ArticleScientific reports2020
Using machine learning tools to predict outcomes for emergency department intensive care unit patients.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Data balancing improves mortality prediction for emergency department patients.BMC medical informatics and decision making · 2026Article
- Machine Learning-Driven Prediction of Intensive Care Units Mortality and Length of Stay: A 11-Year Retrospective Study in Hong Kong Public Hospitals.Journal of medical systems · 2026Article
- Acute biological age as a determinant of adverse outcomes requiring hospitalization in Danish emergency department patients.Communications medicine · 2026Article
- Smart emergency care: a narrative review of predictive machine learning models.Annals of translational medicine · 2025Review
- Article
- Predicting patient deterioration with physiological data using AI: systematic review protocol.BMJ health & care informatics · 2025Article
- Improving the Predictive Accuracy of the National Early Warning Score 2: Protocol for Algorithm Refinement.JMIR research protocols · 2025Article
- Prioritizing Patient Selection in Clinical Trials: A Machine Learning Algorithm for Dynamic Prediction of In-Hospital Mortality for ICU Admitted Patients Using Repeated Measurement Data.Journal of clinical medicine · 2025Article
- Usability of an artificially intelligence-powered triage platform for adult ophthalmic emergencies: a mixed methods study.Scientific reports · 2023Article
- Machine learning-based prediction model for emergency department visits using prescription information in community-dwelling non-cancer older adults.Scientific reports · 2023Article
- Improved patient mortality predictions in emergency departments with deep learning data-synthesis and ensemble models.Scientific reports · 2023Article
- Predicting patient decompensation from continuous physiologic monitoring in the emergency department.NPJ digital medicine · 2023Article
- The use of machine learning and artificial intelligence within pediatric critical care.Pediatric research · 2023Review
- Prognostic Model of ICU Admission Risk in Patients with COVID-19 Infection Using Machine Learning.Diagnostics (Basel, Switzerland) · 2022Article
- Establishment of ICU Mortality Risk Prediction Models with Machine Learning Algorithm Using MIMIC-IV Database.Diagnostics (Basel, Switzerland) · 2022Article
- Administrative Data Is Insufficient to Identify Near-Future Critical Illness: A Population-Based Retrospective Cohort Study.Frontiers in epidemiology · 2022Article
- Predicting the Prognosis of Patients in the Coronary Care Unit: A Novel Multi-Category Machine Learning Model Using XGBoost.Frontiers in cardiovascular medicine · 2022Article
- An Inverse Relation between Hyperglycemia and Skeletal Muscle Mass Predicted by Using a Machine Learning Approach in Middle-Aged and Older Adults in Large Cohorts.Journal of clinical medicine · 2021Article
- Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review.IEEE access : practical innovations, open solutions · 2021Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The number of critically ill patients has increased globally along with the rise in emergency visits. Mortality prediction for critical patients is vital for emergency care, which affects the distribution of emergency resources. Traditional scoring systems are designed for all emergency patients using a classic mathematical method, but risk factors in critically ill patients have complex interactions, so traditional scoring cannot as readily apply to them. As an accurate model for predicting the mortality of emergency department critically ill patients is lacking, this study's objective was to develop a scoring system using machine learning optimized for the unique case of critical patients in emergency departments. We conducted a retrospective cohort study in a tertiary medical center in Beijing, China. Patients over 16 years old were included if they were alive when they entered the emergency department intensive care unit system from February 2015 and December 2015. Mortality up to 7 days after admission into the emergency department was considered as the primary outcome, and 1624 cases were included to derive the models. Prospective factors included previous diseases, physiologic parameters, and laboratory results. Several machine learning tools were built for 7-day mortality using these factors, for which their predictive accuracy (sensitivity and specificity) was evaluated by area under the curve (AUC). The AUCs were 0.794, 0.840, 0.849 and 0.822 respectively, for the SVM, GBDT, XGBoost and logistic regression model. In comparison with the SAPS 3 model (AUC = 0.826), the discriminatory capability of the newer machine learning methods, XGBoost in particular, is demonstrated to be more reliable for predicting outcomes for emergency department intensive care unit patients.
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