Effectual Emergency Severity Adaptation for Improved Triage Care Operations Studies have shown that triage nurses often have difficulty correctly predicting triage level when using the Emergency Severity Index (ESI), even after having been previously trained on the algorithm. This paper examines nursesu0027 difficulties with applying the ESI and previous attempts at electronic triage support systems. United States emergency department data obtained from the National Center for Health Statistics website is analyzed using the random forest algorithm and C5.0 decision tree algorithm to determine if refinements can be made to the current ESI algorithm so that it can be more easily implemented by nurses. A model electronic triage application is produced based on the current ESI algorithm and the results of the decision tree analysis.

DeepTriager: A Neural Attention Model for Emergency Triage with Electronic Health Records As the first pass for emergency patients, triage is the most important factor affecting emergency department (ED) overcrowding. So it is crucial to develop a datadriven and evidencebased triage method to quickly identify acute and severe patients, and prevent the limited emergency resources from overdiagnosis. To address these challenges, we propose an attention based deep learning framework, named DeepTriager. Trained and tested on 70,918 clinical records, DeepTriager achieved highly accurate performance on assessment of acuity level I (endangered patients), with AUC of 0.98, which was 0.16 higher than the clinical scale method MEWS and NEWS, and 0.04 higher than traditional machine learning methods. In summary, we presented a new approach for clinical evidence based discovery using a cohort of Electronic Health Records (EHRs). This approach not only outperforms the traditional word segmentation methods but also provides evidence for interpreting the results.