Ohanian, Diana, Brown, Abigail, Sunnquist, Madison et al. · Neurology (E-Cronicon) · 2016
This study compared symptoms reported by people with ME/CFS to those with multiple sclerosis (MS) to find out which symptoms are most different between these conditions. Researchers used a computer learning method to analyze questionnaire responses and found that five specific symptoms—particularly flu-like feelings and swollen lymph nodes—were the best at telling these conditions apart. The computer correctly identified whether someone had MS or ME/CFS about 81% of the time, and people with ME/CFS reported having more severe symptoms overall.
ME/CFS is frequently misdiagnosed or confused with other conditions like MS, leading to inappropriate treatment and delayed proper care. Identifying key distinguishing symptoms could help clinicians and patients recognize ME/CFS more accurately and avoid unnecessary testing or treatments designed for other diseases. This work demonstrates how machine learning might improve diagnostic accuracy in complex chronic illnesses.
This study does not prove these symptoms cause or define ME/CFS, only that they are reported differently between groups. The 81% accuracy rate means about 19% of cases were misclassified, so these symptoms alone are not perfect diagnostic markers. Additionally, online self-report data may not represent all patients, and the study doesn't establish whether these findings apply to diverse populations or healthcare settings.
About the PEM badge: “PEM required” means post-exertional malaise was an explicit required diagnostic criterion for participant inclusion in this study — not that PEM was studied, observed, or discussed. Studies using criteria that do not require PEM (e.g. Fukuda, Oxford) are tagged “PEM not required”. How the atlas works →
The first block is for the primary paper and is the citation you should use in research work. The atlas-snapshot line only applies if you are specifically referring to this atlas’s reading of the paper on the date shown.
Primary citation
Ohanian, Diana, Brown, Abigail, Sunnquist, Madison, Furst, Jacob, Nicholson, Laura, Klebek, Lauren, et al. (2016). Identifying Key Symptoms Differentiating Myalgic Encephalomyelitis and Chronic Fatigue Syndrome from Multiple Sclerosis.. Neurology (E-Cronicon). https://pubmed.ncbi.nlm.nih.gov/28066845/
BibTeX
@article{mecfsatlas-ohanian-2016-identifying-key,
author = {Ohanian, Diana and Brown, Abigail and Sunnquist, Madison and Furst, Jacob and Nicholson, Laura and Klebek, Lauren and Jason, Leonard A},
title = {Identifying Key Symptoms Differentiating Myalgic Encephalomyelitis and Chronic Fatigue Syndrome from Multiple Sclerosis.},
journal = {Neurology (E-Cronicon)},
year = {2016},
note = {PubMed: 28066845},
url = {https://www.mecfsatlas.com/evidence/ohanian-2016-identifying-key},
}Atlas snapshot reference
ME/CFS Atlas. Generator v1 / Scanner v1.4 / policy v0.1. Accessed 2026-05-30. https://www.mecfsatlas.com/evidence/ohanian-2016-identifying-key
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