Data Mining: Comparing the Empiric CFS to the Canadian ME/CFS Case Definition

J Clin Psychol. 2012 Jan;68(1):41-9. doi: 10.1002/jclp.20827. Epub 2011 Aug 5.

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.

Publication types

  • Comparative Study
  • Research Support, N.I.H., Extramural

MeSH terms

  • Canada
  • Chicago
  • Chronic Disease
  • Data Mining / methods*
  • Diagnostic and Statistical Manual of Mental Disorders
  • Fatigue / diagnosis*
  • Fatigue Syndrome, Chronic / diagnosis*
  • Follow-Up Studies
  • Health Surveys
  • Humans
  • Interview, Psychological
  • Psychiatric Status Rating Scales
  • Psychometrics / instrumentation*
  • Reproducibility of Results
  • Risk
  • Sensitivity and Specificity
  • Severity of Illness Index