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相关概念视频

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

Mechanistic Models: Compartment Models in Individual and Population Analysis

87
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...
87
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

127
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...
127
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

270
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
270
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

112
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
112
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

101
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...
101
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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

228
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...
228

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动态建模和系统识别的用户参与的mHealth干预使用贝叶斯的方法缺失的数据计算计算.

Mohamed El Mistiri1, Steven De La Torre2, Benjamin M Marlin3

  • 1Control Systems Engineering Laboratory in the Chemical Engineering Department, School for Engineering of Matter, Transport at Arizona State University, Tempe, 85282, Arizona, USA.

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概括
此摘要是机器生成的。

数字行为改变干预措施 (DBCI) 改善健康行为,但需要用户参与. 本研究引入了贝叶斯计算方法,以准确地建模参与动态,并处理DBCI中缺失的数据.

关键词:
贝叶斯的方法 贝叶斯的方法以控制为导向的行为干预措施用于社会科学应用的动态建模.电子健康 电子健康缺失的数据 缺失的数据

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科学领域:

  • 控制系统工程 控制系统工程
  • 数字健康数字健康
  • 行为科学 行为科学

背景情况:

  • 数字行为改变干预 (DBCI) 显示出改善健康行为的前景.
  • 用户与数字工具和干预措施的互动对于DBCI的有效性至关重要.
  • 参与模式根据个人背景和心理状态而演变.

研究的目的:

  • 将DBCI中的用户参与模式作为一个动态系统.
  • 用一种新的贝叶斯归因方法来解决参与跟踪中缺少数据的挑战.
  • 为了量化强大的闭环干预设计的归算不确定性.

主要方法:

  • 从HeartSteps II研究中建模参与数据作为一个动态系统.
  • 从系统识别应用预测错误方法.
  • 使用一种新的贝叶斯归因技术来处理丢失的参与数据.

主要成果:

  • 贝叶斯归算方法提供比传统方法更准确的数据归算.
  • 该方法量化了从归算和数据稀缺性中产生的不确定性.
  • 获得了对影响参与行为随时间和背景的因素的洞察力.

结论:

  • 准确的参与动态建模对于有效的DBCI至关重要.
  • 贝叶斯归因为参与研究中处理缺失数据提供了一个强大的解决方案.
  • 这项工作支持开发基于控制工程的数字健康干预措施.