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Interpreting deep embeddings for disease progression clustering
Anna Munoz-Farre, Antonios Poulakakis-Daktylidis, Dilini Mahesha · 2023-07-12 · via stat.ML updates on arXiv.org

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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