data mining – American ME and CFS Society

Using Data Mining and Time Series to Investigate ME and CFS Naming Preferences

Abstract:

There have been numerous iterations of naming convention specified for Myalgic Encephalomyelitis (ME) and Chronic Fatigue Syndrome (CFS). As health care turns to “big data” analytics to gain insights, the Google Trends database was mined to ascertain worldwide trends of public interest in several ME- and CFS-related search categories between 2004 and 2019.

Time series analysis revealed that though “Chronic Fatigue Syndrome” remains the predominant search category in the ME and CFS field, the interest index declined at a rate of 2.77 per month during the 15-year study period. In the same time period, the interest index in “ME/CFS Hybrid” terms increased at a rate of 3.20 per month. Potential causal mechanisms for these trends and implications for patient sentiment analysis are discussed.

Source: Bhatia, S., & Jason, L. A. (2023). Using Data Mining and Time Series to Investigate ME and CFS Naming Preferences. Journal of Disability Policy Studies, 0(0). https://doi.org/10.1177/10442073231154027

Data mining: comparing the empiric CFS to the Canadian ME/CFS case definition

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.

Source: Jason LA, Skendrovic B, Furst J, Brown A, Weng A, Bronikowski C. 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. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3228898/ (Full article)

Meta analysis of Chronic Fatigue Syndrome through integration of clinical, gene expression, SNP and proteomic data

Abstract:

We start by constructing gene-gene association networks based on about 300 genes whose expression values vary between the groups of CFS patients (plus control). Connected components (modules) from these networks are further inspected for their predictive ability for symptom severity, genotypes of two single nucleotide polymorphisms (SNP) known to be associated with symptom severity, and intensity of the ten most discriminative protein features. We use two different network construction methods and choose the common genes identified in both for added validation. Our analysis identified eleven genes which may play important roles in certain aspects of CFS or related symptoms. In particular, the gene WASF3 (aka WAVE3) possibly regulates brain cytokines involved in the mechanism of fatigue through the p38 MAPK regulatory pathway.

Source: Pihur V, Datta S, Datta S. Meta analysis of Chronic Fatigue Syndrome through integration of clinical, gene expression, SNP and proteomic data. Bioinformation. 2011 Apr 22;6(3):120-4. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3089886/ (Full article)