A new survival status prediction system for severe trauma patients based on a multiple classifier system.
OBJECTIVE
Severe trauma patients are those who have several injuries implying a death risk. Prediction systems consider the severity of these injuries to predict whether the patients are likely to survive or not. These systems allow one to objectively compare the quality of the emergency services of trauma centres across different hospitals. However, even the most accurate existing prediction systems are based on the usage of a single model. The aim of this paper is to combine several models to make the prediction, since this methodology usually improves the performance of single models.


METHODS
The two currently used prediction systems by the Hospital of Navarre, which are based on logistic regression models, besides the C4.5 decision tree are combined to conform our proposed multiple classifier system. The quality of the method is tested using the major trauma registry of Navarre, which stores information of 462 trauma patients. A 10x10-fold cross-validation model is applied using as performance measures the specificity, sensitivity and the geometric mean between the two former ones. The results are supported by the usage of the Mann-Whitney's U statistical test.


RESULTS
The proposed method provides 0.8908, 0.6703 and 0.7661 for sensitivity, specificity and geometric mean, respectively. It slightly decreases the sensitivity of the currently used systems but it notably increases the specificity, which implies a large enhancement on the geometric mean. The same behaviour is found when it is compared versus four classical ensemble approaches and the random forest. The statistical analysis supports the quality of our proposal, since the obtained p-values are less than 0.01 in all the cases.


CONCLUSIONS
The obtained results show that the multiple classifier systems is the best choice among the considered methods to obtain a trade-off between sensitivity and specificity.
Departamento de Automatica y Computacion and Institute of Smart Cities, Universidad Publica de Navarra, Campus Arrosadia s/n, P.O. Box 31006, Pamplona, Spain. Electronic address: joseantonio.sanz@unavarra.es.
José
J
Sanz
Departamento de Automatica y Computacion and Institute of Smart Cities, Universidad Publica de Navarra, Campus Arrosadia s/n, P.O. Box 31006, Pamplona, Spain.
Daniel
D
Paternain
Departamento de Automatica y Computacion and Institute of Smart Cities, Universidad Publica de Navarra, Campus Arrosadia s/n, P.O. Box 31006, Pamplona, Spain.
Mikel
M
Galar
Departamento de Automatica y Computacion and Institute of Smart Cities, Universidad Publica de Navarra, Campus Arrosadia s/n, P.O. Box 31006, Pamplona, Spain.
Javier
J
Fernandez
Prehospital Emergency, Navarre Health Services, Pamplona, Spain.
Diego
D
Reyero
Department of Health, Universidad Publica de Navarra, Barañaín Avenue s/n, P.O. Box 31008, Pamplona, Spain; Accident and Emergency Department, Hospital of Navarre, Navarre, Spain.
Tomás
T
Belzunegui
D000328:Adult; D000368:Aged; D000465:Algorithms; D003663:Decision Trees; D004635:Emergency Medicine; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D012042:Registries; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D012984:Software; D013030:Spain; D016896:Treatment Outcome; D014947:Wounds and Injuries / Q000145:classification* / Q000401:mortality*
Classification; Multiple classifier system; Survival status prediction; Trauma patients