ArticleJMIR cancer2021
A Natural Language Processing-Assisted Extraction System for Gleason Scores: Development and Usability Study.
Article in JMIR cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Applications of Natural Language Processing and Large Language Models for Social Determinants of Health: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Performance of Natural Language Processing for Information Extraction From Electronic Health Records Within Cancer: Systematic Review.JMIR medical informatics · 2025Pooled it
- From Theory to Practice: GPT-Supported Data Extraction in Observational Studies on Transcatheter Aortic Valve Replacement.JACC. Advances · 2026Article
- Review
- Automated data collection in cancer care: State of play among registries in the United Kingdom and Europe.Health information management : journal of the Health Information Management Association of Australia · 2026Article
- Applications of Natural Language Processing in Otolaryngology: A Scoping Review.The Laryngoscope · 2025Article
- Automated Extraction of Imaging and Pathology Data From Diverse Prostate Cancer Electronic Records.JCO clinical cancer informatics · 2025Article
- Applications of Natural Language Processing and Large Language Models for Social Determinants of Health: Protocol for a Systematic Review.JMIR research protocols · 2025Article
- Exploring ChatGPT 3.5 for structured data extraction from oncological notes.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Extracting Cognitive Impairment Assessment Information From Unstructured Notes in Electronic Health Records Using Natural Language Processing Tools: Validation with Clinical Assessment Data.Clinical epidemiology · 2025Article
- Natural language processing pipeline to extract prostate cancer-related information from clinical notes.European radiology · 2024Article
- Digital Health Applications in Oncology: An Opportunity to Seize.Journal of the National Cancer Institute · 2022Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
backgroundNatural 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.
objectiveThe 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.
methodsWe 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.
resultsOf 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).
conclusionsWe demonstrated that an NLP-assisted extraction system was able to achieve much faster Gleason score extraction compared to traditional human extraction without sacrificing accuracy.
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