New machine-learning models outperform conventional risk assessment tools in Gastrointestinal bleeding

New machine-learning models outperform conventional risk assessment tools in Gastrointestinal bleeding

Abstract 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...

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Bibliographic Details
Main Authors: Eszter Boros, József Pintér, Roland Molontay, Kristóf Gergely Prószéky, Nóra Vörhendi, Orsolya Anna Simon, Brigitta Teutsch, Dániel Pálinkás, Levente Frim, Edina Tari, Endre Botond Gagyi, Imre Szabó, Roland Hágendorn, Áron Vincze, Ferenc Izbéki, Zsolt Abonyi-Tóth, Andrea Szentesi, Vivien Vass, Péter Hegyi, Bálint Erőss
Format: Article
Language:English
Published: Nature Portfolio 2025-02-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-025-90986-1

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https://doi.org/10.1038/s41598-025-90986-1

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