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

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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.accessioned2026-06-19T06:37:52Z
dc.date.available2026-06-19T06:37:52Z
dc.date.issued2024
dc.departmentMalatya Turgut Özal Üniversitesi
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.
dc.identifier.doi10.3390/diagnostics14050457
dc.identifier.issn2075-4418
dc.identifier.issue5
dc.identifier.orcid0000-0001-5406-098X
dc.identifier.orcid0000-0002-9848-7958
dc.identifier.orcid0000-0002-4543-8853
dc.identifier.orcid0000-0001-6687-979X
dc.identifier.orcid0000-0001-7677-5070
dc.identifier.pmid38472930
dc.identifier.scopus2-s2.0-85187430982
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.3390/diagnostics14050457
dc.identifier.urihttps://hdl.handle.net/20.500.12899/5233
dc.identifier.volume14
dc.identifier.wosWOS:001182806200001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofDiagnostics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260612
dc.subjectSepsis
dc.subjectMachine Learning
dc.subjectExplainable Artificial Intelligence
dc.subjectBiomarker
dc.titleAssessment of Sepsis Risk at Admission to the Emergency Department: Clinical Interpretable Prediction Model
dc.typeArticle

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