A Deep Variational Approach to Clustering Survival Data
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by
Laura Manduchi, Ričards Marcinkevičs, Michela C. Massi, Verena Gotta, Timothy Müller, Flavio Vasella, Marian C. Neidert, Marc Pfister, Julia E. Vogt
2021
Abstract
Survival analysis has gained significant attention in the medical domain and
has many far-reaching applications. Although a variety of machine learning
methods have been introduced for tackling time-to-event prediction in
unstructured data with complex dependencies, clustering of survival data
remains an under-explored problem. The latter is particularly helpful in
discovering patient subpopulations whose survival is regulated by different
generative mechanisms, a critical problem in precision medicine. To this end,
we introduce a novel probabilistic approach to cluster survival data in a
variational deep clustering setting. Our proposed method employs a deep
generative model to uncover the underlying distribution of both the explanatory
variables and the potentially censored survival times. We compare our model to
the related work on survival clustering in comprehensive experiments on a range
of synthetic, semi-synthetic, and real-world datasets. Our proposed method
performs better at identifying clusters and is competitive at predicting
survival times in terms of the concordance index and relative absolute error.
To further demonstrate the usefulness of our approach, we show that our method
identifies meaningful clusters from an observational cohort of hemodialysis
patients that are consistent with previous clinical findings.
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