Jacob Szeszulski, Ghadir Helal Salsa, Paula Cuccaro, Christine M Markham, Mary Martin, Lara S Savas, Melissa Valerio-Shewmaker, Timothy J Walker, Andrew E Springer
Using Community-Academic Partnerships and a Creative Expression Contest to Engage Youth in the Development of Communication Materials for Promoting Behaviors That Prevent COVID-19
Health promotion practice
establish emergency triage model statistical analysis big data particular time period hospital information system improve accuracy triage emergency department ed total 276164 patients visited emergency medicine department beijing chaoyang hospital 2017 2020 included study including 123392 men 152772 women aged 14 112 years baseline characteristics age gender medical records patients condition body temperature heart rate breathing blood pressure consciousness oxygen saturation patients collected data samples randomly allocated 80 training set 20 testing set patients divided levels ii iii iv accordance fourlevel triage standard selected effective extreme gradient boosting xgboost algorithm emergency classification prediction model xgboost model applied simulate thinking process triage nurses de longs test used compare receiver operating characteristic roc curve different models p value obtained calculating variance covariance area curve auc values different roc curves level 4960 18 patients level ii 25646 929 level iii 130664 4731 level iv 114894 416 xgboost model built following logic exercise based traditional manual preinspection triage results verification prediction accuracy 8257 auc disease severity level levels ii iii iv 09629 09554 09120 09296 respectively emergency triage prediction model achieved relatively strong accuracy rate reduce work intensity medical workers improve working efficiency