Characterizing cognitive concerns in subjective cognitive decline using large language models and electronic health record notes
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Updated: Nov 4, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Taiyang Chen1, Justin Wang1,2, Sheril Varghese1,3
1Division of General Internal Medicine and Primary Care Department of Medicine Brigham and Women's Hospital Boston Massachusetts USA.
Introduction:
Subjective cognitive decline (SCD) refers to self- or informant-reported cognitive concerns despite normal objective testing. These concerns are often documented as free text in electronic health records (EHRs), limiting systematic analysis. We characterized cognitive concern domains and assessed differences across demographic subgroups.
Methods:
We studied 2175 individuals aged ≥65 years with an EHR-derived SCD phenotype. Using large language models with expert review, we derived a taxonomy of concern themes and sub-themes and assigned themes to note excerpts. We examined theme frequencies and compared distribution across demographic groups.
Results:
We identified 13 themes and 40 sub-themes. Theme-level classification showed strong performance (micro-F1 = 84.8%). Memory Issues was the most common theme (68.5%), with concerns spanning multiple cognitive domains. Theme distribution varied by age, sex, marital status, and education.
Discussion:
EHR notes captured heterogeneous, multi-domain cognitive concerns among adults with an EHR-derived SCD phenotype. This approach enables scalable, clinically interpretable profiling of concerns that may support early risk stratification.
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