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相关概念视频

Survival Tree01:19

Survival Tree

105
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
105
Survival Curves01:18

Survival Curves

192
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
192
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

272
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
272
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

153
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
153
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

369
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
369
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

178
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
178

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相关实验视频

Updated: Jul 15, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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GNN-surv:使用图形神经网络进行离散时间生存预测.

So Yeon Kim1,2

  • 1Department of Artificial Intelligence, Ajou University, Suwon 16499, Republic of Korea.

Bioengineering (Basel, Switzerland)
|September 28, 2023
PubMed
概括

图形神经网络 (GNN) 通过分析患者相似性网络来改善癌症生存预测. 这些GNN-surv模型为瘤学中的个性化治疗规划提供了更高的准确性.

科学领域:

  • 在瘤学瘤学.
  • 生物信息学是一种生物信息学.
  • 机器学习 机器学习

背景情况:

  • 准确的生存预测对于患者的预后和个性化癌症治疗至关重要.
  • 通过整合患者相似性网络来捕获复杂的数据模式,可以增强传统模型.
  • 图形神经网络 (GNN) 提供了一种强大的方法来利用数据中的网络结构.

研究的目的:

  • 开发和评估基于图形神经网络的生存预测模型 (GNN-surv),以提高准确性.
  • 利用基因组和临床数据构建的患者相似性网络,以改善生存分析.
  • 评估GNN-surv模型与泌尿癌数据集中的传统模型的性能.

主要方法:

  • 使用癌症患者的基因组和临床数据构建患者相似性网络.
  • 培训和评估各种GNN模型与物流危险和概率质量函数 (PMF) 存活模型集成.
  • 将GNN-surv模型性能与多层感知器 (MLP) 模型进行比较,使用时间依赖的一致性指数和集成的Brier评分.

主要成果:

  • 在BLCA和KIRC数据集上,GNN-surv模型在生存预测方面显著优于传统的MLP模型.
  • 性能改进包括时间依赖一致性指数的增加高达14.6%和7.9%.
关键词:
图形神经网络的神经网络离散的生存模型.患者相似性网络患者相似性网络生存预测的预测.时间到事件预测预测.

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  • 综合布莱尔得分的降低也被观察到,BLCA和KIRC分别达到26.7%和24.1%.
  • 模型在不同的图形构造超参数中展示了稳定性,并在不同的GNN架构中展示了有效性.
  • 结论:

    • 通过有效利用患者相似性网络,GNN-surv模型为离散时间生存预测提供了卓越的方法.
    • 增强GNN-surv模型的准确性和稳定性为瘤学和个性化医学的临床医生提供了宝贵的工具.
    • 这些模型的适应性表明,它们在不同类型的癌症中具有广泛的适用性,并有可能与其他生存模型或数据模式集成.