在未被诊断的黑人和白人美国人中识别可能的痴呆症,在退伍军人卫生管理局使用机器学习,电子健康记录
Yijun Shao1,2, Kaitlin Todd3,4, Andrew Shutes-David3,5
1Washington DC VA Medical Center, Washington, DC 20422, USA.
Big data and cognitive computing
|September 18, 2025
概括
在电子健康记录上训练的机器学习模型可以帮助识别未被诊断的痴呆症,特别是在美国黑人中. 与白人退伍军人相比,种族特定的模型在检测黑人退伍军人中痴呆风险方面显示出更高的准确性.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 老年医学 老年医学
背景情况:
- 在电子健康记录 (EHR) 中的机器学习 (ML) 和自然语言处理 (NLP) 显示出减少痴呆症低诊断的希望.
- 然而,不反映少数群体的ML模型可能会延续健康差异,包括痴呆症低诊断.
研究的目的:
- 开发和验证特定种族的ML模型来识别未被诊断的痴呆症,重点是改善黑人美国人的检测.
- 评估从BA和白人美国人 (WA) 的数据上训练的独立支持矢量机器 (SVM) 模型的性能.
主要方法:
- 开发了使用非结构化和结构化EHR数据的特征来分配痴呆症风险得分的SVM ML模型.
- 用电子健康记录训练BA和WA的单独模型,包括通过隐性迪里克莱特分配和稳定主题提取分析的笔记.
- 在独立样本上验证了模型,并使用痴呆症专家的图表审查来评估AUC,NPV,PPV,灵敏度,特异性和准确性等绩效指标.
主要成果:
- 在SVM产生的风险得分和未诊断的痴呆症之间发现了强烈的积极关系.
- 黑人美国人比白人美国人更有可能患有未诊断的痴呆症 (15.3%与9.5%的总体).
- 针对黑人美国人的种族特异性ML模型在与专家图表评论进行验证时,与白人美国人的模型 (AUC = 0.77) 相比,显示出略有更好的性能 (AUC = 0.86).
结论:
- 来自EHR数据的种族特定的ML模型可以有效地帮助识别患有未诊断痴呆症风险更高的黑人美国人.
- 这些发现凸显了定制人工智能方法在减轻痴呆症诊断中的健康差异方面的潜力.
- 未来的研究应该探索模型在不同人群 (包括更多的女性) 和临床实施策略的普遍性.
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