New machine-learning models outperform conventional risk assessment tools in Gastrointestinal bleedi...
New machine-learning models outperform conventional risk assessment tools in Gastrointestinal bleeding
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Full title
Author / Creator
Boros, Eszter , Pintér, József , Molontay, Roland , Prószéky, Kristóf Gergely , Vörhendi, Nóra , Simon, Orsolya Anna , Teutsch, Brigitta , Pálinkás, Dániel , Frim, Levente , Tari, Edina , Gagyi, Endre Botond , Szabó, Imre , Hágendorn, Roland , Vincze, Áron , Izbéki, Ferenc , Abonyi-Tóth, Zsolt , Szentesi, Andrea , Vass, Vivien , Hegyi, Péter and Erőss, Bálint
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
Formats
Publication information
Publisher
London: Nature Publishing Group UK
Subjects
More information
Scope and Contents
Contents
Rapid and accurate identification of high-risk acute gastrointestinal bleeding (GIB) patients is essential. We developed two machine-learning (ML) models to calculate the risk of in-hospital mortality in patients admitted due to overt GIB. We analyzed the prospective, multicenter Hungarian GIB Registry’s data. The predictive performance of XGBoost...
Alternative Titles
Full title
New machine-learning models outperform conventional risk assessment tools in Gastrointestinal bleeding
Authors, Artists and Contributors
Author / Creator
Pintér, József
Molontay, Roland
Prószéky, Kristóf Gergely
Vörhendi, Nóra
Simon, Orsolya Anna
Teutsch, Brigitta
Pálinkás, Dániel
Frim, Levente
Tari, Edina
Gagyi, Endre Botond
Szabó, Imre
Hágendorn, Roland
Vincze, Áron
Izbéki, Ferenc
Abonyi-Tóth, Zsolt
Szentesi, Andrea
Vass, Vivien
Hegyi, Péter
Erőss, Bálint
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Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_4f639a41c9eb4374a3c9621e1c4b48be
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_4f639a41c9eb4374a3c9621e1c4b48be
Other Identifiers
ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-025-90986-1
