Criticality and clinical department prediction of ED patients using machine learning based on heterogeneous medical data.

Journal: Computers in biology and medicine

Published Date: Aug 28, 2023

Abstract

PROBLEM: Emergency triage faces multiple challenges, including limited medical resources and inadequate manual triage nurses, which cause incorrect triage, overcrowding in the emergency department (ED), and long patient waiting time.

Authors

Yi Xiao

Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P. R. China.
Jun Zhang

First School of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China.
Cheng Chi

Department of Emergency, Peking University People's Hospital, Beijing, 100044, China.
Yuqing Ma
Aiguo Song

School of Instrument Science and Engineering, Southeast University, Nanjing 210096, P.R. China.

Keywords

Artificial Intelligence Emergency Service, Hospital Humans Machine Learning Neural Networks, Computer Retrospective Studies Triage

External Resources

View on PubMed Access via DOI PubMed (37659113)

Criticality and clinical department prediction of ED patients using machine learning based on heterogeneous medical data.

Abstract

Authors

Keywords

External Resources

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