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相关概念视频

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...
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Uncertainty: Overview00:59

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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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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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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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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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用 (大) 语言模型进行临床结果预测的不确定性量化.

Zizhang Chen1, Peizhao Li2, Xiaomeng Dong2

  • 1Brandeis University.

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概括
此摘要是机器生成的。

这项研究通过量化电子健康记录 (EHR) 语言模型 (LMs) 的不确定性来提高医疗保健中的AI可靠性. 组合和多任务等方法减少预测不确定性,提高AI透明度和患者安全.

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科学领域:

  • 人工智能在医学中的应用
  • 临床信息学 临床信息学
  • 机器学习用于医疗保健

背景情况:

  • 语言模型 (LMs) 显示出使用电子健康记录 (EHRs) 的临床预测的前景.
  • 高风险的医疗保健应用要求可靠的AI预测,需要强大的不确定性量化.
  • 当前的人工智能模型往往缺乏透明度,对患者安全和道德标准构成风险.

研究的目的:

  • 开发和验证一个框架,用于在EHR任务中量化LM的不确定性.
  • 为了解决白盒 (可访问参数) 和黑盒 (专有LM,如GPT-4) 设置中的不确定性.
  • 提高人工智能驱动的临床预测的可靠性和透明度.

主要方法:

  • 在使用多任务和组合技术的白盒LM中量化不确定性.
  • 扩展不确定性量化到黑子模型,包括专有LM.
  • 在10个预测任务中验证了来自6000多名患者的纵向临床数据的框架.

主要成果:

  • 建议的多任务和组合方法有效地减少了EHR任务中的模型不确定性.
  • 组合和多任务预测提示显示,在各种临床预测场景中,不确定性减少.
  • 该框架在白盒和黑盒设置中成功提高了模型透明度.

结论:

  • 使用组合和多任务的不确定性量化提高了EMS的LM的可靠性.
  • 开发的框架提高了AI在临床决策支持中的透明度和可信度.
  • 这项工作促进了人工智能在医疗保健提供中的安全和道德整合.