Interpreting deep embeddings for disease progression clustering

2023/07/12 by Munoz-Farre, Anna, Poulakakis-Daktylidis, Antonios, Kothalawala, Dilini Mahesha +1
#Computation and Language (cs.CL) #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Quantitative Methods (q-bio.QM)

paper · doi:10.48550/arxiv.2307.06060

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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