The use of machine learning in predicting clinical outcomes in emergency pre-examination triage: A systematic review of the literature.
• Machine learning has a total of 5 models in clinical triage. • Neural networks are suitable for large datasets but not for smaller, single-center experimental clinical trial data. • Gradient Boosting Decision Trees are suitable for using both categorical and continuous variables. • The random fore...
| Publicado en: | International Emergency Nursing Vol. 83 |
|---|---|
| Autores principales: | , , |
| Formato: | research systematic review Journal Article |
| Publicado: |
Elsevier B.V.
Dec2025
|
| 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=189517829&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 189517829 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1755599X 6395 jtl: International Emergency Nursing issn: 1755599X maglogo: N pubinfo: dt: Dec2025 vid: 83 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 189517829 189517829 189517829 10.1016/j.ienj.2025.101705 189517829 ppct: 1 formats: tig: atl: The use of machine learning in predicting clinical outcomes in emergency pre-examination triage: A systematic review of the literature. aug: au: Jiang, Yao Zhao, Jing Juan, Hu affil: Department of Emergency Nursing, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, PR China, 610000 sug: subj: Machine Learning Utilization Outcomes (Health Care) Triage Physical Examination Medical Staff Emergency Service Human Systematic Review PubMed Embase Cochrane Library Convolutional Neural Networks Boosting Machine Learning Algorithms Decision Trees Random Forest Support Vector Machine Hospitalization Intensive Care Units Descriptive Statistics ab: • Machine learning has a total of 5 models in clinical triage. • Neural networks are suitable for large datasets but not for smaller, single-center experimental clinical trial data. • Gradient Boosting Decision Trees are suitable for using both categorical and continuous variables. • The random forest algorithm can improve the performance of a model and generate more effective models. • Support Vector Machines are developed based on small sample statistical theory. To investigate the application status of machine learning model in the prediction of clinical outcomes in emergency pre-examination and triage, and to analyze its characteristics, advantages and disadvantages, so as to add an objective tool for medical staff to predict the clinical outcome of patients in the process of pre-examination and triage. The literature review method was used to search PubMed, Web of Science, Embase, Cochrane Library, China Biomedical Literature Database, CNKI, Wanfang, VIP and other databases, and the literature that met the inclusion criteria was screened and the specific information of the machine learning model in the literature was extracted. A total of 12 articles that met the criteria were included, including 5 machine learning models, which were mainly used in clinical outcomes such as hospital admission, death, intensive care unit admission, hospital transfer, and home. The overall sensitivity of the machine learning model is high, but there are few literature studies on the prediction of clinical outcomes for pre-test triage, so relevant large-sample studies should be carried out in clinical practice to achieve the combination of subjective and objective evaluation tools to improve the accuracy of prediction and ensure patient safety. pubtype: Academic Journal doctype: research systematic review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|