José Sanz1, Daniel Paternain2, Mikel Galar2, Javier Fernandez2, Diego Reyero3, Tomás Belzunegui4. 1. 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. 2. Departamento de Automatica y Computacion and Institute of Smart Cities, Universidad Publica de Navarra, Campus Arrosadia s/n, P.O. Box 31006, Pamplona, Spain. 3. Prehospital Emergency, Navarre Health Services, Pamplona, Spain. 4. 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.
Abstract
BACKGROUND AND 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. MATERIALS AND 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.
BACKGROUND AND OBJECTIVE: Severe traumapatients 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. MATERIALS AND 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 traumapatients. 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.
Authors: Muhammad Faisal; Binish Khatoon; Andy Scally; Donald Richardson; Sally Irwin; Rachel Davidson; David Heseltine; Alison Corlett; Javed Ali; Rebecca Hampson; Sandeep Kesavan; Gerry McGonigal; Karen Goodman; Michael Harkness; Mohammed Mohammed Journal: BMJ Open Date: 2019-06-19 Impact factor: 2.692