Interpreting deep embeddings for disease progression clustering
Anna Munoz-Farre, Antonios Poulakakis-Daktylidis, Dilini Mahesha Kothalawala, Andrea Rodriguez-Martinez
arXiv:2307.06060v21 citationsh-index: 12
Originality Synthesis-oriented
AI Analysis
This work addresses the challenge of interpreting deep learning models for disease progression clustering in healthcare, but appears incremental as it focuses on applying existing methods to a specific medical context.
The authors tackled the problem of interpreting deep embeddings for patient clustering, and demonstrated clinically meaningful insights into disease progression patterns using a dataset of type 2 diabetes participants from the UK Biobank.
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.