Open AccessA Natural Language Processing–Assisted Extraction System for Gleason Scores: Development and Usability Study
Author(s) -
Shun Yu,
Anh N. Le,
Emily Feld,
Emily Schriver,
Peter Gabriel,
Abigail Doucette,
Vivek Narayan,
Michael D. Feldman,
Lauren Schwartz,
Kara N. Maxwell,
Danielle L. Mowery
Publication year - 2021
Publication title -
jmir cancer
Language(s) - English
Resource type - Journals
ISSN - 2369-1999
DOI - 10.2196/27970
Subject(s) - artificial intelligence , medicine , prostate cancer , cohort , natural language processing , categorization , computer science , cancer , pathology
Background Natural language processing (NLP) offers significantly faster variable extraction compared to traditional human extraction but cannot interpret complicated notes as well as humans can. Thus, we hypothesized that an “NLP-assisted” extraction system, which uses humans for complicated notes and NLP for uncomplicated notes, could produce faster extraction without compromising accuracy. Objective The aim of this study was to develop and pilot an NLP-assisted extraction system to leverage the strengths of both human and NLP extraction of prostate cancer Gleason scores. Methods We collected all available clinical and pathology notes for prostate cancer patients in an unselected academic biobank cohort. We developed an NLP system to extract prostate cancer Gleason scores from both clinical and pathology notes. Next, we designed and implemented the NLP-assisted extraction system algorithm to categorize notes into “uncomplicated” and “complicated” notes. Uncomplicated notes were assigned to NLP extraction and complicated notes were assigned to human extraction. We randomly reviewed 200 patients to assess the accuracy and speed of our NLP-assisted extraction system and compared it to NLP extraction alone and human extraction alone. Results Of the 2051 patients in our cohort, the NLP system extracted a prostate surgery Gleason score from 1147 (55.92%) patients and a prostate biopsy Gleason score from 1624 (79.18%) patients. Our NLP-assisted extraction system had an overall accuracy rate of 98.7%, which was similar to the accuracy of human extraction alone (97.5%; P =.17) and significantly higher than the accuracy of NLP extraction alone (95.3%; P <.001). Moreover, our NLP-assisted extraction system reduced the workload of human extractors by approximately 95%, resulting in an average extraction time of 12.7 seconds per patient (vs 256.1 seconds per patient for human extraction alone). Conclusions We demonstrated that an NLP-assisted extraction system was able to achieve much faster Gleason score extraction compared to traditional human extraction without sacrificing accuracy.