ArticleNPJ digital medicine2022
Machine learning for real-time aggregated prediction of hospital admission for emergency patients.
Article in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Anticipating health and care trajectories from routinely collected social care records.npj health systems · 2026Article
- Simulation of covariate and concept drift in machine learning hospital admission prediction from emergency triage.NPJ digital medicine · 2026Article
- Optimising Retraining Frequency for a Paediatric Emergency Department Admission Prediction Model: Development and Temporal Validation Using Real-World Data.Emergency medicine Australasia : EMA · 2026Article
- Article
- Predicting the Unpredictable: A Data-Driven Machine Learning Model for Emergency Department Waiting Room Surge Status.Mayo Clinic proceedings. Digital health · 2026Article
- Enhancing hospital workforce planning, scheduling, and performance evaluation through an AI-driven human resource management system.Scientific reports · 2026Article
- Article
- Development and evaluation of a machine learning model predicting out-of-hospital cardiac arrest using environmental factors.NPJ digital medicine · 2025Article
- Machine learning-based predictions of healthcare contacts following emergency hospitalisation using electronic health records.NPJ digital medicine · 2025Article
- Potentially inappropriate polypharmacy is an important predictor of 30-day emergency hospitalisation in older adults: a machine learning feature validation study.Age and ageing · 2025Article
- Machine Learning Models for Predicting Pediatric Hospitalizations Due to Air Pollution and Humidity: A Retrospective Study.Pediatric pulmonology · 2025Article
- Machine learning-based forecasting of daily acute ischemic stroke admissions using weather data.NPJ digital medicine · 2025Article
- Predicting Inpatient Admissions From Emergency Department Triage Using Machine Learning: A Systematic Review.Mayo Clinic proceedings. Digital health · 2025Review
- Dynamic mortality prediction in critically Ill children during interhospital transports to PICUs using explainable AI.NPJ digital medicine · 2025Article
- Utilizing machine learning to predict hospital admissions for pediatric COVID-19 patients (PrepCOVID-Machine).Scientific reports · 2025Article
- Time series forecasting of bed occupancy in mental health facilities in India using machine learning.Scientific reports · 2025Article
- Deep learning for cardiovascular management: optimizing pathways and cost control under diagnosis-related group models.Frontiers in artificial intelligence · 2025Review
- Acute Kidney Injury Prognosis Prediction Using Machine Learning Methods: A Systematic Review.Kidney medicine · 2025Review
- A risk prediction system for depression in middle-aged and older adults grounded in machine learning and visualization technology: a cohort study.Frontiers in public health · 2025Article
- Development of Time-Aggregated Machine Learning Model for Relapse Prediction in Pediatric Crohn's Disease.Clinical and translational gastroenterology · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Machine learning for hospital operations is under-studied. We present a prediction pipeline that uses live electronic health-records for patients in a UK teaching hospital's emergency department (ED) to generate short-term, probabilistic forecasts of emergency admissions. A set of XGBoost classifiers applied to 109,465 ED visits yielded AUROCs from 0.82 to 0.90 depending on elapsed visit-time at the point of prediction. Patient-level probabilities of admission were aggregated to forecast the number of admissions among current ED patients and, incorporating patients yet to arrive, total emergency admissions within specified time-windows. The pipeline gave a mean absolute error (MAE) of 4.0 admissions (mean percentage error of 17%) versus 6.5 (32%) for a benchmark metric. Models developed with 104,504 later visits during the Covid-19 pandemic gave AUROCs of 0.68-0.90 and MAE of 4.2 (30%) versus a 4.9 (33%) benchmark. We discuss how we surmounted challenges of designing and implementing models for real-time use, including temporal framing, data preparation, and changing operational conditions.
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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.