Evidence map›Paper›PMID 37652910›Full record

ArticleScientific reports2023

A synthetic data generation system for myalgic encephalomyelitis/chronic fatigue syndrome questionnaires.

Marcos Lacasa, Ferran Prados, José Alegre, Jordi Casas-Roma

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 36% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 2 countries.

Marcos LacasaADaS Lab - E-Health Center, Universitat Oberta de Catalunya, Rambla del Poblenou, 156, 08018, Barcelona, Spain. mlacasaca@uoc.edu.ORCID http://orcid.org/0000-0002-8297-2086
Ferran PradosADaS Lab - E-Health Center, Universitat Oberta de Catalunya, Rambla del Poblenou, 156, 08018, Barcelona, Spain.ORCID http://orcid.org/0000-0002-7872-0142
José AlegreME/CFS Unit, Division of Rheumatology, Vall d'Hebron Hospital Research Institute Universitat Autònoma de Barcelona, Barcelona, Spain.ORCID http://orcid.org/0000-0002-7582-7585
Jordi Casas-RomaADaS Lab - E-Health Center, Universitat Oberta de Catalunya, Rambla del Poblenou, 156, 08018, Barcelona, Spain.ORCID http://orcid.org/0000-0002-0617-3303
Universitat Oberta de Catalunya · ESUniversitat Autònoma de Barcelona · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence or machine-learning-based models have proven useful for better understanding various diseases in all areas of health science. Myalgic Encephalomyelitis or chronic fatigue syndrome (ME/CFS) lacks objective diagnostic tests. Some validated questionnaires are used for diagnosis and assessment of disease progression. The availability of a sufficiently large database of these questionnaires facilitates research into new models that can predict profiles that help to understand the etiology of the disease. A synthetic data generator provides the scientific community with databases that preserve the statistical properties of the original, free of legal restrictions, for use in research and education. The initial databases came from the Vall Hebron Hospital Specialized Unit in Barcelona, Spain. 2522 patients diagnosed with ME/CFS were analyzed. Their answers to questionnaires related to the symptoms of this complex disease were used as training datasets. They have been fed for deep learning algorithms that provide models with high accuracy [0.69-0.81]. The final model requires SF-36 responses and returns responses from HAD, SCL-90R, FIS8, FIS40, and PSQI questionnaires. A highly reliable and easy-to-use synthetic data generator is offered for research and educational use in this disease, for which there is currently no approved treatment.

Indexed as

Artificial IntelligenceFatigue Syndrome, ChronicAlgorithmsDatabases, FactualEducational StatusHumans

Identifiers

PMID37652910
PMCPMC10471690
OpenAlexW4386328063

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.