Machine Learning Models for Emergency Department Layout Prediction Using Graph Metrics

Abstract

Challenges in healthcare facilities, especially emergency departments (EDs), persistently arise from increasing patient volumes, pandemics, and complex operational factors. This study explores the use of graph-theoretic methods as powerful tools for optimizing hospital layouts. Initially, we introduced a multi-objective optimization framework informed by graphs for the design of ED layouts, utilizing NSGA-II and GDE3, with the objectives of minimizing patient flow costs and enhancing the spatial proximity of service areas. We propose and confirm the application of both local and global graph-theoretic metrics, including centrality metrics, clustering coefficients, and network efficiency, to assess and rank Pareto-optimal layouts. Simulation results on the layout of an ED in Dalian, China, show that the best-ranked layouts exhibit superior graph-theoretic values, with the NSGA-II and GDE3 solutions reducing patient flow cost by 18.32% and 11.42%, respectively, and improving service area closeness by 14.5% and 18.02%, respectively. Subsequently, we present a layout prediction method based on machine learning that employs multi-output regression models trained on graph-theoretic features extracted from a large set of optimization-based layout solutions. The proposed models, particularly decision trees and random forests, demonstrated impressive prediction accuracies of up to 98.18% and 88.06%, respectively, showcasing their effectiveness in efficiently producing optimal layouts. Collectively, these findings highlight the potential of graph-theoretic models in enhancing hospital design and operational decision-making.