Generative models use latent variables to represent structured variation in high-dimensional data, such as images, audio, and medical waveforms. These latent variables, however, may ignore subtle, yet meaningful features in the data. Some features may predict an outcome of interest (e.g. heart disease) but account for only a small fraction of variation in the data. We propose a generative model training objective that uses a black-box discriminative model as a regularizer to learn representations that preserve this predictive variation. With these discriminatively regularized generative models, we visualize and measure variation in the data that influence a black-box predictive model, enabling an expert to better understand each prediction. With this technique, we study models that use electrocardiograms to predict outcomes of clinical interest. We measure our approach on synthetic and real data with statistical summaries and an experiment carried out by a physician.