Extraction of clinical phenotypes for Alzheimer's disease dementia from clinical notes using natural language processing.

ABSTRACT:

Objectives

There is much interest in utilizing clinical data for developing prediction models for Alzheimer's disease (AD) risk, progression, and outcomes. Existing studies have mostly utilized curated research registries, image analysis, and structured electronic health record (EHR) data. However, much critical information resides in relatively inaccessible unstructured clinical notes within the EHR.

Materials and methods

We developed a natural language processing (NLP)-based pipeline to extract AD-related clinical phenotypes, documenting strategies for success and assessing the utility of mining unstructured clinical notes. We evaluated the pipeline against gold-standard manual annotations performed by 2 clinical dementia experts for AD-related clinical phenotypes includin

SUBMITTER: Oh IY 

PROVIDER: S-EPMC9952043 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature