Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/45495
Title: Prediction of chronic damage in systemic lupus erythematosus by using machine-learning models
Authors: Ceccarelli, Fulvia 
Sciandrone, Marco 
Perricone, Carlo 
Galvan, Giulio 
Morelli, Francesco 
Vicente, Luís Nunes 
Leccese, Ilaria 
Massaro, Laura 
Cipriano, Enrica 
Spinelli, Francesca Romana 
Alessandri, Cristiano 
Valesini, Guido 
Conti, Fabrizio 
Keywords: Adult; Disease Progression; Female; Humans; Longitudinal Studies; Lupus Erythematosus, Systemic; Machine Learning; Male; Sensitivity and Specificity; Severity of Illness Index
Issue Date: 2017
Publisher: Masataka Kuwana, Keio University, Japan
Project: info:eu-repo/grantAgreement/FCT/5876/147205/PT 
Serial title, monograph or event: PloS one
Volume: 12
Issue: 3
Abstract: The increased survival in Systemic Lupus Erythematosus (SLE) patients implies the development of chronic damage, occurring in up to 50% of cases. Its prevention is a major goal in the SLE management. We aimed at predicting chronic damage in a large monocentric SLE cohort by using neural networks.
URI: http://hdl.handle.net/10316/45495
DOI: 10.1371/journal.pone.0174200
Rights: openAccess
Appears in Collections:I&D CMUC - Artigos em Revistas Internacionais

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