Drug-related fall risk in hospitals: a machine learning approach
Autor(a) principal: | |
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Data de Publicação: | 2023 |
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/10174/34324 https://doi.org/http://dx.doi.org/10.37689/acta-ape/2023AO007711 |
Resumo: | Objective: To compare the performance of machine-learning models with the Medication Fall Risk Score (MFRS) in predicting fall risk related to prescription medications. Methods: This is a retrospective case-control study of adult and older adult patients in a tertiary hospital in Porto Alegre, RS, Brazil. Prescription drugs and drug classes were investigated. Data were exported to the RStudio software for statistical analysis. The variables were analyzed using Logistic Regression, Naive Bayes, Random Forest, and Gradient Boosting algorithms. Algorithm validation was performed using 10-fold cross validation. The Youden index was the metric selected to evaluate the models. The project was approved by the Research Ethics Committee. Results: The machine-learning model showing the best performance was the one developed by the Naive Bayes algorithm. The model built from a data set of a specific hospital showed better results for the studied population than did MFRS, a generalizable tool. Conclusion: Risk-prediction tools that depend on proper application and registration by professionals require time and attention that could be allocated to patient care. Prediction models built through machine-learning algorithms can help identify risks to improve patient care. |
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Drug-related fall risk in hospitals: a machine learning approachRisco de queda relacionado a medicamentos em hospitais: abordagem de aprendizado de máquinaFallsPatient SafetyObjective: To compare the performance of machine-learning models with the Medication Fall Risk Score (MFRS) in predicting fall risk related to prescription medications. Methods: This is a retrospective case-control study of adult and older adult patients in a tertiary hospital in Porto Alegre, RS, Brazil. Prescription drugs and drug classes were investigated. Data were exported to the RStudio software for statistical analysis. The variables were analyzed using Logistic Regression, Naive Bayes, Random Forest, and Gradient Boosting algorithms. Algorithm validation was performed using 10-fold cross validation. The Youden index was the metric selected to evaluate the models. The project was approved by the Research Ethics Committee. Results: The machine-learning model showing the best performance was the one developed by the Naive Bayes algorithm. The model built from a data set of a specific hospital showed better results for the studied population than did MFRS, a generalizable tool. Conclusion: Risk-prediction tools that depend on proper application and registration by professionals require time and attention that could be allocated to patient care. Prediction models built through machine-learning algorithms can help identify risks to improve patient care.Acta Paulista de Enfermagem2023-02-14T12:13:49Z2023-02-142023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/34324http://hdl.handle.net/10174/34324https://doi.org/http://dx.doi.org/10.37689/acta-ape/2023AO007711engSilva AP, Santos HD, Rotta AL, Baiocco GG, Vieira R, Urbanetto JS. Drug-related fall risk in hospitals: a machine learning approach. Acta Paul Enferm. 2023;36:eAPE00771.https://acta-ape.org/article/risco-de-queda-relacionado-a-medicamentos-em-hospitais-abordagem-de-aprendizado-de-maquina/ndndndndrenatav@uevora.ptnd283Silva, AmandaSantos, HenriqueRotta, AnaBaiocco, GraziellaVieira, RenataUrbanetto, Janeteinfo: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:RCAAP2024-01-03T19:36:28Zoai:dspace.uevora.pt:10174/34324Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:22:47.959372Repositório 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 |
Drug-related fall risk in hospitals: a machine learning approach Risco de queda relacionado a medicamentos em hospitais: abordagem de aprendizado de máquina |
title |
Drug-related fall risk in hospitals: a machine learning approach |
spellingShingle |
Drug-related fall risk in hospitals: a machine learning approach Silva, Amanda Falls Patient Safety |
title_short |
Drug-related fall risk in hospitals: a machine learning approach |
title_full |
Drug-related fall risk in hospitals: a machine learning approach |
title_fullStr |
Drug-related fall risk in hospitals: a machine learning approach |
title_full_unstemmed |
Drug-related fall risk in hospitals: a machine learning approach |
title_sort |
Drug-related fall risk in hospitals: a machine learning approach |
author |
Silva, Amanda |
author_facet |
Silva, Amanda Santos, Henrique Rotta, Ana Baiocco, Graziella Vieira, Renata Urbanetto, Janete |
author_role |
author |
author2 |
Santos, Henrique Rotta, Ana Baiocco, Graziella Vieira, Renata Urbanetto, Janete |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Silva, Amanda Santos, Henrique Rotta, Ana Baiocco, Graziella Vieira, Renata Urbanetto, Janete |
dc.subject.por.fl_str_mv |
Falls Patient Safety |
topic |
Falls Patient Safety |
description |
Objective: To compare the performance of machine-learning models with the Medication Fall Risk Score (MFRS) in predicting fall risk related to prescription medications. Methods: This is a retrospective case-control study of adult and older adult patients in a tertiary hospital in Porto Alegre, RS, Brazil. Prescription drugs and drug classes were investigated. Data were exported to the RStudio software for statistical analysis. The variables were analyzed using Logistic Regression, Naive Bayes, Random Forest, and Gradient Boosting algorithms. Algorithm validation was performed using 10-fold cross validation. The Youden index was the metric selected to evaluate the models. The project was approved by the Research Ethics Committee. Results: The machine-learning model showing the best performance was the one developed by the Naive Bayes algorithm. The model built from a data set of a specific hospital showed better results for the studied population than did MFRS, a generalizable tool. Conclusion: Risk-prediction tools that depend on proper application and registration by professionals require time and attention that could be allocated to patient care. Prediction models built through machine-learning algorithms can help identify risks to improve patient care. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-02-14T12:13:49Z 2023-02-14 2023-01-01T00:00:00Z |
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/10174/34324 http://hdl.handle.net/10174/34324 https://doi.org/http://dx.doi.org/10.37689/acta-ape/2023AO007711 |
url |
http://hdl.handle.net/10174/34324 https://doi.org/http://dx.doi.org/10.37689/acta-ape/2023AO007711 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Silva AP, Santos HD, Rotta AL, Baiocco GG, Vieira R, Urbanetto JS. Drug-related fall risk in hospitals: a machine learning approach. Acta Paul Enferm. 2023;36:eAPE00771. https://acta-ape.org/article/risco-de-queda-relacionado-a-medicamentos-em-hospitais-abordagem-de-aprendizado-de-maquina/ nd nd nd nd renatav@uevora.pt nd 283 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Acta Paulista de Enfermagem |
publisher.none.fl_str_mv |
Acta Paulista de Enfermagem |
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reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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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 |
reponame_str |
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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1799136710131449856 |