Please use this identifier to cite or link to this item: http://hdl.handle.net/10609/151322
Title: Data science applied to chronic fatigue syndrome
Author: Lacasa-Cazcarra, Marcos  
Director: Casas-Roma, Jordi  
Alegre, Jose  
Abstract: Myalgic encephalomyelitis or chronic fatigue syndrome (ME/CFS) is an organic, debilitating and multifaceted process. Its heterogeneous onset and clinical presentation with additional comorbidities make it difficult to diagnose. There is no evidence of diagnostic tests or biomarkers that can alone determine the diagnosis. Research lines are heterogeneous. It is necessary to define clinical trials to identify effective treatments. This research provides 2 biomarkers that can be used for this purpose: peak oxygen consumption in the exercise test and the result of the CPT3 test to measure cognitive impairment. An application will be developed that provides a multidisciplinary analysis and predicts the physical risk of a patient affected by ME/CFS. It favors the early detection of physical deterioration and the referral to a specialized unit that would favor the detection of the syndrome.
Keywords: machine learning
encephalomyelitis
chronic fagitue
Document type: info:eu-repo/semantics/doctoralThesis
Issue Date: 19-Sep-2024
Publication license: http://creativecommons.org/licenses/by-nc-nd/3.0/es/  
Appears in Collections:Tesis doctorales (Bioinformatics)

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