Accurate Identification of Colonoscopy Quality and Polyp Findings Using Natural Language Processing.

ABSTRACT:

Objectives

The aim of this study was to test the ability of a commercially available natural language processing (NLP) tool to accurately extract examination quality-related and large polyp information from colonoscopy reports with varying report formats.

Background

Colonoscopy quality reporting often requires manual data abstraction. NLP is another option for extracting information; however, limited data exist on its ability to accurately extract examination quality and polyp findings from unstructured text in colonoscopy reports with different reporting formats.

Study design

NLP strategies were developed using 500 colonoscopy reports from Kaiser Permanente Northern California and then tested using 300 separate colonoscopy reports that underwent manual chart review.

SUBMITTER: Lee JK 

PROVIDER: S-EPMC5847417 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature