Development and validation of a machine learning-based prediction model for hepatorenal syndrome in liver cirrhosis patients using MIMIC-IV and eICU databases

Development and validation of a machine learning-based prediction model for hepatorenal syndrome in liver cirrhosis patients using MIMIC-IV and eICU databases

Abstract Hepatorenal syndrome (HRS) is a key contributor to poor prognosis in liver cirrhosis. This study aims to leverage the database to build a predictive model for early identification of high-risk patients. From two sizable public databases, we retrieved pertinent information about the cirrhosi...

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Bibliographic Details
Main Authors: Fengwei Yao, Ji Luo, Qian Zhou, Luhua Wang, Zhijun He
Format: Article
Language:English
Published: Nature Portfolio 2025-01-01
Series:Scientific Reports
Subjects:
Cirrhosis
Hepatorenal syndrome
MIMIC-IV database
LASSO regression
Extreme gradient boosting
Random forest
Online Access:https://doi.org/10.1038/s41598-025-86674-9
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https://doi.org/10.1038/s41598-025-86674-9

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