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相关概念视频

Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
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Reliability and Validity01:29

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Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
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相关实验视频

Updated: Jun 13, 2025

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
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与手动数据提取相比,人工智能作为数据提取工具并不完全可靠.

Baraa Daraqel1, Amer Owayda2, Haris Khan3

  • 1Department of Orthodontics, Oral Health Research and Promotion Unit, Al-Quds University, Jerusalem, Palestine.

Journal of dentistry
|May 31, 2025
PubMed
概括

人工智能 (AI) 工具显示出在系统性审查中提取简单数据的希望,但人类监督对于复杂的信息至关重要,以确保临床证据的准确性和可靠性.

关键词:
在这里,我们可以看到AIAIAI.数据提取数据提取系统性审查 系统性审查

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科学领域:

  • 矯正牙科 矯正牙科是一種矯正牙科.
  • 生物医学信息学 生物医学信息学
  • 数据科学数据科学数据科学

背景情况:

  • 系统性审查对于以证据为基础的实践至关重要,但涉及耗时且容易出错的数据提取.
  • 自动化数据提取可能会提高效率,减少系统审查中的错误.

研究的目的:

  • 评估基于人工智能 (AI) 和人类数据提取方法在正牙系统性审查中的一致性.
  • 评估AI工具用于从科学文献中提取各种数据点的可靠性.

主要方法:

  • 一项系统的搜索确定了在2019年至2024年之间发表的300项正义牙科研究.
  • 两个独立的审稿人手动提取数据,并使用微软Bing AI.
  • 使用类间相关性和卡帕统计数据评估了协议,比较AI和人类提取.

主要成果:

  • 人工智能在出版年份和研究设计之间显示了轻微的差异,这些差异在统计学上并不显著.
  • 人工智能在提取研究设计细节 (P = 0.017) 和中心数量 (P < 0.001) 等复杂变量方面效率较低.
  • 协议范围从轻微 (0.16对于研究设计类型) 到实质性/完美 (0.65-1.00对于大多数其他变量).

结论:

  • 基于AI的数据提取对于简单的变量是有效的,但对于复杂的数据不是完全可靠的.
  • 人类投入对于确保系统审查的准确性和完整性至关重要.
  • 人工智能工具可以提高效率,但人类监督对于保持临床证据完整性至关重要.