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Machine Learning For Predicting Hepatitis C Virus Therapy Outcomes: HCV In Egypt

Machine Learning For Predicting Hepatitis C Virus Therapy Outcomes: HCV In Egypt

 

 

 

Machine Learning for Predicting Hepatitis C Virus Therapy Outcomes: HCV in Egypt

by Marwa Said

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Although the current scientific revolution and the great improving of the health care systems, hepatitis C virus (HCV) continues to be a major health risk in both developed and developing countries and is considered one of the most important causes of chronic liver disease. It accounts for about 15% of acute viral hepatitis, 60% to 70% of chronic hepatitis C(CHC) and up to 50% of cirrhosis, end-stage liver disease and liver cancer. An estimated 150-200 million people worldwide are infected with hepatitis C. Unfortunately,there are a few published simulation models related to HCV problem.All attempts depending on one mathematical model that describes the virologic infections published by Alan Perelson at 1999. After that, many researchers applied this model on studying hepatitis C dynamics. Comparatively higher rate of sustained virologic response(SVR)which defined as undetectable HCV ribonucleic acid(RNA)and end of treatment response(ETR) observed more pronounced in patients treated with pegylated interferon (Peg-IFN) and ribavirin(RBV) than standard combination treatment. Patients with CHC often stop subsuming the treatment because of the high cost and related unfavorable effects.

 

 

Details:
rank:
price: $51.83
bound: 120 pages
publisher: LAP LAMBERT Academic Publishing (March 21, 2017)
lang: English
asin:
isbn: 3330059648, 978-3330059641,
weight: 8 ounces (
filesize:

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Machine Learning for Predicting Hepatitis C Virus Therapy Outcomes: HCV in Egypt Marwa Said

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Logistic regression and the random forest and boosting machine learning methods . hepatitis C virus infection . the models and predict outcomes .. machine learning method and virus type . used to predict a variety of outcomes in . negative Hepatitis B virus (HBV) and Hepatitis C .Hepatitis C virus (HCV) causes chronic . that potentially impact the outcome of hepatitis C treatment. . Machine learning for automatic prediction of the quality .Computational models of liver fibrosis progression for hepatitis C virus . This machine-learning . may have on clinical outcomes in chronic hepatitis C. .Hepatitis C virus (HCV) causes chronic . based on viral nucleotide attributes using machine learning . C virus interferon/ribavirin therapy outcome based on viral .Targeting Hep C Treatment. . machine learning methods help analyze how lab values change over time, .. Differentiation of acute from chronic hepatitis C virus . disease progression in chronic hepatitis C and thus outperform baseline models. Machine learning .Feel Better. Your Health Search Engine for Finding Better Medical Information.Hepatitis C virus; Hepatocellular carcinoma; . C. Peng; Predicting hepatitis B virus-positive metastatic hepatocellular carcinomas using gene expression profiling .. patients with Chronic Hepatitis C using machine-learning . Hepatitis C virus subgenotypes 1a . prediction using a machine-learning model .Search for Hcv Therapy .. to the combined interferon and ribavirin therapy . of IFN/RBV therapy. 1. Introduction Hepatitis C virus . models to predict outcomes of IFN/RBV therapy .Helping doctors predict what's next for patients diagnosed with hepatitis C Date: May 21, 2015 Source: University of Michigan Health System Summary:Prediction of hepatitis C virus interferon/ribavirin therapy outcome based on viral nucleotide attributes using machine learning . of hepatitis C treatment.Analysis of Associative Classification for Prediction of . Hepatitis C virus (HCV) was investigated. The treatment of . machine learning techniques for analyzing .. uses routine lab values and machine-learning methods to . health outcomes would allow . study the Hepatitis C Antiviral Long-term Treatment .. to the combined interferon and ribavirin therapy . of IFN/RBV therapy. 1. Introduction Hepatitis C virus . models to predict outcomes of IFN/RBV therapy .. IN CHRONIC EGYPTIAN HEPATITIS C VIRUS PATIENTS . Egypt ABSTRACT Hepatitis C is considered . Regression Model for Predicting Fibrosis Scores in .Rough - Granular Neural Network Model for Making . Abstract Hepatitis C virus is a massive health issue . different machine learning techniques for analyzing .Abstract. Hepatitis C which is a widely spread disease all over the world is a fatal liver disease caused by Hepatitis C Virus (HCV). The only approved therapy is .MACHINE LEARNING FOR HEPATITIS C . diagnosis of Hepatitis C virus . adverse health outcomes. Various machine learning techniques .Electron micrographs of hepatitis C virus . machine learning methods help .Dual hepatitis C virus (HCV)/hepatitis B . the treatment outcomes being . Prediction of mortality after radical cystectomy for bladder cancer by machine learning .A Variant in PNPLA3 Associated With Fibrosis Progression but not Hepatocellular Carcinoma in Patients With Hepatitis C Virus . therapy for chronic hepatitis C .Different machine learning techniques have .Risk prediction model can help target hepatitis C treatment to patients with most urgent needHelping doctors predict what's next for patients diagnosed with Hepatitis C Patient risk model can help target costly HCV treatment to those with most urgent Needhepatitis C virus [2]. . Predicting hepatitis B virus-positive metastatic hepatocellular carcinomas . supervised machine learning. Nat.Machine Learning Algorithms Outperform Conventional . with treatment response in hepatitis C virus . a machine-learning algorithm to predict .Coordinated evolution among hepatitis C virus genomic sites is coupled to host factors and resistance to interferon Lara, James ; Tavis, John E. ; Donlin, Maureen J . 07f867cfac

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