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  • 1Department of Geriatrics, First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China.

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此摘要是机器生成的。

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科学领域:

  • 老年学是指老年学的学科.
  • 医疗信息学 医疗信息学
  • 计算生物学 计算生物学

背景情况:

  • 全球化增加了老年人认知功能障碍的患病率.
  • 对认知障碍的早期干预可以减少疾病负担和成本.
  • 准确的风险评估工具是需要及时干预的.

研究的目的:

  • 开发一种基于机器学习 (ML) 的风险预测模型,用于老年人的认知功能障碍.
  • 使用ML算法识别认知障碍的关键预测因素.
  • 为医疗保健专业人员和患者提供一个有效风险评估的工具.

主要方法:

  • 1,325名老年参与者接受了认知评估和血液测试.
  • 使用单变量分析,逻辑回归,LASSO和Boruta算法确定了风险因素.
  • 建造和评估了9个ML模型,并使用SHAP进行解释.

主要成果:

  • 随机森林 (RF) 模型实现了最高的预测性能 (AUC).
  • 通过SHAP分析发现的关键预测因素包括年龄,种族,教育,糖尿病和抑郁症.
  • 模型校准和决策曲线证实了强大的预测准确性和临床实用性.

结论:

  • 年龄,种族,教育,糖尿病和抑郁症是认知功能障碍的重要危险因素.
  • 随机森林模型在评估的ML算法中显示出卓越的预测能力.
  • 开发的模型为早期识别和管理认知功能障碍提供了一个有希望的工具.