Natural Language Processing for Automated Extraction of Continuous Glucose Monitoring Data.

ABSTRACT:

Objective

Continuous glucose monitoring (CGM) is essential in diabetes care and research; however, extracting key data (e.g., time above, in, or below range) from CGM reports is manual, time-consuming, and inefficient. Natural language processing (NLP) can extract data from unstructured sources (e.g., images), but its application in CGM remains unexplored. We aimed to evaluate the accuracy of extracting CGM data using NLP.

Research design and methods

We analyzed CGM reports stored as PDF files from the electronic health record at New York University Langone Health. The steps of our algorithm pipeline consist of 1) performing optical character recognition (OCR) to obtain glucose matrix data from CGM reports, 2) determining the type of CGM documents based on keywords in OCR re

SUBMITTER: Zheng Y 

PROVIDER: S-EPMC12635892 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature