A neural network model for combining clinical predictors of antidepressant response in mood disorders

Artificial neural networks (ANN) represent a promising tool for combining multiple predictors in complex diseases. Antidepressant response in mood disorders is a typical complex phenomenon were a number of predictors influence outcome under non-linear interactions. In the present study we tested a neural network strategy for antidepressant outcome in subjects affected by major depression. One hundred and forty-five never reported depressed inpatients were included in this study (major depressives/bipolars: 111/34). A multi layer perceptron network composed of 1 hidden layer with 13 nodes was chosen. The network was performed on the sample of 145 cases divided as follows: train 73+verify 36+test 36. Correlation of predicted versus observed response was 0.46 in the test (independent) sample that corresponds to 21% of variance explained. Number of episodes, side effects, delusional features, baseline HAM-D, length of current episode, lithium augmentation, current medical condition and personality disorders were the main factors identified by the model. Sex, age at onset, polarity, plasma level and baseline VAS score were part of the model but with a lower rank. Overall, our findings suggest that neural networks could be a valid technique for the analysis of psychopharmacology studies. The complex interactions modelled through ANN may be eventually applied at the clinical level for the individualized therapy.