ArticleJournal of gastroenterology and hepatology2021
Early identification of patients with acute gastrointestinal bleeding using natural language processing and decision rules.
Article in Journal of gastroenterology and hepatology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 21 citations in OpenAlex.
- A foundation systematic review of natural language processing applied to gastroenterology & hepatology.BMC gastroenterology · 2025Pooled it
- Using natural language processing in emergency medicine health service research: A systematic review and meta-analysis.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2024Pooled it
- Detection of Antithrombotic-Related Bleeding in Older Inpatients: Multicenter Retrospective Study Using Structured and Unstructured Electronic Health Record Data.Journal of medical Internet research · 2026Article
- Artificial intelligence for early diagnosis in emergency department.Journal of anesthesia, analgesia and critical care · 2026Review
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- Are Scoring Systems Useful in Predicting Mortality from Upper GI Bleeding in Geriatric Patients?Diagnostics (Basel, Switzerland) · 2025Article
- Article
- Validation of an Electronic Health Record-Based Machine Learning Model Compared With Clinical Risk Scores for Gastrointestinal Bleeding.Gastroenterology · 2024Article
- The Role of Large Language Models in Transforming Emergency Medicine: Scoping Review.JMIR medical informatics · 2024Article
- Machine learning in the assessment and management of acute gastrointestinal bleeding.BMJ medicine · 2024Article
- Doctors Identify Hemorrhage Better during Chart Review when Assisted by Artificial Intelligence.Applied clinical informatics · 2023Article
- Reconciling Allergy Information in the Electronic Health Record After a Drug Challenge Using Natural Language Processing.Frontiers in allergy · 2022Article
- Deep learningWorld journal of gastroenterology · 2021Article
- Current Status and Future Perspective of Artificial Intelligence in the Management of Peptic Ulcer Bleeding: A Review of Recent Literature.Journal of clinical medicine · 2021Review
- Advancing care for acute gastrointestinal bleeding using artificial intelligence.Journal of gastroenterology and hepatology · 2021Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
BACKGROUND AND
aimGuidelines recommend risk stratification scores in patients presenting with gastrointestinal bleeding (GIB), but such scores are uncommonly employed in practice. Automation and deployment of risk stratification scores in real time within electronic health records (EHRs) would overcome a major impediment. This requires an automated mechanism to accurately identify ("phenotype") patients with GIB at the time of presentation. The goal is to identify patients with acute GIB by developing and evaluating EHR-based phenotyping algorithms for emergency department (ED) patients.
methodsWe specified criteria using structured data elements to create rules for identifying patients and also developed multiple natural language processing (NLP)-based approaches for automated phenotyping of patients, tested them with tenfold cross-validation for 10 iterations (n = 7144) and external validation (n = 2988) and compared them with a standard method to identify patient conditions, the Systematized Nomenclature of Medicine. The gold standard for GIB diagnosis was the independent dual manual review of medical records. The primary outcome was the positive predictive value.
resultsA decision rule using GIB-specific terms from ED triage and ED review-of-systems assessment performed better than the Systematized Nomenclature of Medicine on internal validation and external validation (positive predictive value = 85% confidence interval:83%-87% vs 69% confidence interval:66%-72%; P < 0.001). The syntax-based NLP algorithm and Bidirectional Encoder Representation from Transformers neural network-based NLP algorithm had similar performance to the structured-data fields decision rule.
conclusionsAn automated decision rule employing GIB-specific triage and review-of-systems terms can be used to trigger EHR-based deployment of risk stratification models to guide clinical decision making in real time for patients with acute GIB presenting to the ED.
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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.