SynthesisBMJ open2021
Machine learning techniques for mortality prediction in emergency departments: a systematic review.
Synthesis in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 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
32 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning to predict hospital admission at triage in paediatric emergency care: A meta-analysis.European journal of pediatrics · 2026Pooled 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 risk stratification in the emergency department (MARS-ED): a randomized controlled trial.Nature communications · 2025Trial
- The Effectiveness of Machine Learning Algorithms in Predicting Healthcare Service Quality Metrics: A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Data balancing improves mortality prediction for emergency department patients.BMC medical informatics and decision making · 2026Article
- SeqBoost: a sequential explainable model for predicting ED revisits within 72 hours.BMC medical informatics and decision making · 2026Article
- Artificial Intelligence-powered tiered early warning framework addressing high false alarm rates for in-hospital mortality prediction.NPJ digital medicine · 2026Article
- The sarcopenia artificial intelligence diagnostic decision support system (SAID DSS) - a multimodal deep learning model.BMC geriatrics · 2026Article
- Diagnostic Accuracy of Artificial Intelligence in Predicting Admission Status, Intensive Care Requirements, and Mortality in the Emergency Department: A Systematic Review and Meta-Analysis.Medical journal of the Islamic Republic of Iran · 2026Review
- Machine learning-based mortality prediction models for emergency department patients: a comparative analysis.Frontiers in medicine · 2026Article
- Do we actually need aging clocks?npj aging · 2025Review
- From prediction to action: a retrospective observational study on the real-world implementation of Critical Interventions (CrIs), an AI-based clinical decision support system changing clinical behavior in the emergency department.BMC medical informatics and decision making · 2025Observational
- Smart emergency care: a narrative review of predictive machine learning models.Annals of translational medicine · 2025Review
- Development and evaluation of a machine learning prediction model for short-term mortality in patients with diabetes or hyperglycemia at emergency department admission.Cardiovascular diabetology · 2025Article
- Deep Learning for Comprehensive Analysis of Retinal Fundus Images: Detection of Systemic and Ocular Conditions.Bioengineering (Basel, Switzerland) · 2025Article
- Validating 8 Area-Based Measures of Social Risk for Predicting Health and Mortality.JAMA health forum · 2025Article
- Predicting Partially Observed Long-Term Outcomes with Adversarial Positive-Unlabeled Domain Adaptation.Proceedings of machine learning research · 2025Article
- Comparing Single-Hospital and National Models to Predict 30-Day Inpatient Mortality.Journal of general internal medicine · 2025Article
- Applications of Artificial Intelligence for Metastatic Gastrointestinal Cancer: A Systematic Literature Review.Cancers · 2025Review
- Development and validation of prognostic machine learning models for short- and long-term mortality among acutely admitted patients based on blood tests.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis systematic review aimed to assess the performance and clinical feasibility of machine learning (ML) algorithms in prediction of in-hospital mortality for medical patients using vital signs at emergency departments (EDs).
designA systematic review was performed.
settingThe databases including Medline (PubMed), Scopus and Embase (Ovid) were searched between 2010 and 2021, to extract published articles in English, describing ML-based models utilising vital sign variables to predict in-hospital mortality for patients admitted at EDs. Critical appraisal and data extraction for systematic reviews of prediction modelling studies checklist was used for study planning and data extraction. The risk of bias for included papers was assessed using the prediction risk of bias assessment tool.
participantsAdmitted patients to the ED.
main outcome measureIn-hospital mortality.
resultsFifteen articles were included in the final review. We found that eight models including logistic regression, decision tree, K-nearest neighbours, support vector machine, gradient boosting, random forest, artificial neural networks and deep neural networks have been applied in this domain. Most studies failed to report essential main analysis steps such as data preprocessing and handling missing values. Fourteen included studies had a high risk of bias in the statistical analysis part, which could lead to poor performance in practice. Although the main aim of all studies was developing a predictive model for mortality, nine articles did not provide a time horizon for the prediction.
conclusionThis review provided an updated overview of the state-of-the-art and revealed research gaps; based on these, we provide eight recommendations for future studies to make the use of ML more feasible in practice. By following these recommendations, we expect to see more robust ML models applied in the future to help clinicians identify patient deterioration earlier.
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Identifiers
What 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.