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Related Concept Videos

Hepatitis01:25

Hepatitis

86
Hepatitis is an inflammatory condition of the liver most commonly caused by hepatotropic viruses (A–E), though non-infectious causes such as alcohol and drugs also exist.Hepatitis AHepatitis A virus (HAV) is a non-enveloped RNA virus of the Picornaviridae family. It is primarily transmitted via the fecal-oral route, typically through ingestion of contaminated food or water. After ingestion, HAV enters the bloodstream through the oropharynx or intestinal epithelium and reaches the liver.
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Viral Hepatitis I: Introduction01:28

Viral Hepatitis I: Introduction

25
Viral hepatitis is an inflammatory condition of the liver caused by infection with hepatotropic viruses, most commonly hepatitis A, B, C, D, and E. Despite variations in structure and transmission, all viruses mentioned infect hepatocytes and provoke immune responses that can hinder liver function. Additionally, some non-hepatotropic viruses can also lead to hepatic inflammation.Hepatitis A VirusHepatitis A virus (HAV) is transmitted through the fecal–oral route, typically by ingestion...
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Related Experiment Video

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The CYP2D6 Animal Model: How to Induce Autoimmune Hepatitis in Mice
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Development and Validation of a Machine Learning-Based Screening Algorithm to Predict High-Risk Hepatitis C

Suk-Chan Jang1, Wei-Hsuan Lo-Ciganic2,3, Pilar Hernandez-Con1

  • 1College of Pharmacy, University of Florida, Gainesville, Florida, USA.

Open Forum Infectious Diseases
|August 28, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning models effectively identify individuals at high risk for hepatitis C virus (HCV) infection. The Gradient Boosting Machine model demonstrated superior performance, offering a promising tool for targeted HCV screening in clinical settings.

Keywords:
HCV infectionhepatitis C virushigh-risk predictionmachine learningscreening algorithm

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Area of Science:

  • Public Health
  • Infectious Diseases
  • Data Science

Background:

  • Hepatitis C virus (HCV) infections are increasing in the US, exacerbated by the opioid epidemic.
  • A significant portion of HCV infections remain undiagnosed due to asymptomatic presentation.
  • There is a critical need for effective screening tools to identify at-risk populations.

Purpose of the Study:

  • To develop and validate machine learning (ML) algorithms for predicting and stratifying HCV infection risk.
  • To create a tool that can assist in targeted screening strategies for HCV.

Main Methods:

  • Utilized the 2016-2023 OneFlorida+ electronic health records database.
  • Included individuals aged 18+ tested for HCV, analyzing 275 features.
  • Developed and validated four ML models: elastic net, random forest, gradient boosting machine, and deep neural network.

Main Results:

  • The Gradient Boosting Machine (GBM) model achieved the highest performance (C statistic: 0.916).
  • GBM demonstrated 79.39% sensitivity and 89.08% specificity, identifying one positive HCV case per six tests.
  • The top three risk deciles captured over 90% of patients with HCV.

Conclusions:

  • ML algorithms provide an effective method for predicting and stratifying HCV infection risk.
  • These models represent a promising tool for targeted HCV screening in clinical practice.
  • The study highlights the potential of data-driven approaches in managing public health challenges like HCV.