Interpreting Deep Embeddings for Disease Progression Clustering
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.
Cite
Text
Munoz-Farre et al. "Interpreting Deep Embeddings for Disease Progression Clustering." ICML 2023 Workshops: IMLH, 2023.Markdown
[Munoz-Farre et al. "Interpreting Deep Embeddings for Disease Progression Clustering." ICML 2023 Workshops: IMLH, 2023.](https://mlanthology.org/icmlw/2023/munozfarre2023icmlw-interpreting/)BibTeX
@inproceedings{munozfarre2023icmlw-interpreting,
title = {{Interpreting Deep Embeddings for Disease Progression Clustering}},
author = {Munoz-Farre, Anna and Poulakakis-Daktylidis, Antonios and Kothalawala, Dilini Mahesha and Rodriguez-Martinez, Andrea},
booktitle = {ICML 2023 Workshops: IMLH},
year = {2023},
url = {https://mlanthology.org/icmlw/2023/munozfarre2023icmlw-interpreting/}
}