Natural language processing of electronic health records for early detection of cognitive decline: a systematic review.

ABSTRACT: This systematic review evaluated natural language processing (NLP) approaches for detecting cognitive impairment in electronic health record clinical notes. Following PRISMA guidelines, we analyzed 18 studies (n = 1,064,530) that employed rule-based algorithms (67%), traditional machine learning (28%), and deep learning (17%). NLP models demonstrated robust performance in identifying cognitive decline, with median sensitivity 0.88 (IQR 0.74-0.91) and specificity 0.96 (IQR 0.81-0.99). Deep learning architectures achieved superior results, with area under the receiver operating characteristic curves up to 0.997. Major implementation challenges included incomplete electronic health record data capture, inconsistent clinical documentation practices, and limited external validation. While NLP d

SUBMITTER: Shankar R 

PROVIDER: S-EPMC11873039 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature