A neural-network approach to predicting admission decisions in a psychiatric emergency room.

Clinical decision making is based on recognizing complex patterns of patients' signs and symptoms. Neural networks have been shown to be very effective at this type of pattern recognition, and in this study a neural-network approach was used to predict which patients seen in a psychiatric emergency room required admission and which did not. Data from all walk-in patients (N = 658) evaluated during normal working hours in a psychiatric emergency room during a one-year period were used either to train a neural network or to test its performance. The network had 53 input nodes, one hidden layer, and an output layer with a single node. The back-propagation method was used to train the network. The neural network's admitting decisions were in substantial agreement with those of the clinicians (kappa coefficient = 0.63). When used as a diagnostic test for admission it had a specificity of 94%, a sensitivity of 70%, and an overall accuracy of 91%. The information gain was 35% of that of a perfect diagnostic test. These results show that a neural network can be trained to make clinical decisions that are in substantial agreement with those of experienced clinicians.