ArticleBMJ health & care informatics2021
Artificial intelligence-based prediction of transfusion in the intensive care unit in patients with gastrointestinal bleeding.
Article in BMJ health & care informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Management of acute variceal bleeding: updated APASL guidelines.Hepatology international · 2025Guideline
- 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
- Evaluation of Six Large Language Models for Clinical Decision Support: Application in Transfusion Decision-making for RhD Blood-type Patients.Annals of laboratory medicine · 2025Article
- Current Perspectives on the Artificial Intelligence in Critical Care Medicine.Anesthesiology clinics · 2025Review
- Implementing Artificial Intelligence in Critical Care Medicine: a consensus of 22.Critical care (London, England) · 2025Review
- [Emerging Diagnostic Techniques and Therapies for Upper Gastrointestinal Bleeding].The Korean journal of gastroenterology = Taehan Sohwagi Hakhoe chi · 2025Review
- Development and validation of a machine learning model to predict hemostatic intervention in patients with acute upper gastrointestinal bleeding.BMC medical informatics and decision making · 2025Article
- Management of gastrointestinal bleed in the intensive care setting, an updated literature review.World journal of critical care medicine · 2025Review
- Proenkephalin improves cardio-renal risk prediction in acute coronary syndromes: the KID-ACS score.European heart journal · 2025Article
- AI Applications in Transfusion Medicine: Opportunities, Challenges, and Future Directions.Acta haematologica · 2025Review
- Validation of an Electronic Health Record-Based Machine Learning Model Compared With Clinical Risk Scores for Gastrointestinal Bleeding.Gastroenterology · 2024Article
- Blood Bonds: Transforming Blood Donation Through Innovation, Inclusion, and Engagement.JMIR perioperative medicine · 2024Article
- A precise blood transfusion evaluation model for aortic surgery: a single-center retrospective study.Journal of clinical monitoring and computing · 2024Article
- Machine learning in the assessment and management of acute gastrointestinal bleeding.BMJ medicine · 2024Article
- Machine learning in transfusion medicine: A scoping review.Transfusion · 2024Article
- Application of Machine Learning Based on Structured Medical Data in Gastroenterology.Biomimetics (Basel, Switzerland) · 2023Review
- Prediction of Transfusion among In-patient Population using Temporal Pattern based Clinical Similarity Graphs.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Chinese experts' consensus on the application of intensive care big data.Frontiers in medicine · 2023Article
- Machine Learning Model for the Prediction of Hemorrhage in Intensive Care Units.Healthcare informatics research · 2022Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
objectiveGastrointestinal (GI) bleeding commonly requires intensive care unit (ICU) in cases of potentialhaemodynamiccompromise or likely urgent intervention. However, manypatientsadmitted to the ICU stop bleeding and do not require further intervention, including blood transfusion. The present work proposes an artificial intelligence (AI) solution for the prediction of rebleeding in patients with GI bleeding admitted to ICU.
methodsA machine learning algorithm was trained and tested using two publicly available ICU databases, the Medical Information Mart for Intensive Care V.1.4 database and eICU Collaborative Research Database using freedom from transfusion as a proxy for patients who potentially did not require ICU-level care. Multiple initial observation time frames were explored using readily available data including labs, demographics and clinical parameters for a total of 20 covariates.
resultsThe optimal model used a 5-hour observation period to achieve an area under the curve of the receiving operating curve (ROC-AUC) of greater than 0.80. The model was robust when tested against both ICU databases with a similar ROC-AUC for all.
conclusionsThe potential disruptive impact of AI in healthcare innovation is acknowledge, but awareness of AI-related risk on healthcare applications and current limitations should be considered before implementation and deployment. The proposed algorithm is not meant to replace but to inform clinical decision making. Prospective clinical trial validation as a triage tool is warranted.
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