PreprintArticleVersion 1This version is not peer-reviewed
Comparison of the Performance of Machine Learning Models in Predicting ESI Triage Levels Using Data of Non‐Traumatic Patients from the Triage Point at the Emergency Medicine Department of Lampang Hospital
Version 1
: Received: 5 August 2024 / Approved: 6 August 2024 / Online: 7 August 2024 (14:10:04 CEST)
How to cite:
Seesuwan, N.; Phetkub, N.; Rojanasumapong, A.; Chuenjai, N. Comparison of the Performance of Machine Learning Models in Predicting ESI Triage Levels Using Data of Non‐Traumatic Patients from the Triage Point at the Emergency Medicine Department of Lampang Hospital. Preprints2024, 2024080428. https://doi.org/10.20944/preprints202408.0428.v1
Seesuwan, N.; Phetkub, N.; Rojanasumapong, A.; Chuenjai, N. Comparison of the Performance of Machine Learning Models in Predicting ESI Triage Levels Using Data of Non‐Traumatic Patients from the Triage Point at the Emergency Medicine Department of Lampang Hospital. Preprints 2024, 2024080428. https://doi.org/10.20944/preprints202408.0428.v1
Seesuwan, N.; Phetkub, N.; Rojanasumapong, A.; Chuenjai, N. Comparison of the Performance of Machine Learning Models in Predicting ESI Triage Levels Using Data of Non‐Traumatic Patients from the Triage Point at the Emergency Medicine Department of Lampang Hospital. Preprints2024, 2024080428. https://doi.org/10.20944/preprints202408.0428.v1
APA Style
Seesuwan, N., Phetkub, N., Rojanasumapong, A., & Chuenjai, N. (2024). Comparison of the Performance of Machine Learning Models in Predicting ESI Triage Levels Using Data of Non‐Traumatic Patients from the Triage Point at the Emergency Medicine Department of Lampang Hospital. Preprints. https://doi.org/10.20944/preprints202408.0428.v1
Chicago/Turabian Style
Seesuwan, N., Auswin Rojanasumapong and Nipada Chuenjai. 2024 "Comparison of the Performance of Machine Learning Models in Predicting ESI Triage Levels Using Data of Non‐Traumatic Patients from the Triage Point at the Emergency Medicine Department of Lampang Hospital" Preprints. https://doi.org/10.20944/preprints202408.0428.v1
Abstract
Emergency departments (EDs) are critical in urgent care, where accurate triage optimizes patient flow and resource allocation. Manual triage faces challenges due to increasing volume and com-plexity. This study compares logistic regression, gradient boosting, neural network, and random forest models in predicting Emergency Severity Index (ESI) triage levels for non-traumatic pa-tients at Lampang Hospital's ED. Using data from January 1, 2023, to April 30, 2024, we ana-lyzed 45,245 complete records. The gradient boosting model achieved the highest accuracy (0.81), significantly outperforming logistic regression (accuracy 0.64). Pain scale, sex, and mean arterial pressure were key predictors. This is the first comparison of these models for ESI triage in a Thai hospital, highlighting their potential to improve triage accuracy and efficiency. Implementing these models could enhance patient outcomes and resource management in ED.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
