title: Categorization of major depression in an outpatient sample.
abstract: Intake Beck Depression Inventory (BDI) item scores of 400 outpatient major depressives were submitted to a categorization algorithm developed for artificial intelligence applications. The algorithm maximizes a function of "category utility" that is preferable in several respects to available clustering methods, and has demonstrated its capacity to locate the most informative, or "basic," level of categorization. The analysis yielded four syndromal subtypes: a common, general depressive type; a common and relatively severe melancholic type; an infrequent 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 the classification of depression are discussed.
authors.affiliation: Department of Psychology, New School for Social Research, New York, New York 10003.
authors.forename: N
authors.initials: N
authors.lastname: Haslam
authors.forename: A T
authors.initials: AT
authors.lastname: Beck
mesh_terms: D000342:Affective Symptoms / Q000145:classification / Q000175:diagnosis / Q000453:epidemiology; D000465:Algorithms; D000553:Ambulatory Care; D001008:Anxiety Disorders / Q000145:classification / Q000175:diagnosis / Q000453:epidemiology; D001185:Artificial Intelligence; D015982:Bias; D016000:Cluster Analysis; D015897:Comorbidity; D003866:Depressive Disorder / Q000145:classification* / Q000175:diagnosis / Q000453:epidemiology; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D010555:Personality Inventory / Q000706:statistics & numerical data*