Interpreting deep embeddings for disease progression clustering

Abstract
We propose a novel approach for interpreting deep embeddings in the context of patient clustering. We evaluate our approach on a dataset of participants with type 2 diabetes from the UK Biobank, and demonstrate clinically meaningful insight into disease progression patterns.