FPBoost: Fully Parametric Gradient Boosting for Survival Analysis

• URL: http://arxiv.org/abs/2409.13363v1
• Date: Fri, 20 Sep 2024 09:57:17 GMT
• Title: FPBoost: Fully Parametric Gradient Boosting for Survival Analysis
• Authors: Alberto Archetti, Eugenio Lomurno, Diego Piccinotti, Matteo Matteucci,
• Abstract summary: We propose a novel paradigm for survival model design based on the weighted sum of individual fully parametric hazard contributions. The proposed model, which we call FPBoost, is the first algorithm to directly optimize the survival likelihood via gradient boosting.
• Score: 4.09225917049674
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Survival analysis is a critical tool for analyzing time-to-event data and extracting valuable clinical insights. Recently, numerous machine learning techniques leveraging neural networks and decision trees have been developed for this task. Among these, the most successful approaches often rely on specific assumptions about the shape of the modeled hazard function. These assumptions include proportional hazard, accelerated failure time, or discrete estimation at a predefined set of time points. In this study, we propose a novel paradigm for survival model design based on the weighted sum of individual fully parametric hazard contributions. We build upon well-known ensemble techniques to deliver a novel contribution to the field by applying additive hazard functions, improving over approaches based on survival or cumulative hazard functions. Furthermore, the proposed model, which we call FPBoost, is the first algorithm to directly optimize the survival likelihood via gradient boosting. We evaluated our approach across a diverse set of datasets, comparing it against a variety of state-of-the-art models. The results demonstrate that FPBoost improves risk estimation, according to both concordance and calibration metrics.

Related papers

• Deep End-to-End Survival Analysis with Temporal Consistency [49.77103348208835]
  We present a novel Survival Analysis algorithm designed to efficiently handle large-scale longitudinal data. A central idea in our method is temporal consistency, a hypothesis that past and future outcomes in the data evolve smoothly over time. Our framework uniquely incorporates temporal consistency into large datasets by providing a stable training signal.
  arXiv  Detail & Related papers  (2024-10-09T11:37:09Z)
• Cumulative Distribution Function based General Temporal Point Processes [49.758080415846884]
  CuFun model represents a novel approach to TPPs that revolves around the Cumulative Distribution Function (CDF) Our approach addresses several critical issues inherent in traditional TPP modeling. Our contributions encompass the introduction of a pioneering CDF-based TPP model, the development of a methodology for incorporating past event information into future event prediction.
  arXiv  Detail & Related papers  (2024-02-01T07:21:30Z)
• Composite Survival Analysis: Learning with Auxiliary Aggregated Baselines and Survival Scores [0.0]
  Survival Analysis (SA) constitutes the default method for time-to-event modeling. We show how to improve the training and inference of SA models by decoupling their full expression into (1) an aggregated baseline hazard, which captures the overall behavior of a given population, and (2) independently distributed survival scores, which model idiosyncratic probabilistic dynamics of its given members, in a fully parametric setting.
  arXiv  Detail & Related papers  (2023-12-10T11:13:22Z)
• Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behavior [51.60683890503293]
  We propose a theoretical framework for studying behavior cloning of complex expert demonstrations using generative modeling. We show that pure supervised cloning can generate trajectories matching the per-time step distribution of arbitrary expert trajectories.
  arXiv  Detail & Related papers  (2023-07-27T04:27:26Z)
• Towards Flexible Time-to-event Modeling: Optimizing Neural Networks via Rank Regression [17.684526928033065]
  We introduce the Deep AFT Rank-regression model for Time-to-event prediction (DART) This model uses an objective function based on Gehan's rank statistic, which is efficient and reliable for representation learning. The proposed method is a semiparametric approach to AFT modeling that does not impose any distributional assumptions on the survival time distribution.
  arXiv  Detail & Related papers  (2023-07-16T13:58:28Z)
• Learning Survival Distribution with Implicit Survival Function [15.588273962274393]
  We propose Implicit Survival Function (ISF) based on Implicit Neural Representation for survival distribution estimation without strong assumptions. Experimental results show ISF outperforms the state-of-the-art methods in three public datasets.
  arXiv  Detail & Related papers  (2023-05-24T02:51:29Z)
• FAStEN: An Efficient Adaptive Method for Feature Selection and Estimation in High-Dimensional Functional Regressions [7.674715791336311]
  We propose a new, flexible and ultra-efficient approach to perform feature selection in a sparse function-on-function regression problem. We show how to extend it to the scalar-on-function framework. We present an application to brain fMRI data from the AOMIC PIOP1 study.
  arXiv  Detail & Related papers  (2023-03-26T19:41:17Z)
• A Free Lunch with Influence Functions? Improving Neural Network Estimates with Concepts from Semiparametric Statistics [41.99023989695363]
  We explore the potential for semiparametric theory to be used to improve neural networks and machine learning algorithms. We propose a new neural network method MultiNet, which seeks the flexibility and diversity of an ensemble using a single architecture.
  arXiv  Detail & Related papers  (2022-02-18T09:35:51Z)
• Uncertainty Modeling for Out-of-Distribution Generalization [56.957731893992495]
  We argue that the feature statistics can be properly manipulated to improve the generalization ability of deep learning models. Common methods often consider the feature statistics as deterministic values measured from the learned features. We improve the network generalization ability by modeling the uncertainty of domain shifts with synthesized feature statistics during training.
  arXiv  Detail & Related papers  (2022-02-08T16:09:12Z)
• Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression [51.770998056563094]
  Probabilistic Gradient Boosting Machines (PGBM) is a method to create probabilistic predictions with a single ensemble of decision trees. We empirically demonstrate the advantages of PGBM compared to existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-06-03T08:32:13Z)
• Estimating Structural Target Functions using Machine Learning and Influence Functions [103.47897241856603]
  We propose a new framework for statistical machine learning of target functions arising as identifiable functionals from statistical models. This framework is problem- and model-agnostic and can be used to estimate a broad variety of target parameters of interest in applied statistics. We put particular focus on so-called coarsening at random/doubly robust problems with partially unobserved information.
  arXiv  Detail & Related papers  (2020-08-14T16:48:29Z)

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.