title: Bayesian Scoring Systems for Military Pelvic and Perineal Blast Injuries: Is it Time to Take a New Approach?
abstract: Various injury severity scores exist for trauma; it is known that they do not correlate accurately to military injuries. A promising anatomical scoring system for blast pelvic and perineal injury led to the development of an improved scoring system using machine-learning techniques.
  
  
  
  An unbiased genetic algorithm selected optimal anatomical and physiological parameters from 118 military cases. A Naïve Bayesian model was built using the proposed parameters to predict the probability of survival. Ten-fold cross validation was employed to evaluate its performance.
  
  
  
  Our model significantly out-performed Injury Severity Score (ISS), Trauma ISS, New ISS, and the Revised Trauma Score in virtually all areas; positive predictive value 0.8941, specificity 0.9027, accuracy 0.9056, and area under curve 0.9059. A two-sample t test showed that the predictive performance of the proposed scoring system was significantly better than the other systems (p < 0.001).
  
  
  
  With limited resources and the simplest of Bayesian methodologies, we have demonstrated that the Naïve Bayesian model performed significantly better in virtually all areas assessed by current scoring systems used for trauma. This is encouraging and highlights that more can be done to improve trauma systems not only for our military injured, but also for civilian trauma victims.
authors.affiliation: Queen Mary University of London, Blizard Institute, Barts and The London School of Medicine and Dentistry, The Blizard Building, 4 Newark Street, London E1 2AT, United Kingdom.
authors.forename: Somayyeh
authors.initials: S
authors.lastname: Mossadegh
authors.affiliation: School of Computer Science Centre for Systems Biology, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom.
authors.forename: Shan
authors.initials: S
authors.lastname: He
authors.affiliation: Royal Centre for Defence Medicine, New Queen Elizabeth Hospital Birmingham, Mindelsohn Way, Edgbaston, Birmingham B15 2GW, United Kingdom.
authors.forename: Paul
authors.initials: P
authors.lastname: Parker
mesh_terms: D000328:Adult; D001499:Bayes Theorem; D001753:Blast Injuries / Q000453:epidemiology; D015331:Cohort Studies; D005260:Female; D006801:Humans; D015601:Injury Severity Score; D008297:Male; D008889:Military Personnel / Q000706:statistics & numerical data; D010388:Pelvis / Q000293:injuries / Q000503:physiopathology; D010502:Perineum / Q000293:injuries / Q000503:physiopathology; D011787:Quality of Health Care; D012042:Registries / Q000706:statistics & numerical data; D012189:Retrospective Studies; D014947:Wounds and Injuries / Q000145:classification*