Trial reportInflammatory bowel diseases2013
Improving case definition of Crohn's disease and ulcerative colitis in electronic medical records using natural language processing: a novel informatics approach.
Trial report in Inflammatory bowel diseases, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 115 papers, 5 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
115 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Emerging applications of NLP and large language models in gastroenterology and hepatology: a systematic review.Frontiers in medicine · 2024Pooled it
- A Systematic Review of Crohn's Disease Case Definitions in Administrative or Claims Databases.Inflammatory bowel diseases · 2023Pooled it
- Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2022Pooled it
- Extracting information from the text of electronic medical records to improve case detection: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2016Pooled it
- An autism case history to review the systematic analysis of large-scale data to refine the diagnosis and treatment of neuropsychiatric disorders.Biological psychiatry · 2015Pooled it
- Trial
- Natural Language Processing Algorithm Accurately Classifies Diverticulitis-Related Complications and Predicts Long-Term Outcomes.Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2026Article
- Beyond ICD-10 Codes: A Multi-class Machine-Learning Approach to Identify True IBD Cases in Electronic Medical Records.Digestive diseases and sciences · 2026Article
- Automating clinical phenotyping using natural language processing.Communications medicine · 2026Article
- Neonatal and Pregnancy Outcomes in Pregnant Adults With Pediatric-Onset Crohn's Disease.Gastro hep advances · 2026Article
- Identification of Cohorts with Inflammatory Bowel Disease Amidst Fragmented Clinical Databases via Machine Learning.Digestive diseases and sciences · 2025Article
- Accurate, Robust, and Scalable Machine Abstraction of Mayo Endoscopic Subscores From Colonoscopy Reports.Inflammatory bowel diseases · 2025Article
- Large Language Models Outperform Traditional Natural Language Processing Methods in Extracting Patient-Reported Outcomes in Inflammatory Bowel Disease.Gastro hep advances · 2025Article
- The impact of artificial intelligence on the endoscopic assessment of inflammatory bowel disease-related neoplasia.Therapeutic advances in gastroenterology · 2025Review
- Electronic Health Records-based identification of newly diagnosed Crohn's Disease cases.Artificial intelligence in medicine · 2025Article
- Large language models outperform traditional natural language processing methods in extracting patient-reported outcomes in IBD.medRxiv : the preprint server for health sciences · 2024Article
- Artificial Intelligence and IBD: Where are We Now and Where Will We Be in the Future?Current gastroenterology reports · 2024Review
- LATTE: Label-efficient incident phenotyping from longitudinal electronic health records.Patterns (New York, N.Y.) · 2024Article
- Applications of natural language processing tools in the surgical journey.Frontiers in surgery · 2024Review
- Advancing rheumatology with natural language processing: insights and prospects from a systematic review.Rheumatology advances in practice · 2024Review
55 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
Abstract
backgroundPrevious studies identifying patients with inflammatory bowel disease using administrative codes have yielded inconsistent results. Our objective was to develop a robust electronic medical record-based model for classification of inflammatory bowel disease leveraging the combination of codified data and information from clinical text notes using natural language processing.
methodsUsing the electronic medical records of 2 large academic centers, we created data marts for Crohn's disease (CD) and ulcerative colitis (UC) comprising patients with ≥1 International Classification of Diseases, 9th edition, code for each disease. We used codified (i.e., International Classification of Diseases, 9th edition codes, electronic prescriptions) and narrative data from clinical notes to develop our classification model. Model development and validation was performed in a training set of 600 randomly selected patients for each disease with medical record review as the gold standard. Logistic regression with the adaptive LASSO penalty was used to select informative variables.
resultsWe confirmed 399 CD cases (67%) in the CD training set and 378 UC cases (63%) in the UC training set. For both, a combined model including narrative and codified data had better accuracy (area under the curve for CD 0.95; UC 0.94) than models using only disease International Classification of Diseases, 9th edition codes (area under the curve 0.89 for CD; 0.86 for UC). Addition of natural language processing narrative terms to our final model resulted in classification of 6% to 12% more subjects with the same accuracy.
conclusionsInclusion of narrative concepts identified using natural language processing improves the accuracy of electronic medical records case definition for CD and UC while simultaneously identifying more subjects compared with models using codified data alone.
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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.