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dc.contributor.authorBopche, Rajeev
dc.contributor.authorGustad, Lise Tuset
dc.contributor.authorAfset, Jan Egil
dc.contributor.authorEhrnström, Birgitta
dc.contributor.authorDamås, Jan Kristian
dc.contributor.authorNytrø, Øystein
dc.date.accessioned2024-10-17T13:02:05Z
dc.date.available2024-10-17T13:02:05Z
dc.date.issued2024-09-14
dc.description.abstractObjective - This study aimed to investigate the predictive capabilities of historical patient records to predict patient adverse outcomes such as mortality, readmission, and prolonged length of stay (PLOS).<p> <p>Methods - Leveraging a de-identified dataset from a tertiary care university hospital, we developed an eXplainable Artificial Intelligence (XAI) framework combining tree-based and traditional machine learning (ML) models with interpretations and statistical analysis of predictors of mortality, readmission, and PLOS.<p> <p>Results - Our framework demonstrated exceptional predictive performance with a notable area under the receiver operating characteristic (AUROC) of 0.9625 and an area under the precision-recall curve (AUPRC) of 0.8575 for 30-day mortality at discharge and an AUROC of 0.9545 and AUPRC of 0.8419 at admission. For the readmission and PLOS risk, the highest AUROC achieved were 0.8198 and 0.9797, respectively. The tree-based models consistently outperformed the traditional ML models in all 4 prediction tasks. The key predictors were age, derived temporal features, routine laboratory tests, and diagnostic and procedural codes.<p> <p>Conclusion - The study underscores the potential of leveraging medical history for enhanced hospital predictive analytics. We present an accurate and intuitive framework for early warning models that can be easily implemented in the current and developing digital health platforms to predict adverse outcomes accurately.en_US
dc.identifier.citationBopche R, Gustad LT, Afset JE, Ehrnström B, Damås JK, Nytrø ØN. In-hospital Mortality, Readmission, and Prolonged Length of Stay Risk Prediction Leveraging Historical Electronic Patient Records. JAMIA Open. 2024en_US
dc.identifier.cristinIDFRIDAID 2287041
dc.identifier.doi10.1093/jamiaopen/ooae074
dc.identifier.issn2574-2531
dc.identifier.urihttps://hdl.handle.net/10037/35287
dc.language.isoengen_US
dc.publisherOxford University Pressen_US
dc.relation.journalJAMIA Open
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2024 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleIn-hospital Mortality, Readmission, and Prolonged Length of Stay Risk Prediction Leveraging Historical Electronic Patient Recordsen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Med mindre det står noe annet, er denne innførselens lisens beskrevet som Attribution 4.0 International (CC BY 4.0)