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

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.

SUBMITTER: Lacasa M

PROVIDER: S-EPMC10471690 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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

Lacasa Marcos M Prados Ferran F Alegre José J Casas-Roma Jordi J

Scientific reports 20230831 1

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 ...[more]

PMID: 37652910

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Project description:IntroductionMyalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a multisystem chronic disease estimated to affect 836,000-2.5 million individuals in the United States. Persons with ME/CFS have a substantial reduction in their ability to engage in pre-illness levels of activity. Multiple symptoms include profound fatigue, post-exertional malaise, unrefreshing sleep, cognitive impairment, orthostatic intolerance, pain, and other symptoms persisting for more than 6 months. Diagnosis is challenging due to fluctuating and complex symptoms. ME/CFS Common Data Elements (CDEs) were identified in the National Institutes of Health (NIH) National Institute of Neurological Disorders and Stroke (NINDS) Common Data Element Repository. This study reviewed ME/CFS CDEs item content.MethodsInclusion criteria for CDEs (measures recommended for ME/CFS) analysis: 1) assesses symptoms; 2) developed for adults; 3) appropriate for patient reported outcome measure (PROM); 4) does not use visual or pictographic responses. Team members independently reviewed CDEs item content using the World Health Organization International Classification of Functioning, Disability and Health (ICF) framework to link meaningful concepts.Results119 ME/CFS CDEs (measures) were reviewed and 38 met inclusion criteria, yielding 944 items linked to 1503 ICF meaningful concepts. Most concepts linked to ICF Body Functions component (b-codes; n = 1107, 73.65%) as follows: Fatiguability (n = 220, 14.64%), Energy Level (n = 166, 11.04%), Sleep Functions (n = 137, 9.12%), Emotional Functions (n = 131, 8.72%) and Pain (n = 120, 7.98%). Activities and Participation concepts (d codes) accounted for a smaller percentage of codes (n = 385, 25.62%). Most d codes were linked to the Mobility category (n = 69, 4.59%) and few items linked to Environmental Factors (e codes; n = 11, 0.73%).DiscussionRelatively few items assess the impact of ME/CFS symptoms on Activities and Participation. Findings support development of ME/CFS-specific PROMs, including items that assess activity limitations and participation restrictions. Development of psychometrically-sound, symptom-based item banks administered as computerized adaptive tests can provide robust assessments to assist primary care providers in the diagnosis and care of patients with ME/CFS.

Dataset Information

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

Publications

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

Similar Datasets

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