A simple machine learning approach for preoperative diagnosis of esophageal burns after caustic substance ingestion in children

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dc.authoridOZCAN SIKI, Fatma/0000-0002-4461-3461
dc.authoridOZTAS, TULIN/0000-0002-1010-3324
dc.authoridAslanyurek, Birol/0000-0002-0792-667X
dc.authoridUsta, Merve/0000-0002-5086-6270
dc.contributor.authorAydin, Emrah
dc.contributor.authorKhanmammadova, Narmina
dc.contributor.authorAslanyurek, Birol
dc.contributor.authorUrganci, Nafiye
dc.contributor.authorUsta, Merve
dc.contributor.authorParlak, Ayse
dc.contributor.authorKaya, Seymanur
dc.date.accessioned2024-10-29T17:58:18Z
dc.date.available2024-10-29T17:58:18Z
dc.date.issued2023
dc.departmentTekirdağ Namık Kemal Üniversitesi
dc.description.abstractPurposeThe unresolved debate about the management of corrosive ingestion is a major problem both for the patients and healthcare systems. This study aims to demonstrate the presence and the severity of the esophageal burn after caustic substance ingestion can be predicted with complete blood count parameters.MethodsA multicenter, national, retrospective cohort study was performed on all caustic substance cases between 2000 and 2018. The classification learner toolbox of MATLAB version R2021a was used for the classification problem. Machine learning algorithms were used to forecast caustic burn.ResultsAmong 1839 patients, 142 patients (7.7%) had burns. The type of the caustic and the PDW (platelet distribution width) values were the most important predictors. In the acid group, the AUC (area under curve) value was 84% while it was 70% in the alkaline group. The external validation had 85.17% accuracy in the acidic group and 91.66% in the alkaline group.ConclusionsArtificial intelligence systems have a high potential to be used in the prediction of caustic burns in pediatric age groups.
dc.identifier.doi10.1007/s00383-023-05602-y
dc.identifier.issn0179-0358
dc.identifier.issn1437-9813
dc.identifier.issue1en_US
dc.identifier.pmid38092997
dc.identifier.scopus2-s2.0-85179727428
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1007/s00383-023-05602-y
dc.identifier.urihttps://hdl.handle.net/20.500.11776/14202
dc.identifier.volume40
dc.identifier.wosWOS:001123631300001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofPediatric Surgery International
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectCaustic substance ingestion
dc.subjectMachine learning
dc.subjectArtificial intelligence
dc.subjectChildren
dc.titleA simple machine learning approach for preoperative diagnosis of esophageal burns after caustic substance ingestion in children
dc.typeArticle

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