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A Digital Twin Platform for Diagnostics and Rehabilitation of Multiple Sclerosis

Authors
  • Petrova-Antonova, Dessislava1
  • Spasov, Ivaylo2
  • Krasteva, Iva1
  • Manova, Ilina2
  • Ilieva, Sylvia1
  • 1 Sofia University,
  • 2 Rila Solutions,
Type
Published Article
Journal
Computational Science and Its Applications – ICCSA 2020
Publication Date
Aug 24, 2020
Volume
12249
Pages
503–518
Identifiers
DOI: 10.1007/978-3-030-58799-4_37
PMCID: PMC7975374
Source
PubMed Central
Keywords
Disciplines
  • Article
License
Unknown

Abstract

30 million people across Europe are affected approximately from rare diseases. The brain disorders (including, but not limited to those affecting mental health) remain a major challenge. The understanding of mental disorders’ determinants and causes, processes and impacts is a key to their prevention, early detection and treatment as well as factor for good health and well-being. In order to improve the health and disease understanding, a close linkage between fundamental, clinical, epidemiological and socio-economic research is required. Effective sharing of data, standardized data processing and the linkage of such data with large-scale cohort studies is a prerequisite for translation of research findings into the clinic. In this context, this paper proposes a platform for exploration of behavioral changes in patients with proven cognitive disorders with a focus on Multiple Sclerosis. It adopts the concept of a digital twin by applying the Big Data and Artificial Intelligence technologies to allow for deep analysis of medical data to estimate human health status, accurate diagnosis and adequate treatment of patients. The platform has two main components. The first component, provides functionality for diagnostics and rehabilitation of Multiple Sclerosis and acts as a main provider of data for the second component. The second component is an advanced analytical application, which provides services for data aggregation, enrichment, analysis and visualization that will be used to produce a new knowledge and support decision in each instance of the transactional component.

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