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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

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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...
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Probability in Statistics

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Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
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Probability Distributions01:32

Probability Distributions

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 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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贝叶斯网络方法用于使用概率建模的弗里德里希衰竭严重程度分类.

Sahan Dissanayake, Ragil Krishna, Pubudu N Pathirana

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
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    这项研究引入了一种新的贝叶斯网络方法来客观地测量弗里德里希 (FRDA) 严重程度,将专家知识与仪器数据相结合,以获得更好的临床试验洞察力.

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    科学领域:

    • 神经学 神经学
    • 生物医学工程 生物医学工程
    • 数据科学数据科学数据科学

    背景情况:

    • 弗里德里希 (FRDA) 缺乏客观的严重程度指标,阻碍了治疗试验.
    • 罕见疾病数据集很小,挑战了传统的机器学习.
    • 现有的定量性无氧症测量不足以利用专家临床知识.

    研究的目的:

    • 使用贝叶斯网络,为FRDA开发一个客观的严重性衡量标准.
    • 整合主观的临床评估和客观的仪器测量.
    • 解决FRDA研究中小型数据集和未充分利用的专家知识的局限性.

    主要方法:

    • 使用与贝叶斯网络 (BNs) 的混合学习方法.
    • 包含主观临床评估和仪器上肢运动数据.
    • 在FRDA患者的数据上训练了BN模型.

    主要成果:

    • 美国国联模型实现了0.93的皮尔森相关性,误差低.
    • 预测的临床尺度具有高准确性:94%的直立稳定/下肢协调.
    • 在功能分期,上肢协调和日常生活活动方面显示了67%的准确性.

    结论:

    • 贝叶斯网络为罕见疾病严重程度评估提供了可行的解决方案.
    • 这种混合方法有效地结合了专家知识和客观数据.
    • 开发的模型可以作为FRDA严重性预测的临床决策支持系统.