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Liu, N., Koh, Z.X., Goh, J., Lin, Z., Haaland, B., Ting, B.P., et al. (2014) Prediction of Adverse Cardiac Events in Emergency Department Patients with Chest Pain Using Machine Learning for Variable Selection. BMC Medical Informatics and Decision Making, 14, Article No. 75.
https://doi.org/10.1186/1472-6947-14-75

has been cited by the following article:

• TITLE: Application Research of Machine Learning in Emergency Field

  AUTHORS: Wei Jiang, Xiaoqian Liu

  KEYWORDS: Safety Engineering, Machine Learning, Data Analysis, Artificial Intelligence

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.15 No.5, May 19, 2025

  ABSTRACT: With the rapid development of science and technology, artificial intelligence plays a significant role across various domains. In recent years, frequent occurrences of natural disasters and human-made accidents have brought increasing attention to emergency management, which constitutes an integral part of China’s governance system. As a crucial tool for data analysis and prediction, machine learning has been extensively applied in emergency management. Through bibliometric analysis using CiteSpace software—examining annual publication volumes, authors, and keywords in literature related to machine learning applications in emergency contexts—it is evident that machine learning, as a core AI technology, has been widely utilized in areas such as data analysis, predictive modeling, and decision-making assistance. The integration of machine learning with emergency management can substantially enhance operational efficiency. This paper explores the application research of machine learning in emergency management and investigates its potential to improve emergency response effectiveness while mitigating disaster-related losses.