Biomedical subjects

Douglas Steinley

Publications and source records attributed to Douglas Steinley.

7 recordsLinked to original sources

PubMed2006

Profiling local optima in K-means clustering: developing a diagnostic technique.

Using the cluster generation procedure proposed by D. Steinley and R. Henson (2005), the author investigated the performance of K-means clustering under the following scenarios: (a) different probabilities of cluster overlap; (b) different types of cluster overlap; (c) varying samples sizes, clusters, and dimensions; (d) different multivariate distributions of clusters; and (e) various multidimensional data structures. The results are evaluated in terms of the Hubert-Arabie adjusted Rand index, and several observations concerning the performance of K-means clustering are made. Finally, the article concludes with the proposal of a diagnostic technique indicating when the partitioning given by a K-means cluster analysis can be trusted. By combining the information from several observable characteristics of the data (number of clusters, number of variables, sample size, etc.) with the prevalence of unique local optima in several thousand implementations of the K-means algorithm, the author provides a method capable of guiding key data-analysis decisions.

Algorithms↗

PubMed2006

Clustering, seriation, and subset extraction of confusion data.

The study of confusion data is a well established practice in psychology. Although many types of analytical approaches for confusion data are available, among the most common methods are the extraction of 1 or more subsets of stimuli, the partitioning of the complete stimulus set into distinct groups, and the ordering of the stimulus set. Although standard commercial software packages can sometimes facilitate these types of analyses, they are not guaranteed to produce optimal solutions. The authors present a MATLAB *.m file for preprocessing confusion matrices, which includes fitting of the similarity-choice model. Two additional MATLAB programs are available for optimally clustering stimuli on the basis of confusion data. The authors also developed programs for optimally ordering stimuli and extracting subsets of stimuli using information from confusion matrices. Together, these programs provide several pragmatic alternatives for the applied researcher when analyzing confusion data. Although the programs are described within the context of confusion data, they are also amenable to other types of proximity data.

Cluster Analysis↗

PubMed2006

Correlates of health literacy in patients with chronic heart failure.

PURPOSE: Many older adults have inadequate health-related literacy, which is associated with poor health outcomes. Thus, it is important to identify determinants of health literacy. We investigated relationships between health literacy and general cognitive and sensory abilities, as well as education, health, and demographic variables, in a community sample of middle-aged and older adults. DESIGN AND METHODS: Participants were 314 community-dwelling adults (67% female, 48% African American) diagnosed with chronic heart failure recruited for a pharmacist-based intervention study to improve adherence to chronic heart failure medications. We adminstered demographic, health, education, cognitive (e.g., processing speed, working memory), and sensory measures, and the Short Test of Functional Health Literacy in Adults (STOFHLA), as part of the baseline condition of this study. RESULTS: STOFHLA scores were lower for participants who were older, less educated, male, African American, had more comorbidities, or scored lower on all cognitive ability measures. Hierarchical linear regression analyses showed that education and cognitive ability were independently associated with the STOFHLA measure and explained age differences in health literacy. IMPLICATIONS: The association of cognitive abilities and literacy has important implications for health literacy models and for interventions to reduce the impact of low health literacy on health outcomes. For example, medication instructions should be designed to reduce comprehension demands on general cognitive abilities as well as literacy skills.

Aged↗

PubMed2006

K-means clustering: a half-century synthesis.

This paper synthesizes the results, methodology, and research conducted concerning the K-means clustering method over the last fifty years. The K-means method is first introduced, various formulations of the minimum variance loss function and alternative loss functions within the same class are outlined, and different methods of choosing the number of clusters and initialization, variable preprocessing, and data reduction schemes are discussed. Theoretic statistical results are provided and various extensions of K-means using different metrics or modifications of the original algorithm are given, leading to a unifying treatment of K-means and some of its extensions. Finally, several future studies are outlined that could enhance the understanding of numerous subtleties affecting the performance of the K-means method.

Algorithms↗

PubMed2005

Improving medication knowledge among older adults with heart failure: a patient-centered approach to instruction design.

PURPOSE: We investigated whether patient-centered instructions for chronic heart failure medications increase comprehension and memory for medication information in older adults diagnosed with chronic heart failure. DESIGN AND METHODS: Patient-centered instructions for familiar and unfamiliar medications were compared with instructions for the same medications from a chain pharmacy (standard pharmacy instructions). Thirty-two adults (age, M = 63.8) read and answered questions about each instruction, recalled medication information (free recall), and then answered questions from memory (cued recall). RESULTS: Patient-centered instructions were better recalled and understood more quickly than the standard instructions. Instructions for the familiar medications also were better recalled. Patient-centered instructions were understood more accurately for the unfamiliar medications, but standard instructions were understood more accurately for the familiar medications. However, the recall measures showed that the advantage of the standard format for familiar medications was short lived. IMPLICATIONS: The findings suggest that the patient-centered format may improve printed medication instructions available in many pharmacies, which should help older adults to better understand how to take their medications.

Aged↗

PubMed2004

Properties of the Hubert-Arabie adjusted Rand index.

This article provides an investigation of cluster validation indices that relates 4 of the indices to the L. Hubert and P. Arabie (1985) adjusted Rand index--the cluster validation measure of choice (G. W. Milligan & M. C. Cooper, 1986). It is shown how these other indices can be "roughly" transformed into the same scale as the adjusted Rand index. Furthermore, in-depth explanations are given of why classification rates should not be used in cluster validation research. The article concludes by summarizing several properties of the adjusted Rand index across many conditions and provides a method for testing the significance of observed adjusted Rand indices.

Artificial Intelligence↗

PubMed2003

Local optima in K-means clustering: what you don't know may hurt you.

The popular K-means clustering method, as implemented in 3 commercial software packages (SPSS, SYSTAT, and SAS), generally provides solutions that are only locally optimal for a given set of data. Because none of these commercial implementations offer a reasonable mechanism to begin the K-means method at alternative starting points, separate routines were written within the MATLAB (Math-Works, 1999) environment that can be initialized randomly (these routines are provided at the end of the online version of this article in the PsycARTICLES database). Through the analysis of 2 empirical data sets and 810 simulated data sets, it is shown that the results provided by commercial packages are most likely locally optimal. These results suggest the need for some strategy to study the local optima problem for a specific data set or to identify methods for finding "good" starting values that might lead to the best solutions possible.

Cluster Analysis↗