Friday, 20 January 2017

Diagnosing ME/CFS the machine learning way?

In today's post I want to draw your attention to the findings reported by Diana Ohanian and colleagues [1] (open-access available here) talking about "the use of machine learning to further explore the unique nature"of various conditions/labels including those typically headed under the label of chronic fatigue syndrome / myalgic encephalomyelitis (CFS/ME).

Including one 'Jason LA' on the authorship list, researchers set about looking at "what key symptoms differentiate Myalgic Encephalomyelitis (ME) and Chronic Fatigue syndrome (CFS) from Multiple Sclerosis (MS)."You may be wondering why such a comparative study was undertaken but a quick trawl of the research literature reveals that these different clinical labels may well have some important commonalities [2].

This was an internet-based research project whereby "106 people with MS and 354 people with ME or CFS fully completed the [DePaul Symptom Questionnairequestionnaire" and based on the responses received "decision trees were used to determine what symptoms differentiated those with MS from those with ME or CFS." Decision trees, as the name suggests, is a statistical technique where binary (0 or 1, no or yes) choices make branches and: "At each branch the computer decides what symptom would best predict classifications, in this case whether someone has MS or ME or CFS." This process continues and continues through the different levels of branches "until the tree reaches a balance between classification accuracy and generalizing to new data." Such a machine learning tool has been previously discussed quite recently on this blog (see here).

Results: "Five symptoms best differentiated the groups." These were: flu-like symptoms, tender lymph nodes, alcohol intolerance, inability to tolerate upright position and next day soreness after strenuous activity. The first two symptoms - flu-like symptoms and tender lymph nodes - were pretty good by themselves at correctly categorising MS or CFS/ME (~80% correct). Indeed, these seemed to be the core differentiators that were examined and as the authors note: "The most important two symptoms that differentiated MS versus ME or CFS existed within the immune domain."

Of course further investigations are warranted to potentially build on these findings. One has however to be slightly cautious about the use of the internet and social media when undertaking such research, especially when very little information about the formal diagnoses of participants is included in the current paper. This is a particular issue when it comes to CFS/ME and the various ways that it can be defined and diagnosed [3].

Still, I can't quibble with the continued rise and rise of machine learning being applied to many areas of medicine, and not before time that it starts to reach ME/CFS. And just before I go, it appears that the research team at DePaul University have been quite busy...

To close, on what retiring Presidents of the USA should do next. I think I would go with George Washington and his whisky business... 🍻

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[1] Ohanian D. et al. Identifying Key Symptoms Differentiating Myalgic Encephalomyelitis and Chronic Fatigue Syndrome from Multiple Sclerosis. Neurology (ECronicon). 2016;4(2):41-45.

[2] Morris G. & Maes M. Myalgic encephalomyelitis/chronic fatigue syndrome and encephalomyelitis disseminata/multiple sclerosis show remarkable levels of similarity in phenomenology and neuroimmune characteristics. BMC Medicine. 2013; 11: 205.

[3] Jason LA. et al. Case definitions integrating empiric and consensus perspectives. Fatigue. 2016;4(1):1-23.

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ResearchBlogging.org Ohanian D, Brown A, Sunnquist M, Furst J, Nicholson L, Klebek L, & Jason LA (2016). Identifying Key Symptoms Differentiating Myalgic Encephalomyelitis and Chronic Fatigue Syndrome from Multiple Sclerosis. Neurology (E-Cronicon), 4 (2), 41-45 PMID: 28066845