Characterizing Dementia Phenotypes from Unstructured EHR Notes with Generative AI and Interpretable Machine Learning.

ABSTRACT: Dementia encompasses diverse clinical syndromes where diseases of the brain can manifest as impaired cognitive abilities, such as in Alzheimer's disease (AD) and behavioral-variant frontotemporal dementia (bvFTD). The diversity of symptom presentations often results in challenges in diagnosis. Crucial clinical information remains in unstructured narrative notes within electronic health records (EHRs). We leverage large language models (LLMs) for symptom phenotyping from notes in UCSF Information Commons, focusing on patients with expert dementia syndrome diagnosed from a multidisciplinary team of specialists from the UCSF Memory and Aging Center. We developed a pipeline to extract findings in a validated structured output, clustered into symptom groups, and then classified patients into sy

SUBMITTER: Tang AS 

PROVIDER: S-EPMC12622152 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature