Exploratory Clustering for Emergency Department Patients.

Georgios Feretzakis,Aikaterini Sakagianni,Dimitris Kalles,Evangelos Loupelis,Lazaros Tzelves,Vasileios Panteris,Rea Chatzikyriakou,Nikolaos Trakas,Stavroula Kolokytha,Polyxeni Batiani,Zoi Rakopoulou,Aikaterini Tika,Stavroula Petropoulou,Ilias Dalainas,Vasileios Kaldis

International Conference on Informatics, Management and Technology in Healthcare (ICIMTH)（2022）

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Abstract

Emergency department (ED) overcrowding is an increasing global problem raising safety concerns for the patients. Elaborating an effective triage system that properly separates patients requiring hospital admission remains difficult. The objective of this study was to compare a clustering-related technique assignment of emergency department patients with the admission output using the k-means algorithm. Incorporating such a model into triage practice could theoretically shorten waiting times and reduce ED overcrowding.

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Key words

Machine learning,clustering,emergency department,hospital admission,k-means,unsupervised learning

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