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Liver Cirrhosis

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Machine learning algorithms reveal gut microbiota signatures associated with chronic hepatitis B-related hepatic fibrosis.

World journal of gastroenterology
BACKGROUND: Hepatic fibrosis (HF) represents a pivotal stage in the progression and potential reversal of cirrhosis, underscoring the importance of early identification and therapeutic intervention to modulate disease trajectory.

Utilizing Machine Learning to Predict Liver Allograft Fibrosis by Leveraging Clinical and Imaging Data.

Clinical transplantation
BACKGROUND AND AIM: Liver transplant (LT) recipients may succumb to graft-related pathologies, contributing to graft fibrosis (GF). Current methods to diagnose GF are limited, ranging from procedural-related complications to low accuracy. With recent...

Machine learning-based models for advanced fibrosis in non-alcoholic steatohepatitis patients: A cohort study.

World journal of gastroenterology
BACKGROUND: The global prevalence of non-alcoholic steatohepatitis (NASH) and its associated risk of adverse outcomes, particularly in patients with advanced liver fibrosis, underscores the importance of early and accurate diagnosis.

Application of Interpretable Machine Learning Models to Predict the Risk Factors of HBV-Related Liver Cirrhosis in CHB Patients Based on Routine Clinical Data: A Retrospective Cohort Study.

Journal of medical virology
Chronic hepatitis B (CHB) infection represents a significant global public health issue, often leading to hepatitis B virus (HBV)-related liver cirrhosis (HBV-LC) with poor prognoses. Early identification of HBV-LC risk is essential for timely interv...

MicroHDF: predicting host phenotypes with metagenomic data using a deep forest-based framework.

Briefings in bioinformatics
The gut microbiota plays a vital role in human health, and significant effort has been made to predict human phenotypes, especially diseases, with the microbiota as a promising indicator or predictor with machine learning (ML) methods. However, the a...

Development and validation of an explainable machine learning model for predicting multidimensional frailty in hospitalized patients with cirrhosis.

Briefings in bioinformatics
We sought to develop and validate a machine learning (ML) model for predicting multidimensional frailty based on clinical and laboratory data. Moreover, an explainable ML model utilizing SHapley Additive exPlanations (SHAP) was constructed. This stud...

Deep Learning Segmentation of Ascites on Abdominal CT Scans for Automatic Volume Quantification.

Radiology. Artificial intelligence
Purpose To evaluate the performance of an automated deep learning method in detecting ascites and subsequently quantifying its volume in patients with liver cirrhosis and patients with ovarian cancer. Materials and Methods This retrospective study in...

Establishment of diagnostic model and identification of diagnostic markers between liver cancer and cirrhosis based on multi-chip and machine learning.

Clinical and experimental pharmacology & physiology
OBJECTIVE: Most cases of hepatocellular carcinoma (HCC) arise as a consequence of cirrhosis. In this study, our objective is to construct a comprehensive diagnostic model that investigates the diagnostic markers distinguishing between cirrhosis and H...

[Development of a grading diagnostic model for schistosomiasis-induced liver fibrosis based on radiomics and clinical laboratory indicators].

Zhongguo xue xi chong bing fang zhi za zhi = Chinese journal of schistosomiasis control
OBJECTIVE: To investigate the feasibility of developing a grading diagnostic model for schistosomiasis-induced liver fibrosis based on B-mode ultrasonographic images and clinical laboratory indicators.