Interpretable machine learning models for pre- and postoperative prediction of early intra-abdominal infections after liver transplantation: a multicenter retrospective cohort study

Background: Early intra-abdominal infections (EIAIs) are among the most frequent and life-threatening complications following liver transplantation (LT). Early identification of high-risk patients remains challenging, and no standardized risk stratification tool is currently available. Objective: To...

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I tiakina i:

Ngā taipitopito rārangi puna kōrero
Ngā kaituhi matua:	Shuang Cao, Hua-Bin Peng, Xin-Zhe Wei, Lei Wang, Ying Liu, Wei Qu, Zhi-Gui Zeng, Hai-Ming Zhang, Lin Wei, Hao-Feng Xiong, Fei Hou, Zhi-Jun Zhu, Wei Gao, Li-Ying Sun
Hōputu:	Artigo
Reo:	Inglês
I whakaputaina:	SAGE Publishing 2026-06-01
Rangatū:	Therapeutic Advances in Infectious Disease
Urunga tuihono:	https://doi.org/10.1177/20499361261453152
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!

Interpretable machine learning models for pre- and postoperative prediction of early intra-abdominal infections after liver transplantation: a multicenter retrospective cohort study

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