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We propose a general framework for visualizing any intermediate embeddingrepresentation used by any neural survival analysis model. Our framework isbased on so-called anchor directions in an embedding space. We show how toestimate these anchor directions using clustering or, alternatively, usinguser-supplied "concepts" defined by collections of raw inputs (e.g., featurevectors all from female patients could encode the concept "female"). Fortabular data, we present visualization strategies that reveal how anchordirections relate to raw clinical features and to survival time distributions.We then show how these visualization ideas extend to handling raw inputs thatare images. Our framework is built on looking at angles between vectors in anembedding space, where there could be "information loss" by ignoring magnitudeinformation. We show how this loss results in a "clumping" artifact thatappears in our visualizations, and how to reduce this information loss inpractice.

A General Framework for Visualizing Embedding Spaces of Neural Survival Analysis Models Based on Angular Information