2026-04-17T06:25:10Zhttps://recercat.cat/oai/requestoai:recercat.cat:11351/102932025-10-24T08:47:47Zcom_2072_378070com_2072_378040com_2072_378071col_2072_378092col_2072_378097
RECERCAT
author
LACASA-CAZCARRA, MARCOS
author
Prados Carrasco, Ferran
author
Alegre, Jose
author
Casas-Roma, Jordi
2025-10-24T08:47:46Z
2025-10-24T08:47:46Z
2023-09-19T08:58:02Z2023-09-19T08:58:02Z2023-08-31
http://hdl.handle.net/11351/10293
Computational models; Data acquisitionModelos computacionales; Adquisición de datosModels computacionals; Adquisició de dadesArtificial 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.
Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess
Intel·ligència artificial
Bases de dades
Síndrome de fatiga crònica - Diagnòstic
INFORMATION SCIENCE::Information Science::Computing Methodologies::Algorithms::Artificial Intelligence
INFORMATION SCIENCE::Information Science::Informatics::Medical Informatics::Medical Informatics Applications::Information Systems::Databases as Topic::Databases, Factual
DISEASES::Musculoskeletal Diseases::Muscular Diseases::Fatigue Syndrome, Chronic
Other subheadings::Other subheadings::/diagnosis
CIENCIA DE LA INFORMACIÓN::Ciencias de la información::metodologías computacionales::algoritmos::inteligencia artificial
CIENCIA DE LA INFORMACIÓN::Ciencias de la información::informática::informática médica::aplicaciones de la informática médica::sistemas de información::bases de datos como asunto::bases de datos factuales
ENFERMEDADES::enfermedades musculoesqueléticas::enfermedades musculares::síndrome de fatiga crónica
Otros calificadores::Otros calificadores::/diagnóstico
A synthetic data generation system for myalgic encephalomyelitis/chronic fatigue syndrome questionnaires
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion