Modestly, the researchers proposed that their findings "hold promise for the development of an empirical CFS case definition as an accurate diagnostic tool." They suggest that "future work should focus on extending these findings to standardized criteria that can be easily implemented in a clinical setting."
The beauty of this elegant instrument is that by adjusting for symptoms that are prominent among some, but not all, people with ME/CFS, it can also be used to identify subgroups.
Without a doubt, other clinical features of this disease that are revealed by immune system, endocrine system, and nervous system tests would have to be added, as well as other objective, measurable abnormalities such as the two-day CPET. But so far, this "unsupervised machine learning and feature selection" tool is the best jumping-off point that has been developed.
Now, I believe, would be a good time for those seeking to devise an accurate case definition for ME/CFS to enter into the 21st century.
Read the full study HERE .
Citation: Samuel P. Watson, Amy S. Ruskin, Valerie Simonis, Leonard A. Jason, Madison Sunnquist, and Jacob D. Furst, "Identifying Defining Aspects of Chronic Fatigue Syndrome via Unsupervised Machine Learning and Feature Selection," International Journal of Machine Learning and Computing vol.4, no. 2, pp. 133-138, 2014.