基于机器学习的风险评分与退伍军人的痴呆症转化有关
Karl Brown1, Andrew Shutes-David1,2, Katie Wilson1,3
1Geriatric Research, Education, and Clinical Center, VISN-20, VA Puget Sound Health Care System, Seattle, WA, USA.
Journal of Alzheimer's disease : JAD
|September 19, 2025
概括
机器学习衍生的阿尔茨海默病和相关痴呆症 (ADRD) 风险评分预测了黑人和白人美国退伍军人未来的ADRD诊断和死亡率. 这些得分显示出在不同人群中早期检测和预后的潜力.
科学领域:
- 神经学 神经学
- 老年学是一门学科.
- 数据科学数据科学数据科学
背景情况:
- 使用机器学习 (ML) 和自然语言处理在黑人和白人美国 (BA和WA) 退伍军人的电子健康记录上开发了阿尔茨海默病和相关痴呆症 (ADRD) 的祖先特定风险评分.
- 以前的研究将这些分数与得分生成时可能的ADRD诊断联系起来,但未来的风险关联不清楚.
研究的目的:
- 评估基线ADRD风险得分与随后的ADRD发病率和全因死亡率之间的关联.
- 评估ML衍生的风险评分在多样化的退伍军人群中没有先前ADRD诊断的预测效用.
主要方法:
- 对没有先前ADRD诊断的BA和WA退伍军人进行了生存分析.
- 利用特定原因的考克斯比例危险模型,将死亡视为竞争风险.
- 根据种族进行分层分析,并分别对BA和WA退伍军人进行分析.
主要成果:
- 在BA (HR=1.98) 和WA (HR=2.13) 的退伍军人中,较高的ADRD风险得分与未来ADRD诊断的风险增加显著相关.
- 较高的ADRD风险分数也与两组的死亡风险增加有关 (BA HR=1.52,WA HR=1.55).
结论:
- ML衍生的ADRD风险得分与未来ADRD发展和死亡率的风险增加有关.
- 这些发现支持ML衍生的风险评分对早期ADRD检测和预后在不同人群中的有用性.
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