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

2019 Jan;53(1):e25-e30.

Lee JK, Jensen CD, Levin TR, Zauber AG, Doubeni CA, Zhao WK, Corley DA.



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.

Rx Data News: Impact of Advanced Data Technologies on Pharma R&D

Originally published in Rx Data News

Author: Jane Reed

Published: 19th June 2019

Jane Z. Reed, Ph.D, is the Linguamatics’ head of life science strategy and responsible for developing the strategic vision for Linguamatics’ growing product portfolio and business development in the life science market.

Automatically identifying social isolation from clinical narratives for patients with prostate cancer

(2019) 19:43 

Vivienne J Zhu, Leslie A Lenert, Brian E Bunnell, Jihad S Obeid, Melanie Jefferson and Chanita Hughes Halbert.