Document Detail

Artificial neural network model is superior to logistic regression model in predicting treatment outcomes of interferon-based combination therapy in patients with chronic hepatitis C.
MedLine Citation:
PMID:  18309244     Owner:  NLM     Status:  MEDLINE    
BACKGROUND/AIMS: Patients with chronic hepatitis C (CHC) can achieve a sustained virologic response if they received pegylated interferon plus ribavirin therapy; however, some of them do not respond or relapse after treatment. The aim of this study was to compare the ability of two statistical models to predict treatment outcomes. METHODS: Clinical data, biochemical values, and liver histological features of 107 patients with CHC were collected and assessed using a logistic regression (LR) model and an artificial neural network (ANN) model. Both the LR and ANN models were compared by receiver-operating characteristics curves. RESULTS: Aspartate aminotransferase (p = 0.017), prothrombin time (p = 0.002), body mass index (BMI; p = 0.003), and fibrosis score of liver histology (p = 0.002) were found to be significant predictive factors by univariate analysis. The independent significant predicting factor was BMI by multivariate LR analysis (p = 0.0095). The area under receiver-operating characteristics of the ANN model was larger than that of the LR model (85 vs. 58.4%). CONCLUSIONS: It was found that BMI is an independent factor for identifying patients with favorable treatment response. A useful ANN model in predicting outcomes of standard treatment for CHC infection was developed and showed greater accuracy than the LR model.
Chun-Hsiang Wang; Lein-Ray Mo; Ruey-Chang Lin; Jen-Juan Kuo; Kuo-Kuan Chang; Jieh-Jen Wu
Related Documents :
19343584 - Quantitative structure-property relationship modelling of the degradability rate consta...
18237004 - Forecasting the onset of an allergic risk to poaceae in nancy and strasbourg (france) w...
11779684 - Artificial neural network assessment of substitutive pharmacological treatments in hosp...
10198534 - Feature-based classification of myoelectric signals using artificial neural networks.
11495114 - Counting models of temporal discrimination.
24109734 - Atlas-based segmentation of white matter structures from dti using tensor invariants an...
Publication Detail:
Type:  Journal Article     Date:  2008-02-29
Journal Detail:
Title:  Intervirology     Volume:  51     ISSN:  1423-0100     ISO Abbreviation:  Intervirology     Publication Date:  2008  
Date Detail:
Created Date:  2008-04-02     Completed Date:  2008-04-18     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0364265     Medline TA:  Intervirology     Country:  Switzerland    
Other Details:
Languages:  eng     Pagination:  14-20     Citation Subset:  IM    
Copyright Information:
Copyright (c) 2008 S. Karger AG, Basel.
Department of Hepatogastroenterology, Tainan Municipal Hospital, Tainan, Taiwan, ROC.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Aspartate Aminotransferases / blood
Body Mass Index
Drug Evaluation / statistics & numerical data*
Hepatitis C, Chronic / drug therapy*,  pathology,  physiopathology,  virology
Interferons / therapeutic use*
Liver / enzymology,  pathology
Liver Cirrhosis / pathology
Logistic Models
Middle Aged
Neural Networks (Computer)
Prothrombin Time
Severity of Illness Index
Treatment Outcome
Viral Load
Reg. No./Substance:
9008-11-1/Interferons; EC Aminotransferases

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

Previous Document:  Role of horizontal transmission in hepatitis B virus spread among household contacts in north India.
Next Document:  Prevalence of hepatitis E virus IgG antibody in Japanese patients with hemophilia.