Scholarlink

<p>Alzheimer’s disease and related dementias (ADRD) develop years before symptoms emerge, making early prediction critical. Electronic health records (EHR) offer a scalable alternative to neuroimaging but are challenged by irregular encounters, data sparsity, and limited interpretability. We propose Gated Recurrent Unit with Decay &amp; Attention (GRU-DA), which integrates GRU-D missingness modeling with RETAIN-style attention for interpretable risk monitoring. The model was trained on the University of Texas Physicians EHR (15,172 ADRD cases with 1:10 matched controls) and externally validated in the All of Us cohort. EHR data up to 10 years before diagnosis were used, with random follow-up initiation to reflect real-world encounters. Both GRU-DA and GRU-D outperformed competing models, particularly beyond 5 years of follow-up and achieved AUROC ~ 0.7 after 8.5 years. Prediction performance depended more on data availability than follow-up length: 1 year with 15% data availability (AUROC 0.75, Average Precision 0.5) was comparable to 7.5 years with 10% availability. For individual cases, GRU-DA produced stable risk predictions with some variability in timestep and feature-level attributions across folds. These results demonstrate EHR data can support dynamic ADRD risk monitoring up to 10 years before diagnosis, with effectiveness strongly influenced by data completeness.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A dynamic risk prediction framework for Alzheimer's disease and related dementias with interpretability

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月15日

查看全文
  • Xiaoyang Ruan,
  • Shuyu Lu,
  • Sunyang Fu,
  • Jaerong Ahn,
  • Fang Chen,
  • Rui Li,
  • Andrew Wen,
  • Liwei Wang,
  • Ezenwa Onyema,
  • Victoria Tang,
  • Hongfang Liu

摘要

Alzheimer’s disease and related dementias (ADRD) develop years before symptoms emerge, making early prediction critical. Electronic health records (EHR) offer a scalable alternative to neuroimaging but are challenged by irregular encounters, data sparsity, and limited interpretability. We propose Gated Recurrent Unit with Decay & Attention (GRU-DA), which integrates GRU-D missingness modeling with RETAIN-style attention for interpretable risk monitoring. The model was trained on the University of Texas Physicians EHR (15,172 ADRD cases with 1:10 matched controls) and externally validated in the All of Us cohort. EHR data up to 10 years before diagnosis were used, with random follow-up initiation to reflect real-world encounters. Both GRU-DA and GRU-D outperformed competing models, particularly beyond 5 years of follow-up and achieved AUROC ~ 0.7 after 8.5 years. Prediction performance depended more on data availability than follow-up length: 1 year with 15% data availability (AUROC 0.75, Average Precision 0.5) was comparable to 7.5 years with 10% availability. For individual cases, GRU-DA produced stable risk predictions with some variability in timestep and feature-level attributions across folds. These results demonstrate EHR data can support dynamic ADRD risk monitoring up to 10 years before diagnosis, with effectiveness strongly influenced by data completeness.