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Results for “cluster analysis”

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PubMed2000

Cluster-analysis & patterns of dissemination of multidrug resistance among clinical strains of Vibrio cholerae in Calcutta, India.

BACKGROUND & OBJECTIVES: Antimicrobial resistance among Vibrio cholerae has been monitored for several years in Calcutta. To investigate the changing trends in multidrug resistance (MDR) among different serogroups of V. cholerae and to perform software assisted cluster analysis the current study was undertaken. METHODS: Strains isolated from patients with cholera and "cholera-like" diarrhoea admitted in the Infectious Diseases Hospital, Calcutta were analysed. Eight hundred and forty V. cholerae strains isolated from 1992 through 1997 were tested for susceptibility to 11 antibiotics. Cluster analysis was done using SPSS software. RESULTS: Most of the strains exhibited MDR with fluctuating trends as the resistance profile diverged each year. A total of 119 different resistance profiles exhibited by V. cholerae O1, O139 and non-O1, non-O139 serogroups were analysed by cluster combination method. During 1993 and 1994, 53 per cent of V. cholerae O139 and 82 per cent of V. cholerae O1 serogroups, respectively, exhibited maximal number of new resistance patterns. The frequency of new resistance patterns among V. cholerae non-O1, non-O139 was constantly high (33-47%) during 1995 to 1997. INTERPRETATION & CONCLUSIONS: With a few exceptions, preponderance of the resistance profiles was generally not confined to any serogroup. The cluster analysis depicted dissemination of some of the resistance patterns commonly found among V. cholerae non-O1, non-O139 belonging to different serogroups to the O139 serogroup in the succeeding years. In this study we have shown that the V. cholerae strains are resistant to several antibiotics with constant change in the MDR profiles. It is imperative to define the susceptibility pattern of the strains to determine the effective drug of choice for the treatment of cholera.

Cluster Analysis↗

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↗

PubMed2003

Coupled two-way clustering analysis of breast cancer and colon cancer gene expression data.

UNLABELLED: We present and review coupled two-way clustering, a method designed to mine gene expression data. The method identifies submatrices of the total expression matrix, whose clustering analysis reveals partitions of samples (and genes) into biologically relevant classes. We demonstrate, on data from colon and breast cancer, that we are able to identify partitions that elude standard clustering analysis. AVAILABILITY: Free, at http://ctwc.weizmann.ac.il.. SUPPLEMENTARY INFORMATION: http://www.weizmann.ac.il/physics/complex/compphys/bioinfo2/

Algorithms↗

PubMed1993

Cluster analysis of MMPI scales of patients involuntarily committed for chemical dependency treatment.

This study investigated empirical clusters of MMPI scale T scores for 186 men and 112 women involuntarily committed to a chemical dependency unit of a state psychiatric hospital. Ward's method of cluster analysis produced six clusters which were similar to those reported in other cluster-analytic studies of alcoholics but with a prominent increase in Scale 6 elevations. Average profiles for the six clusters were Spike 6, 6-2-8-4, within normal limits (Clusters 3 and 4), and 2-6-8-3-4-1-9. Results are discussed in terms of treatment planning for this population.

Adult↗

PubMed2006

Automatic identification of the left ventricle in cardiac cine-MR images: dual-contrast cluster analysis and scout-geometry approaches.

PURPOSE: To evaluate the technical feasibility of two approaches--dual-contrast (DC) cluster analysis, and scout geometry (SG)--for automatic identification of the left ventricular (LV) cavity in short-axis (SA) cine-MR images. MATERIALS AND METHODS: The DC algorithm uses Fuzzy C-Means (FCM) cluster analysis of SA images from a black-blood double-inversion recovery turbo spin-echo (dual IR TSE) sequence, and bright-blood images from a steady-state free precession (SSFP) sequence. The SG algorithm employs geometric information from scout views (i.e., vertical long-axis (VLA) and four-chamber (4CH) views). Both algorithms incorporate additional geometric continuity constraints along with LV region segmentation to identify the LV. The performance of both algorithms was compared on images of eight healthy volunteers, and the SG algorithm was further evaluated on images of 13 clinical patients. RESULTS: The DC algorithm identified the LV in 89% (72/75 at end-diastole (ED) and 47/59 at end-systole (ES)) of the images from healthy volunteers, compared to 98% (74/75 at ED and 57/59 at ES) by the SG algorithm. Both methods are robust against interslice signal variations and misalignment. The DC method suffers from misregistration between the dual IR TSE and SSFP images near the apex at ES. The SG method identified the LV in 91% (112/122 at ED and 91/102 at ES) of the images from clinical patients. CONCLUSION: The SG method requires no additional scan, is robust and accurate, and performs better than the DC method for automatic identification of the LV.

Adult↗

PubMed1992

Cluster analysis of children and adolescents with brain damage and learning disabilities using neuropsychological, psychoeducational, and sociobehavioral variables.

The purpose of the study was to employ psychoeducational, neuropsychological, and sociobehavioral (Conners Rating Scale) variables in determining if definable subtypes exist within a diverse population of subjects with learning disabilities (LD) and documented brain damage. The sample of 95 subjects (27% female and 73% male) had been referred for neuropsychological assessment at a large, Midwestern medical center. Mean age was 10.6 years. Brain damage (BD) was documented for 45% of the sample. The first cluster analysis employed neuropsychological, psychoeducational, and sociobehavioral data and revealed four interpretable clusters. A second cluster analysis excluded sociobehavioral data and yielded two interpretable clusters. In neither analysis did a cluster consist exclusively of BD or LD subjects. Results were interpreted as supporting the importance of the sociobehavioral component in LD subtyping, as well as supporting the contention that parallels may exist in cerebral function and/or structure between the LD and BD classifications.

Adolescent↗

PubMed2004

Associations of empirically derived eating patterns with plasma lipid biomarkers: a comparison of factor and cluster analysis methods.

BACKGROUND: Despite the growing use of patterning methods in nutritional epidemiology, a direct comparison of factor and cluster analysis methods has not been performed. OBJECTIVE: Our main objective was to compare patterns derived from the cluster and factor analysis procedures with measures of plasma lipids. DESIGN: This cross-sectional study included 459 healthy subjects who participated in the Baltimore Longitudinal Study of Aging and had measures of diet and plasma lipids. Eating patterns were derived by using both factor and cluster analysis methods. RESULTS: In separate multivariate-adjusted regression models, subjects in the healthy cluster had lower plasma triacylglycerols than did those not in the healthy cluster (beta = -15.97; 95% CI: -29.51, -2.43; P < 0.05), and factor 1 (reduced-fat dairy products, fruit, and fiber) was inversely related to plasma triacylglycerols (beta = -7.02 mg/dL for a one-unit increase in z score; 95% CI: -12.92, -1.12; P < 0.05). Those in the alcohol cluster had higher total cholesterol concentrations than did those not in the alcohol cluster (beta = 12.81; 95% CI: 2.74, 22.88; P < 0.05), and factor 2 (protein and alcohol) was also directly associated with total cholesterol (beta = 1.59 for a one-unit increase in z score; 95% CI: 0.55, 2.63; P < 0.05). The multivariate model containing all of the clusters was not significantly different from the model containing all of the factors in predicting each lipid outcome. CONCLUSION: Our study provides evidence of comparability between cluster and factor analysis methods in relation to plasma lipid biomarkers.

Adult↗

PubMed1999

Using multidimensional scaling and cluster analysis for understanding information processing and schizophrenia.

This comprehensive review of information processing theory together with multidimensional scaling and cluster analysis methods is used to examine proposed models, developing taxonomies, and treatment for schizophrenia. Studies cited link attention deficit with schizophrenia, suggest that for some tasks, the irregular performance of people with schizophrenia is the major problem, add evidence of treatment effects on sufferers' mental organization, provide a critique of current diagnostic instruments, and highlight the importance of supporting coping skills. The author concludes that multidimensional scaling and cluster analysis are important tools for the development of theory and knowledge and act as a check against theoretical bias.

Adaptation, Psychological↗

PubMed2003

Cluster analysis classification of SF-36 profiles for patients with spinal pain.

STUDY DESIGN: A k-means cluster analysis of patients with spinal and radicular pain based on the SF-36 Health Survey scales. OBJECTIVE: The aim was to determine whether spine patients fall into clusters according to self-reported health status as measured by the SF-36 and to determine if clustering is similar across four common diagnostic categories: herniated disc, spinal stenosis, spondylosis, and chronic pain syndrome. SUMMARY OF BACKGROUND DATA: Cognitive-behavioral classifications of chronic pain patients have previously identified three patient groups described as Dysfunctional, Interpersonally Distressed, and Minimizers/Adaptive Copers. The purpose of these classifications is to facilitate and direct treatment based not only on biomedical diagnosis but also on emotional, social, and behavioral diagnoses. This type of analysis has not been done on the quality-of-life scores of patients with specific spinal diagnoses. METHODS: Health status data were reviewed from the initial visits of 15,748 spine patients in the National Spine Network database. Based on the eight scales of the SF-36, k-means cluster analysis divided the National Spine Network population into distinct clusters of similar patients. Clustering was performed separately for each clinical diagnosis group. RESULTS: In all four diagnostic categories, cluster analysis classified patients into three groups. Group 1 had fairly high (relative to the entire sample) scores on all scales and was labeled "Highly Functional." Group 2 had low measures on physical variables but comparatively high scores on the mental scales. These were labeled "Emotional Adapters." Group 3 had low scores on all scales. These patients were labeled "Dysfunctional." Although patients in each diagnostic category fell into one of the three groups, the proportion of patients within each group was quite different among chronic pain patients as compared to the other three diagnostic groups. For example, 29% of herniated disc patients were in the Highly Functional group, whereas only 14% of patients in chronic pain were categorized as Highly Functional. Thirty-three percent of spondylosis patients were classified as Dysfunctional compared with 51% of chronic pain patients. CONCLUSIONS: Patients with spinal pain fall into three groups according to their profile of scores on the SF-36 Health Survey. It is proposed that such empirical groupings can guide decision-making in selecting the most appropriate therapies.

Cluster Analysis↗

PubMed2004

Studies on acute human infections using FTIR microspectroscopy and cluster analysis.

A novel methodology for the diagnosis of acute infections using FTIR microspectroscopy (FTIR-MSP) data on blood components and cluster analysis is presented. Blood samples were collected from 11 patients suffering from various infections and 16 age-matched healthy human controls. Blood components such as white blood cells, red blood cells, and plasma were isolated using standard procedures and FTIR-MSP of these components was utilized. A cluster analysis of the FTIR spectra was performed. The spectra obtained from the three blood components of patients were different from those of controls. The FTIR spectra of white blood cells from patients suffering infections were significantly different from the controls. Cluster analyses of averaged FTIR-MSP spectra of white blood cells provided 100% classification between patients and healthy controls.

Acute Disease↗

PubMed2004

[Use of cluster analysis for the study of self-stigmatization phenomenon].

Cluster analysis was used in the study of self-stigmatization phenomenon measured with the original self-stigmatization questionnaire in 81 patients with schizophrenia, affective and neurotic disorders. The answers presented by digital codes were analyzed statistically. Six factors of self-stigmatization were as follows: "a victim of stigma", "deidentification from the healthy", "predisposition to identification with mentally ill", "avoidance of mentally ill", "excuse by mental illness" and "generalization of stigma".

Adult↗

PubMed1991

A cluster analysis of the multidimensional pain inventory.

Seventy-nine patients with chronic headaches of diverse causes, recruited from a headache clinic's biofeedback facility, were administered the Multidimensional Pain Inventory (MPI) for measuring the cognitive, behavioral, and affective dimensions of pain. Using the statistical technique of cluster analysis to organize the results, three clusters emerged, and were similar in their characteristics to those named "Dysfunctional", "Interpersonally Distressed", and "Adaptive Coper" by other authors who had applied the Inventory and the cluster analysis technique to other populations (one population containing heterogenous groups of chronic pain patients, and another population of patients suffering from "temporomandibular joint disorders". Additional analyses of our results confirmed that the three groups were distinct from one another; and that age, sex, duration of complaint, and diagnosis, were not factors in the formation of the groups. Our results suggest that the MPI is a valid measure of the cognitive, behavioral and affective aspects of pain. Rather than apply a similar intervention program to all headache patients, it might be more effective to tailor treatment to the variations in these aspects exhibited by patients in the three different clusters.

Adolescent↗

PubMed1998

Cluster analysis applied to building-related illness.

Identifying remediable causes of occupant symptoms in building-related illness is frequently difficult. This is particularly true when the building-wide prevalence of symptoms is comparable to that reported in non-problem buildings. This analysis applied an epidemiological approach to an assessment of a problem building, allowing investigators to visually identify an area of apparent increased symptom density. A cluster analysis approach permitted biostatistical confirmation of the visual cluster. Building-related symptom reporting was statistically significantly associated with a prior physician diagnosis of dust and/or mold allergy. The likely etiology of building occupant symptoms was identified within the region implicated by the cluster analysis. This approach may be useful to focus building evaluations on both the likely physical source and general characteristics of suspect etiologic agents.

Air Pollution, Indoor↗

PubMed2001

Cluster analysis and patterns of findings on cranial magnetic resonance imaging of the elderly: the Cardiovascular Health Study.

OBJECTIVE: To characterize patterns of findings on cranial magnetic resonance imaging (MRI) of the elderly using a statistical technique called cluster analysis. SUBJECTS AND METHODS: The Cardiovascular Health Study is a population-based, longitudinal study of 5888 people 65 years and older. Of these, 3230 underwent cranial MRI scans, which were coded for presence of infarcts and grades for white matter, ventricles, and sulci. Cluster analysis separated participants into 5 clusters based solely on patterns of MRI findings. Participants comprising each cluster were contrasted with respect to cardiovascular risk factors and clinical manifestations. RESULTS: One cluster was low on all the MRI findings (normal) and another was high on all of them (complex infarcts). Another cluster had evidence for infarcts alone (simple infarcts), whereas the last 2 clusters lacked infarcts, one having enlarged ventricles and sulci (atrophy) and the other having prominent white matter changes and enlarged ventricles (leukoaraiosis). Factors that distinguished these clusters in a discriminant analysis were age, sex, several measures of hypertension, internal carotid artery wall thickness, smoking, and prevalent claudication before the MRI. The atrophy group had the highest percentage of men and the normal group had the lowest. Cognitive and motor performance also differed across clusters, with the atrophy cluster performing better than may have been expected. CONCLUSIONS: These MRI patterns identified participants with different vascular disease risk factors and clinical manifestations. Results of these exploratory analyses warrant consideration in other populations of elderly people. Such patterns may provide clues about the pathophysiology of structural brain changes in the elderly.

Aged↗

PubMed1997

Differences in symptom structure between panic attack and limited symptom panic attack: a study using cluster analysis.

We had investigated the clinical characteristics of panic disorder (PD) in a Japanese outpatient population comprised of more than 250 patients diagnosed as having PD during a 13-year study period and observed that some PD patients had both panic attacks (PA) and limited symptom panic attacks (LPA). In the criteria for PD based on the Diagnostic and Statistics Manual of Mental Disorders, third edition-revised (DSM-III-R), episodes involving four or more symptoms are classified as PA, while those involving fewer than four symptoms are described as LPA. Therefore, LPA is identified as part of an episode of PA, since the difference between the two episodes is only in the number of symptoms. However, some recent research suggests that there is a distinct subgroup of individuals who suffer LPA. Using cluster analysis, we investigated the differences between PA and LPA groups in terms of the structures of several panic symptoms, which included anticipatory anxiety, agoraphobia and 13 clinical symptoms based on the DSM-III-R at the time of panic attacks, in 247 patients with PD. Cluster analysis revealed clusters of three and four panic symptoms in the PA group and LPA group, respectively, and there were also differences in symptom structure between the two groups. These results suggest that there may be a subgroup of individuals who show LPA among PD patients.

Adult↗

PubMed2002

Geographical pattern of malignant neoplasm by cluster analysis using standardized mortality ratios (SMRs) in Ibaraki Prefecture, Japan.

We investigated the geographical patterns of mortality from eight (males)/ten (females) sites of malignant neoplasm, using cluster analysis with Standardized Mortality Ratios (SMRs), and examined the relationship between the mortality structure and urbanization. To explore the geographical tendencies is important for the prevention of cancers; such as noticing risk factors associated with regional variance. The death rates, by site, gender and age from 1990 to 1994 in Japan, were obtained from Vital Statistics. The deaths and population in municipalities were obtained from "Population of Ibaraki Prefecture". These were represented as averaged values in five-year periods. As an indicator of urbanization and mortality structure, the population density of municipalities and the overall rank scores of SMRs were used, respectively. Cluster analysis formed some distinctive structures. For males, Cluster 1 included four municipalities and three of these were located in the mountainous area in northwest Ibaraki, characterized by high SMRs from bone marrow. Cluster 5 consisted of the mid-south areas, characterized by high SMRs from stomach cancer. For females, the clusters seemed to be characterized by SMRs from esophagus cancer. An association between mortality structure and urbanization was found for females, 0.364 (p<0.01), but not for males, 0.162 (p=0.14).

Cluster Analysis↗

PubMed2005

Cluster analysis of contaminated sediment data: nodal analysis.

The objective of the present study was to explore the use of multivariate statistical methods as a means to discern relationships between contaminants and biological and/or toxicological effects in a representative data set from the National Status and Trends (NS&T) Program. Data from the National Oceanic and Atmospheric Administration, NS&T Program's Bioeffects Survey of Delaware Bay, USA, were examined using various univariate and multivariate statistical techniques, including cluster analysis. Each approach identified consistent patterns and relationships between the three types of triad data. The analyses also identified factors that bias the interpretation of the data, primarily the presence of rare and unique species and the dependence of species distributions on physical parameters. Sites and species were clustered with the unweighted pair-group method using arithmetic averages clustering with the Jaccard coefficient that clustered species and sites into mutually consistent groupings. Pearson product moment correlation coefficients, normalized for salinity, also were clustered. The most informative analysis, termed nodal analysis, was the intersection of species cluster analysis with site cluster analysis. This technique produced a visual representation of species association patterns among site clusters. Site characteristics, such as salinity and grain size, not contaminant concentrations, appeared to be the primary factors determining species distributions. This suggests the sediment-quality triad needs to use physical parameters as a distinct leg from chemical concentrations to improve sediment-quality assessments in large bodies of water. Because the Delaware Bay system has confounded gradients of contaminants and physical parameters, analyses were repeated with data from northern Chesapeake Bay, USA, with similar results.

Animals↗

PubMed2003

Fuzzy cluster analysis of high-field functional MRI data.

Functional magnetic resonance imaging (fMRI) based on blood-oxygen level dependent (BOLD) contrast today is an established brain research method and quickly gains acceptance for complementary clinical diagnosis. However, neither the basic mechanisms like coupling between neuronal activation and haemodynamic response are known exactly, nor can the various artifacts be predicted or controlled. Thus, modeling functional signal changes is non-trivial and exploratory data analysis (EDA) may be rather useful. In particular, identification and separation of artifacts as well as quantification of expected, i.e. stimulus correlated, and novel information on brain activity is important for both, new insights in neuroscience and future developments in functional MRI of the human brain. After an introduction on fuzzy clustering and very high-field fMRI we present several examples where fuzzy cluster analysis (FCA) of fMRI time series helps to identify and locally separate various artifacts. We also present and discuss applications and limitations of fuzzy cluster analysis in very high-field functional MRI: differentiate temporal patterns in MRI using (a) a test object with static and dynamic parts, (b) artifacts due to gross head motion artifacts. Using a synthetic fMRI data set we quantitatively examine the influences of relevant FCA parameters on clustering results in terms of receiver-operator characteristics (ROC) and compare them with a commonly used model-based correlation analysis (CA) approach. The application of FCA in analyzing in vivo fMRI data is shown for (a) a motor paradigm, (b) data from multi-echo imaging, and (c) a fMRI study using mental rotation of three-dimensional cubes. We found that differentiation of true "neural" from false "vascular" activation is possible based on echo time dependence and specific activation levels, as well as based on their signal time-course. Exploratory data analysis methods in general and fuzzy cluster analysis in particular may help to identify artifacts and add novel and unexpected information valuable for interpretation, classification and characterization of functional MRI data which can be used to design new data acquisition schemes, stimulus presentations, neuro(physio)logical paradigms, as well as to improve quantitative biophysical models.

Artifacts↗