2021
DOI: 10.1016/j.semarthrit.2021.02.005
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Comprehensive disease control in systemic lupus erythematosus

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Cited by 8 publications
(3 citation statements)
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“… 131 152 The top performing predictive models for treatment response used a simple neural network (AUC 0.9735) 134 and an RF model (AUC 0.92). 131 Predictors of disease remission (SVM, AUC 0.713) 139 and response to B cell therapies (RF, AUC 0.88) 77 were examined as well. Lastly, cluster analysis by k-means and consensus cluster to identify different SLE endotypes based on treatment response revealed a wide range of results, for example, the number of reported clusters ranged from 3 to 39.…”
Section: Key Sle Findings By ML Reportsmentioning
confidence: 99%
“… 131 152 The top performing predictive models for treatment response used a simple neural network (AUC 0.9735) 134 and an RF model (AUC 0.92). 131 Predictors of disease remission (SVM, AUC 0.713) 139 and response to B cell therapies (RF, AUC 0.88) 77 were examined as well. Lastly, cluster analysis by k-means and consensus cluster to identify different SLE endotypes based on treatment response revealed a wide range of results, for example, the number of reported clusters ranged from 3 to 39.…”
Section: Key Sle Findings By ML Reportsmentioning
confidence: 99%
“…14 Furthermore, an inverse correlation between BMD and chronic damage measured by the Systemic Lupus International Collaborating Clinics damage index (SDI) has been observed. [15][16][17] Therefore, current guidelines for treating SLE patients have prioritized the management of bone health. 18 Accurately predicting the risk of FF is essential for developing effective prevention strategies and minimizing the burden of this complication.…”
Section: Introductionmentioning
confidence: 99%
“…Risk factors for damage include older age at diagnosis, longer duration of SLE, African-Caribbean or Asian ethnicity, high disease activity at diagnosis and greater overall activity during the disease course [ 12 ]. We previously showed that machine learning models could predict the development of chronic damage and the achievement of the Lupus Comprehensive Disease Control ( LupusCDC ) [ 13 , 14 ]. These models have suggested that despite the control of disease activity and the absence of adverse drug events, the chronic damage progresses in some patients, meaning that there may be other risk factors such as genetic background.…”
Section: Introductionmentioning
confidence: 99%