Applying a Digital Twin Approach for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

Abstract

Introduction Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic condition for which no diagnostic biomarkers have been validated, and most treatments globally are limited to managing the symptoms. The technological advantages for complex computational processing, achieved in population cohorts, for the study of cardiovascular diseases and cancer, can offer a potential new avenue for research in ME/CFS. Methods Two such digital research approaches are considered here: (1) the potential implementation of digital twins as a dynamic monitoring system and (2) the potential implementation of synthetic data as a way of augmenting available data sets while having the potential to protect the privacy of individual patients’ data. Results Utilizing such computational methodologies has the potential to provide novel angles in the investigation of ME/CSF; however, they remain in the early stages of their development, and doing so would be computationally expensive, and would require the training of professional staff in new technologies, as well as the clinical validation of the predictive modelling. Conclusion Thus, carefully targeted implementations of these new technologies are anticipated in the immediate future, at least in the biomarker discovery field.