Early predicting of hospital admission using machine learning algorithms: Priority queues approach

2026-01-21

AI Summary

Emergency department crowding jeopardizes patient safety and operational efficiency, necessitating accurate prediction of admission demand. This study leverages data from a tertiary Australian hospital spanning 2017–2021 to develop a novel forecasting framework that stratifies emergency admissions across eight ward categories and clinical complexity levels. We integrate SARIMAX, XGBoost, and LSTM models to predict daily admissions over a seven-day horizon and employ Prophet to generate counterfactual data during pandemic periods, thereby correcting for anomalous disruptions. All models outperform a seasonal naïve baseline: XGBoost achieves a mean absolute error (MAE) of 6.63 for total admissions, while SARIMAX attains an MAE of 3.77 for high-complexity cases, though challenges persist in capturing sudden surges. Our approach innovatively combines multidimensional stratification with synthetic counterfactual adjustment to enhance predictive robustness.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Multiagent Systems: Adversarial Agents
• Machine Learning: Time-Series/Data Streams

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Emergency Department overcrowding
• hospital admission prediction
• demand forecasting
• clinical complexity
• resource allocation

Innovation

• demand stratification
• counterfactual data synthesis
• clinical complexity stratification
• multi-model comparison
• ED admission forecasting