ArticleJournal of critical care medicine (Universitatea de Medicina si Farmacie din Targu-Mures)2022
Using Machine Learning Techniques to Predict Hospital Admission at the Emergency Department.
Article in Journal of critical care medicine (Universitatea de Medicina si Farmacie din Targu-Mures), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review.BMC emergency medicine · 2024Pooled it
- Using machine learning methods to predict all-cause somatic hospitalizations in adults: A systematic review.PloS one · 2024Pooled it
- Systems Bioengineering of Septic Shock Metabolism: Citrulline, β-Hydroxybutyrate and Plasma Biomarker-Based Phenotyping.Biomolecules · 2026Review
- 30-day hospital admission among older adults initially managed at home by a mobile emergency unit: a retrospective cohort study.BMC emergency medicine · 2026Article
- Development of a Triage-Level Predictive Model for Hospitalization in the Emergency Department.Journal of clinical medicine · 2026Article
- Use of machine learning for risk stratification of chest pain patients in the emergency department.BMC medical informatics and decision making · 2025Article
- Machine Learning Models for Predicting Pediatric Hospitalizations Due to Air Pollution and Humidity: A Retrospective Study.Pediatric pulmonology · 2025Article
- Predicting total healthcare demand using machine learning: separate and combined analysis of predisposing, enabling, and need factors.BMC health services research · 2025Article
- Predicting emergency department admissions using a machine-learning algorithm: a proof of concept with retrospective study.BMC emergency medicine · 2025Article
- Predictors of Medical and Dental Clinic Closure by Machine Learning Methods: Cross-Sectional Study Using Empirical Data.Journal of medical Internet research · 2024Article
- Identification of avoidable patients at triage in a Paediatric Emergency Department: a decision support system using predictive analytics.BMC emergency medicine · 2024Article
- Machine learning in diagnostic support in medical emergency departments.Scientific reports · 2024Article
- Predicting Outcomes in Frail Older Community-Dwellers in Western Australia: Results from the Community Assessment of Risk Screening and Treatment Strategies (CARTS) Programme.Healthcare (Basel, Switzerland) · 2024Article
- Use of Artificial Intelligence in Triage in Hospital Emergency Departments: A Scoping Review.Cureus · 2024Article
- Artificial Intelligence Models in Health Information Exchange: A Systematic Review of Clinical Implications.Healthcare (Basel, Switzerland) · 2023Review
- Predicting Hospital Ward Admission from the Emergency Department: A Systematic Review.Journal of personalized medicine · 2023Review
- Artificial Intelligence: The Next Blockbuster Drug in Critical Care?Journal of critical care medicine (Universitatea de Medicina si Farmacie din Targu-Mures) · 2023Article
- The Aspects of Running Artificial Intelligence in Emergency Care; a Scoping Review.Archives of academic emergency medicine · 2023Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: One of the most important tasks in the Emergency Department (ED) is to promptly identify the patients who will benefit from hospital admission. Machine Learning (ML) techniques show promise as diagnostic aids in healthcare. Aim of the study: Our objective was to find an algorithm using ML techniques to assist clinical decision-making in the emergency setting. Material and methods: We assessed the following features seeking to investigate their performance in predicting hospital admission: serum levels of Urea, Creatinine, Lactate Dehydrogenase, Creatine Kinase, C-Reactive Protein, Complete Blood Count with differential, Activated Partial Thromboplastin Time, DDi-mer, International Normalized Ratio, age, gender, triage disposition to ED unit and ambulance utilization. A total of 3,204 ED visits were analyzed. Results: The proposed algorithms generated models which demonstrated acceptable performance in predicting hospital admission of ED patients. The range of F-measure and ROC Area values of all eight evaluated algorithms were [0.679-0.708] and [0.734-0.774], respectively. The main advantages of this tool include easy access, availability, yes/no result, and low cost. The clinical implications of our approach might facilitate a shift from traditional clinical decision-making to a more sophisticated model. Conclusions: Developing robust prognostic models with the utilization of common biomarkers is a project that might shape the future of emergency medicine. Our findings warrant confirmation with implementation in pragmatic ED trials.
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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.