Neural network subtyping of depression.
OBJECTIVE
To examine the applicability of a neural network classification strategy to examine the independent contribution of psychomotor disturbance (PMD) and endogeneity symptoms to the DSM-III-R definition of melancholia.


METHODS
We studied 407 depressed patients with the clinical dataset comprising 17 endogeneity symptoms and the 18-item CORE measure of behaviourally rated PMD. A multilayer perception neural network was used to fit non-linear models of varying complexity. A linear discriminant function analysis was also used to generate a model for comparison with the non-linear models.


RESULTS
Models (linear and non-linear) using PMD items only and endogeneity symptoms only had similar rates of successful classification, while non-linear models combining both PMD and symptoms scores achieved the best classifications.


CONCLUSIONS
Our current non-linear model was superior to a linear analysis, a finding which may have wider application to psychiatric classification. Our non-linear analysis of depressive subtypes supports the binary view that melancholic and non-melancholic depression are separate clinical disorders rather than different forms of the same entity. This study illustrates how non-linear modelling with neural networks is a potentially fruitful approach to the study of the diagnostic taxonomy of psychiatric disorders and to clinical decision-making.
Florio
T M
TM
Psychiatry Unit, Prince of Wales Hospital, Randwick, New South Wales, Australia.
Parker
G
G
Austin
M P
MP
Hickie
I
I
Mitchell
P
P
Wilhelm
K
K
D001526:Behavioral Symptoms / Q000145:classification; D002493:Central Nervous System Diseases / Q000145:classification; D003866:Depressive Disorder / Q000145:classification*; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009069:Movement Disorders / Q000145:classification*; D016571:Neural Networks, Computer; D011569:Psychiatric Status Rating Scales; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index