Paper ID: 2307.06060

Interpreting deep embeddings for disease progression clustering

Anna Munoz-Farre, Antonios Poulakakis-Daktylidis, Dilini Mahesha Kothalawala, Andrea Rodriguez-Martinez

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.

Submitted: Jul 12, 2023