Do clinical and paraclinical findings have the power to predict critical conditions of injured patients after traumatic injury resuscitation? Using data mining artificial intelligence.
10.1016/j.cjtee.2020.11.009
- VernacularTitle:Do clinical and paraclinical findings have the power to predict critical conditions of injured patients after traumatic injury resuscitation? Using data mining artificial intelligence
- Author:
Shahram PAYDAR
1
;
Elahe PARVA
2
;
Zahra GHAHRAMANI
1
;
Saeedeh POURAHMAD
3
;
Leila SHAYAN
1
;
Vahid MOHAMMADKARIMI
4
;
Golnar SABETIAN
1
Author Information
1. Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Shiraz University of Medical Sciences, Shiraz, Iran.
2. Technical and Vocational University, Shiraz, Iran. Electronic address: paydarsh@sums.ac.ir.
3. Department of Biostatistics, Shiraz University of Medical Sciences, Shiraz, Iran.
4. Department of Internal Medicine, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
- Publication Type:Journal Article
- Keywords:
Artificial Intelligence;
Data mining;
Traumatic injuries
- From:
Chinese Journal of Traumatology
2021;24(1):48-52
- CountryChina
- Language:English
-
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.
