Observational studyAnnals of emergency medicine2021
Predicting Progression to Septic Shock in the Emergency Department Using an Externally Generalizable Machine-Learning Algorithm.
Observational study in Annals of emergency medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 3 of them syntheses that pooled it.
What it found
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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.
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Who cites it
43 citing papers in PubMed, 3 syntheses or guidelines pooled it, 104 citations in OpenAlex.
- Evaluating Artificial Intelligence for Sepsis Prediction in Emergency Departments: A Systematic Review and Meta Analysis.Journal of medical systems · 2026Pooled 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
- A bibliometric analysis of artificial intelligence research in critical illness: a quantitative approach and visualization study.Frontiers in medicine · 2025Pooled it
- 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
- Explainable machine learning model for predicting septic shock in critically sepsis patients based on coagulation indexes: A multicenter cohort study.Chinese journal of traumatology = Zhonghua chuang shang za zhi · 2025Article
- Unreasonable effectiveness of training AI models locally.BMJ quality & safety · 2025Article
- Review
- Personalized and real time hemodynamic management in critical care using Dynamic Cohort Ensemble Learning (DynaCEL).NPJ digital medicine · 2025Article
- Mortality and antibiotic timing in deep learning-derived surviving sepsis campaign risk groups: a multicenter study.Critical care (London, England) · 2025Observational
- Early Prediction of Septic Shock in Emergency Department Using Serum Metabolites.Journal of the American Society for Mass Spectrometry · 2025Article
- AI-driven healthcare: Fairness in AI healthcare: A survey.PLOS digital health · 2025Review
- Early sepsis mortality prediction model based on interpretable machine learning approach: development and validation study.Internal and emergency medicine · 2025Article
- The role of artificial intelligence in sepsis in the Emergency Department: a narrative review.Annals of translational medicine · 2025Review
- Artificial Intelligence in Sepsis Management: An Overview for Clinicians.Journal of clinical medicine · 2025Review
- Predicting emergency department admissions using a machine-learning algorithm: a proof of concept with retrospective study.BMC emergency medicine · 2025Article
- Exploring the Potentials of Artificial Intelligence in Sepsis Management in the Intensive Care Unit.Critical care research and practice · 2025Review
- Development, deployment, and continuous monitoring of a machine learning model to predict respiratory failure in critically ill patients.JAMIA open · 2024Article
- Transatlantic transferability and replicability of machine-learning algorithms to predict mental health crises.NPJ digital medicine · 2024Article
- Article
- Development and Validation of a Deep Learning Model for Prediction of Adult Physiological Deterioration.Critical care explorations · 2024Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
STUDY
objectiveMachine-learning algorithms allow improved prediction of sepsis syndromes in the emergency department (ED), using data from electronic medical records. Transfer learning, a new subfield of machine learning, allows generalizability of an algorithm across clinical sites. We aim to validate the Artificial Intelligence Sepsis Expert for the prediction of delayed septic shock in a cohort of patients treated in the ED and demonstrate the feasibility of transfer learning to improve external validity at a second site.
methodsThis was an observational cohort study using data from greater than 180,000 patients from 2 academic medical centers between 2014 and 2019, using multiple definitions of sepsis. The Artificial Intelligence Sepsis Expert algorithm was trained with 40 input variables at the development site to predict delayed septic shock (occurring greater than 4 hours after ED triage) at various prediction windows. We then validated the algorithm at a second site, using transfer learning to demonstrate generalizability of the algorithm.
resultsWe identified 9,354 patients with severe sepsis, of whom 723 developed septic shock at least 4 hours after triage. The Artificial Intelligence Sepsis Expert algorithm demonstrated excellent area under the receiver operating characteristic curve (>0.8) at 8 and 12 hours for the prediction of delayed septic shock. Transfer learning significantly improved the test characteristics of the Artificial Intelligence Sepsis Expert algorithm and yielded comparable performance at the validation site.
conclusionThe Artificial Intelligence Sepsis Expert algorithm accurately predicted the development of delayed septic shock. The use of transfer learning allowed significantly improved external validity and generalizability at a second site. Future prospective studies are indicated to evaluate the clinical utility of this model.
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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.