Artificial neural networks help to identify disease subsets and to predict lymphoma in primary Sjögren's syndrome.
Journal:
Clinical and experimental rheumatology
PMID:
30156549
Abstract
OBJECTIVES: 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.
Authors
Keywords
Aged
Autoantibodies
Biomarkers
Data Mining
Decision Support Techniques
Diagnosis, Computer-Assisted
Diagnosis, Differential
Disease Progression
Female
Humans
Lymphoma
Male
Middle Aged
Neural Networks, Computer
Phenotype
Predictive Value of Tests
Principal Component Analysis
Prognosis
Reproducibility of Results
Risk Factors
Serologic Tests
Severity of Illness Index
Sjogren's Syndrome