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Scaling Survival Analysis in Healthcare with Federated Survival Forests: A Comparative Study on Heart Failure and Breast Cancer Genomics [PDF]
, 2023 Survival analysis is a fundamental tool in medicine, modeling the time until an event of interest occurs in a population. However, in real-world applications, survival data are often incomplete, censored, distributed, and confidential, especially in healthcare settings where privacy is critical.
arxiv +1 more source
Federated Survival Forests [PDF]
, 2023 Survival analysis is a subfield of statistics concerned with modeling the occurrence time of a particular event of interest for a population. Survival analysis found widespread applications in healthcare, engineering, and social sciences. However, real-world applications involve survival datasets that are distributed, incomplete, censored, and ...
arxiv +1 more source
Survival stacking: casting survival analysis as a classification problem [PDF]
arXiv, 2021 While there are many well-developed data science methods for classification and regression, there are relatively few methods for working with right-censored data. Here, we present "survival stacking": a method for casting survival analysis problems as classification problems, thereby allowing the use of general classification methods and software in a ...
arxiv
Reverse Survival Model (RSM): A Pipeline for Explaining Predictions of Deep Survival Models [PDF]
arXiv, 2022 The aim of survival analysis in healthcare is to estimate the probability of occurrence of an event, such as a patient's death in an intensive care unit (ICU). Recent developments in deep neural networks (DNNs) for survival analysis show the superiority of these models in comparison with other well-known models in survival analysis applications ...
arxiv
Learning Survival Distribution with Implicit Survival Function [PDF]
arXiv, 2023 Survival analysis aims at modeling the relationship between covariates and event occurrence with some untracked (censored) samples. In implementation, existing methods model the survival distribution with strong assumptions or in a discrete time space for likelihood estimation with censorship, which leads to weak generalization.
arxiv
Interpretable Survival Analysis for Heart Failure Risk Prediction [PDF]
arXiv, 2023 Survival analysis, or time-to-event analysis, is an important and widespread problem in healthcare research. Medical research has traditionally relied on Cox models for survival analysis, due to their simplicity and interpretability. Cox models assume a log-linear hazard function as well as proportional hazards over time, and can perform poorly when ...
arxiv
Neural networks to predict survival from RNA-seq data in oncology [PDF]
arXiv, 2021 Survival analysis consists of studying the elapsed time until an event of interest, such as the death or recovery of a patient in medical studies. This work explores the potential of neural networks in survival analysis from clinical and RNA-seq data. If the neural network approach is not recent in survival analysis, methods were classically considered
arxiv
FedPseudo: Pseudo value-based Deep Learning Models for Federated Survival Analysis [PDF]
arXiv, 2022 Survival analysis, time-to-event analysis, is an important problem in healthcare since it has a wide-ranging impact on patients and palliative care. Many survival analysis methods have assumed that the survival data is centrally available either from one medical center or by data sharing from multi-centers.
arxiv
Multi-Source Survival Domain Adaptation [PDF]
arXiv, 2022 Survival analysis is the branch of statistics that studies the relation between the characteristics of living entities and their respective survival times, taking into account the partial information held by censored cases. A good analysis can, for example, determine whether one medical treatment for a group of patients is better than another. With the
arxiv
Survival Forests under Test: Impact of the Proportional Hazards Assumption on Prognostic and Predictive Forests for ALS Survival [PDF]
Statistical Methods in Medical Research (2020), 2019 We investigate the effect of the proportional hazards assumption on prognostic and predictive models of the survival time of patients suffering from amyotrophic lateral sclerosis (ALS). We theoretically compare the underlying model formulations of several variants of survival forests and implementations thereof, including random forests for survival ...
arxiv +1 more source