Lacasa, Marcos, Prados, Ferran, Alegre, José et al. · Scientific reports · 2023 · DOI
This study created an artificial intelligence tool that can predict what ME/CFS patients might answer on symptom questionnaires. Researchers trained the tool using responses from 2,522 ME/CFS patients from a hospital in Spain, and it learned to predict answers accurately. The tool could help researchers study the disease more easily by generating realistic patient data for testing new ideas.
Because ME/CFS lacks objective diagnostic tests, researchers rely heavily on patient questionnaires to understand the disease. This synthetic data generator could accelerate research by providing large datasets that preserve disease patterns while protecting patient privacy, potentially helping scientists develop better diagnostic tools and understand disease mechanisms.
This study does not prove that the synthetic data generator can diagnose ME/CFS in new patients or that it works equally well across different populations or healthcare settings. It also does not establish that the model captures all the complexity of ME/CFS or that predictions based on SF-36 responses alone are sufficient for clinical decision-making. The accuracy metrics reported may not reflect performance on truly independent patient populations.
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
Lacasa, Marcos, Prados, Ferran, Alegre, José, & Casas-Roma, Jordi (2023). A synthetic data generation system for myalgic encephalomyelitis/chronic fatigue syndrome questionnaires.. Scientific reports. https://doi.org/10.1038/s41598-023-40364-6
BibTeX
@article{mecfsatlas-lacasa-2023-synthetic-data,
author = {Lacasa, Marcos and Prados, Ferran and Alegre, José and Casas-Roma, Jordi},
title = {A synthetic data generation system for myalgic encephalomyelitis/chronic fatigue syndrome questionnaires.},
journal = {Scientific reports},
year = {2023},
doi = {10.1038/s41598-023-40364-6},
note = {PubMed: 37652910},
url = {https://www.mecfsatlas.com/evidence/lacasa-2023-synthetic-data},
}Atlas snapshot reference
ME/CFS Atlas. Generator v1 / Scanner v1.4 / policy v0.1. Accessed 2026-05-29. https://www.mecfsatlas.com/evidence/lacasa-2023-synthetic-data
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