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

2023-01Journal article Open access
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dc.contributor.authorGouareb, Racha
dc.contributor.authorBornet, Alban
dc.contributor.authorProios, Dimitrios
dc.contributor.authorPereira, Sónia Gonçalves
dc.contributor.authorTeodoro, Douglas
dc.date.accessioned2024-01-17T11:39:49Z
dc.date.available2024-01-17T11:39:49Z
dc.date.issued2023-01
dc.date.updated2023-12-24T15:42:43Z
dc.descriptionFunding: 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.pt_PT
dc.description.abstractBackground: 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.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationGouareb 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.0099pt_PT
dc.identifier.doihttps://doi. org/10.34133/hds.0099pt_PT
dc.identifier.eissn2765-8783
dc.identifier.other0099
dc.identifier.slugcv-prod-3444907
dc.identifier.urihttp://hdl.handle.net/10400.8/9299
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherAmerican Association for the Advancement of Sciencept_PT
dc.relationNot Available
dc.relationCenter for Innovative Care and Health Technology
dc.relation.publisherversionhttps://spj.science.org/doi/10.34133/hds.0099pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectMultidrug-Resistant Enterobacteriaceae Infectionpt_PT
dc.subjectGraph Neural Networkspt_PT
dc.subjectPatients at Riskpt_PT
dc.subjectMultidrug-resistant (MDR) Enterobacteriaceaept_PT
dc.subjectEnterobacteriaceae bacteriapt_PT
dc.subjectMDR Enterobacteriaceaept_PT
dc.titleDetection of Patients at Risk of Multidrug-Resistant Enterobacteriaceae Infection Using Graph Neural Networks: A Retrospective Studypt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleNot Available
oaire.awardTitleCenter for Innovative Care and Health Technology
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/CEEC INST 2018/CEECINST%2F00051%2F2018%2FCP1566%2FCT0001/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05704%2F2020/PT
oaire.citation.titleHealth Data Sciencept_PT
oaire.citation.volume3pt_PT
oaire.fundingStreamCEEC INST 2018
oaire.fundingStream6817 - DCRRNI ID
person.familyNameGonçalves Pereira
person.givenNameSónia
person.identifier461252
person.identifier.ciencia-idEB1F-4DB2-B0C9
person.identifier.orcid0000-0001-8197-1850
person.identifier.ridM-7588-2017
person.identifier.scopus-author-id7202024712
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.cv.cienciaid8615-92D1-7858 | Sónia Gonçalves Pereira
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication259ad2cc-f9ff-4c33-ad6d-10f5b3ff341a
relation.isAuthorOfPublication.latestForDiscovery259ad2cc-f9ff-4c33-ad6d-10f5b3ff341a
relation.isProjectOfPublication79a941dc-2813-4f89-850b-4228ff5224cc
relation.isProjectOfPublicationd421b07d-3471-4026-aa43-def80b8e142b
relation.isProjectOfPublication.latestForDiscoveryd421b07d-3471-4026-aa43-def80b8e142b

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