AIMC Topic: Liver Cirrhosis
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Quantitative fibrosis identifies biliary tract involvement and is associated with outcomes in pediatric autoimmune liver disease.
Hepatology communications
Dec 11, 2024
BACKGROUND: Children with autoimmune liver disease (AILD) may develop fibrosis-related complications necessitating a liver transplant. We hypothesize that tissue-based analysis of liver fibrosis by second harmonic generation (SHG) microscopy with art...
Construction and validation of a machine learning-based prediction model for short-term mortality in critically ill patients with liver cirrhosis.
Clinics and research in hepatology and gastroenterology
Nov 30, 2024
OBJECTIVE: Critically ill patients with liver cirrhosis generally have a poor prognosis due to complications such as multiple organ failure. This study aims to develop a machine learning-based prediction model to forecast short-term mortality in crit...
Predictive model of in-hospital mortality in liver cirrhosis patients with hyponatremia: an artificial neural network approach.
Scientific reports
Nov 20, 2024
Hyponatremia can worsen the outcomes of patients with liver cirrhosis. However, it remains unclear about how to predict the risk of death in cirrhotic patients with hyponatremia. Patients with liver cirrhosis and hyponatremia were screened. Eligible ...
Accurate non-invasive detection of MASH with fibrosis F2-F3 using a lightweight machine learning model with minimal clinical and metabolomic variables.
Metabolism: clinical and experimental
Nov 19, 2024
BACKGROUND: There are no known non-invasive tests (NITs) designed for accurately detecting metabolic dysfunction-associated steatohepatitis (MASH) with liver fibrosis stages F2-F3, excluding cirrhosis-the FDA-defined range for prescribing Resmetirom ...
Machine-learning methodologies to predict disease progression in chronic hepatitis B in Africa.
Hepatology communications
Nov 15, 2024
BACKGROUND: Little is known about the determinants of disease progression among African patients with chronic HBV infection.
Explainable machine learning model for predicting the risk of significant liver fibrosis in patients with diabetic retinopathy.
BMC medical informatics and decision making
Nov 11, 2024
BACKGROUND: Diabetic retinopathy (DR), a prevalent complication in patients with type 2 diabetes, has attracted increasing attention. Recent studies have explored a plausible association between retinopathy and significant liver fibrosis. The aim of ...
Predicting Portal Pressure Gradient in Patients with Decompensated Cirrhosis: A Non-invasive Deep Learning Model.
Digestive diseases and sciences
Oct 28, 2024
BACKGROUND: A high portal pressure gradient (PPG) is associated with an increased risk of failure to control esophagogastric variceal hemorrhage and refractory ascites in patients with decompensated cirrhosis. However, direct measurement of PPG is in...
Machine Learning Models for Predicting Significant Liver Fibrosis in Patients with Severe Obesity and Nonalcoholic Fatty Liver Disease.
Obesity surgery
Oct 25, 2024
PURPOSE: Although noninvasive tests can be used to predict liver fibrosis, their accuracy is limited for patients with severe obesity and nonalcoholic fatty liver disease (NAFLD). We developed machine learning (ML) models to predict significant liver...
Enhancing the accuracy and effectiveness of diagnosis of spontaneous bacterial peritonitis in cirrhotic patients: A machine learning approach utilizing clinical and laboratory data.
Advances in medical sciences
Oct 16, 2024
PURPOSE: Spontaneous bacterial peritonitis (SBP) is a bacterial infection of ascitic fluid that develops naturally, without being triggered by any surgical conditions or procedures, and is a common complication of cirrhosis. With a potential mortalit...
Evaluating the positive predictive value of code-based identification of cirrhosis and its complications utilizing GPT-4.
Hepatology (Baltimore, Md.)
Oct 8, 2024
BACKGROUND AND AIMS: Diagnosis code classification is a common method for cohort identification in cirrhosis research, but it is often inaccurate and augmented by labor-intensive chart review. Natural language processing using large language models (...
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