In 17,661 patients presenting with suspected cardiovascular disease, the distribution of triage of level 1, level 2, level 3 and level 4 were 1.3 %, 18.6 %, 76.5 %, and 3.6 % respectively. The area under the receiver operating characteristic curve (AUC), accuracy and macro- F1 were calculated for each model. For each model, 80 % of the data set was used for training and 20 % was used to test the models. Four machine learning models вЂ“ multinomial logistic regression (multinomial LR), eXtreme gradient boosting (XGBoost), random forest (RF) and gradient-boosted decision tree (GBDT) вЂ“ were compared. Demographic information, vital signs, blood glucose, and other available triage scores were collected. This cross-sectional study in the second Affiliated Hospital of Guangzhou Medical University was conducted from August 2015 to December 2018 inclusive. Using data available from patients with suspected cardiovascular disease presenting at ED triage, this study aimed to train and compare the performance of four common machine learning models to assist in decision making of triage levels. The CETS is a reliable system for ED triage and can promote rapid and effective triage in mainland China.ДЄccurate differentiation and prioritization in emergency department (ED) triage is important to identify high-risk patients and to efficiently allocate of finite resources. The chi-square test showed that there was a significant difference in triage time between the CETS levels (p < 0.001). The average triage time of ED nurses in all sites was 151.5 В± 26.3 s, using the computer-based triage instrument. Trauma cases were assigned to triage with greater accuracy than were nontrauma cases, both for the scenarios and for active patients. Inter-rater reliability values for triage nurses and research nurses were 0.96 (95% CI = 0.95-0.97), based on the quadratic weight Оє. There was no difference in accuracy between sites or according to nurse experience. Mean pooled accuracy from all sites was 89.4% (95% CI = 86.9%-91.8%), and the proportion of over-triage slightly exceeded under-triage (6.5% vs. The ability of the CETS to predict ED mortality was assessed via the receiver-operating characteristic (ROC) area under the curve (AUC). The relationship between the CETS acuity levels triaged by the research nurse and four possible outcomes was assessed with a cross-classification table, using a chi-square test. The criterion-related validity of the CETS was evaluated criteria included ED mortality, number of patients discharged, and admission to ICU or general ward. The triage time and clinical outcome of 1000 active patients per site also were collected. Inter-rater reliability was measured by weighted Оє to compare the triage nurse's rating with the research nurse's acuity level for each patient. Accuracy was defined by concordance with the key for the scenarios and was calculated as percentages. Standardized triage scenarios and active patients were assigned to nurses who used the CETS for evaluative purposes. The aim of this study was to assess the reliability and validity of the CETS by emergency department (ED) nurses in eight EDs in mainland China.ДЄ cross-sectional multi-center study was conducted.Д®ight EDs in the eastern, western, northern, and central areas of mainland China.ДЄ total of 51 ED nurses and 8000 active ED patients participated in the study between May and September 2018 in eight EDs. The Chinese Emergency Triage Scale (CETS) was developed based on vital complaints and vital parameters, according to Chinese data. The QGIS Python API and the QGIS C++ API are the ultimate references for plugins creators.An effective emergency triage system is lacking in mainland China.The pyQGIS cookbook contains a section on developing plugins and is an ongoing effort to collect tips and tricks about QGIS python programming generaly.If no information is available, please report it to the Developer mailing-list.

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