Exploratory Clustering for Emergency Department Patients.
International Conference on Informatics, Management and Technology in Healthcare (ICIMTH)(2022)
摘要
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.
更多查看译文
关键词
Machine learning,clustering,emergency department,hospital admission,k-means,unsupervised learning
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要