公共卫生 公共卫生
Debby W Tsuang1,2, Karl Brown3, Eugene Shao4
1University of Washington, Seattle, WA, USA.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 23, 2025
概括
机器学习模型可以识别高风险的退伍军人阿尔茨海默病和相关痴呆症 (ADRD). 与美国白人退伍军人相比,美国黑人退伍军人患ADRD的风险不成比例地高.
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:
- 阿尔茨海默病和相关痴呆症 (ADRD) 诊断不足,导致患者,家庭和VA医疗保健系统的不利结果.
- 缺少对ADRD的诊断加剧了现有的医疗保健差异,与白人相比,黑人美国人 (BA) 受到不成比例的影响.
研究的目的:
- 开发和验证一种机器学习 (ML) 模型,用于识别面临未诊断ADRD风险的退伍军人.
- 评估退伍军人在不久的将来患上痴呆症的风险,使用ML衍生风险得分.
主要方法:
- 开发了一个ML模型,使用2万名BA和2万名WA退伍军人的结构化和非结构化电子健康记录的850多个变量.
- 卡普兰-梅尔曲线和考克斯比例危险模型被用来分析基于种族和ML分数五分位数的ADRD风险.
主要成果:
- 2.20的危险比率 (95% CI,1.93-2.51) 表明较高的ML得分增加了ADRD风险.
- 卡普兰-梅尔分析显示,在最高两个五分位数中的BA和最高五分位数中的WA中,ADRD风险曲线更.
结论:
- 较高的ML分数与患ADRD的风险更高相关.
- 研究样本中的黑人美国退伍军人与具有类似ML分数的白人美国退伍军人相比,患ADRD的风险更高.
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