1. Predicting Macular Edema Recurrence from Spatio-Temporal Signatures in Optical Coherence Tomography Image

    Predicting Macular Edema Recurrence from Spatio-Temporal Signatures in Optical Coherence Tomography Image

    Abstract: 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 ...

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