Interpreting deep embeddings for disease progression clustering

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

Keywords: Electronic Health Records, Language Modelling, Representation Learning, Patient clustering, Time Series, Embedding interpretation

TL;DR: We propose a novel approach for interpreting deep embeddings in the context of patient clustering, and evaluate it for type 2 diabetes.

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 insights into disease progression patterns.

Submission Number: 58