To apply an artificial neural network (ANN) in patients with craniocerebral injury to characterize its prognostic ability.

Based on the completed quadratic polynomial stepwise regression analysis, data of 130 patients with brain injury from January 2013 to August 2017 in the Department of Critical Care Medicine of the Affiliated Hospital of Yangzhou University were collected. A prognostic prediction model was established based on artificial neural network technology. Then the clinical data of 46 patients with craniocerebral injury admitted from October 2017 to March 2019 were used for external verification, the correlation coefficient, sensitivity and specificity were calculated, and compared with the quadratic polynomial stepwise regression model.

Artificial neural network technology could be used to establish a prognosis prediction model for patients with craniocerebral injury. The correlation coefficient was 0.8935. In internal verification, the sensitivity of "poor outcome" (GOS score 1 or 2 points) was 94.8% (55/58), the specificity was 82.1% (55/67); the sensitivity of "good outcome" (GOS score 4 or 5 points) was 95.5% (42/44), and the specificity was 87.5% (42/48). The external verification correlation coefficient was 0.7138, the sensitivity of "poor outcome" was 43.8% (7/16), the specificity was 100.0% (7/7); the sensitivity of "good outcome" was 100.0% (26/26), the specificity was 66.7% (26/39).

The mathematical model established by artificial neural network technology has a better fitting than the quadratic polynomial stepwise regression model for predicting the prognosis of patients with brain injury. However, for the sensitivity and specificity of prognostic evaluation, the neural network model does not show obvious advantages.