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相关概念视频

Cognitive Development During Adulthood01:30

Cognitive Development During Adulthood

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Cognitive development continues throughout adulthood, undergoing significant shifts across early, middle, and late stages. Individual transition occurs from adolescent idealism to pragmatic and adaptable thinking in early adulthood. During this period, individuals learn to integrate personal beliefs with the recognition that other perspectives are equally valid. Exposure to the complexities of modern society, diverse experiences, and higher education contribute to this adaptive thought process,...
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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.
 Building a Survival Tree
Constructing a...
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Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
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Decision Making: P-value Method01:09

Decision Making: P-value Method

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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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Information Processing Approach01:30

Information Processing Approach

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The information-processing theory of cognitive development centers on fundamental mental processes, including attention, memory, and problem-solving skills. Researchers in this field examine how cognitive abilities, such as working memory, evolve and influence children's overall development. Studies indicate that children with stronger working memory tend to excel in reading comprehension, math, and problem-solving compared to peers with less efficient memory skills. Low working memory is...
18
Introduction to Cognitive Psychology01:20

Introduction to Cognitive Psychology

237
Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
This field emerged in the mid-20th century, following a period dominated by behaviorism, which...
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使用决策树模型预测和评估老龄化人口的认知状态.

Zhidi Luo1,2, Stella Ping Wang1,3, Emily H Ho3

  • 1Health Sciences Integrated Program, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.

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概括
此摘要是机器生成的。

机器学习使用几个关键测试准确识别正常认知 (NC),轻度认知障碍 (MCI) 和阿尔茨海默病 (AD). 这种方法有助于临床医生诊断认知衰退,并支持患者的护理决策.

关键词:
装甲队 装甲队 装甲队 装甲队阿尔茨海默氏症是阿尔茨海默氏症的一种疾病.美国国立卫生研究院工具箱认知障碍是一种认知障碍.机器学习是机器学习.预测建模预测建模

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科学领域:

  • 神经学 神经学
  • 人工智能的人工智能
  • 生物统计学 生物统计学

背景情况:

  • 认知障碍,包括正常认知 (NC),轻度认知障碍 (MCI) 和阿尔茨海默病 (AD),构成了重大的公共卫生挑战.
  • 准确的识别对于及时干预和管理至关重要.

研究的目的:

  • 开发和验证一个机器学习模型,以区分NC,MCI和AD.
  • 确定用于预测这些条件的关键认知评估变量.

主要方法:

  • 使用ARMADA数据和NIH工具箱评估,训练了一个递归分区树模型.
  • 人口和临床变量被纳入预测认知状态.
  • 使用AUC,精度,回忆,F1得分和交叉验证来评估模型性能.

主要成果:

  • 该模型实现了高性能指标,宏观AUC为0.92 (培训) 和0.89 (测试).
  • 关键预测因素包括图片序列记忆测试和列表排序工作记忆测试.
  • 交叉验证证明了70.22%的准确性和0.52.2的卡帕.

结论:

  • 机器学习模型可以有效地区分NC,MCI和AD,使用有限的一组评估.
  • 这种方法提供了一种有价值的,数据驱动的工具,以支持认知障碍诊断中的临床决策.
  • 建议在多种不同人群中进行进一步验证,以提高可通用性.