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10.1371/journal.pone.0174200 http://scihub22266oqcxt.onion/10.1371/journal.pone.0174200 C5362169!5362169!28329014     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2017 ; 12 (3): ä Nephropedia Template TP {{tp|p=28329014|t=2017. Prediction of chronic damage in systemic lupus erythematosus by using machine-learning models |pdf=|usr=}}{{28329014}} gab.com Text Prediction of chronic damage in systemic lupus erythematosus by using machine-learning models JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=28329014 #FOAvip Objective: 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. Methods: We enrolled 413 SLE patients (M/F 30/383; mean age ± SD 46.3±11.9 years; mean disease duration ± SD 174.6 ± 112.1 months). Chronic damage was assessed by the SLICC/ACR Damage Index (SDI). We applied Recurrent Neural Networks (RNNs) as a machine-learning model to predict the risk of chronic damage. The clinical data sequences registered for each patient during the follow-up were used for building and testing the RNNs. Results: At the first visit in the Lupus Clinic, 35.8% of patients had an SDI?1. For the RNN model, two groups of patients were analyzed: patients with SDI = 0 at the baseline, developing damage during the follow-up (N = 38), and patients without damage (SDI = 0). We created a mathematical model with an AUC value of 0.77, able to predict damage development. A threshold value of 0.35 (sensitivity 0.74, specificity 0.76) seemed able to identify patients at risk to develop damage. Conclusion: We applied RNNs to identify a prediction model for SLE chronic damage. The use of the longitudinal data from the Sapienza Lupus Cohort, including laboratory and clinical items, resulted able to construct a mathematical model, potentially identifying patients at risk to develop damage. Twit Text FOAVip Prediction of chronic damage in systemic lupus erythematosus by using machine-learning models ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=28329014 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28329014 #FOAvip English Wikipedia <ref>{{Cite pmid|28329014}}</ref> Prediction of chronic damage in systemic lupus erythematosus by using machine-learning models #MMPMID28329014 Ceccarelli F; Sciandrone M; Perricone C; Galvan G; Morelli F; Vicente LN; Leccese I; Massaro L; Cipriano E; Spinelli FR; Alessandri C; Valesini G; Conti F PLoS One 2017[]; 12 (3): ä PMID28329014show ga Objective: 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. Methods: We enrolled 413 SLE patients (M/F 30/383; mean age ± SD 46.3±11.9 years; mean disease duration ± SD 174.6 ± 112.1 months). Chronic damage was assessed by the SLICC/ACR Damage Index (SDI). We applied Recurrent Neural Networks (RNNs) as a machine-learning model to predict the risk of chronic damage. The clinical data sequences registered for each patient during the follow-up were used for building and testing the RNNs. Results: At the first visit in the Lupus Clinic, 35.8% of patients had an SDI?1. For the RNN model, two groups of patients were analyzed: patients with SDI = 0 at the baseline, developing damage during the follow-up (N = 38), and patients without damage (SDI = 0). We created a mathematical model with an AUC value of 0.77, able to predict damage development. A threshold value of 0.35 (sensitivity 0.74, specificity 0.76) seemed able to identify patients at risk to develop damage. Conclusion: We applied RNNs to identify a prediction model for SLE chronic damage. The use of the longitudinal data from the Sapienza Lupus Cohort, including laboratory and clinical items, resulted able to construct a mathematical model, potentially identifying patients at risk to develop damage. äPrediction of chronic damage in systemic lupus erythematosus by using (epmc).pdf DeepDyve Pubget Overpricing