Application of neural networks and sensitivity analysis to improved prediction of trauma survival.
The performance of trauma departments is widely audited by applying predictive models that assess probability of survival, and examining the rate of unexpected survivals and deaths. Although the TRISS methodology, a logistic regression modelling technique, is still the de facto standard, it is known that neural network models perform better. A key issue when applying neural network models is the selection of input variables. This paper proposes a novel form of sensitivity analysis, which is simpler to apply than existing techniques, and can be used for both numeric and nominal input variables. The technique is applied to the audit survival problem, and used to analyse the TRISS variables. The conclusions discuss the implications for the design of further improved scoring schemes and predictive models.
Hunter
A
A
Department of Computer Science, University of Sunderland, St Peter's Campus, Tyne and Wear, Sunderland, UK.
Kennedy
L
L
Henry
J
J
Ferguson
I
I
D000704:Analysis of Variance; D003198:Computer Simulation; D016208:Databases, Factual; D006801:Humans; D016015:Logistic Models; D008485:Medical Audit / Q000639:trends; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011336:Probability; D012680:Sensitivity and Specificity; D017741:Survivors / Q000706:statistics & numerical data*; D014193:Trauma Centers; D014947:Wounds and Injuries / Q000401:mortality*