A NEURAL-NETWORK APPROACH TO PREDICTING ADMISSION DECISIONS IN A PSYCHIATRIC EMERGENCY ROOM

Clinical decision making is based on recognizing complex patterns of p atients signs and symptoms. Neural networks have been shown to be very effective at this type of pattern recognition, and in this study a ne ural-network approach was used to predict which patients seen in a psy chiatric emergency room required admission and which did not. Data fro m all walk-in patients (N = 658) evaluated during normal working hours in a psychiatric emergency room during a one-year period were used ei ther 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 sing le node. The back-propagation method was used to train the network. Th e neural network's admitting decisions were in substantial agreement w ith those of the clinicians (kappa coefficient = 0.63). When used as a diagnostic test for admission it had a specificity of 94%, a sensitiv ity of 70%, and an overall accuracy of 91%. The information gain was 3 5% of that of a perfect diagnostic test. These results show that a neu ral network can be trained to make clinical decisions that are in subs tantial agreement with those of experienced clinicians.