A Machine Learning–Based Prognostication Model Enhances Prediction of Early Hepatic Encephalopathy in Patients With Noncancer-Related Cirrhosis: Multicenter Longitudinal Cohort Study in Taiwan
Abstract BackgroundHepatic encephalopathy (HE) contributes significantly to mortality among patients with liver cirrhosis. Early prediction of HE is essential for clinical decision-making, yet remains challenging—particularly in noncancer-related cirrhosis due to the unpredict...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2025-08-01
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| Series: | JMIR Medical Informatics |
| Online Access: | https://medinform.jmir.org/2025/1/e71229 |
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| Summary: | Abstract
BackgroundHepatic encephalopathy (HE) contributes significantly to mortality among patients with liver cirrhosis. Early prediction of HE is essential for clinical decision-making, yet remains challenging—particularly in noncancer-related cirrhosis due to the unpredictable disease course.
ObjectiveThis study aimed to develop a novel machine learning (ML) model to improve early prediction of HE in patients with noncancer-related cirrhosis.
MethodsA multicenter, retrospective cohort study was conducted from January 2010 to December 2017 across all Chang Gung Memorial Hospital branches in northern, middle, and southern Taiwan. We applied several ML models to evaluate HE predictability and compared their performance in the training dataset and testing dataset. Optimal sensitivity and specificity were determined using the Youden index. The best ML model was interpreted by the Shapley Additive Explanations plot.
ResultsA total of 5878 patients with cirrhosis were included in the analysis, of whom 1187 (20.2%) subsequently developed HE. Compared to the non-HE group, patients with HE were older (median age 55, IQR 46‐65 vs median age 54, IQR 44‐66 years; PPPPPPPP
ConclusionsWe developed a novel ML model for predicting HE in patients with noncancer-related cirrhosis. This model provides a practical guide to help physicians and these patients in shared decision-making regarding treatment strategy, with the ultimate goal of improving clinical care and reducing the burden of HE-related morbid complications. |
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| ISSN: | 2291-9694 |