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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

69
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...
69
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

490
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...
490
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

53
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...
53
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

62
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
62
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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

123
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...
123
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

106
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
106

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相关实验视频

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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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提高数据驱动的动态建模与自动区分的预测能力:库普曼和神经ODE方法.

C Ricardo Constante-Amores1, Alec J Linot2, Michael D Graham1

  • 1Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA.

Chaos (Woodbury, N.Y.)
|April 4, 2024
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概括

一种新方法改进了库普曼运算符近似用于复杂系统动态预测. 这种数据驱动的方法优于用词典学习 (EDMD-DL) 进行扩展动态模式分解,并提供与状态空间模型相比具有竞争力的结果.

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科学领域:

  • 动态系统和控制理论.
  • 机器学习用于科学发现
  • 计算物理与工程 计算物理与工程

背景情况:

  • 预测复杂的动态系统的时间演变在科学和工程方面至关重要.
  • 数据驱动的库普曼运算子近似为分析非线性动态提供了强大的框架.
  • 扩展动态模式分解与字典学习 (EDMD-DL) 是一个突出的但可以改进的方法.

研究的目的:

  • 开发一种修改后的EDMD-DL方法,同时优化可观测的词典和库普曼运算子近似.
  • 对各种基于库普曼和状态空间的方法进行评估,以评估拟议方法的性能.
  • 评估各种动态系统的预测准确性,包括具有复杂吸引力的ODEs和PDEs.

主要方法:

  • 引入了一种新的EDMD-DL变体,利用自动差异化来通过伪反向实现基于梯度的优化.
  • 将拟议的方法与"纯粹的"库普曼方法 (可观测空间中的时间整合) 相比较.
  • 评估了交替状态可观测空间库普曼方法和神经普通微分方程 (状态空间) 方法.

主要成果:

  • 拟议的修改EDMD-DL框架显著优于标准EDMD-DL.
  • 状态空间方法表现出比"纯粹"库普曼方法更好的预测性能.
  • 交替状态可观测空间库普曼方法实现了与状态空间方法相比的预测准确性.

结论:

  • 开发的数据驱动框架增强了对复杂系统动态的库普曼操作员近似度.
  • 该研究强调了不同库普曼运算符实现和状态空间模型之间的权衡.
  • 这项工作为预测复杂动态系统的时间演变提供了更强大,更准确的工具.