SynthesisDigestive diseases and sciences2019
Machine Learning to Predict Outcomes in Patients with Acute Gastrointestinal Bleeding: A Systematic Review.
Synthesis in Digestive diseases and sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 8 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
50 citing papers in PubMed, 8 syntheses or guidelines pooled it.
- A meta-analysis of the diagnostic test accuracy of artificial intelligence predicting emergency department dispositions.BMC medical informatics and decision making · 2025Pooled it
- Machine Learning for Predicting Stillbirth: A Systematic Review.Reproductive sciences (Thousand Oaks, Calif.) · 2025Pooled it
- Diagnostic Performance of Artificial Intelligence in Detection of Primary Malignant Bone Tumors: a Meta-Analysis.Journal of imaging informatics in medicine · 2024Pooled it
- Application of machine learning in acute upper gastrointestinal bleeding: bibliometric analysis.Frontiers in medicine · 2024Pooled it
- Clinical applications of machine learning in predicting 3D shapes of the human body: a systematic review.BMC bioinformatics · 2022Pooled it
- Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review.BMC medical research methodology · 2022Pooled it
- Can minimal clinically important differences in patient reported outcome measures be predicted by machine learning in patients with total knee or hip arthroplasty? A systematic review.BMC medical informatics and decision making · 2022Pooled it
- Pooled it
- Prediction of Blood Transfusion Need and Dose in Patients With Upper Gastrointestinal Bleeding: Retrospective Multicenter Prediction Model Study.JMIR medical informatics · 2026Article
- HOLD-B: A Practical Score for Early Risk Stratification in Upper Gastrointestinal Bleeding.Medicina (Kaunas, Lithuania) · 2026Article
- Artificial Intelligence in Gastrointestinal Endoscopy and Hemostatic Decision-Making: Current Evidence, Clinical Implications and Implementation Barriers.Life (Basel, Switzerland) · 2026Review
- Development and internal validation of the AGATE hybrid score for pre-endoscopic risk stratification in acute upper gastrointestinal bleeding: a single-centre, retrospective cohort study.BMJ open gastroenterology · 2026Article
- Explainable machine learning predicts mortality in critically ill patients with nonvariceal upper gastrointestinal bleeding: a MIMIC-IV study with external validation.BMC gastroenterology · 2026Article
- Revolutionizing non-traumatic acute care: a review of the role of artificial intelligence and machine learning in triaging and diagnosis.Acute and critical care · 2026Article
- A Clustering-Based Machine Learning Approach for Mortality Prediction in Gastrointestinal Bleeding: Development and Validation.Gastro hep advances · 2026Article
- Machine learning-based prediction of gout flares during hospitalization in patients with upper gastrointestinal bleeding: a retrospective cohort study.Frontiers in medicine · 2026Article
- Evaluation of Care Quality for Atrial Fibrillation Across Non-Interoperable Electronic Health Record Data using a Retrieval-Augmented Generation-enabled Large Language Model.medRxiv : the preprint server for health sciences · 2025Article
- 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
- New machine-learning models outperform conventional risk assessment tools in Gastrointestinal bleeding.Scientific reports · 2025Article
- Major Bleeding in the Emergency Department: A Practical Guide for Optimal Management.Journal of clinical medicine · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Risk stratification of patients with gastrointestinal bleeding (GIB) is recommended, but current risk assessment tools have variable performance. Machine learning (ML) has promise to improve risk assessment. We performed a systematic review to evaluate studies utilizing ML techniques for GIB. Bibliographic databases and conference abstracts were searched for studies with a population of overt GIB that used an ML algorithm with outcomes of mortality, rebleeding, hemostatic intervention, and/or hospital stay. Two independent reviewers screened titles and abstracts, reviewed full-text studies, and extracted data from included studies. Risk of bias was assessed with an adapted Quality in Prognosis Studies tool. Area under receiver operating characteristic curves (AUCs) were the primary assessment of performance with AUC ≥ 0.80 predefined as an acceptable threshold of good performance. Fourteen studies with 30 assessments of ML models met inclusion criteria. No study had low risk of bias. Median AUC reported in validation datasets for predefined outcomes of mortality, intervention, or rebleeding was 0.84 (range 0.40-0.98). AUCs were higher with artificial neural networks (median 0.93, range 0.78-0.98) than other ML models (0.81, range 0.40-0.92). ML performed better than clinical risk scores (Glasgow-Blatchford, Rockall, Child-Pugh, MELD) for mortality in upper GIB. Limitations include heterogeneity of ML models, inconsistent comparisons of ML models with clinical risk scores, and high risk of bias. ML generally provided good-excellent prognostic performance in patients with GIB, and artificial neural networks tended to outperform other ML models. ML was better than clinical risk scores for mortality in upper GIB.
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31055722What OpenQuestion holds
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.