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.
Cite
@article{arxiv.2307.06060,
title = {Interpreting deep embeddings for disease progression clustering},
author = {Anna Munoz-Farre and Antonios Poulakakis-Daktylidis and Dilini Mahesha Kothalawala and Andrea Rodriguez-Martinez},
journal= {arXiv preprint arXiv:2307.06060},
year = {2023}
}
Comments
Workshop on Interpretable ML in Healthcare at International Conference on Machine Learning (ICML), Honolulu, Hawaii, USA. 2023