ArticleScientific reports2017
Predictive models of minimal hepatic encephalopathy for cirrhotic patients based on large-scale brain intrinsic connectivity networks.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Machine learning techniques in hepatic encephalopathy: a scoping review.BMC medical informatics and decision making · 2025Article
- The role of multifocal visual evoked potential in detection of minimal hepatic encephalopathy in patients with compensated liver cirrhosis.BMC neurology · 2025Article
- Prognostic models for breast cancer: based on logistics regression and Hybrid Bayesian Network.BMC medical informatics and decision making · 2023Article
- Prediction of the occurrence of leprosy reactions based on Bayesian networks.Frontiers in medicine · 2023Article
- Artificial Intelligence and Its Application to Minimal Hepatic Encephalopathy Diagnosis.Journal of personalized medicine · 2021Review
- Cognition-tracking-based strategies for diagnosis and treatment of minimal hepatic encephalopathy.Metabolic brain disease · 2020Review
- Identifying Mild Hepatic Encephalopathy Based on Multi-Layer Modular Algorithm and Machine Learning.Frontiers in neuroscience · 2020Article
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Authors and funding
6 authors.
Funding
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Abstract
We aimed to find the most representative connectivity patterns for minimal hepatic encephalopathy (MHE) using large-scale intrinsic connectivity networks (ICNs) and machine learning methods. Resting-state fMRI was administered to 33 cirrhotic patients with MHE and 43 cirrhotic patients without MHE (NMHE). The connectivity maps of 20 ICNs for each participant were obtained by dual regression. A Bayesian machine learning technique, called Graphical Model-based Multivariate Analysis, was applied to determine ICN regions that characterized group differences. The most representative ICNs were evaluated by the performance of three machine learning methods (support vector machines (SVMs), multilayer perceptrons (MLP), and C4.5). The clinical significance of these potential biomarkers was further tested. The temporal lobe network (TLN), and subcortical network (SCN), and sensorimotor network (SMN) were selected as representative ICNs. The distinct functional integration patterns of the representative ICNs were significantly correlated with behavior criteria and Child-Pugh scores. Our findings suggest the representative ICNs based on GAMMA can distinguish MHE from NMHE and provide supplementary information to current MHE diagnostic criteria.
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