ArticleJournal of medical systems2020
A Transparent and Adaptable Method to Extract Colonoscopy and Pathology Data Using Natural Language Processing.
Article in Journal of medical systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A foundation systematic review of natural language processing applied to gastroenterology & hepatology.BMC gastroenterology · 2025Pooled it
- Emerging applications of NLP and large language models in gastroenterology and hepatology: a systematic review.Frontiers in medicine · 2024Pooled it
- Article
- Large language models for extracting histopathologic diagnoses of colorectal cancer and dysplasia from electronic health records.BMJ open gastroenterology · 2025Article
- A Novel Ensemble Framework for Comprehensive Early-Stage Colorectal Cancer Diagnosis, Prognosis, and Treatment: Integration of Gastroenterology-Specific Transformer Language Models and Multiple Decision Trees.Journal of clinical medicine · 2025Article
- Conversational LLM Chatbot ChatGPT-4 for Colonoscopy Boston Bowel Preparation Scoring: An Artificial Intelligence-to-Head Concordance Analysis.Diagnostics (Basel, Switzerland) · 2024Article
- Identification of pancreatic cancer risk factors from clinical notes using natural language processing.Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.] · 2024Article
- Applications of natural language processing tools in the surgical journey.Frontiers in surgery · 2024Review
- 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
- 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
- Automated extraction of information of lung cancer staging from unstructured reports of PET-CT interpretation: natural language processing with deep-learning.BMC medical informatics and decision making · 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
- Natural Language Processing for Information Extraction of Gastric Diseases and Its Application in Large-Scale Clinical Research.Journal of clinical medicine · 2022Article
- Deep Learning-Based Natural Language Processing in Radiology: The Impact of Report Complexity, Disease Prevalence, Dataset Size, and Algorithm Type on Model Performance.Journal of medical systems · 2021Article
- A BERT model generates diagnostically relevant semantic embeddings from pathology synopses with active learning.Communications medicine · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Key variables recorded as text in colonoscopy and pathology reports have been extracted using natural language processing (NLP) tools that were not easily adaptable to new settings. We aimed to develop a reliable NLP tool with broad adaptability. During 1996-2016, Kaiser Permanente Northern California performed 401,566 colonoscopies with linked pathology. We randomly sampled 1000 linked reports into a Training Set and developed an NLP tool using SAS® PERL regular expressions. The NLP tool captured five colonoscopy and pathology variables: type, size, and location of polyps; extent of procedure; and quality of bowel preparation. We used a Validation Set (N = 3000) to confirm the variables' classifications using manual chart review as the reference. Performance of the NLP tool was assessed using the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and Cohen's κ. Cohen's κ ranged from 93 to 99%. The sensitivity and specificity ranged from 95 to 100% across all categories. For categories with prevalence exceeding 10%, the PPV ranged from 97% to 100% except for adequate quality of preparation (prevalence 92%), for which the PPV was 65%. For categories with prevalence below 10%, the PPVs ranged from 62% to 100%. NPVs ranged from 94% to 100% except for the "complete" extent of procedure, for which the NPV was 73%. Using information from a large community-based population, we developed a transparent and adaptable NLP tool for extracting five colonoscopy and pathology variables. The tool can be readily tested in other healthcare settings.
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Registered trials
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