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相关概念视频

Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

887
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
4.7K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.1K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

176
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
176
Decision Making: P-value Method01:09

Decision Making: P-value Method

5.7K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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Uncertainty: Overview00:59

Uncertainty: Overview

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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相关实验视频

Updated: Sep 13, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

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隐式与显式贝叶斯先验对于临床决策支持中的认识不确定性估计.

Malte Blattmann1, Adrian Lindenmeyer1, Stefan Franke1

  • 1Innovation Center Computer Assisted Surgery (ICCAS), Leipzig University, Semmelweisstraße 14, Leipzig, Germany.

PLOS digital health
|July 29, 2025
PubMed
概括

深度学习模型可以帮助个性化医疗,但与不确定性作斗争. 像SNGP这样的显式远程感知贝叶斯深度学习方法,为临床决策支持提供更可靠的不确定性估计.

科学领域:

  • 人工智能的人工智能
  • 生物医学信息学 生物医学信息学
  • 机器学习 机器学习

背景情况:

  • 深度学习模型显示出个性化医疗的前景.
  • 可靠性问题随着分布外数据和过度自信的预测而出现.
  • 量化认识体系的不确定性对于可靠的临床决策支持至关重要.

研究的目的:

  • 将近似贝叶斯深度学习方法与不确定性量化方法进行比较.
  • 评估模型在预测前列腺癌死亡率方面的性能.
  • 确定可靠临床决策支持工具的方法.

主要方法:

  • 对前列腺癌死亡率数据 (PLCO试验) 应用了三种近似贝叶斯深度学习方法.
  • 将隐式功能先验方法 (NN集,VBNNs) 与显式距离感知先验 (SNGP) 进行比较.
  • 评估区分性表现 (AUROC) 和认识不确定性估计的校准.

主要成果:

  • 所有方法都取得了强的性能 (AUROC = 0.86),并且在分布中进行了精确校准的概率.
  • 隐式功能先验方法显示了降低的忠实度和偏的认识系统不确定性估计.
  • 明确的距离感知SNGP模型提供了更准确的后方近似和可靠的不确定性量化.

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Last Updated: Sep 13, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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结论:

  • 显式远程感知贝叶斯深度学习架构提供了优越的不确定性量化.
  • 这些方法对开发可靠的临床决策支持系统充满希望.
  • 准确的不确定性估计是医疗保健中可靠的AI的关键.