ArticleClinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association2013
Natural language processing accurately categorizes findings from colonoscopy and pathology reports.
Article in Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.
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Who cites it
39 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Emerging applications of NLP and large language models in gastroenterology and hepatology: a systematic review.Frontiers in medicine · 2024Pooled it
- Accurate and Scalable Classification of Colonoscopy Neoplasia Using Machine Learning and Natural Language Processing.Clinical and translational gastroenterology · 2026Article
- Leveraging Machine Learning and Robotic Process Automation to Identify and Convert Unstructured Colonoscopy Results Into Actionable Data: Proof-of-Concept Study.JMIR medical informatics · 2025Article
- Large language models for extracting histopathologic diagnoses of colorectal cancer and dysplasia from electronic health records.BMJ open gastroenterology · 2025Article
- A natural language processing algorithm accurately classifies steatotic liver disease pathology to estimate the risk of cirrhosis.Hepatology communications · 2024Article
- Development and Validation of an Automated, Real-time Adenoma Detection Rate and Colonoscopy Quality Metrics Calculator.Techniques and innovations in gastrointestinal endoscopy · 2024Article
- Opportunities for the use of large language models in hepatology.Clinical liver disease · 2023Article
- Performance of natural language processing in identifying adenomas from colonoscopy reports: a systematic review and meta-analysis.iGIE : innovation, investigation and insights · 2023Article
- Natural Language Processing Can Automate Extraction of Barrett's Esophagus Endoscopy Quality Metrics.medRxiv : the preprint server for health sciences · 2023Article
- Combining text mining with clinical decision support in clinical practice: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Deep learning approach to detection of colonoscopic information from unstructured reports.BMC medical informatics and decision making · 2023Article
- Leveraging Natural Language Processing to Extract Features of Colorectal Polyps From Pathology Reports for Epidemiologic Study.JCO clinical cancer informatics · 2023Article
- Assessment of Electronic Health Record for Cancer Research and Patient Care Through a Scoping Review of Cancer Natural Language Processing.JCO clinical cancer informatics · 2022Article
- Natural Language Processing for Information Extraction of Gastric Diseases and Its Application in Large-Scale Clinical Research.Journal of clinical medicine · 2022Article
- Natural Language Processing for Assessing Quality Indicators in Free-Text Colonoscopy and Pathology Reports: Development and Usability Study.JMIR medical informatics · 2022Article
- Application of Artificial Intelligence to Clinical Practice in Inflammatory Bowel Disease - What the Clinician Needs to Know.Journal of Crohn's & colitis · 2022Article
- Automated detection of cecal intubation with variable bowel preparation using a deep convolutional neural network.Endoscopy international open · 2021Article
- Simplifying Measurement of Adenoma Detection Rates for Colonoscopy.Digestive diseases and sciences · 2021Article
- Evolving Role and Future Directions of Natural Language Processing in Gastroenterology.Digestive diseases and sciences · 2021Review
- Natural Language Processing for Surveillance of Cervical and Anal Cancer and Precancer: Algorithm Development and Split-Validation Study.JMIR medical informatics · 2020Article
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4 authors.
Funding
Abstract
BACKGROUND &
aimsLittle is known about the ability of natural language processing (NLP) to extract meaningful information from free-text gastroenterology reports for secondary use.
methodsWe randomly selected 500 linked colonoscopy and pathology reports from 10,798 nonsurveillance colonoscopies to train and test the NLP system. By using annotation by gastroenterologists as the reference standard, we assessed the accuracy of an open-source NLP engine that processed and extracted clinically relevant concepts. The primary outcome was the highest level of pathology. Secondary outcomes were location of the most advanced lesion, largest size of an adenoma removed, and number of adenomas removed.
resultsThe NLP system identified the highest level of pathology with 98% accuracy, compared with triplicate annotation by gastroenterologists (the standard). Accuracy values for location, size, and number were 97%, 96%, and 84%, respectively.
conclusionsThe NLP can extract specific meaningful concepts with 98% accuracy. It might be developed as a method to further quantify specific quality metrics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.