ACM Computing Surveys (CSUR)
Self-Organizing Maps
Integration of self-organizing feature map and K-means algorithm for market segmentation
Computers and Operations Research
Data Mining to Support Simulation Modeling of Patient Flow in Hospitals
Journal of Medical Systems
Functional analysis for operating emergency department of a general hospital
WSC '04 Proceedings of the 36th conference on Winter simulation
Clustering Indian stock market data for portfolio management
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Logistic-based patient grouping for multi-disciplinary treatment
Artificial Intelligence in Medicine
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Variability and unpredictability are typical characteristics of complex systems such as emergency department (ED) where the patient demand is high and patient conditions are diverse. To tackle the uncertain nature of ED and improve the resource management, it is beneficial to group patients with common features. This paper aims to use self-organizing map (SOM), k-means, and hierarchical methods to group patients based on their medical procedures and make comparisons among these methods. It can be reasonably assumed that the medical procedures received by the patients are directly associated with ED resource consumption. Different grouping techniques are compared using a validity index and the resulting groups are distinctive in the length of treatment (LOT) of patients and their presenting complaints. This paper also discusses how the resulting patient groups can be used to enhance the ED resource planning, as well as to redesign the ED charging policy.