Effective Predictive Modelling for Emergency Department Visits and Evaluating Exogenous Variables Impact: Using Explainable Meta-learning Gradient Boosting

Neshat, M; Phipps, M; Jha, N; Khojasteh, D; Tong, M; Gandomi, AH

Effective Predictive Modelling for Emergency Department Visits and Evaluating Exogenous Variables Impact: Using Explainable Meta-learning Gradient Boosting

Neshat, M

Phipps, M Jha, N Khojasteh, D Tong, M Gandomi, AH

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Publisher:: Association for Computing Machinery (ACM)
Publication Type:: Journal Article
Citation:: ACM Transactions on Computing for Healthcare, 2026, 7, (1), pp. 1-33
Issue Date:: 2026-01-31

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Field	Value	Language
dc.contributor.author	Neshat, M https://orcid.org/0000-0002-9537-9513
dc.contributor.author	Phipps, M
dc.contributor.author	Jha, N
dc.contributor.author	Khojasteh, D
dc.contributor.author	Tong, M
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2026-02-03T04:58:41Z
dc.date.available	2026-02-03T04:58:41Z
dc.date.issued	2026-01-31
dc.identifier.citation	ACM Transactions on Computing for Healthcare, 2026, 7, (1), pp. 1-33
dc.identifier.issn	2691-1957
dc.identifier.issn	2637-8051
dc.identifier.uri	http://hdl.handle.net/10453/192723
dc.description.abstract	Accurately predicting Emergency Department (ED) visits is essential for optimising resource allocation, including staffing adjustments and Operating Room scheduling. Despite the proliferation of AI-driven models, effective ED visit prediction remains challenging due to limited generalisability, susceptibility to overfitting and underfitting, scalability, and the complexity of fine-tuning hyper-parameters. To address these challenges, we propose a novel Meta-learning Gradient Booster (Meta-ED) approach to forecast daily ED visits. Meta-ED leverages a comprehensive dataset spanning 23 years from Canberra Hospital, incorporating exogenous variables such as socio-demographic characteristics, healthcare usage, chronic diseases, diagnoses, and climate parameters. Meta-ED combines four foundational learners—CatBoost, Random Forest, Extra Trees, and LightGBM—with a Multi-Layer Perceptron (MLP) as the master-level learner, thereby enhancing predictive precision by integrating the strengths of diverse base models. Our comparative analysis, which involved testing 23 models against a set of predefined criteria, demonstrates the superior performance of Meta-ED, achieving an accuracy of 85.7% (95% CI [85.4%, 86.0%]) and outperforming prominent models like XGBoost, Random Forest, AdaBoost, LightGBM, and Extra Trees by up to 106.3%. Furthermore, incorporating climate features resulted in a 3.25% improvement in prediction accuracy, effectively capturing seasonal variations that influence patient volumes. These results underscore the potential of Meta-ED to advance predictive analytics in complex healthcare environments.
dc.language	en
dc.publisher	Association for Computing Machinery (ACM)
dc.relation.ispartof	ACM Transactions on Computing for Healthcare
dc.relation.isbasedon	10.1145/3768317
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Effective Predictive Modelling for Emergency Department Visits and Evaluating Exogenous Variables Impact: Using Explainable Meta-learning Gradient Boosting
dc.type	Journal Article
utslib.citation.volume	7
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Data Science Institute (DSI)
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2026-02-03T04:57:54Z
pubs.issue	1
pubs.publication-status	Published online
pubs.volume	7
utslib.citation.issue	1

Abstract:

Accurately predicting Emergency Department (ED) visits is essential for optimising resource allocation, including staffing adjustments and Operating Room scheduling. Despite the proliferation of AI-driven models, effective ED visit prediction remains challenging due to limited generalisability, susceptibility to overfitting and underfitting, scalability, and the complexity of fine-tuning hyper-parameters. To address these challenges, we propose a novel Meta-learning Gradient Booster (Meta-ED) approach to forecast daily ED visits. Meta-ED leverages a comprehensive dataset spanning 23 years from Canberra Hospital, incorporating exogenous variables such as socio-demographic characteristics, healthcare usage, chronic diseases, diagnoses, and climate parameters. Meta-ED combines four foundational learners—CatBoost, Random Forest, Extra Trees, and LightGBM—with a Multi-Layer Perceptron (MLP) as the master-level learner, thereby enhancing predictive precision by integrating the strengths of diverse base models. Our comparative analysis, which involved testing 23 models against a set of predefined criteria, demonstrates the superior performance of Meta-ED, achieving an accuracy of 85.7% (95% CI [85.4%, 86.0%]) and outperforming prominent models like XGBoost, Random Forest, AdaBoost, LightGBM, and Extra Trees by up to 106.3%. Furthermore, incorporating climate features resulted in a 3.25% improvement in prediction accuracy, effectively capturing seasonal variations that influence patient volumes. These results underscore the potential of Meta-ED to advance predictive analytics in complex healthcare environments.

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http://hdl.handle.net/10453/192723