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

Project Page [ OpenReview]

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.

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