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相关概念视频

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

73
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
73
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

56
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.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
56
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

526
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
526
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

529
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...
529
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

43
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...
43
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

138
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
138

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相关实验视频

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A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
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一般化的贝叶斯方法用于与模型错误规范的反向问题.

Youngsoo Baek1, Wilkins Aquino2, Sayan Mukherjee1,3,4,5

  • 1Department of Statistical Science, Duke University, Durham, NC, United States of America.

Inverse problems
|November 22, 2023
PubMed
概括

我们引入了一个新的概率框架,用于解决基于偏微分方程 (PDE) 的反向问题,而不需要假设概率模型. 这种方法增强了不确定性量化和复杂应用的模型选择.

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科学领域:

  • 计算数学 计算数学 计算数学
  • 应用数学 应用数学 应用数学
  • 科学计算科学计算

背景情况:

  • 贝叶斯方法是反向问题的不确定性量化标准.
  • 它们需要准确的概率模型,这些模型通常是不可用的或难以指定的.
  • 这限制了它们在现实场景中的应用,因为数据生成过程很复杂.

研究的目的:

  • 为基于PDE的反向问题开发概率解决方案的一般框架.
  • 解决贝叶斯推理中未知概率模型的挑战.
  • 引入基于预测性能的新型模型比较框架.

主要方法:

  • 使用吉布斯后方框架,在概率分布的空间上解决正则化的变量问题.
  • 开发一个模型比较框架,通过预测性能来评估损失函数的最佳性.
  • 实施调整参数校准和损失函数比较的交叉验证.

主要成果:

  • 证明了基于PDE的反向问题的新型概率框架.
  • 介绍了吉布斯后部的理论性质.
  • 用超声波振动计模拟示例验证了框架,用于动脉血管表征.

结论:

  • 拟议的框架为在不确定性模型未知时的不确定性量化提供了一个强大的替代方案.
  • 模型比较方法有助于选择最佳的损失函数.
  • 该方法在医学成像和其他领域的应用方面表现有前途.