Bayesian statistical procedures use probabilistic models and probability distributions to summarize data, estimate unknown quantities of interest, and predict future observations. The procedures borrow strength from other observations in the dataset by using prior distributions and/or hierarchical model specifications. The unique posterior sampling techniques can handle different issues, e.g., missing data, imputation, and extraction of parameters (and their functional forms) that would otherwise be difficult to address using conventional methods. In this dissertation, we propose Bayesian modeling strategies to address various challenges arising in the fields of neuroscience and medicine. Specifically, we propose a sparse Bayesian hierarchical Vector Autoregressive (VAR) model to map human brain connectivity using multi-subject multi-session functional magnetic resonance image (fMRI) data. We use the same model on patient diary databases, focusing on patient-level prediction of medical conditions using posterior predictive samples. We also propose a Bayesian model with an augmented Markov Chain Monte Carlo (MCMC) algorithm on repeat Electrical Stimulation Mappings (ESM) to evaluate the variability of localization in brain sites responsible for language function. We close by using Bayesian disproportionality analyses on spontaneous reporting system (SRS) databases for post-market drug safety surveillance, illustrating the caution required in real-world analysis and decision making.

Nationally, physicians have been slow to adapt to the use of the Internet as a tool of communication for the business of medicine. To understand the reasons for this and to determine if physicians in Indiana follow this national trend, a Q analysis was used to measure their attitudes toward the Internet. Q analysis allowed the researcher to quantify subjectivity.This study focused on physicians who are members of the Indiana State Medical Association, an organization representing nearly 8,000 of the state's doctors. Physicians were of different race, sex, age, specialty and geographic location as to be representative of Indiana physicians.For this study, twenty-eight physicians ranked a series of forty-eight statements along a bipolar continuum called a Q sort. Each subject was asked to rate the forty-eight statements on a nine-point most agree (+4) and least agreed (-4) scale.Analysis of the twenty-eight Q sorts collected for this study provided three factor types. Those who comprised Factor I were labeled the "Futurists." The view that emerged from this group was that the Internet is seeping into every facet of the American health experience and will change the business of medicine. The highest number of physicians fell into Factor 2. This group comprised 39 percent of the participants. Factor 2, named the "Skeptics," showed interest in the Internet along with many concerns. Those physicians from Factor 3 strongly disagree that the health care system is behind the times in the information revolution. Named "Easy Navigators," physicians in this group responded that they effortlessly find their way around the Internet. Analysis suggests Indiana physician attitudes toward the Internet are concurrent with those nationally. / Department of Journalism

Indiana University-Purdue University Indianapolis (IUPUI) / With the availability of electronic medical records, medication dispensing data offers an unprecedented opportunity for researchers to explore complex relationships among longterm medication use, disease progression and potential side-effects in large patient populations. However, these data also pose challenges to existing statistical models because both medication exposure status and its intensity vary over time. This dissertation focused on flexible models to investigate the association between time-varying exposures and different types of outcomes. First, a penalized functional regression model was developed to estimate the effect of time-varying exposures on multivariate longitudinal outcomes. Second, for survival outcomes, a regression spline based model was proposed in the Cox proportional hazards (PH) framework to compare disease risk among different types of time-varying exposures. Finally, a penalized spline based Cox PH model with functional interaction terms was developed to estimate interaction effect between multiple medication classes. Data from a primary care patient cohort are used to illustrate the proposed approaches in determining the association between antidepressant use and various outcomes. / NIH grants, R01 AG019181 and P30 AG10133.

Translational Biomedical Informatics (TBMI) is an emerging discipline expanding beyond traditional bioinformatics, with a focus on developing computational technologies for real-world biomedical practice. The goal of my Ph.D. research is to address a few key challenges in TBI, including: (1) the high quality and reproducibility required by medical applications when processing high throughput data, (2) the need for knowledge management solutions that allow molecular data to be handled and evaluated by researchers, regulators, and doctors collectively, (3) the need for near real-time, efficient access to decision-oriented visualizations of integrated data and data processing results, and (4) the need for an integrated solution that can evolve as medical consensus evolves, without requiring retraining, overhaul or replacement. This dissertation resulted in the development and adoption of concrete web-based application deliverables in regular use by bioinformaticians, clinicians, biologists and nanotechnologists. These include: the Chip Artifact Correction (caCORRECT) web site and grid services, the ArrayWiki community microarray repository, and the SimpleVisGrid visualization grid services (including eGOMiner, nanoDRIVE, PathwayVis and SphingoVisGrid).

[Truncated abstract] Hypothesis: There is deficiency in the evidence base and scientific underpinning of existing diagnostic imaging pathways (DIP) for diagnostic endpoints. Objective: a) To carry out systematic review of literature in relation to use of diagnostic imaging tests for diagnosis and investigation of 78 common clinical problems, b) To identify deficiencies and controversies in existing diagnostic imaging pathways, and to develop a new set of consensus based pathways for diagnostic imaging (DIP) supported by evidence as an education and decision support tool for hospital based doctors and general practitioners, c) To carry out a trial dissemination, implementation and evaluation of DIP. Methods: 78 common clinical presentations were chosen for development of DIP. For general practitioners, clinical topics were selected based on the following criteria: common clinical problem, complex in regards to options available for imaging, subject to inappropriate imaging resulting in unnecessary expenditure and /or radiation exposure, and new options for imaging of which general practitioners may not be aware. For hospital based junior doctors and medical students, additional criteria included: acute presentation when immediate access to expert radiological opinion may be lacking and clinical problem for which there is a need for education. Systematic review of the literature in relation to each of the 78 topics was carried out using Ovid, Pubmed and Cochrane Database of Systematic Reviews. ... The electronic environment and the method of delivery provided a satisfactory medium for dissemination. Getting DIP implemented required vigorous effort. Knowledge of diagnostic imaging and requesting behaviour tended to become more aligned with DIP following a period of intensive marketing. Conclusions: Systematic review of literature and input and feedback from various clinicians and radiologists led to the development of 78 consensus based Diagnostic Imaging Pathways supported by evidence. These pathways are a valuable decision support tool and are a definite step towards incorporating evidence based medicine in patient management. The clinical and academic content of DIP is of practical use to a wide range of clinicians in hospital and general practice settings. It is source of high level knowledge; a reference tool for the latest available and most effective imaging test for a particular clinical problem. In addition, it is an educational tool for medical students, junior doctors, medical imaging technologists, and allied health care personnel.

This research was instigated by testimony of the failure and the subsequent crisis in the South African public healthcare system. Official investigations had brought to light alarming operational deficiencies in institutions under the aegis of the Gauteng Department of Health. The South African public sector and government departments, in general, are currently challenged by a complex transformation process which has a prime objective to ameliorate public accountability, service delivery and budgetary control. They are likewise faced with the equally labyrinthine and demanding task of establishing a public-sector organisation with meaningful and effective operational processes that are, in addition, expected to synchronise with the modern economy. The researcher is unequivocal: the success of the GDH in responding to the challenges of improving organisational performance and healthcare service delivery depends essentially on their knowledge management strategy. Knowledge, across public-sector organisations, is increasingly being acknowledged, not only as a strategic resource but also as a valuable organisational asset. In the context of this research, knowledge is defined as the experience that resides in the minds of people; termed tacit knowledge (as opposed to formal, codified or explicit knowledge). In an analogous manner, research studies abound with evidence that has identified knowledge management as having an influence on operational performance for healthcare service delivery. Yet, a brief inquest indicated that our overall understanding of the existence of the relationship between knowledge management and operational performance for healthcare service delivery in the South African public sector is, at best, exiguous. With these appraisals in mind, the researcher developed a theoretical model that revealed factors that could influence organisational performance and healthcare service delivery. The model focused on knowledge management capabilities and organisational performance. The prime objective was to operationalise the theoretically derived knowledge management capabilities constructs, identify statistically the enhancing or impeding factors that impact on organisational performance and develop a structural equation model to verify this theoretical paradigm. The ambition of this study was similarly to investigate the use of knowledge management by the Gauteng Department of Health for its transformation to achieve improved organisational performance and healthcare service delivery. This study in essence addressed four research questions: Firstly, what was the level of understanding of knowledge management in the Gauteng Department of Health and related healthcare facilities? Secondly, how were knowledge management strategies and practices aligned with the Gauteng Department of Health strategies and operational objectives? Thirdly, how was knowledge management used by the employees in the Gauteng Department of Health? and fourthly, how could the results of the literature review and the empirical data be used to create a knowledge-management culture and a collaborative working environment for the Gauteng Department of Health? The elemental, hypothesised pursuit governing the study was to determine the existence of a relationship between the use of knowledge management and an improvement in organisational performance and healthcare service delivery. Collaterally, what enabling environment would be instituted by the gatekeepers of the institutional praxes to capacitate other staff members specifically so as to include the succession planning conundrum? To accomplish this and after reviewing the literature, the effective factors in knowledge management were identified, namely, knowledge infrastructure proficiencies and knowledge process capabilities. The research followed the parallel mixed-methods approach in gathering and analysing research data. Data was collected using questionnaires with 496 respondents and interviews with 35 interviewees. The sample used in this study comprised employees of the Gauteng Department of Health and its regional healthcare centres. The survey respondents and interview participants were the general staff and executive/senior managers of the Gauteng Department of Health. These individuals were considered to possess the most comprehensive knowledge about their organisation’s characteristics and strategy, which included knowledge management adoption. In order to identify the relationships between the model elements, appropriate tests were initiated using the Statistical Package for Social Sciences. Exploratory and confirmatory factor analyses and structural equation modelling were utilised and the proposed model was then extracted and content analysis was applied in evaluating the resulting qualitative data. The findings of this study furthermore indicated that knowledge management concepts were not universally understood in the Gauteng Department of Health. A structural equation model development strategy, postulated in the factor analysis, also produced a new best-fitting knowledge management capability model based on the new constructs. The structural equation model suggested that significant factors influencing the improvement of the organisational performance and healthcare service delivery are those of knowledge management capability. The regression analysis showed that most of the inter-correlations were significant, thus confirming the theory that knowledge management capabilities have a direct influence on organisational performance and healthcare service delivery. The research contributed theoretically to a comprehensive understanding of the relationship between knowledge management principles and factors that influence organisational performance and healthcare service delivery. Practically, the research contributed to confirming the use of knowledge management by the Gauteng Department of Health could to the improvement of its overall organisational performance and healthcare service delivery. The study further demonstrated the impact of knowledge management activities as a driving force for organisational change and the effect of knowledge management on the improvement of workforce productivity and organisational effectiveness. The new knowledge management capability model could additionally assist the Gauteng Department of Health determining the extent to which knowledge management is used and where to focus in developing and implementing knowledge management strategy. The study encourages practitioners to take cognisance of the fact that organisations are unique and that the factors which enhance or impede knowledge management are to be thoroughly examined. This case study was endorsed for its significant contribution to regional healthcare system, as well as the broader national healthcare structure. / School of computing / Ph. D. (Information Systems)

University of Macau / Faculty of Science and Technology / Department of Computer and Information Science

No description available.

Senior Health Care System (SHCS) is created for users to enter participants' conditions and store information in a central database. When users are ready for quarterly assessments the system generates a simple summary that can be reviewed, modified, and saved as part of the summary assessments, which are required by Federal and California law.

Indiana University-Purdue University Indianapolis (IUPUI) / Object segmentation, quantification, and shape modeling are important areas inmedical image processing. By combining these techniques, researchers can find valuableways to extract and represent details on user-desired structures, which can functionas the base for subsequent analyses such as feature classification, regression, and prediction. This thesis presents a new framework for building a three-dimensional (3D) hippocampal atlas model with subfield information mapped onto its surface, with which hippocampal surface registration can be done, and the comparison and analysis can be facilitated and easily visualized. This framework combines three powerful tools for automatic subcortical segmentation and 3D surface modeling. Freesurfer and Functional magnetic resonance imaging of the brain's Integrated Registration and Segmentation Tool (FIRST) are employed for hippocampal segmentation and quantification, while SPherical HARMonics (SPHARM) is employed for parametric surface modeling. This pipeline is shown to be effective in creating a hippocampal surface atlas using the Alzheimer's Disease Neuroimaging Initiative Grand Opportunity and phase 2 (ADNI GO/2) dataset. Intra-class Correlation Coefficients (ICCs) are calculated for evaluating the reliability of the extracted hippocampal subfields. The complex folding anatomy of the hippocampus offers many analytical challenges, especially when informative hippocampal subfields are usually ignored in detailed morphometric studies. Thus, current research results are inadequate to accurately characterize hippocampal morphometry and effectively identify hippocampal structural changes related to different conditions. To address this challenge, one contribution of this study is to model the hippocampal surface using a parametric spherical harmonic model, which is a Fourier descriptor for general a 3D surface. The second contribution of this study is to extend hippocampal studies by incorporating valuable hippocampal subfield information. Based on the subfield distributions, a surface atlas is created for both left and right hippocampi. The third contribution is achieved by calculating Fourier coefficients in the parametric space. Based on the coefficient values and user-desired degrees, a pair of averaged hippocampal surface atlas models can be reconstructed. These contributions lay a solid foundation to facilitate a more accurate, subfield-guided morphometric analysis of the hippocampus and have the potential to reveal subtle hippocampal structural damage associated.