Detection of Patients at Risk of Multidrug-Resistant Enterobacteriaceae Infection Using Graph Neural Networks: A Retrospective Study

Detalhes bibliográficos
Autor(a) principal: Gouareb, Racha
Data de Publicação: 2023
Outros Autores: Bornet, Alban, Proios, Dimitrios, Pereira, Sónia Gonçalves, Teodoro, Douglas
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.8/9299
Resumo: Funding: This research was funded by the Joint Swiss–Portuguese Academic Program from the University of Applied Sciences and Arts Western Switzerland (HES-SO) and the Fundação para a Ciência e Tecnologia (FCT). S.G.P. also acknowledges FCT for her direct funding (CEECINST/00051/2018) and her research unit (UIDB/05704/2020). Funders were not involved in the study design, data pre-processing, data analysis, interpretation, or report writing. Author contributions: R.G. and A.B. designed and implemented the models, and ran the experiments and analyses. R.G. and D.T. wrote the manuscript draft. D.T. and S.G.P. conceptualized the experiments and acquired funding. R.G., D.P., and S.G.P. curated the data. R.G., A.B., D.P., and D.T. analyzed the data. All authors reviewed and approved the manuscript. Competing interests: The authors declare that they have no competing interests.
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spelling Detection of Patients at Risk of Multidrug-Resistant Enterobacteriaceae Infection Using Graph Neural Networks: A Retrospective StudyMultidrug-Resistant Enterobacteriaceae InfectionGraph Neural NetworksPatients at RiskMultidrug-resistant (MDR) EnterobacteriaceaeEnterobacteriaceae bacteriaMDR EnterobacteriaceaeFunding: This research was funded by the Joint Swiss–Portuguese Academic Program from the University of Applied Sciences and Arts Western Switzerland (HES-SO) and the Fundação para a Ciência e Tecnologia (FCT). S.G.P. also acknowledges FCT for her direct funding (CEECINST/00051/2018) and her research unit (UIDB/05704/2020). Funders were not involved in the study design, data pre-processing, data analysis, interpretation, or report writing. Author contributions: R.G. and A.B. designed and implemented the models, and ran the experiments and analyses. R.G. and D.T. wrote the manuscript draft. D.T. and S.G.P. conceptualized the experiments and acquired funding. R.G., D.P., and S.G.P. curated the data. R.G., A.B., D.P., and D.T. analyzed the data. All authors reviewed and approved the manuscript. Competing interests: The authors declare that they have no competing interests.Background: While Enterobacteriaceae bacteria are commonly found in the healthy human gut, their colonization of other body parts can potentially evolve into serious infections and health threats. We investigate a graph-based machine learning model to predict risks of inpatient colonization by multidrug-resistant (MDR) Enterobacteriaceae. Methods: Colonization prediction was defined as a binary task, where the goal is to predict whether a patient is colonized by MDR Enterobacteriaceae in an undesirable body part during their hospital stay. To capture topological features, interactions among patients and healthcare workers were modeled using a graph structure, where patients are described by nodes and their interactions are described by edges. Then, a graph neural network (GNN) model was trained to learn colonization patterns from the patient network enriched with clinical and spatiotemporal features. Results: The GNN model achieves performance between 0.91 and 0.96 area under the receiver operating characteristic curve (AUROC) when trained in inductive and transductive settings, respectively, up to 8% above a logistic regression baseline (0.88). Comparing network topologies, the configuration considering ward-related edges (0.91 inductive, 0.96 transductive) outperforms the configurations considering caregiver-related edges (0.88, 0.89) and both types of edges (0.90, 0.94). For the top 3 most prevalent MDR Enterobacteriaceae, the AUROC varies from 0.94 for Citrobacter freundii up to 0.98 for Enterobacter cloacae using the best-performing GNN model. Conclusion: Topological features via graph modeling improve the performance of machine learning models for Enterobacteriaceae colonization prediction. GNNs could be used to support infection prevention and control programs to detect patients at risk of colonization by MDR Enterobacteriaceae and other bacteria families.American Association for the Advancement of ScienceIC-OnlineGouareb, RachaBornet, AlbanProios, DimitriosPereira, Sónia GonçalvesTeodoro, Douglas2024-01-17T11:39:49Z2023-012023-12-24T15:42:43Z2023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.8/9299engGouareb R, Bornet A, Proios D, Pereira SG, Teodoro D. Detection of Patients at Risk of Multidrug-Resistant Enterobacteriaceae Infection Using Graph Neural Networks: A Retrospective Study. Health Data Sci. 2023;3:Article 0099. https://doi. org/10.34133/hds.00990099cv-prod-3444907https://doi. org/10.34133/hds.00992765-8783info: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:RCAAP2024-01-21T02:30:56Zoai:iconline.ipleiria.pt:10400.8/9299Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:52:43.961613Repositório 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 Detection of Patients at Risk of Multidrug-Resistant Enterobacteriaceae Infection Using Graph Neural Networks: A Retrospective Study
title Detection of Patients at Risk of Multidrug-Resistant Enterobacteriaceae Infection Using Graph Neural Networks: A Retrospective Study
spellingShingle Detection of Patients at Risk of Multidrug-Resistant Enterobacteriaceae Infection Using Graph Neural Networks: A Retrospective Study
Gouareb, Racha
Multidrug-Resistant Enterobacteriaceae Infection
Graph Neural Networks
Patients at Risk
Multidrug-resistant (MDR) Enterobacteriaceae
Enterobacteriaceae bacteria
MDR Enterobacteriaceae
title_short Detection of Patients at Risk of Multidrug-Resistant Enterobacteriaceae Infection Using Graph Neural Networks: A Retrospective Study
title_full Detection of Patients at Risk of Multidrug-Resistant Enterobacteriaceae Infection Using Graph Neural Networks: A Retrospective Study
title_fullStr Detection of Patients at Risk of Multidrug-Resistant Enterobacteriaceae Infection Using Graph Neural Networks: A Retrospective Study
title_full_unstemmed Detection of Patients at Risk of Multidrug-Resistant Enterobacteriaceae Infection Using Graph Neural Networks: A Retrospective Study
title_sort Detection of Patients at Risk of Multidrug-Resistant Enterobacteriaceae Infection Using Graph Neural Networks: A Retrospective Study
author Gouareb, Racha
author_facet Gouareb, Racha
Bornet, Alban
Proios, Dimitrios
Pereira, Sónia Gonçalves
Teodoro, Douglas
author_role author
author2 Bornet, Alban
Proios, Dimitrios
Pereira, Sónia Gonçalves
Teodoro, Douglas
author2_role author
author
author
author
dc.contributor.none.fl_str_mv IC-Online
dc.contributor.author.fl_str_mv Gouareb, Racha
Bornet, Alban
Proios, Dimitrios
Pereira, Sónia Gonçalves
Teodoro, Douglas
dc.subject.por.fl_str_mv Multidrug-Resistant Enterobacteriaceae Infection
Graph Neural Networks
Patients at Risk
Multidrug-resistant (MDR) Enterobacteriaceae
Enterobacteriaceae bacteria
MDR Enterobacteriaceae
topic Multidrug-Resistant Enterobacteriaceae Infection
Graph Neural Networks
Patients at Risk
Multidrug-resistant (MDR) Enterobacteriaceae
Enterobacteriaceae bacteria
MDR Enterobacteriaceae
description Funding: This research was funded by the Joint Swiss–Portuguese Academic Program from the University of Applied Sciences and Arts Western Switzerland (HES-SO) and the Fundação para a Ciência e Tecnologia (FCT). S.G.P. also acknowledges FCT for her direct funding (CEECINST/00051/2018) and her research unit (UIDB/05704/2020). Funders were not involved in the study design, data pre-processing, data analysis, interpretation, or report writing. Author contributions: R.G. and A.B. designed and implemented the models, and ran the experiments and analyses. R.G. and D.T. wrote the manuscript draft. D.T. and S.G.P. conceptualized the experiments and acquired funding. R.G., D.P., and S.G.P. curated the data. R.G., A.B., D.P., and D.T. analyzed the data. All authors reviewed and approved the manuscript. Competing interests: The authors declare that they have no competing interests.
publishDate 2023
dc.date.none.fl_str_mv 2023-01
2023-12-24T15:42:43Z
2023-01-01T00:00:00Z
2024-01-17T11:39:49Z
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/10400.8/9299
url http://hdl.handle.net/10400.8/9299
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Gouareb R, Bornet A, Proios D, Pereira SG, Teodoro D. Detection of Patients at Risk of Multidrug-Resistant Enterobacteriaceae Infection Using Graph Neural Networks: A Retrospective Study. Health Data Sci. 2023;3:Article 0099. https://doi. org/10.34133/hds.0099
0099
cv-prod-3444907
https://doi. org/10.34133/hds.0099
2765-8783
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 American Association for the Advancement of Science
publisher.none.fl_str_mv American Association for the Advancement of Science
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
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