RESUMO
For many decades, the clinical unmet needs of primary Sjögren's Syndrome (pSS) have been left unresolved due to the rareness of the disease and the complexity of the underlying pathogenic mechanisms, including the pSS-associated lymphomagenesis process. Here, we present the HarmonicSS cloud-computing exemplar which offers beyond the state-of-the-art data analytics services to address the pSS clinical unmet needs, including the development of lymphoma classification models and the identification of biomarkers for lymphomagenesis. The users of the platform have been able to successfully interlink, curate, and harmonize 21 regional, national, and international European cohorts of 7,551 pSS patients with respect to the ethical and legal issues for data sharing. Federated AI algorithms were trained across the harmonized databases, with reduced execution time complexity, yielding robust lymphoma classification models with 85% accuracy, 81.25% sensitivity, 85.4% specificity along with 5 biomarkers for lymphoma development. To our knowledge, this is the first GDPR compliant platform that provides federated AI services to address the pSS clinical unmet needs.
RESUMO
Primary Sjögren's Syndrome (pSS) has been characterized as a hypersensitivity reaction type II systemic autoimmune chronic disease causing exocrine gland dysfunction mainly affecting women near the menopausal age. pSS patients exhibit dryness of the main mucosal surfaces and are highly prone to lymphoma development. This paper presents a first biomedical ontology for pSS based on a reference model which was determined by pSS clinical experts. The ensuing ontology constitutes the fundamental basis for mapping pSS-related ontologies from international cohorts to a common ontology. The ontology mapping (i.e., schematic interlinking) procedure is, in fact, a preliminary step to harmonize heterogeneous medical data obtained from various cohorts.