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.
A new capsule network that converges faster on our healthcare benchmark experiments. Presents a variant of capsule networks that instead of using EM routing employs a linear subspace spanned by the dominant eigenvector on the weighted votes matrix from the previous capsule. The paper proposes an improved routing method, which employs tools of eigendecomposition to find capsule activation and pose.