• Corpus ID: 235390687

A Deep Variational Approach to Clustering Survival Data

@article{Manduchi2021ADV,
  title={A Deep Variational Approach to Clustering Survival Data},
  author={Laura Manduchi and Ricards Marcinkevics and Michela Carlotta Massi and Verena Gotta and Timothy M{\"u}ller and Flavio Vasella and Marian Christoph Neidert and Marc Pfister and Julia E. Vogt},
  journal={ArXiv},
  year={2021},
  volume={abs/2106.05763}
}
In this work, we study the problem of clustering survival data — a challenging and so far under-explored task. We introduce a novel semi-supervised probabilistic approach to cluster survival data by leveraging recent advances in stochastic gradient variational inference. In contrast to previous work, our proposed method employs a deep generative model to uncover the underlying distribution of both the explanatory variables and censored survival times. We compare our model to the related work on… 
1 Citations
Time-series deep survival prediction for hemodialysis patients using an attention-based Bi-GRU network
  • Ziyue Yang, Yu Tian, +4 authors Jingsong Li
  • Computer Science, Medicine
    Comput. Methods Programs Biomed.
  • 2021
TLDR
This study found that even after controlling the initial body mass index (BMI) values, different 3-month BMI trends could produce different survival outcomes, and proposed a more effective and interpretable method to use time-series information in survival analysis.

References

SHOWING 1-10 OF 82 REFERENCES
A Deep Learning Approach for Survival Clustering without End-of-life Signals
TLDR
A loss function is introduced that differentiates between the empirical lifetime distributions of the clusters using a modified Kuiper statistic, and a deep neural network is learned by optimizing this loss, that performs a soft clustering of users into survival groups.
Semi-crowdsourced Clustering with Deep Generative Models
TLDR
This work proposes a new approach that includes a deep generative model (DGM) to characterize low-level features of the data, and a statistical relational model for noisy pairwise annotations on its subset, and develops its fully Bayesian variant.
Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders
TLDR
It is shown that a heuristic called minimum information constraint that has been shown to mitigate this effect in VAEs can also be applied to improve unsupervised clustering performance with this variant of the variational autoencoder model with a Gaussian mixture as a prior distribution.
Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering
TLDR
Variational Deep Embedding (VaDE) is proposed, a novel unsupervised generative clustering approach within the framework of Variational Auto-Encoder (VAE), which shows its capability of generating highly realistic samples for any specified cluster, without using supervised information during training.
Deep Survival Analysis
TLDR
Deep survival analysis is introduced, a hierarchical generative approach to survival analysis that scalably handles heterogeneous data types that occur in the EHR and is significantly superior in stratifying patients according to their risk.
Survival cluster analysis
TLDR
This paper proposes a Bayesian nonparametrics approach that represents observations (subjects) in a clustered latent space, and encourages accurate time-to-event predictions and clusters (subpopulations) with distinct risk profiles, and shows consistent improvements in predictive performance and interpretability relative to existing state-of-the-art survival analysis models.
Clustering method for censored and collinear survival data
TLDR
A Dirichlet process mixture model for censored survival data with covariates for mixtures of Weibull distributions, which can be used to model survival times and also allow for censoring, and a real application to sleep surveys in older women from The Australian Longitudinal Study on Women's Health is presented.
Image-Based Survival Prediction for Lung Cancer Patients Using CNNS
TLDR
This work shows that by simplifying survival analysis to median survival classification, convolutional neural networks can be trained with small batch sizes and learn features that predict survival equally well as end-to-end hazard prediction networks.
Auto-Encoding Variational Bayes
TLDR
A stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case is introduced.
Deep learning cardiac motion analysis for human survival prediction
TLDR
A fully convolutional neural network is used to create time-resolved three-dimensional dense segmentations of heart images that can efficiently predict human survival.
...
1
2
3
4
5
...