Observational studyScientific reports2022
Machine learning-based suggestion for critical interventions in the management of potentially severe conditioned patients in emergency department triage.
Observational study in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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
12 citing papers in PubMed, 21 citations in OpenAlex.
- Workflow Bottlenecks and Staff Readiness in an NHS Emergency Urology Clinic: A Prospective Service Evaluation to Inform Future AI-Supported Triage.Healthcare (Basel, Switzerland) · 2026Article
- Artificial intelligence-driven triage in emergency departments: a comparative study of machine learning approaches.Revista da Associacao Medica Brasileira (1992) · 2026Article
- 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
- Review
- Development and validation of a transformer model-based early warning score for real-time prediction of adverse outcomes in the emergency department.Scientific reports · 2025Article
- Predicting triage of pediatric patients in the emergency department using machine learning approach.International journal of emergency medicine · 2025Article
- The role of artificial intelligence in sepsis in the Emergency Department: a narrative review.Annals of translational medicine · 2025Review
- Applications and Performance of Machine Learning Algorithms in Emergency Medical Services: A Scoping Review.Prehospital and disaster medicine · 2024Article
- Use of Artificial Intelligence in Triage in Hospital Emergency Departments: A Scoping Review.Cureus · 2024Article
- Development and validation of a reinforcement learning model for ventilation control during emergence from general anesthesia.NPJ digital medicine · 2023Article
- Clinical support system for triage based on federated learning for the Korea triage and acuity scale.Heliyon · 2023Article
- Artificial intelligence decision points in an emergency department.Clinical and experimental emergency medicine · 2022Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Providing timely intervention to critically ill patients is a challenging task in emergency departments (ED). Our study aimed to predict early critical interventions (CrIs), which can be used as clinical recommendations. This retrospective observational study was conducted in the ED of a tertiary hospital located in a Korean metropolitan city. Patient who visited ED from January 1, 2016, to December 31, 2018, were included. Need of six CrIs were selected as prediction outcomes, namely, arterial line (A-line) insertion, oxygen therapy, high-flow nasal cannula (HFNC), intubation, Massive Transfusion Protocol (MTP), and inotropes and vasopressor. Extreme gradient boosting (XGBoost) prediction model was built by using only data available at the initial stage of ED. Overall, 137,883 patients were included in the study. The areas under the receiver operating characteristic curve for the prediction of A-line insertion was 0·913, oxygen therapy was 0.909, HFNC was 0.962, intubation was 0.945, MTP was 0.920, and inotropes or vasopressor administration was 0.899 in the XGBoost method. In addition, an increase in the need for CrIs was associated with worse ED outcomes. The CrIs model was integrated into the study site's electronic medical record and could be used to suggest early interventions for emergency physicians.
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