ArticleTherapeutic advances in gastrointestinal endoscopy
Development and validation of machine learning models to predict the need for haemostatic therapy in acute upper gastrointestinal bleeding.
Article in Therapeutic advances in gastrointestinal endoscopy. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Artificial Intelligence in Gastrointestinal Endoscopy and Hemostatic Decision-Making: Current Evidence, Clinical Implications and Implementation Barriers.Life (Basel, Switzerland) · 2026Review
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- Predicting Mortality in Non-Variceal Upper Gastrointestinal Bleeding: Machine Learning Models Versus Conventional Clinical Risk Scores.Journal of clinical medicine · 2025Article
- Artificial intelligence in clinical thrombosis and hemostasis: A review.Research and practice in thrombosis and haemostasis · 2025Review
- Risk factors for peptic ulcer bleeding one year after the initial episode.Gut pathogens · 2024Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Acute upper gastrointestinal bleeding (AUGIB) is a major cause of morbidity and mortality. This presentation however is not universally high risk as only 20-30% of bleeds require urgent haemostatic therapy. Nevertheless, the current standard of care is for all patients admitted to an inpatient bed to undergo endoscopy within 24 h for risk stratification which is invasive, costly and difficult to achieve in routine clinical practice. Objectives: To develop novel non-endoscopic machine learning models for AUGIB to predict the need for haemostatic therapy by endoscopic, radiological or surgical intervention. Design: A retrospective cohort study. Method: We analysed data from patients admitted with AUGIB to hospitals from 2015 to 2020 ( Results: The random forest classifier [AUROC 0.84 (0.80-0.87)] had the best performance and was superior to the GBS [AUROC 0.75 (0.72-0.78), Conclusion: We developed and validated a machine learning algorithm with high accuracy and specificity in predicting the need for haemostatic therapy in AUGIB. This could be used to risk stratify high-risk patients to urgent endoscopy.
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