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相关概念视频

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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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...
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Uncertainty: Confidence Intervals00:54

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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...
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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...
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The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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对于临床文本分类的深度学习不确定性量化.

Alina Peluso1, Ioana Danciu1, Hong-Jun Yoon1

  • 1Oak Ridge National Laboratory, Oak Ridge, TN 37830, United States.

Journal of biomedical informatics
|December 15, 2023
PubMed
概括
此摘要是机器生成的。

新的选择性分类方法提高了癌症注册的深度神经网络 (DNN) 的可靠性. 这些方法实现了高准确度,拒绝率低于现有分类器,减少了手动审查的需求.

关键词:
拒绝的分类者 拒绝的分类者准确度 准确度 准确度 准确度 准确度在美国,CNN是CNN.DNN DNN 在线深度学习是一种深度学习.这就是HisAN.NCI SEER 其他国家病理学报告报告病理学报告.选择性分类是一种选择性分类.文字分类 文本分类 文本分类不确定性量化不确定性的量化.

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科学领域:

  • 计算病理学计算病理学
  • 机器学习在医疗保健中的应用
  • 癌症登记处信息学 癌症登记处信息学

背景情况:

  • 深度神经网络 (DNN) 是用于分类任务的最先进的技术.
  • DNN的可靠性和校准对于人类与人工智能在决策中的合作至关重要.
  • 自动化从病理学报告中提取信息对于癌症注册表至关重要.

研究的目的:

  • 展示基于DNN的分类,用于从病理学报告中自动提取癌症诊断和手术信息.
  • 引入选择性分类方法,以实现目标准确性,同时尽量减少不可靠的预测.
  • 将拟议的方法与当前基于深度学习的弃权分类器 (DAC) 进行比较.

主要方法:

  • 使用DNN的多种选择性分类方法的开发和应用.
  • 从电子病理学报告中自动提取信息,用于美国国家癌症研究所 (NCI) 监测,流行病学和最终结果 (SEER) 注册表.
  • 对基于深度学习的弃权分类器 (DAC) 对分布内和分布外数据进行拟议方法的比较分析.

主要成果:

  • 所有提出的选择性分类方法都实现了目标准确性,并最大限度地降低了排斥率.
  • 与DAC相比,拟议的方法在分销和分销之外的测试数据上显示了较低的拒绝率.
  • 与DAC相比,最好的建议方法实现了高精度 (≥97%) 和较低的排斥率.

结论:

  • 选择性分类方法有效地平衡了准确性和拒绝率,以获得可靠的DNN预测.
  • 拟议的方法保留了更大一部分可靠的预测,而不需要重新培训,从而降低了计算成本.
  • 这些进步提高了癌症登记处信息提取的自动化,支持人类注释器.