Neural network and logistic regression diagnostic prediction models for giant cell arteritis: development and validation.

Purpose: To develop and validate neural network (NN) vs logistic regression (LR) diagnostic prediction models in patients with suspected giant cell arteritis (GCA). Design: Multicenter retrospective chart review.

Methods: An audit of consecutive patients undergoing temporal artery biopsy (TABx) for suspected GCA was conducted at 14 international medical centers.

Support Vector Machines and logistic regression to predict temporal artery biopsy outcomes.

Objective: Support vector machines (SVM) is a newer statistical method that has been reported to be advantageous to traditional logistic regression for clinical classification. We determine if SVM can better predict the results of temporal artery biopsy (TABx) for giant cell arteritis compared to logistic regression.

Method: A database of 530 TABx patients with 10 covariates was used and randomly split into training and test sets.

Threshold-free measures for assessing the performance of medical screening tests.

Background: The area under the receiver operating characteristic curve (AUC) is frequently used as a performance measure for medical tests. It is a threshold-free measure that is independent of the disease prevalence rate. We evaluate the utility of the AUC against an alternate measure called the average positive predictive value (AP), in the setting of many medical screening programs where the disease has a low prevalence rate.