Latent Structure Analysis in Pharmaceutical Formulations Using Kohonen's Self-Organizing Map and a Bayesian Network

2011 ◽  
Vol 100 (3) ◽  
pp. 964-975 ◽  
Author(s):  
Shingo Kikuchi ◽  
Yoshinori Onuki ◽  
Akihito Yasuda ◽  
Yoshihiro Hayashi ◽  
Kozo Takayama
Keyword(s):  
Bayesian Network ◽  
Structure Analysis ◽  
Pharmaceutical Formulations ◽  
Latent Structure ◽  
Self Organizing Map ◽  
Latent Structure Analysis ◽  
Self Organizing

Clinical target syndromes in psychiatry: latent structure analysis versus factor analysis

1991 ◽  
Vol 6 (6) ◽  
pp. 301-306 ◽  
Author(s):  
P Bech
Keyword(s):  
Factor Analysis ◽  
Structure Analysis ◽  
Statistical Models ◽  
Rating Scales ◽  
Rating Scale ◽  
Latent Structure ◽  
Depression And Anxiety ◽  
Sufficient Statistics ◽  
Latent Structure Analysis ◽  
Symptom Rating

SummaryThe algorithms for the demonstration of shared phenomenology of psychiatric syndromes in DSM-III are resistant to quantification. In contrast, the rating scale approach quantifies clinical target syndromes in psychiatry. The two most useful statistical models for quantifying shared phenomenology by symptom rating scales have been reviewed; namely factor analysis and latent structure analysis. Results have shown that factor analysis has demonstrated dimensions of dementia, delirium, schizophrenia, mania, outward aggression, depression and anxiety. Latent structure analysis has confirmed that the items of brief rating scales (such as the Melancholia Scale) are additively related implying that their total scores are sufficient statistics for the measurement of these factors or dimensions. Latent structure analysis should be considered as a psychometric “glasnost” compared to algorithm-resistant logic of quantification in DSM-III.

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