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相关概念视频

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

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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
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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The Buckingham Pi Theorem01:09

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The Buckingham Pi theorem provides a structured method to simplify fluid dynamics problems by reducing complex systems of variables to dimensionless terms.
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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相关实验视频

Updated: Jun 16, 2025

Particle Image Velocimetry Investigation of Hemodynamics via Aortic Phantom
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贝叶斯参数推断和不确定性量化用于使用高斯过程的计算肺血液动力学模型.

Amirreza Kachabi1, Sofia Altieri Correa1, Naomi C Chesler1

  • 1Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center, Department of Biomedical Engineering, University of California, Irvine, CA, USA.

Computers in biology and medicine
|June 13, 2025
PubMed
概括

这项研究开发了一种慢性血栓栓塞性肺高血压 (CTEPH) 的计算模型,可以有效评估微血管疾病的严重程度. 模型 模型的模型

关键词:
在CTEPH的基础上,血液动力学 血液动力学一维流体力学的一维流体力学.统计模拟 统计模拟不确定性量化不确定性的量化.

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科学领域:

  • 心血管研究研究心血管研究
  • 计算流体动力学的流体动力学.
  • 肺高血压建模 肺高血压建模

背景情况:

  • 肺高血压的临床诊断具有局限性.
  • 具体学科模型提供了洞察力,但需要计算效率和不确定性量化.
  • 慢性血栓栓塞性肺高血压 (CTEPH) 可能在手术后由于微血管疾病而持续存在.

研究的目的:

  • 开发一个计算高效,不确定性意识的1D流体动力学模型,用于评估CTEPH中的微血管疾病.
  • 单独对个别肺部进行建模,以捕捉CTEPH异质性.
  • 为了将模型衍生的微血管参数与疾病严重程度的临床标志物相关联.

主要方法:

  • 利用1D流体动力学模型与来自CTEPH的狗模型的实验数据.
  • 集成的高斯过程 (GP) 模拟器用于加速模型校准和不确定性估计.
  • 单独模拟每个肺部,以分析肺部特定的微血管收窄和抵抗.

主要成果:

  • CTEPH诱导了异质的微血管适应,具有明显的参数转移.
  • 推断模型参数与疾病严重程度的临床标志物有很强的相关性.
  • 该模型在临床上可行的时间范围内成功估计了微血管参数及其不确定性.

结论:

  • 开发的框架提供了一个快速,不确定性意识的方法来评估CTEPH中的微血管功能障碍.
  • 这种方法可以支持持续性肺高血压的更有针对性的治疗策略.
  • 使用GP模拟器进行特定主体建模,提高了心血管研究的临床适用性.