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dc.contributorVall d'Hebron Barcelona Hospital Campus
dc.contributor.authorKui, Balázs
dc.contributor.authorPintér, József
dc.contributor.authorMolontay, Roland
dc.contributor.authorNagy, Marcell
dc.contributor.authorFarkas, Nelli
dc.contributor.authorGede, Noémi
dc.contributor.authorPando Rau, Elizabeth
dc.contributor.authorAlberti Delgado, Piero Arturo
dc.contributor.authorGomez Jurado, Maria Jose
dc.date.accessioned2022-09-09T08:31:53Z
dc.date.available2022-09-09T08:31:53Z
dc.date.issued2022-06
dc.identifier.citationKui B, Pintér J, Molontay R, Nagy M, Farkas N, Gede N, et al. EASY-APP: An artificial intelligence model and application for early and easy prediction of severity in acute pancreatitis. Clin Transl Med. 2022 Jun;12(6):e842.
dc.identifier.issn2001-1326
dc.identifier.urihttp://hdl.handle.net/11351/8098
dc.descriptionAcute pancreatitis; Artificial intelligence; Severity prediction
dc.description.abstractBackground Acute pancreatitis (AP) is a potentially severe or even fatal inflammation of the pancreas. Early identification of patients at high risk for developing a severe course of the disease is crucial for preventing organ failure and death. Most of the former predictive scores require many parameters or at least 24 h to predict the severity; therefore, the early therapeutic window is often missed. Methods The early achievable severity index (EASY) is a multicentre, multinational, prospective and observational study (ISRCTN10525246). The predictions were made using machine learning models. We used the scikit-learn, xgboost and catboost Python packages for modelling. We evaluated our models using fourfold cross-validation, and the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), and accuracy metrics were calculated on the union of the test sets of the cross-validation. The most critical factors and their contribution to the prediction were identified using a modern tool of explainable artificial intelligence called SHapley Additive exPlanations (SHAP). Results The prediction model was based on an international cohort of 1184 patients and a validation cohort of 3543 patients. The best performing model was an XGBoost classifier with an average AUC score of 0.81 ± 0.033 and an accuracy of 89.1%, and the model improved with experience. The six most influential features were the respiratory rate, body temperature, abdominal muscular reflex, gender, age and glucose level. Using the XGBoost machine learning algorithm for prediction, the SHAP values for the explanation and the bootstrapping method to estimate confidence, we developed a free and easy-to-use web application in the Streamlit Python-based framework (http://easy-app.org/). Conclusions The EASY prediction score is a practical tool for identifying patients at high risk for severe AP within hours of hospital admission. The web application is available for clinicians and contributes to the improvement of the model.
dc.language.isoeng
dc.publisherWiley
dc.relation.ispartofseriesClinical and Translational Medicine;12(6)
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceScientia
dc.subjectPancreatitis - Diagnòstic
dc.subjectIntel·ligència artificial - Aplicacions a la medicina
dc.subject.meshPancreatitis
dc.subject.mesh/diagnosis
dc.subject.meshArtificial Intelligence
dc.titleEASY-APP: An artificial intelligence model and application for early and easy prediction of severity in acute pancreatitis
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.1002/ctm2.842
dc.subject.decspancreatitis
dc.subject.decs/diagnóstico
dc.subject.decsinteligencia artificial
dc.relation.publishversionhttps://doi.org/10.1002/ctm2.842
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.audienceProfessionals
dc.contributor.organismesInstitut Català de la Salut
dc.contributor.authoraffiliation[Kui B] Department of Medicine, University of Szeged, Szeged, Hungary. Centre for Translational Medicine, Department of Medicine, University of Szeged, Szeged, Hungary. [Pintér J, Nagy M] Department of Stochastics, Institute of Mathematics, Budapest University of Technology and Economics, Budapest, Hungary. [Molontay R] Department of Stochastics, Institute of Mathematics, Budapest University of Technology and Economics, Budapest, Hungary. MTA-BME Stochastics Research Group, Budapest, Hungary. [Farkas N] Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Pécs, Hungary. Institute of Bioanalysis, Medical School, University of Pécs, Pécs, Hungary. [Gede N] Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Pécs, Hungary. [Pando E, Alberti P, Gómez-Jurado MJ] Servei de Cirurgia Hepatobiliopancreàtica i Trasplantaments, Vall d’Hebron Hospital Universitari, Barcelona, Spain. Universitat Autònoma de Barcelona, Bellaterra, Spain
dc.identifier.pmid35653504
dc.identifier.wos000804849400001
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess


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