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相关概念视频

Changes in the Appendicular Skeleton with Age01:09

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The upper and lower limb initially develops as a small bulge called a limb bud, which appears on the lateral side of the early embryo. The upper limb bud appears near the end of the fourth week of development, with the lower limb bud appearing shortly after.
Initially, the limb buds consist of a core of mesenchyme covered by a layer of ectoderm. The ectoderm at the end of the limb bud thickens to form a narrow crest called the apical ectodermal ridge. This ridge stimulates the underlying...
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一种用于预测中年成人肉眼的机器学习模型:发展和外部验证

Hye Jin Chong1

  • 1Department of Nursing, Sunchon National University, Suncheon-si, Republic of Korea.

JMIR medical informatics
|August 27, 2025
PubMed
概括

这项研究开发了一种机器学习模型,用于预测中年人的肉症风险. 该模型准确地识别了有风险的个体, 使得早期干预能够改善长期健康.

科学领域:

  • 老年学与公共卫生
  • 生物医学信息学
  • 肌肉生理学

背景情况:

  • 肉症是老年人常见的肌肉疾病,需要在中年人群中早期检测以改善健康结果.
  • 早期识别肉症可以减少未来的医疗负担,提高老年人的生活质量.
  • 机器学习 (ML) 提供了分析复杂数据集的潜力,以确定肉眼的危险因素,解决未满足的临床需求.

研究的目的:

  • 开发和外部验证机器学习 (ML) 模型,用于预测中年成年人的肉风险.
  • 使用具有全国代表性的数据集,以确保模型的通用性和适用性.
  • 在中年人群中建立早期识别和干预的工具.

主要方法:

  • 来自2022年韩国国家健康和营养检查调查 (KNHANES) 的1926名中年人 (40-64岁) 的数据分析.
  • 根据2019年亚洲癌症工作组的标准 (肌肉质量低和肌肉强度降低) 诊断出癌症.
  • 使用了四种ML算法 (随机森林,SVM,XGBoost,物流回归),顶级模型在外部队列 (2247名参与者,2023名KNHANES) 上得到了验证.

主要成果:

  • 后勤回归模型表现最好,AUC为0.85,灵敏度为0.92,F2得分为0.66.
  • 使用2023年KNHANES数据集进行的外部验证证实了该模型的强大预测能力.
关键词:
机器学习中年成人预测模型危险因素癌症患者

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  • 开发的ML模型显示了广泛应用的巨大潜力.

    • 结论:

    • 一个外部验证的ML模型准确地识别了中年成年人的肉症风险.
    • 早期检测和适合中年人群的干预措施对于缓解肉症和优化长期健康至关重要.
    • 这项研究强调了ML在利用国家卫生数据为积极的公共卫生战略中的有用性.