2014
DOI: 10.1136/annrheumdis-2014-eular.2853
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SAT0284 Large Vessel Involvement in GIANT Cell Arteritis - Incidence, Distribution and Predictors

Abstract: Background Over the years it has become increasingly clear that vascular involvement in giant cell arteritis (GCA) is widespread, that vasculitic changes may become evident many years after onset of the disease, and that these anomalies may not always be symptomatic. Objectives To investigate the cumulative incidence of large vessel involvement (LVI), defined as aneurysm/ectasia/stenosis of the aorta and its main branches, in patients with biopsy-proven GCA, and to describe the distribution of LVI. Furthermo… Show more

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“…txt') ,P1=load('f:;\P1.txt') and T1=load('f:\T1.txt') import files formatted 'txt' from f disk whose name is P,T,P1 and T1 as the P,T,P1,and T1 input matrix and this can reduce workload of manual input. Net=newff(minmax(p), [20,7],{'tansig','purelin'},'trainlm') construct a BP neural network whose input is P matrix and hidden layer contains 20 neurons. The out layer of the BP network contains 7 neurons (corresponding to the T matrix).…”
Section: The Analysis Of Fault Diagnosis Examplementioning
confidence: 99%
See 1 more Smart Citation
“…txt') ,P1=load('f:;\P1.txt') and T1=load('f:\T1.txt') import files formatted 'txt' from f disk whose name is P,T,P1 and T1 as the P,T,P1,and T1 input matrix and this can reduce workload of manual input. Net=newff(minmax(p), [20,7],{'tansig','purelin'},'trainlm') construct a BP neural network whose input is P matrix and hidden layer contains 20 neurons. The out layer of the BP network contains 7 neurons (corresponding to the T matrix).…”
Section: The Analysis Of Fault Diagnosis Examplementioning
confidence: 99%
“…It is an error back propagation algorithm for training multilayer feedback network. The topology of BP network model including input layer, hide layer and output layer [6] [7].…”
Section: Introductionmentioning
confidence: 99%