Bayesian Scoring Systems for Military Pelvic and Perineal Blast Injuries: Is it Time to Take a New Approach?
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.Metho...
| Publicado en: | Military Medicine Vol. 181; pp. 127 - 132 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
May2016 Supplement
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=115356224&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115356224 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00264075 4DV jtl: Military Medicine issn: 00264075 maglogo: N pubinfo: dt: May2016 Supplement vid: 181 pid: 622 pub: Oxford University Press / USA artinfo: ui: 115356224 115356224 NLM27168562 115356224 10.7205/MILMED-D-15-00171 NLM27168562 115356224 ppf: 127 ppct: 5 formats: fmt: @attributes: type: P tig: atl: Bayesian Scoring Systems for Military Pelvic and Perineal Blast Injuries: Is it Time to Take a New Approach? aug: au: Mossadegh, Somayyeh Shan He Parker, Paul He, Shan affil: Queen Mary University of London, Blizard Institute, Barts and The London School of Medicine and Dentistry, The Blizard Building, 4 Newark Street, London El 2AT, United Kingdom. sug: subj: Probability Wounds and Injuries Classification Trauma Severity Indices Quality of Health Care Pelvis Injuries Adult Prospective Studies Perineum Physiopathology Blast Injuries Epidemiology Military Personnel Statistics and Numerical Data Pelvis Physiopathology Male Retrospective Design Data Collection Female Perineum Injuries Human Clinical Assessment Tools Adult: 19-44 years Male Female ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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