Early prediction of the severe course, survival, and ICU requirements in acute pancreatitis by artificial intelligence

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dc.authorid0000-0001-8863-8348
dc.contributor.authorİnce, Ali Tüzün
dc.contributor.authorSilahtaroğlu, Gökhan
dc.contributor.authorSeven, Gülseren
dc.contributor.authorKoçhan, Koray
dc.contributor.authorYıldız, Kemal
dc.contributor.authorŞentürk, Hakan
dc.date.accessioned2023-07-05T12:35:11Z
dc.date.available2023-07-05T12:35:11Z
dc.date.issued2023
dc.departmentİstanbul Medipol Üniversitesi, İşletme ve Yönetim Bilimleri Fakültesi, Yönetim Bilişim Sistemleri Bölümü
dc.description.abstractObjective: To evaluate the success of artificial intelligence for early prediction of severe course, survival, and intensive care unit(ICU) requirement in patients with acute pancreatitis(AP).Methods: Retrospectively, 1334 patients were included the study. Severity is determined according to the Revised Atlanta Classification(RAC). The success of machine learning(ML) method was evaluated by 13 simple demographic, clinical, etiologic, and laboratory features obtained on ER admission. Additionally, it was evaluated whether Balthazar-computerized tomography severity index(CTSI) at 48-h contributed to success. The dataset was split into two parts, 90% for ML(of which 70% for learning and 30% for testing) and 10% for validation and 5-fold stratified sampling has been utilized. Variable Importance was used in the selection of features during training phase of machine. The Gradient Boost Algorithm trained the machine by KNIME analytics platform. SMOTE has been applied to increase the minority classes for training. The combined effects of the measured features were examined by multivariate logistic regression analysis and reciever operating curve curves of the prediction and confidence of the target variables were obtained.Results: Accuracy values for the early estimation of Atlanta severity score, ICU requirement, and survival were found as 88.20%, 98.25%, and 92.77% respectively. When Balthazar-CTSI score is used, results were found as 91.02%, 92.25%, and 98% respectively.Conclusions: The ML method we used successfully predicted the severe course, ICU requirement and survival, with promising accuracy values of over 88%. If 48-h Balthazar-CTSI is included in the calculation, the severity score and survival rates increase even more.
dc.identifier.citationİnce, A. T., Silahtaroğlu, G., Seven, G., Koçhan, K., Yıldız, K. ve Şentürk, H. (2023). Early prediction of the severe course, survival, and ICU requirements in acute pancreatitis by artificial intelligence. Pancreatology, 23(2), 176-186. https://dx.doi.org/10.1016/j.pan.2022.12.005
dc.identifier.doi10.1016/j.pan.2022.12.005
dc.identifier.endpage186
dc.identifier.issn1424-3903
dc.identifier.issn1424-3911
dc.identifier.issue2
dc.identifier.pmid36610872
dc.identifier.scopus2-s2.0-85146030177
dc.identifier.scopusqualityQ1
dc.identifier.startpage176
dc.identifier.urihttps://dx.doi.org/10.1016/j.pan.2022.12.005
dc.identifier.urihttps://hdl.handle.net/20.500.12511/11147
dc.identifier.volume23
dc.identifier.wos001005610200001en_US
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorSilahtaroğlu, Gökhan
dc.language.isoen
dc.publisherElsevier B.V.
dc.relation.ispartofPancreatologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectArtificial Intelligence
dc.subjectMachine Learning
dc.subjectAcute Pancreatitis
dc.subjectSeverity
dc.subjectBalthazar CTSI
dc.subjectRevised Atlanta Classification
dc.titleEarly prediction of the severe course, survival, and ICU requirements in acute pancreatitis by artificial intelligence
dc.typeArticle

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