Prediction of treatment responses from available data is key to optimizing personalized treatment. Retinal diseases are treated over long periods and patients' response patterns differ substantially, ranging from a complete response to a recurrence of the disease and need for re-treatment at different intervals. Linking observable variables in high-dimensional observations to outcome is challenging. In this paper, we present and evaluate two different data-driven machine learning approaches operating in a high-dimensional feature space: sparse logistic regression and random forests-based extra trees (ET). Both identify spatio-temporal signatures based on retinal thickness features measured in longitudinal spectral-domain optical coherence tomography (OCT) imaging data and predict individual patient outcome using these quantitative characteristics. We demonstrate on a data set of monthly SD-OCT scans of 155 patients with central retinal vein occlusion (CRVO) and 92 patients with branch retinal vein occlusion (BRVO) followed over one year that we can predict from initial three observations if the treated disease will recur within the covered interval. ET predicts the outcome on fivefold cross-validation with an area under the receiver operating characteristic curve (AuC) of 0.83 for BRVO and 0.76 for CRVO. Logistic regression achieved an AuC of 0.78 and 0.79, respectively. At the same time, the methods identified stable predictive signatures in the longitudinal imaging data that are the basis for accurate prediction. Furthermore, our results show that taking spatio-temporal features into account improves accuracy compared with features extracted at a single time-point. Our results demonstrate the feasibility of mining longitudinal data for predictive signatures, and building predictive models based on observed data.