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

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dc.contributor.authorİnce A. T.
dc.contributor.authorSilahtaroğlu G.
dc.contributor.authorSeven G.
dc.contributor.authorKoçhan K.
dc.contributor.authorYıldız K.
dc.contributor.authorŞentürk H.
dc.contributor.institutionauthorİNCE, ALİ TÜZÜN
dc.contributor.institutionauthorSEVEN, GÜLSEREN
dc.contributor.institutionauthorKOÇHAN, KORAY
dc.contributor.institutionauthorŞENTÜRK, HAKAN
dc.date.accessioned2023-01-06T00:07:48Z
dc.date.available2023-01-06T00:07:48Z
dc.date.issued2023-01-01
dc.description.abstractAbstractObjectiveTo 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).MethodsRetrospectively, 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.ResultsAccuracy 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.ConclusionsThe 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., Şentürk H., "Early prediction of the severe course, survival, and ICU requirements in acute pancreatitis by artificial intelligence", PANCREATOLOGY, cilt.111, sa.111, ss.1-10, 2023
dc.identifier.doi10.1016/j.pan.2022.12.005
dc.identifier.endpage10
dc.identifier.issn1424-3903
dc.identifier.issue111
dc.identifier.startpage1
dc.identifier.urihttps://www.sciencedirect.com/science/article/abs/pii/S1424390322008274
dc.identifier.urihttps://hdl.handle.net/20.500.12645/34581
dc.identifier.volume111
dc.relation.ispartofPANCREATOLOGY
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectTıp
dc.subjectDahili Tıp Bilimleri
dc.subjectİç Hastalıkları
dc.subjectGastroenteroloji-(Hepatoloji)
dc.subjectSağlık Bilimleri
dc.subjectMedicine
dc.subjectInternal Medicine Sciences
dc.subjectInternal Diseases
dc.subjectGastroenterology and Hepatology
dc.subjectHealth Sciences
dc.subjectKlinik Tıp (MED)
dc.subjectKlinik Tıp
dc.subjectGASTROENTEROLOJİ VE HEPATOLOJİ
dc.subjectTIP, GENEL & DAHİLİ
dc.subjectClinical Medicine (MED)
dc.subjectCLINICAL MEDICINE
dc.subjectGASTROENTEROLOGY & HEPATOLOGY
dc.subjectMEDICINE, GENERAL & INTERNAL
dc.subjectGenel Sağlık Meslekleri
dc.subjectPatofizyoloji
dc.subjectTemel Bilgi ve Beceriler
dc.subjectDeğerlendirme ve Teşhis
dc.subjectDahiliye
dc.subjectHepatoloji
dc.subjectGastroenteroloji
dc.subjectAile Sağlığı
dc.subjectTıp (çeşitli)
dc.subjectGenel Tıp
dc.subjectGeneral Health Professions
dc.subjectPathophysiology
dc.subjectFundamentals and Skills
dc.subjectAssessment and Diagnosis
dc.subjectInternal Medicine
dc.subjectHepatology
dc.subjectGastroenterology
dc.subjectFamily Practice
dc.subjectMedicine (miscellaneous)
dc.subjectGeneral Medicine
dc.titleEarly prediction of the severe course, survival, and ICU requirements in acute pancreatitis by artificial intelligence
dc.typearticle
dspace.entity.typePublication
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