ArticleBMJ open2018
Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU.
Article in BMJ open, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 150 papers, 9 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.
Gram Type Infection-Specific Sepsis Identification Using Machine Learning
Unsupervised Machine Learning for Clustering of Septic Patients to Determine Optimal Treatment
Who cites it
150 citing papers in PubMed, 9 syntheses or guidelines pooled it.
- Artificial Intelligence-Based Predictive Modeling for Early Detection of Sepsis in Hospitalized Patients: A Systematic Review and Meta-Analysis.Critical care explorations · 2025Pooled it
- Diagnostic performance of machine-learning algorithms for sepsis prediction: An updated meta-analysis.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Pooled it
- Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis.Frontiers in medicine · 2024Pooled it
- Predicting sepsis onset in ICU using machine learning models: a systematic review and meta-analysis.BMC infectious diseases · 2023Pooled it
- Deployment of machine learning algorithms to predict sepsis: systematic review and application of the SALIENT clinical AI implementation framework.Journal of the American Medical Informatics Association : JAMIA · 2023Pooled it
- Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review.Frontiers in medicine · 2021Pooled it
- Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy.Intensive care medicine · 2020Pooled it
- Use of machine learning to analyse routinely collected intensive care unit data: a systematic review.Critical care (London, England) · 2019Pooled it
- Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review.F1000Research · 2019Pooled it
- A comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis.PloS one · 2021Trial
- Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs.Computers in biology and medicine · 2019Trial
- Evaluation of Sepsis Algorithmic Alerts Among Hospitalized Pediatric Patients in Qatar: A Retrospective Cohort Study.Health science reports · 2026Article
- Process Improvement Before Artificial Intelligence and Automation: Building Trust With the Understand-Transform-Sustain Framework.Mayo Clinic proceedings. Innovations, quality & outcomes · 2026Article
- Interpretable Machine Learning Model for Predicting Sepsis and Septic Shock Among Patients with Documented Fever at Emergency Department Triage Using Patients' Historical Data.Journal of medical systems · 2026Article
- Prediction of bronchopulmonary dysplasia seven days after birth using respiratory and oxygenation timeseries with machine learning.Pediatric research · 2026Article
- Online Sepsis Prediction Using Vital Signs and Multiscale Temporal-Aware Contrastive Learning: Model Development and Validation Study.JMIR medical informatics · 2026Article
- From scores to signals: evolution and innovations in pediatric early warning systems.Acute and critical care · 2026Article
- Lightweight large language models for early sepsis prediction via a semantic abstraction rule engine.Scientific reports · 2026Article
- Machine learning predicts sepsis deterioration trajectories.NPJ digital medicine · 2026Article
- Development of a machine learning-based sepsis prediction model for real-world clinical settings in South Korea: a single-center retrospective study.Journal of Korean biological nursing science · 2026Article
90 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
objectivesWe validate a machine learning-based sepsis-prediction algorithm (
designA machine-learning algorithm with gradient tree boosting. Features for prediction were created from combinations of six vital sign measurements and their changes over time.
settingA mixed-ward retrospective dataset from the University of California, San Francisco (UCSF) Medical Center (San Francisco, California, USA) as the primary source, an intensive care unit dataset from the Beth Israel Deaconess Medical Center (Boston, Massachusetts, USA) as a transfer-learning source and four additional institutions' datasets to evaluate generalisability.
participants684 443 total encounters, with 90 353 encounters from June 2011 to March 2016 at UCSF.
interventionsNone. PRIMARY AND SECONDARY OUTCOME MEASURES: Area under the receiver operating characteristic (AUROC) curve for detection and prediction of sepsis, severe sepsis and septic shock.
resultsFor detection of sepsis and severe sepsis,
conclusions
Indexed as
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.