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相关概念视频

Survival Curves01:18

Survival Curves

194
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...
194
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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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
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...
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Life Histories01:29

Life Histories

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Overview
18.0K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

469
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
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Protein Networks02:26

Protein Networks

4.0K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Three-Dimensional In Vitro Biomimetic Model of Neuroblastoma Using Collagen-Based Scaffolds
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生存混合物密度网络 生存混合物密度网络

Xintian Han1, Mark Goldstein1, Rajesh Ranganath1

  • 1New York University.

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|September 14, 2023
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此摘要是机器生成的。

生存混合密度网络 (Survival MDNs) 为时间对事件建模提供了一种高效和灵活的方法. 这种新的方法改进了生存分析中的现有连续和离散模型,证明了更快的训练和可比或优异的性能.

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科学领域:

  • 计算生物学 计算生物学
  • 生物统计学 生物统计学
  • 机器学习 机器学习

背景情况:

  • 生存分析对于临床治疗决策至关重要,建模时间到事件数据.
  • 最近使用神经常规微分方程 (ODEs) 的连续时间模型显示出有希望的结果,但由于计算复杂性而受到缓慢的训练.
  • 离散时间模型面临与捆绑问题有关的局限性.

研究的目的:

  • 为生存分析提出一个高效和灵活的连续时间模型.
  • 引入生存混合密度网络 (Survival MDNs) 作为计算密集型神经ODEs的替代方案.
  • 评估生存MDN的性能和效率与现有的生存分析模型相比.

主要方法:

  • 幸存MDN使用混合密度网络 (MDN) 具有可逆正函数.
  • 这个可逆函数将MDN的灵活实值分布映射到时间域中,保持可处理的密度.
  • 该模型在四个不同的数据集上进行了评估.

主要成果:

  • 幸存期MDN在关键指标上实现了与连续和离散时间基线相似或更好的性能:一致性,集成的Brier得分和集成的二项式日志概率.
  • 与基于ODE的模型相比,提出的生存MDNs显示训练时间明显更快.
  • 生存MDN有效地解决了离散生存模型固有的包装限制.

结论:

  • 生存混合密度网络为连续时间生存分析提供了一种高效,灵活和高性能的替代方案.
  • 这种方法克服了神经ODEs的计算瓶和离散模型的捆绑问题.
  • 生存MDN代表了临床和生物医学研究中时间对事件建模的宝贵进步.