Poster
in
Workshop: 3rd Workshop on Interpretable Machine Learning in Healthcare (IMLH)
Interpreting deep embeddings for disease progression clustering
Anna Munoz-Farre · Antonios Poulakakis-Daktylidis · Dilini Kothalawala · Andrea Rodriguez-Martinez
Keywords: [ Representation Learning ] [ Time Series ] [ Embedding interpretation ] [ Patient clustering ] [ Electronic Health Records ] [ Language Modelling ]
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.