Integrating artificial neural networks with rule-based expert systems
Decision Support Systems - Special issue on neural networks for decision support
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Expert Systems with Applications: An International Journal
Forecasting of the electric energy demand trend and monthly fluctuation with neural networks
Computers and Industrial Engineering
Forecasting Thailand's rice export: Statistical techniques vs. artificial neural networks
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
EMS call volume predictions: A comparative study
Computers and Operations Research
A multivariate time series approach to modeling and forecasting demand in the emergency department
Journal of Biomedical Informatics
Artificial intelligence diagnosis algorithm for expanding a precision expert forecasting system
Expert Systems with Applications: An International Journal
Interday Forecasting and Intraday Updating of Call Center Arrivals
Manufacturing & Service Operations Management
Medical doctor rostering problem in a hospital emergency department by means of genetic algorithms
Computers and Industrial Engineering
Forecasting tourist arrivals by using the adaptive network-based fuzzy inference system
Expert Systems with Applications: An International Journal
A two-stage dynamic sales forecasting model for the fashion retail
Expert Systems with Applications: An International Journal
Analyzing supply chain operation models with the PC-algorithm and the neural network
Expert Systems with Applications: An International Journal
Combining linear and nonlinear model in forecasting tourism demand
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A two-stage analysis of the influences of employee alignment on effecting business-IT alignment
Decision Support Systems
A medical procedure-based patient grouping method for an emergency department
Applied Soft Computing
Hi-index | 0.00 |
Emergency Department (ED) plays a critical role in healthcare systems by providing emergency care to patients in need. The quality of ED services, measured by waiting time and length of stay, is significantly affected by patient arrivals. Increased patient arrivals could undermine service timeliness, thus putting patients in severe conditions at risk. These factors lead to the following research questions that have rarely been studied before: What are the variables directly associated with patient arrivals in the ED? What is the nature of association between these variables and patient arrivals? Which variable is the most influential and why? To address the above questions, we proposed a three-stage method in this paper. First, a data-driven method is used to identify contributing variables directly correlated with the daily arrivals of Categories 3 and 4 patients (i.e., non-critical patients). Second, the association between contributing variables and daily patient arrival is modeled by using artificial neural network (ANN), and the modeling ability is compared with that of nonlinear least square regression (NLLSR) and multiple linear regression (MLR) in terms of mean average percentage error (MAPE). Third, four types of relative importance (RI) of input variables based on ANN are compared, and their statistical reliability is tested by the MLR-based RI. We applied this three-stage method to one year of data of patient arrivals at a local ED. The contribution of this paper is twofold. Theoretically, this paper emphasizes the importance of using data-driven selection of variables for complex system modeling, and then provides a comprehensive comparison of RI using different computational methods. Practically, this work is a novel attempt of applying ANN to model patient arrivals, and the result can be used to aid in strategic decision-making on ED resource planning in response to predictable arrival variations.