@article{ref1,
title="Do clinical and paraclinical findings have the power to predict the critical conditions of injured patients after traumatic injury resuscitation? Using data mining artificial intelligence",
journal="Chinese journal of traumatology",
year="2020",
author="Paydar, Shahram and Parva, Elahe and Ghahramani, Zahra and Pourahmad, Saeedeh and Shayan, Leila and Mohammadkarimi, Vahid and Sabetian, Golnar",
volume="ePub",
number="ePub",
pages="ePub-ePub",
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. <br><br>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. <br><br>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. <br><br>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.<p /> <p>Language: en</p>",
language="en",
issn="1008-1275",
doi="10.1016/j.cjtee.2020.11.009",
url="http://dx.doi.org/10.1016/j.cjtee.2020.11.009"
}