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<p>Alzheimer disease (AD) is a neurodegenerative disorder marked by gradual decline in memory and thinking. New treatments for early symptomatic AD have increased the need for early and accurate AD diagnosis. This study aimed to identify which patients presenting to a specialty memory clinic were diagnosed with symptomatic AD by examining electronic health record (EHR) data encompassing demographic data, comorbidities, medications, biomarkers, and clinical notes analyzed using large language models. We developed clinical diagnosis prediction machine learning models to predict future diagnosis of symptomatic AD in specialty memory clinic patients presenting with cognitive concerns. The models could accurately predict a diagnosis of symptomatic AD one year prior to clinical diagnosis (AUROC 0.858, 95% CI: [0.835, 0.881]). Key features in the model included age, repeating statements, and hypertension. Our findings suggested that models using multifaceted EHR-derived phenotypes could predict diagnosis of symptomatic AD, potentially enabling earlier intervention.</p>

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Identification of memory clinic patients diagnosed with alzheimer disease using electronic health records data and large language models

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月19日

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  • William J. B. Powell,
  • Anna Hofmann,
  • Inez Y. Oh,
  • Suzanne E. Schindler,
  • Barbara Joy Snider,
  • Madeline Paczynski,
  • Nupur Ghoshal,
  • Philip R. O. Payne,
  • Albert M. Lai,
  • Mackenzie R. Hofford,
  • Aditi Gupta

摘要

Alzheimer disease (AD) is a neurodegenerative disorder marked by gradual decline in memory and thinking. New treatments for early symptomatic AD have increased the need for early and accurate AD diagnosis. This study aimed to identify which patients presenting to a specialty memory clinic were diagnosed with symptomatic AD by examining electronic health record (EHR) data encompassing demographic data, comorbidities, medications, biomarkers, and clinical notes analyzed using large language models. We developed clinical diagnosis prediction machine learning models to predict future diagnosis of symptomatic AD in specialty memory clinic patients presenting with cognitive concerns. The models could accurately predict a diagnosis of symptomatic AD one year prior to clinical diagnosis (AUROC 0.858, 95% CI: [0.835, 0.881]). Key features in the model included age, repeating statements, and hypertension. Our findings suggested that models using multifaceted EHR-derived phenotypes could predict diagnosis of symptomatic AD, potentially enabling earlier intervention.