PMID: 30156549Aug 30, 2018Paper

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

Clinical and Experimental Rheumatology
Chiara BaldiniEnzo Grossi

Abstract

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. Out of a historic cohort of 850 patients, we selected 542 cases of pSS fulfilling the AECG criteria 2002. Thirty-seven variables were analysed including: patient demographics, glandular symptoms, systemic features, biological abnormalities and MALT-NHLs. AutoCM was used to compute the association of strength of each variable with all other variables in the dataset. PCA was applied to the same data set. Both PCA and AutoCM confirmed the associations between autoantibody positivity and several pSS clinical manifestations, ...Continue Reading

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