Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks

• URL: http://arxiv.org/abs/2003.01176v3
• Date: Wed, 9 Jun 2021 12:09:21 GMT
• Title: Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks
• Authors: Chirag Nagpal, Xinyu Rachel Li and Artur Dubrawski
• Abstract summary: We describe a new approach to estimating relative risks in time-to-event prediction problems with censored data. Our approach does not require making strong assumptions of constant proportional hazard of the underlying survival distribution. This is the first work involving fully parametric estimation of survival times with competing risks in the presence of censoring.
• Score: 14.928328404160299
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We describe a new approach to estimating relative risks in time-to-event prediction problems with censored data in a fully parametric manner. Our approach does not require making strong assumptions of constant proportional hazard of the underlying survival distribution, as required by the Cox-proportional hazard model. By jointly learning deep nonlinear representations of the input covariates, we demonstrate the benefits of our approach when used to estimate survival risks through extensive experimentation on multiple real world datasets with different levels of censoring. We further demonstrate advantages of our model in the competing risks scenario. To the best of our knowledge, this is the first work involving fully parametric estimation of survival times with competing risks in the presence of censoring.

Related papers

• Data-Adaptive Tradeoffs among Multiple Risks in Distribution-Free Prediction [55.77015419028725]
  We develop methods that permit valid control of risk when threshold and tradeoff parameters are chosen adaptively. Our methodology supports monotone and nearly-monotone risks, but otherwise makes no distributional assumptions.
  arXiv  Detail & Related papers  (2024-03-28T17:28:06Z)
• Predictive Uncertainty Quantification via Risk Decompositions for Strictly Proper Scoring Rules [7.0549244915538765]
  Uncertainty in predictive modeling often relies on ad hoc methods. This paper introduces a theoretical approach to understanding uncertainty through statistical risks. We show how to split pointwise risk into Bayes risk and excess risk.
  arXiv  Detail & Related papers  (2024-02-16T14:40:22Z)
• SurvivalGAN: Generating Time-to-Event Data for Survival Analysis [121.84429525403694]
  Imbalances in censoring and time horizons cause generative models to experience three new failure modes specific to survival analysis. We propose SurvivalGAN, a generative model that handles survival data by addressing the imbalance in the censoring and event horizons. We evaluate this method via extensive experiments on medical datasets.
  arXiv  Detail & Related papers  (2023-02-24T17:03:51Z)
• Optimizing the Performative Risk under Weak Convexity Assumptions [0.0]
  In performative prediction, a predictive model impacts the distribution that generates future data. Prior work has identified a pair of general conditions on the loss and the mapping from model parameters to distributions that implies convexity the performative risk. In this paper, we relax these assumptions, without sacrificing the amenability of the performative minimization risk problem for iterative optimization methods.
  arXiv  Detail & Related papers  (2022-09-02T01:07:09Z)
• Detecting and Mitigating Test-time Failure Risks via Model-agnostic Uncertainty Learning [30.86992077157326]
  This paper introduces Risk Advisor, a novel post-hoc meta-learner for estimating failure risks and predictive uncertainties of any already-trained black-box classification model. In addition to providing a risk score, the Risk Advisor decomposes the uncertainty estimates into aleatoric and epistemic uncertainty components. Experiments on various families of black-box classification models and on real-world and synthetic datasets show that the Risk Advisor reliably predicts deployment-time failure risks.
  arXiv  Detail & Related papers  (2021-09-09T17:23:31Z)
• Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning [57.88785630755165]
  Empirical risk minimization (ERM) is the workhorse of machine learning, but its model-agnostic guarantees can fail when we use adaptively collected data. We study a generic importance sampling weighted ERM algorithm for using adaptively collected data to minimize the average of a loss function over a hypothesis class. For policy learning, we provide rate-optimal regret guarantees that close an open gap in the existing literature whenever exploration decays to zero.
  arXiv  Detail & Related papers  (2021-06-03T09:50:13Z)
• DeepHazard: neural network for time-varying risks [0.6091702876917281]
  We propose a new flexible method for survival prediction: DeepHazard, a neural network for time-varying risks. Our approach is tailored for a wide range of continuous hazards forms, with the only restriction of being additive in time. Numerical examples illustrate that our approach outperforms existing state-of-the-art methodology in terms of predictive capability evaluated through the C-index metric.
  arXiv  Detail & Related papers  (2020-07-26T21:01:49Z)
• Learning Bounds for Risk-sensitive Learning [86.50262971918276]
  In risk-sensitive learning, one aims to find a hypothesis that minimizes a risk-averse (or risk-seeking) measure of loss. We study the generalization properties of risk-sensitive learning schemes whose optimand is described via optimized certainty equivalents.
  arXiv  Detail & Related papers  (2020-06-15T05:25:02Z)
• Enabling Counterfactual Survival Analysis with Balanced Representations [64.17342727357618]
  Survival data are frequently encountered across diverse medical applications, i.e., drug development, risk profiling, and clinical trials. We propose a theoretically grounded unified framework for counterfactual inference applicable to survival outcomes.
  arXiv  Detail & Related papers  (2020-06-14T01:15:00Z)
• 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.