Identification of Prediabetes Discussions in Unstructured Clinical Documentation: Validation of a Natural Language Processing Algorithm.

ABSTRACT:

Background

Prediabetes affects 1 in 3 US adults. Most are not receiving evidence-based interventions, so understanding how providers discuss prediabetes with patients will inform how to improve their care.

Objective

This study aimed to develop a natural language processing (NLP) algorithm using machine learning techniques to identify discussions of prediabetes in narrative documentation.

Methods

We developed and applied a keyword search strategy to identify discussions of prediabetes in clinical documentation for patients with prediabetes. We manually reviewed matching notes to determine which represented actual prediabetes discussions. We applied 7 machine learning models against our manual annotation.

Results

Machine learning classifiers were able to achieve

SUBMITTER: Schwartz JL 

PROVIDER: S-EPMC8914791 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature