Assessment of Sepsis Risk at Admission to the Emergency Department: Clinical Interpretable Prediction Model

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dc.authoridYagin, Fatma Hilal/0000-0002-9848-7958
dc.authoridArdigò, Luca Paolo/0000-0001-7677-5070
dc.authoridOzkan, Ahmet Selim/0000-0002-4543-8853
dc.authoridCOLAK, Cemil/0000-0001-5406-098X
dc.authorwosidYagin, Fatma Hilal/ABI-8066-2020
dc.authorwosidArdigò, Luca Paolo/H-8955-2019
dc.authorwosidOzkan, Ahmet Selim/ABH-2918-2020
dc.contributor.authorAygun, Umran
dc.contributor.authorYagin, Fatma Hilal
dc.contributor.authorYagin, Burak
dc.contributor.authorYasar, Seyma
dc.contributor.authorColak, Cemil
dc.contributor.authorOzkan, Ahmet Selim
dc.contributor.authorArdigo, Luca Paolo
dc.date.accessioned2024-08-04T20:55:09Z
dc.date.available2024-08-04T20:55:09Z
dc.date.issued2024
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThis study aims to develop an interpretable prediction model based on explainable artificial intelligence to predict bacterial sepsis and discover important biomarkers. A total of 1572 adult patients, 560 of whom were sepsis positive and 1012 of whom were negative, who were admitted to the emergency department with suspicion of sepsis, were examined. We investigated the performance characteristics of sepsis biomarkers alone and in combination for confirmed sepsis diagnosis using Sepsis-3 criteria. Three different tree-based algorithms-Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost)-were used for sepsis prediction, and after examining comprehensive performance metrics, descriptions of the optimal model were obtained with the SHAP method. The XGBoost model achieved accuracy of 0.898 (0.868-0.929) and area under the ROC curve (AUC) of 0.940 (0.898-0.980) with a 95% confidence interval. The five biomarkers for predicting sepsis were age, respiratory rate, oxygen saturation, procalcitonin, and positive blood culture. SHAP results revealed that older age, higher respiratory rate, procalcitonin, neutrophil-lymphocyte count ratio, C-reactive protein, plaque, leukocyte particle concentration, as well as lower oxygen saturation, systolic blood pressure, and hemoglobin levels increased the risk of sepsis. As a result, the Explainable Artificial Intelligence (XAI)-based prediction model can guide clinicians in the early diagnosis and treatment of sepsis, providing more effective sepsis management and potentially reducing mortality rates and medical costs.en_US
dc.identifier.doi10.3390/diagnostics14050457
dc.identifier.issn2075-4418
dc.identifier.issue5en_US
dc.identifier.pmid38472930en_US
dc.identifier.scopus2-s2.0-85187430982en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/diagnostics14050457
dc.identifier.urihttps://hdl.handle.net/11616/101869
dc.identifier.volume14en_US
dc.identifier.wosWOS:001182806200001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofDiagnosticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectsepsisen_US
dc.subjectmachine learningen_US
dc.subjectexplainable artificial intelligenceen_US
dc.subjectbiomarkeren_US
dc.titleAssessment of Sepsis Risk at Admission to the Emergency Department: Clinical Interpretable Prediction Modelen_US
dc.typeArticleen_US

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