Linked Life Data

1538201

Source:http://linkedlifedata.com/resource/pubmed/id/1538201

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
1992-3-31
pubmed:abstractText
While there have been many applications of cluster analysis in psychiatric classification research, there are no studies in which cluster analysis is used to discover the taxonomic structure implicit in the DSM-III itself. In order to do so, the symptom index in the DSM-III-R manual was summarized in a two-way matrix of disorders by symptoms and then analyzed using a hierarchical classes model and companion algorithm (HICLAS) that permits overlap among classes. A novel feature of this model is that superordinate-subordinate relationships among diagnostic and symptom classes are explicitly represented. The HICLAS analysis revealed that there are several discrete symptom classes in DSM-III-R and that many psychiatric disorders can be modeled as combinations of one or more of these classes. The disorders associated with these symptom classes tend to fit the hierarchical classes model relatively well, particularly the mood disorders and the psychotic disorders. However, disorders such as adjustment, personality, and sexual disorder fit the model poorly or not at all. The results are in line with the conjecture that the taxonomic model implicit in DSM-III-R is a hybrid of discrete symptom classes and some other structure, perhaps a dimensional one.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
AIM
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
0022-3018
pubmed:author
pubmed:issnType
Print
pubmed:volume
180
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
11-9
pubmed:dateRevised
2007-11-15
pubmed:meshHeading
pubmed:year
1992
pubmed:articleTitle
DSM-III-R as a taxonomy. A cluster analysis of diagnoses and symptoms.
pubmed:affiliation
University of Medicine and Dentistry of New Jersey-Community Mental Health Center, Piscataway 08855-1392.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Research Support, U.S. Gov't, Non-P.H.S.