Author: Marcos Lacasa-Cazcarra
Programme: Doctoral Programme in Computer Science, Technology and Multimedia
Language: English
Supervision: Jordi Casas-Roma, Jose Alegre
Faculty / Institute: Doctoral School UOC
Subjects: Bioinformatics
Key words: Machine learning, Encephalomyelitis, Chronic fatigue
Area of knowledge: Computer Science, Technology and Multimedia
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.