公共卫生 公共卫生
Paul Heider1, Sara Knox1, Stephanie Aghamoosa1
1Medical University of South Carolina, Charleston, SC, USA.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 23, 2025
概括
这项研究探索了使用临床笔记识别阿尔茨海默病和相关痴呆症 (ADRD) 的新方法. 虽然ICD代码显示偏差,但随机森林模型为ADRD电子表型提供了更平衡的方法.
科学领域:
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
- 老年学是一门学科.
背景情况:
- 早期识别阿尔茨海默氏症和相关痴呆症 (ADRD) 对于有效的患者管理至关重要.
- 传统的方法,如手动查看图表,耗时,对于大型研究研究来说是不可扩展的.
- 基于ICD-10代码的慢性疾病数据仓库 (CCW) e-phenotype是一种常见的,但潜在的有限的方法.
研究的目的:
- 用非结构化临床笔记评估ADRD电子表型的新方法.
- 为了比较基于ICD代码的模型与使用临床笔记的模型的性能.
- 在ADRDe-phenotyping模型中调查性别之间的绩效差异.
主要方法:
- 分析了1576名患有或没有ADRD的患者的非结构化临床笔记.
- 开发各种e-phenotyping模型,CCW作为参考标准.
- 根据200名患者的手册图表审查获得的黄金标准的验证,使用近似随机化进行统计比较.
主要成果:
- 虽然ICD-10代码方法 (F1=0.83) 的总体F1分数比随机森林 (RF) 模型 (F1=0.81) 的总体F1分数更高,但这种差异在统计学上并不显著.
- 国际疾病控制中心的方法表现出显著的基于性别的偏见,而RF模型则没有.
- 所有模型都在女性患者的笔记上显示出数量上更高的F1分数,而不管均衡的研究体.
结论:
- 没有一个单一的e-phenotyping模型在所有性能指标 (AUC,准确性,特异性,精度,回忆/敏感性) 中始终超过其他模型.
- 选择e-phenotyping模型应与特定的研究需求保持一致,优先考虑回忆或特异性等指标.
- 人口分类,特别是按性别分类,对于全面了解模型性能和ADRD电子表型中的潜在偏差至关重要.
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