Interpreting deep embeddings for disease progression clustering

07/12/2023

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by   Anna Munoz-Farre, et al.

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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 insights into disease progression patterns.