Artificial neural networks help to identify disease subsets and to predict lymphoma in primary Sjögren's syndrome

Primary Sjögren's syndrome (pSS) is a complex chronic systemic disorder, for which specific and effective therapeutic interventions are still lacking. In this era of precision medicine, there is a clear need for a better definition of disease phenotypes to foster the research of novel specific biomarkers and new therapeutic targets. The main objectives of this work are: 1) to compare Auto Contractive Map (AutoCM), a data mining tool based on an artificial neural network (ANN) versus conventional Principal Component Analysis (PCA) in discriminating different pSS subsets and 2) to specifically focus on variables predictive of MALT-NHL development, assessing the previsional gain of the predictive models developed