Machine learning based CAGIB score predicts in-hospital mortality of cirrhotic patients with acute gastrointestinal bleeding

Abstract Acute gastrointestinal bleeding (AGIB) is a potentially lethal complication in cirrhosis. In this prospective international multi-center study, the performance of CAGIB score for predicting the risk of in-hospital death in 2467 cirrhotic patients with AGIB was validated. Machine learning (M...

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Detaylı Bibliyografya
Asıl Yazarlar:	Zhaohui Bai, Su Lin, Mingyu Sun, Shanshan Yuan, Mariana Barros Marcondes, Dapeng Ma, Qiang Zhu, Yiling Li, Yingli He, Cyriac Abby Philips, Xiaofeng Liu, Kanokwan Pinyopornpanish, Lichun Shao, Nahum Méndez-Sánchez, Metin Basaranoglu, Yunhai Wu, Yu Chen, Ling Yang, Andrea Mancuso, Frank Tacke, Bimin Li, Lei Liu, Fanpu Ji, Xingshun Qi
Materyal Türü:	Artigo
Dil:	Inglês
Baskı/Yayın Bilgisi:	Nature Portfolio 2025-07-01
Seri Bilgileri:	npj Digital Medicine
Online Erişim:	https://doi.org/10.1038/s41746-025-01883-w
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Machine learning based CAGIB score predicts in-hospital mortality of cirrhotic patients with acute gastrointestinal bleeding

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