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相关概念视频

Aging01:26

Aging

26
Aging is a complex biological phenomenon influenced by various processes that affect cellular and systemic functions. Several prominent theories attempt to explain its mechanisms, highlighting cellular limitations, oxidative damage, and hormonal changes as central factors in aging.
Cellular Clock Theory
The cellular clock theory posits that the human lifespan is closely tied to the finite capacity of cells to divide, a phenomenon governed by telomeres, which are protective caps at the ends of...
26

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相关实验视频

Updated: May 13, 2025

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health
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人工智能驱动的生物年龄预测模型使用综合健康检查数据:开发和验证研究研究

Chang-Uk Jeong1, Jacob S Leiby1, Dokyoon Kim1,2

  • 1Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.

JMIR aging
|April 15, 2025
PubMed
概括
此摘要是机器生成的。

这项研究开发了一个人工智能驱动的衰老时钟,使用健康数据准确预测生物年龄. 该模型显示出高度准确性和临床相关性,用于个性化健康监测和疾病预防.

关键词:
在这里,我们可以看到AIAIAI.老化的老化 衰老的老化时钟的老化时间人工智能的人工智能是人工智能.生物年龄 生物年龄临床相关性 临床相关性年长的老人老年人老年人.老年人的医疗服务老年病学 老年病学是一门学科.健康检查健康检查历史 历史 历史 历史 历史预期寿命 预期寿命机器学习是机器学习.死亡率 死亡率年长的年长的年长的年长的预测 预测 预测 预测预测性 预测性 预测性记录记录记录记录记录记录记录

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科学领域:

  • 生物医学科学 生物医学科学
  • 老年学是一门学科.
  • 医疗保健中的人工智能

背景情况:

  • 全球预期寿命正在上升,但健康的预期寿命滞后,需要准确的生物年龄评估.
  • 与衰老相关的疾病和社会经济负担需要先进的方法来评估生物衰老.
  • 现有的生物年龄预测模型受到传统统计和有限的临床数据的限制.

研究的目的:

  • 开发和验证基于人工智能 (AI) 的老化时钟模型.
  • 用全面的健康检查数据来预测生物年龄.
  • 评估人工智能驱动的生物年龄预测的临床相关性.

主要方法:

  • 利用了来自韩国人的健康检查数据.
  • 结合了27个临床因素,并采用了各种机器学习算法 (例如,梯度增强,随机森林).
  • 使用调整后的R2和平均平方误差 (MSE) 评估模型性能;使用Shapley添加式解释 (SHAP) 进行解释.

主要成果:

  • 梯度提升模型表现出卓越的性能 (MSE:4.219,R2:0.967).
  • SHAP分析确定了关键预测因素:功能,性别,HbA1c,肝功能和人体测量.
  • 预测的生物年龄与代谢状态,身体组成,脂肪肝,吸烟和肺功能有很强的相关性.

结论:

  • 开发的衰老时钟模型提供了高的预测准确性和临床实用性.
  • 这种人工智能工具可以增强个性化的健康监测和干预.
  • 整合到例行健康检查中可以改善健康管理,并鼓励定期查.