BACKGROUND Dropout rates in binge eating disorder (BED) treatment are high (17-30%), and predictors of dropout are unknown. METHOD Participants were 376 patients following an intensive outpatient cognitive behavioural therapy programme for BED, 82 of whom (21.8%) dropped out of treatment. An exploratory logistic regression was performed using eating disorder variables, general psychopathology...

Despite several modifications of the continuous ambulatory peritoneal dialysis (CAPD) technique over the last decade, peritonitis remains a major source of morbidity and is the leading cause of dropout for patients maintained on CAPD therapy. Recently, Baxter Healthcare introduced the Ultra Twin bag system, which uses drainage and infusion bags both secured to Y connecting tubing. Previous nonr...

In networked control systems, time delay and data dropout can degrade the performance of the control system and even destabilize the system. In the present paper, the Extended Kalman filter is employed to compensate the effects of time delay and data dropout in feedforward and feedback paths of networked control systems. In the proposed method, the extended Kalman filter is used as an observer ...

BACKGROUND Diabetes self-management involves adherence to healthy daily habits typically involving blood glucose monitoring, medication, exercise, and diet. To support self-management, some providers have begun testing remote interventions for monitoring and assisting patients between clinic visits. Although some studies have shown success, there are barriers to widespread adoption. OBJECTIVE...

Dropout is often used as an outcome measure in clinical trials of antipsychotic medication. Previous research is inconclusive regarding (a) differences in dropout rates between first- and second-generation antipsychotic medications and (b) how trial design features reduce dropout. Meta-analysis of randomized controlled trials (RCTs) of antipsychotic medication was conducted to compare dropout r...

Dropout is a popular technique for regularizing artificial neural networks. Dropout networks are generally trained by minibatch gradient descent with a dropout mask turning off some of the units—a different pattern of dropout is applied to every sample in the minibatch. We explore a very simple alternative to the dropout mask. Instead of masking dropped out units by setting them to zero, we per...

Dropout is typically interpreted as bagging a large number of models sharing parameters. We show that using dropout in a network can also be interpreted as a kind of data augmentation in the input space without domain knowledge. We present an approach to projecting the dropout noise within a network back into the input space, thereby generating augmented versions of the training data, and we sh...

Dropout has recently emerged as a powerful and simple method for training neural networks preventing co-adaptation by stochastically omitting neurons. is currently not grounded in explicit modelling assumptions which so far precluded its adoption Bayesian modelling. Using entropic reasoning we show that dropout can be interpreted optimal inference under constraints. We demonstrate this on an an...