CATEGORIZATION OF MAJOR DEPRESSION IN AN OUTPATIENT SAMPLE

Intake Beck Depression Inventory (BDI) item scores of 400 outpatient m ajor depressives were submitted to a categorization algorithm develope d for artificial intelligence applications. The algorithm maximizes a function of ''category utility'' that is preferable in several respect s to available clustering methods, and has demonstrated its capacity t o locate the most informative, or ''basic'', level of categorization. The analysis yielded four syndromal subtypes: a common, general depres sive type; a common and relatively severe melancholic type; an infrequ ent type characterized by self-critical features, generalized anxiety, and an absence of melancholic features; and an infrequent, mild type distinguished by enervation and anhedonic features. Implications for t he classification of depression are discussed.