AI Article Synopsis

  • * Various techniques were employed in the study, including symptom scoring and data mining, to pinpoint the most distinguishing symptoms between patients and healthy individuals.
  • * Key symptoms identified as differentiating factors include fatigue, post-exertional malaise, neurocognitive issues, and unrefreshing sleep, indicating that empirically derived symptoms may lead to a more reliable case definition.

Article Abstract

Current case definitions of Myalgic Encephalomyelitis (ME) and chronic fatigue syndrome (CFS) have been based on consensus methods, but empirical methods could be used to identify core symptoms and thereby improve the reliability. In the present study, several methods (i.e., continuous scores of symptoms, theoretically and empirically derived cut off scores of symptoms) were used to identify core symptoms best differentiating patients from controls. In addition, data mining with decision trees was conducted. Our study found a small number of core symptoms that have good sensitivity and specificity, and these included fatigue, post-exertional malaise, a neurocognitive symptom, and unrefreshing sleep. Outcomes from these analyses suggest that using empirically selected symptoms can help guide the creation of a more reliable case definition.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4443921PMC
http://dx.doi.org/10.1080/21642850.2015.1014489DOI Listing

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