ArticlePloS one2016
Machine Learning Classification of Cirrhotic Patients with and without Minimal Hepatic Encephalopathy Based on Regional Homogeneity of Intrinsic Brain Activity.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Brain Gray Matter Alterations in Hepatic Encephalopathy: A Voxel-Based Meta-Analysis of Whole-Brain Studies.Frontiers in human neuroscience · 2022Pooled it
- Machine learning techniques in hepatic encephalopathy: a scoping review.BMC medical informatics and decision making · 2025Article
- Ammonia and beyond - biomarkers of hepatic encephalopathy.Metabolic brain disease · 2025Review
- Interpretable machine learning model for predicting covert hepatic encephalopathy in patients with cirrhosis: a multicenter study.Frontiers in medicine · 2025Article
- Can Artificial Intelligence and Machine Learning Transform Prediction and Treatment of Post-Transjugular Intrahepatic Portosystemic Shunt (TIPS) Overt Hepatic Encephalopathy?Gastro hep advances · 2025Article
- Advancements in Artificial Intelligence-Enhanced Imaging Diagnostics for the Management of Liver Disease-Applications and Challenges in Personalized Care.Bioengineering (Basel, Switzerland) · 2024Review
- Changes in dynamic and static brain fluctuation distinguish minimal hepatic encephalopathy and cirrhosis patients and predict the severity of liver damage.Frontiers in neuroscience · 2023Article
- Artificial Intelligence and Its Application to Minimal Hepatic Encephalopathy Diagnosis.Journal of personalized medicine · 2021Review
- Identification of minimal hepatic encephalopathy based on dynamic functional connectivity.Brain imaging and behavior · 2021Article
- Identifying Mild Hepatic Encephalopathy Based on Multi-Layer Modular Algorithm and Machine Learning.Frontiers in neuroscience · 2020Article
- Reduced Cortical Complexity in Cirrhotic Patients with Minimal Hepatic Encephalopathy.Neural plasticity · 2020Article
- Multimodal MR imaging in hepatic encephalopathy: state of the art.Metabolic brain disease · 2018Review
- Abnormal Regional Homogeneity and Functional Connectivity of Baseline Brain Activity in Hepatitis B Virus-Related Cirrhosis With and Without Minimal Hepatic Encephalopathy.Frontiers in human neuroscience · 2018Article
- FXR-Mediated Cortical Cholesterol Accumulation Contributes to the Pathogenesis of Type A Hepatic Encephalopathy.Cellular and molecular gastroenterology and hepatology · 2018Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning-based approaches play an important role in examining functional magnetic resonance imaging (fMRI) data in a multivariate manner and extracting features predictive of group membership. This study was performed to assess the potential for measuring brain intrinsic activity to identify minimal hepatic encephalopathy (MHE) in cirrhotic patients, using the support vector machine (SVM) method. Resting-state fMRI data were acquired in 16 cirrhotic patients with MHE and 19 cirrhotic patients without MHE. The regional homogeneity (ReHo) method was used to investigate the local synchrony of intrinsic brain activity. Psychometric Hepatic Encephalopathy Score (PHES) was used to define MHE condition. SVM-classifier was then applied using leave-one-out cross-validation, to determine the discriminative ReHo-map for MHE. The discrimination map highlights a set of regions, including the prefrontal cortex, anterior cingulate cortex, anterior insular cortex, inferior parietal lobule, precentral and postcentral gyri, superior and medial temporal cortices, and middle and inferior occipital gyri. The optimized discriminative model showed total accuracy of 82.9% and sensitivity of 81.3%. Our results suggested that a combination of the SVM approach and brain intrinsic activity measurement could be helpful for detection of MHE in cirrhotic patients.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.