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dc.contributor.authorKocbek, Primoz
dc.contributor.authorFijacko, Nino
dc.contributor.authorSoguero-Ruiz, Cristina
dc.contributor.authorMikalsen, Karl Øyvind
dc.contributor.authorMaver, Uros
dc.contributor.authorBrzan, Petra Povalej
dc.contributor.authorStozer, Andraz
dc.contributor.authorJenssen, Robert
dc.contributor.authorSkrøvseth, Stein Olav
dc.contributor.authorStiglic, Gregor
dc.date.accessioned2019-08-21T10:47:39Z
dc.date.available2019-08-21T10:47:39Z
dc.date.issued2019-02-19
dc.description.abstractThis study describes a novel approach to solve the surgical site infection (SSI) classification problem. Feature engineering has traditionally been one of the most important steps in solving complex classification problems, especially in cases with temporal data. The described novel approach is based on abstraction of temporal data recorded in three temporal windows. Maximum likelihood L1-norm (lasso) regularization was used in penalized logistic regression to predict the onset of surgical site infection occurrence based on available patient blood testing results up to the day of surgery. Prior knowledge of predictors (blood tests) was integrated in the modelling by introduction of penalty factors depending on blood test prices and an early stopping parameter limiting the maximum number of selected features used in predictive modelling. Finally, solutions resulting in higher interpretability and cost-effectiveness were demonstrated. Using repeated holdout cross-validation, the baseline C-reactive protein (CRP) classifier achieved a mean AUC of 0.801, whereas our best full lasso model achieved a mean AUC of 0.956. Best model testing results were achieved for full lasso model with maximum number of features limited at 20 features with an AUC of 0.967. Presented models showed the potential to not only support domain experts in their decision making but could also prove invaluable for improvement in prediction of SSI occurrence, which may even help setting new guidelines in the field of preoperative SSI prevention and surveillance.en_US
dc.description.sponsorshipSlovenian Research Agency Spanish Government Institute of Health Carlos III, Spainen_US
dc.descriptionSource at <a href=https://doi.org/10.1155/2019/2059851>https://doi.org/10.1155/2019/2059851. </a>en_US
dc.identifier.citationKocbek, P., Fijacko, N., Soguero-Ruiz, C.,, Mikalsen, K.Ø., Maver, U., Brzan, P.P. ... Stiglic, G. (2019). Maximizing Interpretability and Cost-Effectiveness of Surgical Site Infection (SSI) Predictive Models Using Feature-Specific Regularized Logistic Regression on Preoperative Temporal Data. <i>Computational & Mathematical Methods in Medicine</i>, 2059851. https://doi.org/10.1155/2019/2059851en_US
dc.identifier.cristinIDFRIDAID 1707886
dc.identifier.doi10.1155/2019/2059851
dc.identifier.issn1748-670X
dc.identifier.issn1748-6718
dc.identifier.urihttps://hdl.handle.net/10037/15987
dc.language.isoengen_US
dc.publisherHindawien_US
dc.relation.journalComputational & Mathematical Methods in Medicine
dc.relation.projectIDinfo:eu-repo/grantAgreement/RCN/IKTPLUSS/297186/Norway/Northern Lights Deep Learning Workshop 2019//en_US
dc.rights.accessRightsopenAccessen_US
dc.subjectVDP::Technology: 500::Medical technology: 620en_US
dc.subjectVDP::Teknologi: 500::Medisinsk teknologi: 620en_US
dc.titleMaximizing Interpretability and Cost-Effectiveness of Surgical Site Infection (SSI) Predictive Models Using Feature-Specific Regularized Logistic Regression on Preoperative Temporal Dataen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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