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At least 19 recordsLinked to original sources
PubMed1978

Classification of suicide attempters by cluster analysis.

Cluster analytic procedures for classification were carried out on a sample of 236 suicide attempters. Rating variables concerned previous suicidal behaviour, details of the recent attempt and its motivation, mental state, and demographic characteristics. Results suggested the existence of three groups of attempters. The first comprised patients taking overdoses, on the whole showing less risk to life, less psychiatric disturbance, and more evidence of interpersonal rather than self-destructive motivation. The second groups, fewer in number, made severe attempts with more self-destructive motivation, by violent methods rather than overdose. The third and smallest group had a previous history of many attempts and gestures, made relatively mild attempts and were overtly hostile, engendering reciprocal hostility in the treating psychiatrist. These groups show some resemblance to those found in other studies.

Adult↗
PubMed1978

A note on cluster analysis and depression: disparities in results produced by the application of different clustering methods.

Cluster analysis is the most logically suited method for establishing psychiatric classifications. Different mathematical methods of clustering do, however, produce disparate results when applied to the same set of data. This study attempted to quantify the extent of such disparities, and found them to be marked. It was concluded that until cluster analysis has undergone further mathematical and statistical development, it should be used with caution.

Adult↗
PubMed2026

Whole exome sequencing and cluster analysis reveal that EPB41L4A mutation may trigger tooth agenesis.

OBJECTIVE: To detect and analyze the correlation between commonly mutated genes and known genes associated with tooth agenesis in patients with non-syndromic tooth agenesis. The aim is to explore new genes that may be associated with tooth agenesis, to provide a genetic reference for its prevention as well as for the clinical diagnosis and treatment of tooth agenesis. METHODS: Genomic DNA was extracted from the peripheral blood of 18 congenitally edentulous subjects, and related gene mutations were identified by whole-exome sequencing. The genes related to maxillofacial development and the known pathogenic gene sequences of congenital tooth agenesis were selected for local alignment analysis of pairwise sequences, and the metric relationship of related sequences was determined. Hierarchical and fuzzy clustering methods were used for cluster analysis. RESULTS: Hierarchical clustering and fuzzy clusterings yielded consistent results. The EPB41L4A gene clustered with a large number of well-known and well-defined genes associated with tooth agenesis. From the perspective of cluster analysis, it can be inferred that the genes clustered together generally have similar functions. CONCLUSION: EPB41L4A, which is involved in the Wnt pathway, may be a candidate gene warranting further investigation.

Humans↗
PubMed1979

Standardization of measures prior to cluster analysis.

A common problem in cluster analysis is the determination of a scale-free measure of distance between individuals. This paper presents a procedure for scaling measurements using a reference individual as a standard of comparison. The procedure is particularly useful in classification of the results of laboratory procedures, where a reference standard is routinely produced. An example is the clustering of patterns that result from crossed antigen-antibody electrophoresis for determining the phenotype of the serum protein alpha 1-antitrypsin. The procedure appears to remove extraneous variability while retaining the information necessary for classification.

Blood Protein Electrophoresis↗
PubMed1978

Entropy in the hierarchical cluster analysis of hospitals.

A new technique integrating concepts from cluster analysis and information theory was applied to the classification of Michigan hospitals. First, a number of cost-related variables that describe the hospitals and their surroundings were used in a cluster analysis to produce a hierarchy of classifications. Then for each classification, the within-group entropy was computed for each group of hospitals and averaged over the classification. Finally, this average entropy was used as an aid to judgment in deciding which of the many classifications in the hierarchy yields the most reasonable groupings of hospitals.

Blue Cross Blue Shield Insurance Plans↗
PubMed1979

Application of cluster analysis for characterization of spatial distribution of particles by stereological methods.

A method for the detection and characterization of clusters of particles observed in section with the electron microscope is presented. Cluster analysis is performed by the division method described by Berthet et al. (1976). Starting from a single cluster, profiles from each electron micrograph are successively classified in sets containing an increasing number of clusters. The decrease in the mean free distance, lambda, between profiles in the clusters, is used for terminating the subdivision procedure. The function relating the mean free distance with the number of clusters is evaluated in each subdivision set. The actual number of clusters is selected on the basis of the slope of that function, at a point where lambda has a value close to the average profile diameter. The method assumes a convex shape for the clusters; the salient feature is that it provides a physical delineation of clusters in the section. Hence, an evaluation of some characteristics of clusters in the three-dimensional sample may be obtained by using standard stereological procedures. Characterization of the volume to which the individual particles of a population are eventually restricted can as a result be performed. Practical problems in the acquisition of the data needed for cluster analysis are discussed and a system using for that purpose a Quantimet 720 image analyser in a basic configuration, connected on line with a PDP 11/10 minicomputer, is presented. Application of the method is illustrated by the analysis of lysosomes in cultured hepatoma (HTC) cells, at the end of mitosis and during the S phase. Cluster analysis shows that in cells actively synthesizing DNA they are grouped in clusters representing 5.7% of the cellular volume. Moreover, the average number of particles per cluster falls from a minimum of thirteen at mitosis to only six at the S phase.

Cells, Cultured↗
PubMed1992

Cluster analysis and related techniques in medical research.

In this paper we review methods of cluster analysis in the context of classifying patients on the basis of clinical and/or laboratory type observations. Both hierarchical and non-hierarchical methods of clustering are considered, although the emphasis is on the latter type, with particular attention devoted to the mixture likelihood-based approach. For the purposes of dividing a given data set into g clusters, this approach fits a mixture model of g components, using the method of maximum likelihood. It thus provides a sound statistical basis for clustering. The important but difficult question of how many clusters are there in the data can be addressed within the framework of standard statistical theory, although theoretical and computational difficulties still remain. Two case studies, involving the cluster analysis of some haemophilia and diabetes data respectively, are reported to demonstrate the mixture likelihood-based approach to clustering.

Algorithms↗
PubMed1975

Cluster analysis of preference ratings of pictorial stimuli.

The present research attempted to utilize cluster analysis to develop a comprehensive array of objective pictorial scales that could be used to identify the distinguishing perceptual and personality characteristics of various medical and psychiatric hospital populations. A cluster analysis of individuals' preference ratings of a large number of picture stimuli was described.

Body Image↗
PubMed1978

Behavioural syndromes identified by cluster analysis in a sample of 100 severely and profoundly retarded adults.

Very little is known about psychiatric disorders in severely and profoundly retarded adults. We have investigated these disorders by systematically recording and collecting data about the behaviour of 100 severely and profoundly retarded hospitalized adults and subjecting the data thus derived to cluster analysis. Eight clusters were isolated. The clinical psychiatric significance of these clusters is discussed and their relationship to cause retardation, duration of stay in hospital and visiting is considered. A diagnostic framework for psychiatric disorder in severely and profoundly retarded adults is put forward and some possible treatment approaches are suggested.

Adult↗
PubMed1992

[Computed cluster analysis of citations as a tool in studying structures of research trends].

The method of computer cluster-analysis of citation to determine the structure of a scientific trend is suggested. The map of the current status of research in neurobiology of aging as one of the most promising research areas in gerontology is under consideration. The possibilities and perspectives of this method as a tool of science-of-science research in medicine are analyzed.

Aging↗
PubMed1976

Cluster analysis profiles of suicide attempters.

The author reports the results of a three-stage cluster analysis of data on 298 patients who attempted suicide. Patients were allocated by the analysis to seven identifiable profile types that correlated with six outcome variables. These profiles include data on the social setting and the seriousness of the attempt and judgments about overall functioning and interpersonal conflict as well as the input of the patients' significant others. The author states that these profiles have prognostic significance as well as implications for the treatment of suicide attempters and the education of emotionally involved significant others.

Anxiety↗
PubMed1979

Application of stepwise cluster analysis in medical research.

A stepwise clustering algorithm, a method of multivariate statistical analysis, is suggested in this paper. The algorithm is designed for solving problems connected with stepwise regression. It is efficient not only in handling both continuous and discrete variables, but also in the nonlinear relationships between the variables. The above procedure was used in an attempt to find out the causal association of esophageal cancer with its precursors, i.e. nitrates and nitrites of nitrosamines, some of which are known to be carcinogenic. An analysis has been made of the correlation between esophageal cancer as well as severe epithelial hyperplasia of the esophagus and the concentrations of NO3- and NO2- in the drinking water. The samples used were collected from 495 wells in 49 production brigades of the Yaocun Commune in Linxian County, Honan Province. The result indicates that esophageal cancer is definitely connected with the levels of NO3- (summer) and NO2- (spring) in the drinking water. Severe epithelial hyperplasia is defintely connected with the contents of NO2- and NO3- in the drinking water collected in spring, autumn and winter. Our preliminary analysis shows that the stepwise clustering algorithm is a useful statistical method to be used for medical research.

Humans↗
PubMed1978

Cluster analysis of respiratory time series.

We have investigated the respiratory control system with the hypothesis that, although many variables such as minute ventilation (VI), tidal volume (VT), breathing period (TT), inspiratory duration (TI), and expiratory duration (TE) may be observed, the controller functions more simply by manipulating only 2 or 3 of these. Thus, if tidal volume is the only independent variable, TI being determined by the "off-switch" threshold, these variables should have very similar time courses. Anesthetized dogs were subjected to CO2 breathing and carotid sinus perfusion to stimulate both chemoreceptors. The time series of the variables VI, VT, TT, TE, and TI as well as PACO2 were determined on a breath by breath basis. Derived characteristics of these time series were compared using Cluster Analysis and the latent dimensionality of respiratory control determined by Factor Analysis. The characteristics of the time series clustered into 4 groups: magnitude (of the response), speed, variability and relative change. One respiratory factor accounted for 86% of the variance for the variability characteristics, 2 factors for magnitude (91%) and relative change (85%) and 3 factors for speed (89%). The respiratory variables were analysed for each of the 4 groups of characteristics with the following results: VT and TI clustered together only for the magnitude and relative change characteristics where as TT and TE clustered closely for all four characteristics. One latent factor was associated with the [TT-TE] group and the other usually with PACO2.

Animals↗
PubMed1976

Use of cluster analysis in the development of structure-activity relations for antitumor triazenes.

A series of antitumor triazenes in which the members of the series are physicochemically distinct was designed using the cluster analysis approach as proposed by Hansch and his co-workers. The series that resulted was tested against Sarcoma 180 in the mouse and the antitumor activities were analyzed using regression techniques. The structure-activity relations that resulted are discussed in terms of proposed mechanisms of action.

Animals↗