A neural network approach to the diagnosis of morbidity outcomes in trauma care.

This paper introduces the application of artificial neural networks to trauma complications assessment. The potential financial benefits of improving on trauma center diagnostic specificity in complications assessment are illustrated and the operational feasibility of the use of diagnostic neural models across institutions is discussed. A prototype neural network model is described, which, after training, succeeds in diagnosing the complication of sepsis in victims of traumatic blunt injury. Its diagnostic performance with 100% sensitivity and 96.5% specificity is accomplished with test data from a regional trauma center. The model is further shown to have correctly detected, during training, incorrectly coded data. The potential this suggests, for parsimonious database scrubbing through the use of neural network models, is discussed.