Patient by psychiatric symptom data

Downloads: case_study_psychiatrist.arff, case_study_psychiatrist.csv, case_study_psychiatrist.RData

If you use this data set in publications please cite
Van Mechelen, I., & De Boeck, P. (1989). Implicit taxonomy in pstchiatric diagnosis: A case study. Journal of Social and Clinical Psychology, 8, 276-287.

Summary of the data

Contributor:

Iven Van Mechelen

License:

CC0 Creative Commons "No Rights Reserved"

General information about the data

Abstract:

Presence/absence ratings of 24 psychiatric symptoms in 30 psychiatric inpatients made by an individual psychiatrist.

Subject matter background:

The data have been collected in a case study of an individual psychiatrist to identify his implicit taxonomy.

Data structure:

object x variables data matrix

Data objects and variables:

1. objects (30): psychiatric inpatients

2. variables (28):

2.1 internal variables (24): psychiatric symptoms based on headings of Psychiatric Status Schedule (Spitzer, Endicott, Fleiss, & Cohen, 1970): 1. inappropriate affect; appearance or behavior; 2. interview belligerence - negativism; 3. agitation - excitement; 4. retardation; 5. lack of emotions; 6. speech disorganization; 7. grandiosity; 8. suspicion - ideas of persecution; 9. hallucinations - delusions; 10. overt anger; 11. depression; 12. anxiety; 13. obsession - compulsion; 14. suicide; 15. self injury; 16. somatic concerns; 17. social isolation; 18. daily routine impairment; 19. leisure time impairment; 20. antisocial impulses or acts; 21. alcohol abuse; 22. drug abuse; 23. disorientation; 24. memory impairment

2.2 external variables (4):

2.2.1 global assessment (1): 25. rating on Global Assessment Scale (Endicott, Spitzer, Fleiss, & Cohen, 1976), a 101-point scale for overall severity of psychiatric disturbance

2.2.2 DSM-III Axis 1 diagnosis, coded into three dummy variables (3): 26. Affective (Affective Disorder or Anxiety Disorder); 27. Psychotic (Schizophrenic Disorder or Paranoid Disorder); 28. Substance abuse (Substance Use Disorder or Substance-Induced Disorder)

Data values:

psychiatric symptoms (1-24) and psychiatric diagnoses (26-28): 0 (absence) - 1 (presence)

Global Assessment Scale: 0-100 (with lower values indicating higher severity)

no missing values

Preprocessing:

Optionally, one could consider to replace symptoms 21 (alcohol abuse) and 22 (drug abuse) by a new dummy variable (alcohol or drug abuse), i.e., the Boolean sum of 21 and 22, as substance use disorder is a far from monothetic category from the perspective of the different substances that can be used.

(In most previous analyses of these data Symptom 15 (self injury), which was absent in all patients, was removed from the data.)

Other relevant papers:

background and result of two-mode overlapping cluster analysis (HICLAS): Van Mechelen, I., & De Boeck, P. (1989). Implicit taxonomy in pstchiatric diagnosis: A case study. Journal of Social and Clinical Psychology, 8, 276-287.

result of application of Bayesian extension of two-mode overlapping cluster analysis (HICLAS) used in the previous paper: Leenen, I., Van Mechelen, I., Gelman, A., & De Knop, S. (2008). Bayesian hierarchical classes analysis. Psychometrika, 73, 39-64.

result of latent class analysis: De Soete, G. (1993). Using latent class analysis in categorization research. In I. Van Mechelen et al. (Eds.), Categories and concepts: Theoretical views and inductive data analysis (pp. 309-330). London: Academic Press.

result of Bayesian latent class analysis: Berkhof, J., Van Mechelen, I., & Gelman, A. (2003). A Bayesian approach to the selection and testing of mixture models. Statistica Sinica, 13, 423-442.

result of Galois lattice analysis: Guénoche, A., & Van Mechelen, I. (1993). Galois approach to the induction of concepts. In I. Van Mechelen et al. (Eds.), Categories and concepts: Theoretical views and inductive data analysis (pp. 287-308). London: Academic Press.

Justification for clustering:

A clustering of the patients may reveal the implicit taxonomic system of the psychiatrist under study. (Optionally, a joint clustering (biclustering) of patients and symptoms may reveal this system along with its underlying symptom patterns/diagnostic principles.)

External criteria for clustering quality

External variable that represents the underlying true clustering

One may expect the implicit taxonomy of the psychiatrist to be related to overall severity of psychiatric disturbance (25) (with, e.g., patient clusters characterized by psychotic symptoms taking lower values on the Global Assessment Scale). Also, one may expect the implicit taxonomy to correspond partly to the explicit DSM-III diagnoses (26-28).

Substantive justification

The psychiatrist can be expected to respect more or less the "offical" nosological DSM-III rules; yet, significant idiosyncratic discrepancies between the implicit and explicit clustering (reflecting, e.g., treatment-related concerns of the psychiatrist under study) should come at no surprise either.

Internal criteria for clustering quality: cluster membership

Number of clusters

At least two, reflecting the basic split between schizophrenia-like and affective disorders.

All objects clustered or not

Optionally, more healthy patients could be left out of the clustering.

Cluster overlap

Overlap between patient clusters may reflect implicit syndrome co-morbidity. (Overlap between optional symptom clusters may reflect symptom overlap between implicit syndromes.) In both cases overlap is optional and neither nestedness nor a full hierarchy is required.

Internal criteria for clustering quality: within and between cluster features

Ground for objects to belong to the same cluster:

Common pattern of symptoms or at least symptoms that meaningfully go together (e.g., related to a dysfunctioning in the same system such as the affective one)
Weight: 4, moderately important

Further aspects

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