Development of an emergency department length-of-stay prediction model based on machine learning
The LightGBM model suggests that the emergency waiting time, age, and emergency arrival time may be used to predict the EDLOS.
Authors
- Huilin Jiang (ORCID: https://orcid.org/0000-0002-9268-4424)
- Min Sun (ORCID: https://orcid.org/0000-0002-8528-0050)
- Junrong Mo
- Haifeng Mao (ORCID: https://orcid.org/0000-0003-4799-3073)
- Yunmei Li (ORCID: https://orcid.org/0000-0001-8116-942X)
- Yanling Li (ORCID: https://orcid.org/0000-0001-5114-4125)
- Weiming Wu (ORCID: https://orcid.org/0000-0002-7366-708X)
- Xiaohong Chen (ORCID: https://orcid.org/0000-0002-9797-8384)
- Min� Li (ORCID: https://orcid.org/0000-0002-1163-8961)
- Yongcheng Zhu
- Gongxu Zhu
Institutions
- Second Affiliated Hospital of Guangzhou Medical University (CN)
- Health Biomed (China) (CN)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- World Journal of Emergency Medicine
- Published
- 2025-01-01
- DOI
- https://doi.org/10.5847/wjem.j.1920-8642.2025.048
- Citations
- 2
- Primary Topic
- Trauma and Emergency Care Studies
- Type
- article
- Field-Weighted Citation Impact
- 4.51