Deep Clustering Survival Machines with Interpretable Expert Distributions

• URL: http://arxiv.org/abs/2301.11826v4
• Date: Mon, 8 Apr 2024 14:57:42 GMT
• Title: Deep Clustering Survival Machines with Interpretable Expert Distributions
• Authors: Bojian Hou, Hongming Li, Zhicheng Jiao, Zhen Zhou, Hao Zheng, Yong Fan,
• Abstract summary: We propose a hybrid survival analysis method, referred to as deep clustering survival machines. We learn weights of the expert distributions for individual instances according to their features discriminatively. This method also facilitates interpretable subgrouping/clustering of all instances according to their associated expert distributions.
• Score: 14.938859205541014
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Conventional survival analysis methods are typically ineffective to characterize heterogeneity in the population while such information can be used to assist predictive modeling. In this study, we propose a hybrid survival analysis method, referred to as deep clustering survival machines, that combines the discriminative and generative mechanisms. Similar to the mixture models, we assume that the timing information of survival data is generatively described by a mixture of certain numbers of parametric distributions, i.e., expert distributions. We learn weights of the expert distributions for individual instances according to their features discriminatively such that each instance's survival information can be characterized by a weighted combination of the learned constant expert distributions. This method also facilitates interpretable subgrouping/clustering of all instances according to their associated expert distributions. Extensive experiments on both real and synthetic datasets have demonstrated that the method is capable of obtaining promising clustering results and competitive time-to-event predicting performance.

Related papers

• HACSurv: A Hierarchical Copula-based Approach for Survival Analysis with Dependent Competing Risks [51.95824566163554]
  HACSurv is a survival analysis method that learns structures and cause-specific survival functions from data with competing risks. By capturing the dependencies between risks and censoring, HACSurv achieves better survival predictions.
  arXiv  Detail & Related papers  (2024-10-19T18:52:18Z)
• Mixture of Efficient Diffusion Experts Through Automatic Interval and Sub-Network Selection [63.96018203905272]
  We propose to reduce the sampling cost by pruning a pretrained diffusion model into a mixture of efficient experts. We demonstrate the effectiveness of our method, DiffPruning, across several datasets.
  arXiv  Detail & Related papers  (2024-09-23T21:27:26Z)
• Clustering Survival Data using a Mixture of Non-parametric Experts [0.0]
  This study introduces SurvMixClust, a novel algorithm for survival analysis that integrates clustering with survival function prediction. Our evaluations on five public datasets show that SurvMixClust creates balanced clusters with distinct survival curves, outperforms clustering baselines, and competes with non-clustering survival models in predictive accuracy.
  arXiv  Detail & Related papers  (2024-05-24T20:47:58Z)
• Collaborative Heterogeneous Causal Inference Beyond Meta-analysis [68.4474531911361]
  We propose a collaborative inverse propensity score estimator for causal inference with heterogeneous data. Our method shows significant improvements over the methods based on meta-analysis when heterogeneity increases.
  arXiv  Detail & Related papers  (2024-04-24T09:04:36Z)
• Ambiguous Medical Image Segmentation using Diffusion Models [60.378180265885945]
  We introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights. Our proposed model generates a distribution of segmentation masks by leveraging the inherent sampling process of diffusion. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks.
  arXiv  Detail & Related papers  (2023-04-10T17:58:22Z)
• Heterogeneous Datasets for Federated Survival Analysis Simulation [6.489759672413373]
  This work proposes a novel technique for constructing realistic heterogeneous datasets by starting from existing non-federated datasets in a reproducible way. Specifically, we provide two novel dataset-splitting algorithms based on the Dirichlet distribution to assign each data sample to a carefully chosen client. The implementation of the proposed methods is publicly available in favor of and to encourage common practices to simulate federated environments for survival analysis.
  arXiv  Detail & Related papers  (2023-01-28T11:37:07Z)
• A Deep Variational Approach to Clustering Survival Data [5.871238645229228]
  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.
  arXiv  Detail & Related papers  (2021-06-10T14:10:25Z)
• GENs: Generative Encoding Networks [4.269725092203672]
  We propose and analyze the use of nonparametric density methods to estimate the Jensen-Shannon divergence for matching unknown data distributions to known target distributions. This analytical method has several advantages: better behavior when training sample quantity is low, provable convergence properties, and relatively few parameters, which can be derived analytically.
  arXiv  Detail & Related papers  (2020-10-28T23:40:03Z)
• Good Classifiers are Abundant in the Interpolating Regime [64.72044662855612]
  We develop a methodology to compute precisely the full distribution of test errors among interpolating classifiers. We find that test errors tend to concentrate around a small typical value $varepsilon*$, which deviates substantially from the test error of worst-case interpolating model. Our results show that the usual style of analysis in statistical learning theory may not be fine-grained enough to capture the good generalization performance observed in practice.
  arXiv  Detail & Related papers  (2020-06-22T21:12:31Z)
• Survival Cluster Analysis [93.50540270973927]
  There is an unmet need in survival analysis for identifying subpopulations with distinct risk profiles. An approach that addresses this need is likely to improve characterization of individual outcomes.
  arXiv  Detail & Related papers  (2020-02-29T22:41:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.