Bayesian scoring systems for military pelvic and perineal blast injuries: Is it time to take a new approach?

Research output: Contribution to journalArticlepeer-review


  • Somayyeh Mossadegh
  • Shan He
  • Paul Parker

Colleges, School and Institutes

External organisations

  • Queen Mary University
  • School of Computer Science Centre for Systems Biology
  • University Hospital Birmingham


Background: 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. Methods: 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. Results: 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). Conclusion: 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.

Bibliographic note

Publisher Copyright: © Association of Military Surgeons of the U.S. All rights reserved. Copyright: Copyright 2016 Elsevier B.V., All rights reserved.


Original languageEnglish
Pages (from-to)127-131
Number of pages5
JournalMilitary medicine
Issue number5
Publication statusPublished - May 2016