ArticleDigestive diseases and sciences2013
Automated identification of surveillance colonoscopy in inflammatory bowel disease using natural language processing.
Article in Digestive diseases and sciences, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 41 citations in OpenAlex.
- Artificial intelligence in endoscopy related to inflammatory bowel disease: A systematic review.Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology · 2024Pooled it
- Emerging applications of NLP and large language models in gastroenterology and hepatology: a systematic review.Frontiers in medicine · 2024Pooled it
- Leveraging Natural Language Processing to Accurately Identify Histologically Confirmed Esophageal and Gastric Premalignant and Malignant Conditions Using a Large-Scale Health-Record Linked Genomic Biobank.Gastro hep advances · 2026Article
- Artificial Intelligence in Inflammatory Bowel Disease Endoscopy.Diagnostics (Basel, Switzerland) · 2025Review
- Accurate, Robust, and Scalable Machine Abstraction of Mayo Endoscopic Subscores From Colonoscopy Reports.Inflammatory bowel diseases · 2025Article
- An Insight into Patients' Perspectives of Ulcerative Colitis Flares via Analysis of Online Public Forum Posts.Inflammatory bowel diseases · 2024Article
- Artificial Intelligence in Coloproctology: A Review of Emerging Technologies and Clinical Applications.Journal of clinical medicine · 2024Review
- Deep learning approach to detection of colonoscopic information from unstructured reports.BMC medical informatics and decision making · 2023Article
- Artificial Intelligence for Inflammatory Bowel Diseases (IBD); Accurately Predicting Adverse Outcomes Using Machine Learning.Digestive diseases and sciences · 2022Article
- 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
- Transfer language space with similar domain adaptation: a case study with hepatocellular carcinoma.Journal of biomedical semantics · 2022Article
- Artificial intelligence in gastroenterology: A state-of-the-art review.World journal of gastroenterology · 2021Review
- Evolving Role and Future Directions of Natural Language Processing in Gastroenterology.Digestive diseases and sciences · 2021Review
- Emerging use of artificial intelligence in inflammatory bowel disease.World journal of gastroenterology · 2020Review
- Review
- Application of Big Data analysis in gastrointestinal research.World journal of gastroenterology · 2019Review
- Big data in IBD: a look into the future.Nature reviews. Gastroenterology & hepatology · 2019Review
- Natural Language Processing Accurately Identifies Dysphagia Indications for Esophagogastroduodenoscopy Procedures in a Large US Integrated Healthcare System: Implications for Classifying Overuse and Quality Measurement.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- A Scalable Machine Learning Approach for Inferring Probabilistic US-LI-RADS Categorization.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- Accurate Identification of Fatty Liver Disease in Data Warehouse Utilizing Natural Language Processing.Digestive diseases and sciences · 2017Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundDifferentiating surveillance from non-surveillance colonoscopy for colorectal cancer in patients with inflammatory bowel disease (IBD) using electronic medical records (EMR) is important for practice improvement and research purposes, but diagnosis code algorithms are lacking. The automated retrieval console (ARC) is natural language processing (NLP)-based software that allows text-based document-level classification.
aimsThe purpose of this study was to test the feasibility and accuracy of ARC in identifying surveillance and non-surveillance colonoscopy in IBD using EMR.
methodsWe performed a split validation study of electronic reports of colonoscopy pathology for patients with IBD from the Michael E. DeBakey VA Medical Center. A gastroenterologist manually classified pathology reports as either derived from surveillance or non-surveillance colonoscopy. Pathology reports were randomly split into two sets: 70 % for algorithm derivation and 30 % for validation. An ARC generated classification model was applied to the validation set of pathology reports. The performance of the model was compared with manual classification for surveillance and non-surveillance colonoscopy.
resultsA total of 575 colonoscopy pathology reports were available on 195 IBD patients, of which 400 reports were designated as training and 175 as testing sets. Within the testing set, a total of 69 pathology reports were classified as surveillance by manual review, whereas the ARC model classified 66 reports as surveillance for a recall of 0.77, precision of 0.80, and specificity of 0.88.
conclusionsARC was able to identify surveillance colonoscopy for IBD without customized software programming. NLP-based document-level classification may be used to differentiate surveillance from non-surveillance colonoscopy in IBD.
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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.