ArticleWorld journal of emergency medicine2025
Development of an emergency department length-of-stay prediction model based on machine learning.
Article in World journal of emergency medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Real-time dynamic prediction of in-hospital mortality in hyperglycemic crisis patients using temporal deep learning.World journal of emergency medicine · 2026Article
- Interpretative machine learning for predicting 60-day mortality in burn patients with suspected infection.World journal of emergency medicine · 2026Article
- Patient-level, Temporal, and Dynamic Operational Indicators of Emergency Department Prolonged Length of Stay: A Retrospective Cohort Study.Archives of academic emergency medicine · 2026Article
- DNA damage repair gene defects combined predict the efficacy of immune checkpoint inhibitors in metastatic castration-resistant prostate cancer: a retrospective cohort study.Frontiers in genetics · 2026Article
- Development and validation of machine learning-based in-hospital mortality predictive models for acute aortic syndrome in emergency departments.World journal of emergency medicine · 2026Article
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Authors and funding
11 authors.
Funding
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Abstract
backgroundThe problem of prolonged emergency department length of stay (EDLOS) is becoming increasingly crucial. This study aims to develop a machine learning (ML) model to predict EDLOS, with EDLOS as the outcome variable and demographic characteristics, triage level, and medical resource utilization as predictive factors.
methodsA retrospective analysis was performed on the patients who visited the emergency department of the Second Affiliated Hospital of Guangzhou Medical University from March 2019 to September 2021, and a total of 321,012 cases were identified. According to the inclusion and exclusion criteria, 187,028 cases were finally included in the analysis. ML analysis was performed using R-squared (R
resultsIn the comparative analysis of the six ML models, light gradient boosting machine (LightGBM) model demonstrated the lowest MAE (443.519) and RMSE (826.783), and the highest R² value (0.48), indicating better model fit and predictive performance. Among the top 10 predictive factors associated with EDLOS according to the LightGBM model, the emergency waiting time, age, and emergency arrival time had the most significant impact on the EDLOS.
conclusionThe LightGBM model suggests that the emergency waiting time, age, and emergency arrival time may be used to predict the EDLOS.
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