FastCPH: Efficient Survival Analysis for Neural Networks
- URL: http://arxiv.org/abs/2208.09793v1
- Date: Sun, 21 Aug 2022 03:35:29 GMT
- Title: FastCPH: Efficient Survival Analysis for Neural Networks
- Authors: Xuelin Yang, Louis Abraham, Sejin Kim, Petr Smirnov, Feng Ruan,
Benjamin Haibe-Kains, Robert Tibshirani
- Abstract summary: We propose FastCPH, a new method that runs in linear time and supports both the standard Breslow and Efron methods for tied events.
We also demonstrate the performance of FastCPH combined with LassoNet, a neural network that provides interpretability through feature sparsity.
- Score: 57.03275837523063
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The Cox proportional hazards model is a canonical method in survival analysis
for prediction of the life expectancy of a patient given clinical or genetic
covariates -- it is a linear model in its original form. In recent years,
several methods have been proposed to generalize the Cox model to neural
networks, but none of these are both numerically correct and computationally
efficient. We propose FastCPH, a new method that runs in linear time and
supports both the standard Breslow and Efron methods for tied events. We also
demonstrate the performance of FastCPH combined with LassoNet, a neural network
that provides interpretability through feature sparsity, on survival datasets.
The final procedure is efficient, selects useful covariates and outperforms
existing CoxPH approaches.
Related papers
- CoxKAN: Kolmogorov-Arnold Networks for Interpretable, High-Performance Survival Analysis [0.3213991044370425]
Kolmogorov-Arnold Networks (KANs) were recently proposed as an interpretable and accurate alternative to multi-layer perceptrons (MLPs)
We introduce CoxKAN, a Cox proportional hazards Kolmogorov-Arnold Network for interpretable, high-performance survival analysis.
arXiv Detail & Related papers (2024-09-06T13:59:58Z) - Kernel Cox partially linear regression: building predictive models for
cancer patients' survival [4.230753712933184]
We build a kernel Cox proportional hazards semi-parametric model and propose a novel regularized garrotized kernel machine (RegGKM) method to fit the model.
We use the kernel machine method to describe the complex relationship between survival and predictors, while automatically removing irrelevant parametric and non-parametric predictors.
Our results can help classify patients into groups with different death risks, facilitating treatment for better clinical outcomes.
arXiv Detail & Related papers (2023-10-11T04:27:54Z) - tdCoxSNN: Time-Dependent Cox Survival Neural Network for Continuous-time
Dynamic Prediction [19.38247205641199]
We propose a time-dependent Cox survival neural network (tdCoxSNN) to predict its progression using longitudinal fundus images.
We evaluate and compare our proposed method with joint modeling and landmarking approaches through extensive simulations.
arXiv Detail & Related papers (2023-07-12T03:03:40Z) - Low-rank extended Kalman filtering for online learning of neural
networks from streaming data [71.97861600347959]
We propose an efficient online approximate Bayesian inference algorithm for estimating the parameters of a nonlinear function from a potentially non-stationary data stream.
The method is based on the extended Kalman filter (EKF), but uses a novel low-rank plus diagonal decomposition of the posterior matrix.
In contrast to methods based on variational inference, our method is fully deterministic, and does not require step-size tuning.
arXiv Detail & Related papers (2023-05-31T03:48:49Z) - Continuous time recurrent neural networks: overview and application to
forecasting blood glucose in the intensive care unit [56.801856519460465]
Continuous time autoregressive recurrent neural networks (CTRNNs) are a deep learning model that account for irregular observations.
We demonstrate the application of these models to probabilistic forecasting of blood glucose in a critical care setting.
arXiv Detail & Related papers (2023-04-14T09:39:06Z) - Variable selection for nonlinear Cox regression model via deep learning [0.0]
We extend the recently developed deep learning-based variable selection model LassoNet to survival data.
We apply the proposed methodology to analyze a real data set on diffuse large B-cell lymphoma.
arXiv Detail & Related papers (2022-11-17T01:17:54Z) - Comparative Analysis of Interval Reachability for Robust Implicit and
Feedforward Neural Networks [64.23331120621118]
We use interval reachability analysis to obtain robustness guarantees for implicit neural networks (INNs)
INNs are a class of implicit learning models that use implicit equations as layers.
We show that our approach performs at least as well as, and generally better than, applying state-of-the-art interval bound propagation methods to INNs.
arXiv Detail & Related papers (2022-04-01T03:31:27Z) - An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks.
We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem.
We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
arXiv Detail & Related papers (2020-12-06T18:55:07Z) - A Bayesian Perspective on Training Speed and Model Selection [51.15664724311443]
We show that a measure of a model's training speed can be used to estimate its marginal likelihood.
We verify our results in model selection tasks for linear models and for the infinite-width limit of deep neural networks.
Our results suggest a promising new direction towards explaining why neural networks trained with gradient descent are biased towards functions that generalize well.
arXiv Detail & Related papers (2020-10-27T17:56:14Z) - 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)
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.