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相关概念视频

Longitudinal Research02:20

Longitudinal Research

11.9K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
11.9K
Longitudinal Studies01:26

Longitudinal Studies

151
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
151
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

47
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...
47
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

35
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...
35
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

448
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...
448
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

119
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
119

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相关实验视频

Updated: Jun 18, 2025

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

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对高维纵向数据进行正交混合效应建模:一种无监督学习方法.

Ming Chen, Yijun Bian, Nanguang Chen

    IEEE transactions on medical imaging
    |July 30, 2024
    PubMed
    概括

    无监督直角混合效应轨迹建模 (UOMETM) 通过将全球和个体轨迹分开,有效地分析高维纵向数据. 这种新的方法对阿尔茨海默病的分类和预测有很大的希望.

    科学领域:

    • 生物统计学 生物统计学
    • 机器学习 机器学习
    • 神经科学是一个神经科学.

    背景情况:

    • 线性混合效应模型是纵向数据的标准,但在高维度方面存在困难.
    • 在复杂的数据集中描述全球和个体轨迹仍然是一个挑战.

    研究的目的:

    • 为高维纵向数据引入无监督直角混合效应轨迹建模 (UOMETM).
    • 通过无监督学习有效地分离和表示全球和个人轨迹.

    主要方法:

    • 开发了一个带有隐藏空间中直角约束的自编码器.
    • 实现了交叉重建损失以获得一致性和增强直角性.
    • 使用图像模拟和纵向阿尔茨海默病 (AD) 数据集进行验证.

    主要成果:

    • 在识别纵向模式方面,UOMETM在最先进的方法中表现优越.
    • 在AD分类和转换预测中实现了更低的重建错误,更好的正交,以及更高的准确性.
    • 个人轨迹对AD分类比全球轨迹更为关键,表明成功分离.

    结论:

    • UOMETM为分析高维纵向数据提供了一种强大且可通用的方法.

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  • 该模型显示出临床应用的巨大潜力,特别是在阿尔茨海默病研究中.
  • 全球和个体轨迹的明确分离增强了解释性和预测能力.