Yagin, Fatma Hilal, Shateri, Ahmadreza, Nasiri, Hamid et al. · PeerJ. Computer science · 2024 · DOI
Researchers used artificial intelligence to analyze blood samples from 32 women with ME/CFS and 19 healthy controls, examining 832 different substances in their blood. The AI model identified just 50 key substances that could distinguish ME/CFS patients from healthy people with 98.85% accuracy. The study found that ME/CFS patients had different levels of five specific metabolites in their blood, which could potentially be used as biomarkers for diagnosis.
ME/CFS currently lacks a definitive diagnostic test, making this study significant as it provides potential metabolic biomarkers that could accelerate diagnosis and improve clinical outcomes. The use of explainable AI makes the findings transparent and clinically actionable, potentially supporting the development of cost-effective diagnostic tools. Identifying these metabolites may also offer insights into ME/CFS pathophysiology and inform future therapeutic targets.
This study does not prove that the identified metabolites cause ME/CFS or that measuring them alone is sufficient for clinical diagnosis. The small sample size (51 total participants, all female) means findings cannot yet be generalized to male patients or larger populations. As a proof-of-concept study, it does not establish whether these biomarkers are stable over time or valid for disease monitoring and prognosis.
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
Yagin, Fatma Hilal, Shateri, Ahmadreza, Nasiri, Hamid, Yagin, Burak, Colak, Cemil, & Alghannam, Abdullah F (2024). Development of an expert system for the classification of myalgic encephalomyelitis/chronic fatigue syndrome.. PeerJ. Computer science. https://doi.org/10.7717/peerj-cs.1857
BibTeX
@article{mecfsatlas-yagin-2024-development-expert,
author = {Yagin, Fatma Hilal and Shateri, Ahmadreza and Nasiri, Hamid and Yagin, Burak and Colak, Cemil and Alghannam, Abdullah F},
title = {Development of an expert system for the classification of myalgic encephalomyelitis/chronic fatigue syndrome.},
journal = {PeerJ. Computer science},
year = {2024},
doi = {10.7717/peerj-cs.1857},
note = {PubMed: 38660205},
url = {https://www.mecfsatlas.com/evidence/yagin-2024-development-expert},
}Atlas snapshot reference
ME/CFS Atlas. Generator v1 / Scanner v1.4 / policy v0.1. Accessed 2026-05-25. https://www.mecfsatlas.com/evidence/yagin-2024-development-expert
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