Teaching Models To Survive: Proper Scoring Rule and Stochastic Optimization with Competing Risks

• URL: http://arxiv.org/abs/2406.14085v1
• Date: Thu, 20 Jun 2024 08:00:42 GMT
• Title: Teaching Models To Survive: Proper Scoring Rule and Stochastic Optimization with Competing Risks
• Authors: Julie Alberge, Vincent Maladière, Olivier Grisel, Judith Abécassis, Gaël Varoquaux,
• Abstract summary: When data are right-censored, survival analysis can compute the "time to event" We introduce a strictly proper censoring-adjusted separable scoring rule that can be optimized on a subpart of the data. Compared to 11 state-of-the-art models, this model, MultiIncidence, performs best in estimating the probability of outcomes in survival and competing risks.
• Score: 6.9648613217501705
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: When data are right-censored, i.e. some outcomes are missing due to a limited period of observation, survival analysis can compute the "time to event". Multiple classes of outcomes lead to a classification variant: predicting the most likely event, known as competing risks, which has been less studied. To build a loss that estimates outcome probabilities for such settings, we introduce a strictly proper censoring-adjusted separable scoring rule that can be optimized on a subpart of the data because the evaluation is made independently of observations. It enables stochastic optimization for competing risks which we use to train gradient boosting trees. Compared to 11 state-of-the-art models, this model, MultiIncidence, performs best in estimating the probability of outcomes in survival and competing risks. It can predict at any time horizon and is much faster than existing alternatives.

Related papers

• Survival Models: Proper Scoring Rule and Stochastic Optimization with Competing Risks [6.9648613217501705]
  SurvivalBoost outperforms 12 state-of-the-art models on 4 real-life datasets. It provides great calibration, the ability to predict across any time horizon, and computation times faster than existing methods.
  arXiv  Detail & Related papers  (2024-10-22T07:33:34Z)
• Risk and cross validation in ridge regression with correlated samples [72.59731158970894]
  We provide training examples for the in- and out-of-sample risks of ridge regression when the data points have arbitrary correlations. We further extend our analysis to the case where the test point has non-trivial correlations with the training set, setting often encountered in time series forecasting. We validate our theory across a variety of high dimensional data.
  arXiv  Detail & Related papers  (2024-08-08T17:27:29Z)
• Rejection via Learning Density Ratios [50.91522897152437]
  Classification with rejection emerges as a learning paradigm which allows models to abstain from making predictions. We propose a different distributional perspective, where we seek to find an idealized data distribution which maximizes a pretrained model's performance. Our framework is tested empirically over clean and noisy datasets.
  arXiv  Detail & Related papers  (2024-05-29T01:32:17Z)
• TripleSurv: Triplet Time-adaptive Coordinate Loss for Survival Analysis [15.496918127515665]
  We propose a time-adaptive coordinate loss function, TripleSurv, to handle the complexities of learning process and exploit valuable survival time values. Our TripleSurv is evaluated on three real-world survival datasets and a public synthetic dataset.
  arXiv  Detail & Related papers  (2024-01-05T08:37:57Z)
• CenTime: Event-Conditional Modelling of Censoring in Survival Analysis [49.44664144472712]
  We introduce CenTime, a novel approach to survival analysis that directly estimates the time to event. Our method features an innovative event-conditional censoring mechanism that performs robustly even when uncensored data is scarce. Our results indicate that CenTime offers state-of-the-art performance in predicting time-to-death while maintaining comparable ranking performance.
  arXiv  Detail & Related papers  (2023-09-07T17:07:33Z)
• Rethinking Missing Data: Aleatoric Uncertainty-Aware Recommendation [59.500347564280204]
  We propose a new Aleatoric Uncertainty-aware Recommendation (AUR) framework. AUR consists of a new uncertainty estimator along with a normal recommender model. As the chance of mislabeling reflects the potential of a pair, AUR makes recommendations according to the uncertainty.
  arXiv  Detail & Related papers  (2022-09-22T04:32:51Z)
• Predictive Multiplicity in Probabilistic Classification [25.111463701666864]
  We present a framework for measuring predictive multiplicity in probabilistic classification. We demonstrate the incidence and prevalence of predictive multiplicity in real-world tasks. Our results emphasize the need to report predictive multiplicity more widely.
  arXiv  Detail & Related papers  (2022-06-02T16:25:29Z)
• Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression [91.3373131262391]
  Uncertainty is the only certainty there is. Traditionally, the direct regression formulation is considered and the uncertainty is modeled by modifying the output space to a certain family of probabilistic distributions. How to model the uncertainty within the present-day technologies for regression remains an open issue.
  arXiv  Detail & Related papers  (2021-03-25T06:56:09Z)
• Continuous Mean-Covariance Bandits [39.820490484375156]
  We propose a novel Continuous Mean-Covariance Bandit model to take into account option correlation. In CMCB, there is a learner who sequentially chooses weight vectors on given options and observes random feedback according to the decisions. We propose novel algorithms with optimal regrets (within logarithmic factors) and provide matching lower bounds to validate their optimalities.
  arXiv  Detail & Related papers  (2021-02-24T06:37:05Z)
• 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.