Application of Multistrategy Improvement Gray Wolf Algorithm to Optimize Extreme Gradient Boosting in Emergency Triage.
Effective triage in the emergency department (ED) is essential for optimizing resource allocation, improving efficiency, and enhancing patient outcomes. Conventional systems rely heavily on clinical judgment and standardized guidelines, which may be insufficient under growing patient volumes and inc...
| Publicado en: | Journal of Emergency Nursing Vol. 52; no. 1; pp. 170 - 186 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Jan2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=190692456&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 190692456 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00991767 1FUG jtl: Journal of Emergency Nursing issn: 00991767 maglogo: N pubinfo: dt: Jan2026 vid: 52 iid: 1 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 190692456 190692456 190692456 10.1016/j.jen.2025.07.015 190692456 ppf: 170 ppct: 16 formats: tig: atl: Application of Multistrategy Improvement Gray Wolf Algorithm to Optimize Extreme Gradient Boosting in Emergency Triage. aug: au: Huang, Tichen Jiang, Yuyan Gan, Rumeijiang Wang, Heping Wang, Fuyu Li, Yan sug: subj: Emergency Medical Services Triage Algorithms Artificial Intelligence Human Machine Learning Decision Support Systems, Clinical Models, Statistical Medical Informatics Clinical Competence ab: Effective triage in the emergency department (ED) is essential for optimizing resource allocation, improving efficiency, and enhancing patient outcomes. Conventional systems rely heavily on clinical judgment and standardized guidelines, which may be insufficient under growing patient volumes and increasingly complex presentations. We developed a machine learning triage model, MIGWO-XGBOOST, which incorporates a Multi-strategy Improved Gray Wolf Optimization (MIGWO) algorithm for parameter tuning. Missing data were processed, and the dataset was randomly split into 80 percent for training and 20 percent for testing. Model performance was evaluated against standard XGBOOST, GWO XGBOOST, AdaBoost, LSTM, and CNN-BiGRU. MIGWO-XGBOOST improved accuracy by 8.5 percent over unoptimized XGBOOST and reduced optimization time by 9,285 seconds relative to GWO-XGBOOST. Compared with other benchmarks, accuracy gains were 12.5 percent over AdaBoost, 3.3 percent over LSTM, and 1.9 percent over CNN-BiGRU. These results demonstrate both predictive strength and computational efficiency in complex data environments. MIGWO-XGBOOST provides a robust framework for rapid and precise triage decisions in the ED. By enhancing accuracy while substantially reducing computational time, this approach demonstrates the potential of advanced machine learning to support emergency decision-making and optimize patient care pathways. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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