利用临床记录的地理位置来改善使用DMV框架的阿尔茨海默病诊断
1Khoury College of Computer Sciences, Northeastern University, Seattle, WA 98109, USA.
Biomedicines
|October 29, 2025
概括
将患者的地理位置与临床文本的自然语言处理 (NLP) 整合起来,可以显著改善阿尔茨海默病 (AD) 风险预测. 这种数据驱动的方法通过结合临床数据和环境因素来提高早期AD检测.
科学领域:
- 计算语言学计算语言学
- 医疗信息学医学信息学
- 公共卫生 公共卫生
背景情况:
- 早期发现阿尔茨海默氏病 (AD) 是至关重要的,但由昂贵的临床和神经影像方法阻碍.
- 临床记录的自然语言处理 (NLP) 提供了一个可扩展的检测替代方案.
- 整合地理位置可能会捕获AD的环境风险信号.
研究的目的:
- 开发和评估一个框架 (DMV) 早期发现AD使用NLP和地理位置.
- 为了评估不同语言模型 (Llama3-70B,GPT-4o,GPT-5) 与随机森林回归器相结合的性能.
- 为了确定将空间背景纳入AD风险估计准确度的影响.
主要方法:
- DMV框架将早期AD检测定为预测连续风险得分的回归任务.
- 评估了来自Llama3-70B,GPT-4o和GPT-5的嵌入,与随机森林回归器相结合.
- 利用了大量来自CDC的数据集 (约284k条记录),进行了10倍的交叉验证,评估了MSE,MAE和R2.
主要成果:
- 地理定位在所有评估方法中始终提高了模型性能.
- 随机森林基线显示,随着地理位置的变化,MSE下降了48.6%.
- 与地理定位相结合的GPT-5嵌入实现了最佳性能 (MSE=14.03,MAE=2.37,R2=0.978),具有统计学上显著的错误减少 (p < 0.001).
结论:
- 将高级文本嵌入与患者地理位置相结合,可以显著提高阿尔茨海默病风险预测.
- 将空间背景与临床文本相结合,有助于考虑环境和区域风险因素.
- 这种方法提供了一个可扩展的,数据驱动的工作流程,以改善早期AD检测.
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