MLJul 12, 2023
Interpreting deep embeddings for disease progression clusteringAnna Munoz-Farre, Antonios Poulakakis-Daktylidis, Dilini Mahesha Kothalawala et al.
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.