Early predicting of hospital admission using machine learning algorithms: Priority queues approach
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Abstract
Emergency Department overcrowding is a critical issue that compromises patient safety and operational efficiency, necessitating accurate demand forecasting for effective resource allocation. This study evaluates and compares three distinct predictive models: Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors (SARIMAX), EXtreme Gradient Boosting (XGBoost) and Long Short-Term Memory (LSTM) networks for forecasting daily ED arrivals over a seven-day horizon. Utilizing data from an Australian tertiary referral hospital spanning January 2017 to December 2021, this research distinguishes itself by decomposing demand into eight specific ward categories and stratifying patients by clinical complexity. To address data distortions caused by the COVID-19 pandemic, the study employs the Prophet model to generate synthetic counterfactual values for the anomalous period. Experimental results demonstrate that all three proposed models consistently outperform a seasonal naive baseline. XGBoost demonstrated the highest accuracy for predicting total daily admissions with a Mean Absolute Error of 6.63, while the statistical SARIMAX model proved marginally superior for forecasting major complexity cases with an MAE of 3.77. The study concludes that while these techniques successfully reproduce regular day-to-day patterns, they share a common limitation in underestimating sudden, infrequent surges in patient volume.Keywords
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This publication has 32 references indexed in Scilit:
- Markov decision process and approximate dynamic programming for a patient assignment scheduling problemAnnals of Operations Research, 2025
- Combining machine learning and optimization for the operational patient-bed assignment problemHealth Care Management Science, 2023
- Prediction of hospitalization and waiting time within 24 hours of emergency department patients with unstructured text dataHealth Care Management Science, 2023
- Predicting daily emergency department visits using machine learning could increase accuracyThe American Journal of Emergency Medicine, 2022
- Predicting hospital emergency department visits with deep learning approachesBiocybernetics and Biomedical Engineering, 2022
- Forecasting patient arrivals at emergency department using calendar and meteorological informationApplied Intelligence, 2022
- Forecasting emergency department hourly occupancy using time series analysisThe American Journal of Emergency Medicine, 2021
- Forecasting emergency department overcrowding: A deep learning frameworkChaos, Solitons & Fractals, 2020
- Prediction of emergency department patient disposition decision for proactive resource allocation for admissionHealth Care Management Science, 2019
- Modeling Patient Flows Using a Queuing Network with BlockingHealth Care Management Science, 2005