2010
DOI: 10.1111/j.1872-034x.2009.00607.x
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A predictive model of response to peginterferon ribavirin in chronic hepatitis C using classification and regression tree analysis

Abstract: A decision tree model that includes hepatic steatosis, LDL-C, age, blood sugar, and GGT may be useful for the prediction of response before PEG-IFN plus RBV therapy, and has the potential to support clinical decisions in selecting patients for therapy and may provide a rationale for treating metabolic factors to improve the efficacy of antiviral therapy.

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Cited by 60 publications
(61 citation statements)
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“…There are many different classification tools, including neural networks, Bayesian networks, and support vector machines, but models based on these may be more difficult to interpret or apply in clinical practice. On the other hand, decision tree approaches such as C4.5 and CART are widely used in biomedical studies [37][38][39] and provide a simple and intuitive hierarchical format that in many cases can be used without a computer.…”
Section: Discussionmentioning
confidence: 99%
“…There are many different classification tools, including neural networks, Bayesian networks, and support vector machines, but models based on these may be more difficult to interpret or apply in clinical practice. On the other hand, decision tree approaches such as C4.5 and CART are widely used in biomedical studies [37][38][39] and provide a simple and intuitive hierarchical format that in many cases can be used without a computer.…”
Section: Discussionmentioning
confidence: 99%
“…The results of the analysis are presented as a tree structure, which is intuitive and facilitates the allocation of patients into subgroups by following the flow chart form [20]. We have recently reported the usefulness of decision tree analysis for the prediction of early virological response (undetectable HCVRNA within 12 weeks of therapy) to PEG-IFN and RBV combination therapy in chronic hepatitis C [21].…”
Section: Introductionmentioning
confidence: 99%
“…Moreover, they added that GGT is a biochemical marker with low cost that may be incorporated with other predictive pretreatment factors. RVR and EVR are described in the study Kurosaki et al (2010) (E2) . The authors used the decision tree model (CART -The Classification and Regression Tree), which analyzes the predictors of response to therapy with PEG-IFN and RBV.…”
Section: Characteristics Of Therapeutic Response Of the Sr Studiesmentioning
confidence: 99%