AIMC Topic: Liver Failure, Acute

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Evaluating the synergistic use of advanced liver models and AI for the prediction of drug-induced liver injury.

Expert opinion on drug metabolism & toxicology Feb 2, 2025
INTRODUCTION: Drug-induced liver injury (DILI) is a leading cause of acute liver failure. Hepatotoxicity typically occurs only in a subset of individuals after prolonged exposure and constitutes a major risk factor for the termination of drug develop...
Risk Factors Animals Risk Assessment Humans Machine Learning Chemical and Drug Induced Liver Injury Liver Failure, Acute Models, Biological Drug Evaluation, Preclinical Drug Development Liver
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Dynamics of Systemic Inflammation as a Function of Developmental Stage in Pediatric Acute Liver Failure.

Frontiers in immunology Jan 15, 2021
The Pediatric Acute Liver Failure (PALF) study is a multicenter, observational cohort study of infants and children diagnosed with this complex clinical syndrome. Outcomes in PALF reflect interactions among the child's clinical condition, response to...
Inflammation Mediators Liver Failure, Acute Predictive Value of Tests Liver Transplantation Inflammation Age Factors Machine Learning Decision Support Techniques Prognosis Female Biomarkers Diagnosis, Computer-Assisted Adolescent Humans Child, Preschool Male Reproducibility of Results Time Factors Child Infant Risk Factors
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Predicting daily outcomes in acetaminophen-induced acute liver failure patients with machine learning techniques.

Computer methods and programs in biomedicine Apr 11, 2019
BACKGROUND/OBJECTIVE: Assessing prognosis for acetaminophen-induced acute liver failure (APAP-ALF) patients during the first week of hospitalization often presents significant challenges. Current models such as the King's College Criteria (KCC) and t...
Severity of Illness Index Liver Failure, Acute ROC Curve Retrospective Studies Linear Models Databases, Factual Male Area Under Curve Female Humans Acetaminophen Middle Aged Adult Prognosis Bayes Theorem Machine Learning Treatment Outcome Sensitivity and Specificity Registries Predictive Value of Tests
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Development and Validation of an Explainable Machine Learning Model for Warning of Hepatitis E Virus-Related Acute Liver Failure.

Liver international : official journal of the International Association for the Study of the Liver Jun 1, 2025
BACKGROUND AND AIMS: Early identification of patients with acute hepatitis E (AHE) who are at high risk of progressing to hepatitis E virus-related acute liver failure (HEV-ALF) is crucial for enabling timely monitoring and intervention. This multice...
Male Middle Aged Female Adult Hepatitis E Liver Failure, Acute Hepatitis E virus Machine Learning Risk Assessment Retrospective Studies China Humans
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A deep learning approach for acute liver failure prediction with combined fully connected and convolutional neural networks.

Technology and health care : official journal of the European Society for Engineering and Medicine Jan 1, 2024
BACKGROUND: Acute Liver Failure (ALF) is a critical medical condition with rapid development, often caused by viral infections, hepatotoxic drug abuse, or other severe liver diseases. Timely and accurate prediction of ALF occurrence is clinically cru...
Deep Learning Liver Failure, Acute Female Male Humans Neural Networks, Computer
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