In search for more accurate predictive models, we customize capsule networks for the learning to diagnose problem. We also propose Spectral Capsule Networks, a novel variation of capsule networks, that converge faster than capsule network with EM routing. Spectral capsule networks consist of spatial coincidence filters that detect entities based on the alignment of extracted features on a one-dimensional linear subspace. Experiments on a public benchmark learning to diagnose dataset not only shows the success of capsule networks on this task, but also confirm the faster convergence of the spectral capsule networks.