Full title: Data mining: comparing the empiric CFS to the Canadian ME/CFS case definition.
Authors: Jason LA, Skendrovic B, Furst J, Brown A, Weng A, Bronikowski C.
Source: DePaul University.
Publication: J Clin Psychol.
Publication date: 5 Aug 2011
Abstract
This article contrasts two case definitions for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). We compared the empiric CFS case definition (Reeves et al., 2005) and the Canadian ME/CFS clinical case definition (Carruthers et al., 2003) with a sample of individuals with CFS versus those without. Data mining with decision trees was used to identify the best items to identify patients with CFS. Data mining is a statistical technique that was used to help determine which of the survey questions were most effective for accurately classifying cases. The empiric criteria identified about 79% of patients with CFS and the Canadian criteria identified 87% of patients. Items identified by the Canadian criteria had more construct validity. The implications of these findings are discussed. © 2011 Wiley Periodicals, Inc. J Clin Psychol 67:1-9, 2011.
© 2011 Wiley Periodicals, Inc.
PMID: 21823124 [PubMed - as supplied by publisher]
View the abstract in PubMed.
