Machine Learning-Based Model to Classify Emergency Severity Index Levels 1-3 in Febrile Patients With Tachycardia: Thailand Triage Prediction System
XGBoost was selected for integration into the Smart ER system as the Thailand Triage Prediction System (TTPS), providing real-time prediction to enhance triage accuracy, support decision-making, and improve workflow.
Authors
- Thanin Lokeskrawee (ORCID: https://orcid.org/0000-0003-1808-2086)
- Suppachai Lawanaskol (ORCID: https://orcid.org/0000-0002-9846-1491)
- Jarupa Yaowalaorng (ORCID: https://orcid.org/0009-0005-7675-3265)
- Wanwisa Bumrungpagdee
- Sagoontee Inkate
- Natthaphon Pruksathorn (ORCID: https://orcid.org/0009-0007-2030-9995)
- Chawalit Lakdee
- Chanitda Wicha
- Jayanton Patumanond
- Suwapim Chanlaor
Institutions
- Buddhachinaraj Hospital (TH)
- Lampang Hospital (TH)
- Naresuan University (TH)
Publication Details
- Journal
- Journal of Clinical Medicine Research
- Published
- 2025-12-01
- DOI
- https://doi.org/10.14740/jocmr6371
- Primary Topic
- Emergency and Acute Care Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00