Abstract
Purpose:The triage and initial care of injured patients and a subsequent right level of care is paramount for an overall outcome after traumatic injury.Early recognition of patients is an important case of such decision-making with risk of worse prognosis.This article is to answer if clinical and paraclinical signs can predict the critical conditions of injured patients after traumatic injury resuscitation.Methods:The study included 1107 trauma patients,16 years and older.The patients were trauma victims of Levels I and Ⅱ triage and admitted to the Rajaee(Emtiaz)Trauma Hospital,Shiraz,in 2014-2015.The cross-industry process for data mining methodology and modeling was used for assessing the best early clinical and paraclinical variables to predict the patients'prognosis.Five modeling methods including the support vector machine,K-nearest neighbor algorithms,Bagging and Adaboost,and the neural network were compared by some evaluation criteria.Results:Learning algorithms can predict the deterioration of injured patients by monitoring the Bagging and SVM models with 99％accuracy.The most-fitted variables were Glasgow Coma Scale score,base deficit,and diastolic blood pressure especially after initial resuscitation in the algorithms for overall outcome predictions.Conclusion:Data mining could help in triage,initial treatment,and further decision-making for outcome measures in trauma patients.Clinical and paraclinical variables after resuscitation could predict short-term outcomes much better than variables on arrival.With artificial intelligence modeling system,diastolic blood pressure after resuscitation has a greater association with predicting early mortality rather than systolic blood pressure after resuscitation.Artificial intelligence monitoring may have a role in trauma care and should be further investigated.
NETL
NSTL
维普
万方数据