Using machine learning and natural language processing in triage for prediction of clinical disposition in the emergency department
This study developed and validated machine learning models that integrate structured and unstructured triage data to predict patient dispositions, distinguishing between general ward and critical conditions like ICU admissions and ED deaths. Integrating both structured and unstructured data significantly improved model performance.
Authors
- Yu-Ting Chung
- Charles C. N. Wang (ORCID: https://orcid.org/0000-0002-7305-3061)
- Dar-Min Chen
- Fen-Wei Huang (ORCID: https://orcid.org/0009-0003-2651-8529)
- Yu-Hsin Chang (ORCID: https://orcid.org/0000-0002-7444-2667)
- Ying-Chen Lin (ORCID: https://orcid.org/0000-0002-9794-1426)
- Wei-Kung Chen
Institutions
- Asia University (TW)
- National Yang Ming Chiao Tung University (TW)
- China Medical University (TW)
- China Medical University Hospital (TW)
Publication Details
- Journal
- BMC Emergency Medicine
- Published
- 2024-12-18
- DOI
- https://doi.org/10.1186/s12873-024-01152-1
- Citations
- 12
- Primary Topic
- Emergency and Acute Care Studies
- Type
- article
- Field-Weighted Citation Impact
- 8.29
Funders
- China Medical University Hospital
- Asia University