Trial reportBMC geriatrics2021
Predicting outcomes in older ED patients with influenza in real time using a big data-driven and machine learning approach to the hospital information system.
Trial report in BMC geriatrics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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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
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The Use of Machine Learning for Analyzing Real-World Data in Disease Prediction and Management: Systematic Review.JMIR medical informatics · 2025Pooled it
- A meta-analysis of the diagnostic test accuracy of artificial intelligence predicting emergency department dispositions.BMC medical informatics and decision making · 2025Pooled it
- Using artificial intelligence to predict adverse outcomes in emergency department patients with hyperglycemic crises in real time.BMC endocrine disorders · 2023Trial
- 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
- Collaboration Between the Government and Healthcare Professional Organizations for the Implementation of Geriatric Emergency Departments in Taiwan.Journal of acute medicine · 2025Article
- Review
- Development of an early prediction model for risk of influenza A and influenza B based on complete blood count examination.BMC infectious diseases · 2025Article
- Machine Learning Reveals the Value of Unconventional T Lymphocytes in Sepsis and Prognosis of Elderly Patients With Severe Lower Respiratory Tract Infections.Journal of clinical laboratory analysis · 2025Article
- Machine Learning-Based Prediction Model for Multidrug-Resistant Organisms Infections: Performance Evaluation and Interpretability Analysis.Infection and drug resistance · 2025Article
- Implementing Explainable Machine Learning Models for Practical Prediction of Early Neonatal Hypoglycemia.Diagnostics (Basel, Switzerland) · 2024Article
- Multisite development and validation of machine learning models to predict severe outcomes and guide decision-making for emergency department patients with influenza.Journal of the American College of Emergency Physicians open · 2024Article
- Design and Implementation of a Comprehensive AI Dashboard for Real-Time Prediction of Adverse Prognosis of ED Patients.Healthcare (Basel, Switzerland) · 2022Article
- Use of Artificial Intelligence to Manage Patient Flow in Emergency Department during the COVID-19 Pandemic: A Prospective, Single-Center Study.International journal of environmental research and public health · 2022Article
- Development of an Interactive AI System for the Optimal Timing Prediction of Successful Weaning from Mechanical Ventilation for Patients in Respiratory Care Centers.Diagnostics (Basel, Switzerland) · 2022Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPredicting outcomes in older patients with influenza in the emergency department (ED) by machine learning (ML) has never been implemented. Therefore, we conducted this study to clarify the clinical utility of implementing ML.
methodsWe recruited 5508 older ED patients (≥65 years old) in three hospitals between 2009 and 2018. Patients were randomized into a 70%/30% split for model training and testing. Using 10 clinical variables from their electronic health records, a prediction model using the synthetic minority oversampling technique preprocessing algorithm was constructed to predict five outcomes.
resultsThe best areas under the curves of predicting outcomes were: random forest model for hospitalization (0.840), pneumonia (0.765), and sepsis or septic shock (0.857), XGBoost for intensive care unit admission (0.902), and logistic regression for in-hospital mortality (0.889) in the testing data. The predictive model was further applied in the hospital information system to assist physicians' decisions in real time.
conclusionsML is a promising way to assist physicians in predicting outcomes in older ED patients with influenza in real time. Evaluations of the effectiveness and impact are needed in the future.
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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.