Medical prediction from missing data with max-minus negative regularized dropout.

Missing data is a naturally common problem faced in medical research. Imputation is a widely used technique to alleviate this problem. Unfortunately, the inherent uncertainty of imputation would make the model overfit the observed data distribution, which has a negative impact on the model generalization performance. R-Drop is a powerful technique to regularize the training of deep neural networks. However, it fails to differentiate the positive and negative samples, which prevents the model from learning robust representations. To handle this problem, we propose a novel negative regularization enhanced R-Drop scheme to boost performance and generalization ability, particularly in the context of missing data. The negative regularization enhanced R-Drop additionally forces the output distributions of positive and negative samples to be inconsistent with each other. Especially, we design a new max-minus negative sampling technique that uses the maximum in-batch values to minus the mini-batch to yield the negative samples to provide sufficient diversity for the model. We test the resulting max-minus negative regularized dropout method on three real-world medical prediction datasets, including both missing and complete cases, to show the effectiveness of the proposed method.