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Chronic Kidney Disease (CKD) progressively impairs multiple body systems due to the accumulation of uremic toxins, which disrupt cellular functions across various organs.Neurologic symptomsNeurologic symptoms often arise early in CKD, as uremic toxin buildup drives changes in cognitive and motor functions. Patients frequently experience fatigue, headache, confusion, difficulty concentrating, and, in severe cases, seizures. Peripheral neuropathy commonly manifests as burning sensations in the...
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Coronary Artery Disease (CAD) is a primary health risk worldwide, leading to significant morbidity and mortality. The condition arises from the buildup of atherosclerotic plaques within the coronary arteries, resulting in diminished blood supply to the heart muscle.The clinical manifestations of CAD vary widely, from asymptomatic stages to severe, life-threatening conditions. Understanding these manifestations is crucial for early diagnosis and effective management.Angina Pectoris: The Warning...
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临床表现 临床表现

Yignan He1, Yu Leng1, Ana-Maria Vranceanu1

  • 1Massachusetts General Hospital, Boston, MA, USA.

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概括
此摘要是机器生成的。

健康的社会决定因素 (SDoH) 数据可以预测认知衰退,为早期发现认知障碍提供了一种可访问的方法. 这项研究开发了一个强大的堆叠组合模型,使用SDoH改进痴呆症查.

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科学领域:

  • 老年学是一门学科.
  • 公共卫生 公共卫生
  • 机器学习 机器学习

背景情况:

  • 早期诊断阿尔茨海默病和相关痴呆症 (AD/ADRD) 是至关重要的,但目前的方法面临着可访问性挑战.
  • 健康的社会决定因素 (SDoH) 和生活方式因素为预测认知衰退提供了可访问的替代方案.

研究的目的:

  • 使用SDoH数据开发和验证用于早期检测认知障碍的堆叠组合模型.
  • 评估基于SDoH的认知衰退预测的可行性和性能.

主要方法:

  • 利用了来自墨西哥健康与衰老研究 (MHAS) 的数据,这是一项对50岁以上成年人进行的纵向调查.
  • 采用双层堆叠组合架构,使用人口,经济,健康和生活方式变量预测全球认知.
  • 在多年来收集的数据上训练并测试了模型,以预测认知功能.

主要成果:

  • 整体模型实现了39.25的根-平均-平方偏差 (RMSE),表明大约10.2%的偏差与完整的认知得分范围.
  • 轻GBM是表现最好的基本模型,功能重要性分析确定了关键的社会和生活方式因素.
  • 敏感性分析证实了模型的稳定性,尽管在某些子组中由于数据集表示而存在偏差.

结论:

  • SDoH数据为早期发现认知障碍提供了一种可行的方法,平衡预测准确性和可解释性.
  • 开发的模型展示了可访问数据对认知健康评估的潜力.
  • 未来的研究应该专注于预测认知得分的变化,考虑到SDoH的影响.