Interpreting deep embeddings for disease progression clustering

Anna Munoz-Farre, Antonios Poulakakis-Daktylidis, Dilini Mahesha Kothalawala, Andrea Rodriguez-Martinez
7/12/2023
stat.MLcs.CLcs.LG

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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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Cite this paper

@article{munozfarre2023interpreting,
  title  = {Interpreting deep embeddings for disease progression clustering},
  author = {Anna Munoz-Farre and Antonios Poulakakis-Daktylidis and Dilini Mahesha Kothalawala and Andrea Rodriguez-Martinez},
  year   = {2023},
  eprint = {2307.06060},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2307.06060v2}
}

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