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相关概念视频

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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

62
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...
62
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

79
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...
79
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

59
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...
59
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

737
Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
737
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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

155
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...
155
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

83
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
83

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Controlling Flow Speeds of Microtubule-Based 3D Active Fluids Using Temperature
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机器学习方法用于追踪器动态建模.

Isabelle Miederer1, Kuangyu Shi2,3, Thomas Wendler3,4

  • 1Department of Nuclear Medicine, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.

Nuklearmedizin. Nuclear medicine
|October 11, 2023
PubMed
概括
此摘要是机器生成的。

机器学习可以简化复杂的动态PET追踪器动态建模,用于定量成像. 这种方法增强了动脉输入功能的预测,动力参数估计和模型选择,减少了临床应用的处理时间.

科学领域:

  • 核医学是一种核医学.
  • 量化功能成像技术的使用.

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  • 生物医学工程 生物医学工程
  • 背景情况:

    • 使用动态正子发射断层扫描 (PET) 进行标志物动态建模对于核医学中的定量功能成像至关重要.
    • 动态PET追踪器动态建模的临床实施受到复杂性和高计算成本的阻碍.
    • 精确的运动建模对于下游应用,如瘤检测至关重要.

    结论:

    • 机器学习为通过改进的追踪器动态建模来推进定量功能成像提供了转变的机会.
    • 在这个领域应用ML有望提供更高效和更容易获得的定量成像解决方案.
    • 本次审查强调了ML在彻底改变核医学实践方面的不断增长的潜力.