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相关概念视频

Functional Classification of Joints01:09

Functional Classification of Joints

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Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
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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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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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相关实验视频

Updated: Jul 23, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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在中老年和老年中国人口中使用机器学习预测功能依赖.

Qi Yu1, Zihan Li1, Chenyu Yang1

  • 1Department of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.

Archives of gerontology and geriatrics
|July 16, 2023
PubMed
概括

机器学习模型可以预测中国老年人的功能依赖. 日常生活活动 (ADL) 的关键预测因素包括关节炎和年龄,而认知功能和年龄预测日常生活 (IADL) 工具性活动的依赖性.

关键词:
群组研究是一项群组研究.组合学习学习 组合学习功能依赖关系 功能依赖关系机器学习是机器学习.预测模型的预测模型.

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Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
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A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
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相关实验视频

Last Updated: Jul 23, 2025

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
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A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
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科学领域:

  • 老年学是一门学科.
  • 公共卫生 公共卫生
  • 医疗保健中的人工智能

背景情况:

  • 功能依赖性对全球老龄化人口构成了重大挑战.
  • 预测功能依赖的出现对于及时干预和医疗保健规划至关重要.
  • 中国健康与退休纵向研究 (CHARLS) 为调查中国老年人的健康趋势提供了有价值的数据集.

研究的目的:

  • 开发和验证中老年和老年中国成年人功能依赖的预测模型.
  • 确定与日常生活活动 (ADL) 和日常生活工具活动 (IADL) 相关的关键风险因素.

主要方法:

  • 使用查尔斯队列 (≥45岁) 的数据构建了堆叠的整体机器学习模型.
  • 模型在2011-2015年的数据上进行了训练和测试,并在2015-2018年的数据上进行了外部验证.
  • 用SHapley添加式扩展 (SHAP) 来确定预测器显著性.

主要成果:

  • 堆叠组合模型表现出良好的预测性能,在训练队列中,曲线下的面积 (AUC) 值从0.690到0.750不等,在验证队列中,从0.719到0.727.
  • 一个简化的紧型模型保持了类似的预测准确性.
  • 对ADL依赖的重要预测因素包括关节炎,年龄,自我报告的健康状况和腰围.
  • IADL依赖的预测因素包括认知功能,年龄,农村生活和椅子站测试表现.

结论:

  • 堆叠组合模型是有效的工具,用于识别在中国人口中存在功能依赖风险的个体.
  • 特定的临床和人口因素可以预测未来的ADL和IADL限制,从而实现有针对性的预防策略.