620-7518
person.identifier.orcid0000-0002-7327-2109
person.identifier.orcid0000-0003-0341-2839
person.identifier.orcid0000-0002-6810-2447
person.identifier.scopus-author-id56348667700
person.identifier.scopus-author-id6507997502
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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