title: Survival prediction of trauma patients: a study on US National Trauma Data Bank.
abstract: BACKGROUND
  Exceptional circumstances like major incidents or natural disasters may cause a huge number of victims that might not be immediately and simultaneously saved. In these cases it is important to define priorities avoiding to waste time and resources for not savable victims. Trauma and Injury Severity Score (TRISS) methodology is the well-known and standard system usually used by practitioners to predict the survival probability of trauma patients. However, practitioners have noted that the accuracy of TRISS predictions is unacceptable especially for severely injured patients. Thus, alternative methods should be proposed.
  
  
  METHODS
  In this work we evaluate different approaches for predicting whether a patient will survive or not according to simple and easily measurable observations. We conducted a rigorous, comparative study based on the most important prediction techniques using real clinical data of the US National Trauma Data Bank.
  
  
  RESULTS
  Empirical results show that well-known Machine Learning classifiers can outperform the TRISS methodology. Based on our findings, we can say that the best approach we evaluated is Random Forest: it has the best accuracy, the best area under the curve, and k-statistic, as well as the second-best sensitivity and specificity. It has also a good calibration curve. Furthermore, its performance monotonically increases as the dataset size grows, meaning that it can be very effective to exploit incoming knowledge. Considering the whole dataset, it is always better than TRISS. Finally, we implemented a new tool to compute the survival of victims. This will help medical practitioners to obtain a better accuracy than the TRISS tools.
  
  
  CONCLUSIONS
  Random Forests may be a good candidate solution for improving the predictions on survival upon the standard TRISS methodology.
authors.affiliation: Faculty of Sciences of Tetouan, University Abdelmalek Essaadi, Tétouan, Morocco. sefrioui.imane@gmail.com.
authors.forename: I
authors.identifier: http://orcid.org/0000-0001-7506-0495
authors.initials: I
authors.lastname: Sefrioui
authors.affiliation: Department of Computing and Information Systems, The University of Melbourne, Melbourne, Australia.
authors.forename: R
authors.initials: R
authors.lastname: Amadini
authors.affiliation: Department of Informatics, University of Oslo, Oslo, Norway.
authors.forename: J
authors.initials: J
authors.lastname: Mauro
authors.affiliation: National School of Applied Sciences of Tetouan, University Abdelmalek Essaadi, Tétouan, Morocco.
authors.forename: A
authors.initials: A
authors.lastname: El Fallahi
authors.affiliation: Department of Computer Science, University of Bologna/Lab. Focus INRIA, Bologna, Italy.
authors.forename: M
authors.initials: M
authors.lastname: Gabbrielli
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D002675:Child, Preschool; D016208:Databases, Factual; D005260:Female; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D015599:Trauma Severity Indices; D014481:United States; D014947:Wounds and Injuries / Q000401:mortality*; D055815:Young Adult
keywords: Classification; Machine learning; National trauma data bank; Survival prediction; TRISS methodology; Trauma patients