As part of the Accelerated Shared Growth Initiative of South Africa (AsgiSA), the South African government is focusing on improving the country's export performance (Department of Trade and Industry, 2006a). One of the objectives of the Department of Trade and Industry (DTI) is to increase the level of South African exports through the development and implementation of strategies for targeted markets. The DTI commissioned a study to scientifically identify priority products and markets in order to justify its export promotion activities and to ensure that government resources are effectively allocated. Tills led to the development of the Decision Support Model (DSM) by Viviers and Pearson (2007) as an instrument to identify realistic export opportunities for South Africa. The Viviers and Pearson (2007) study adapted and refined the methodology of Cuyvers et al. (1995) to the South African circumstances and used a sequential filtering process to identify realistic product/market export combinations for South Africa. The outcome of the DSM for South Africa was 9690 SITC product/market opportunities in seventy-four countries, clustered in twelve geographic regions. One of these regions was the Middle East. The DTI indicated the need for a study on South Africa's export opportunities to the Middle East as a result of their prioritisation of regions for export promotion. This need is therefore the rationale of this study. The research objectives were to analyse the identified Middle East countries and determine the product with the most realistic export opportunities to two of the Middle East countries and lastly, to develop a market profile to assist the DTI in promoting the exports of this product to these countries. In this study, several methods of product and market selection were investigated in order to determine the most suitable method to identify the product/market opportunities from the DSM to the Middle East. The product selection method selected involved a three-phase filtering process to determine the product with the highest export potential to the Middle East. The analysis was based on a cluster-selection process. It was determined that fruit juice was the product with the highest export potential to the region. The market selection process used a composite market potential index to determine which country in the Middle East had the highest potential for exports of fruit juice from South Africa. The two countries in the Middle East with the highest potential to import fruit juice from South Africa were Oman and Qatar. A market profile for the export of fruit juice to Oman and Qatar was developed. The market profile provided an economic overview of the two markets, analysed the market potential of fruit juice and provided technical information regarding the requirements for the export of fruit juice by South Africa to Oman and Qatar. The results of this study firstly indicate to the DTI and exporters that South Africa should export fruit juice to Oman and Qatar and secondly presents a market profile with detailed information of the process to follow in exporting fruit juice to these countries. / Thesis (M.Com. (International Commerce))--North-West University, Potchefstroom Campus, 2010.

The world we live in today demands systems that make our lives easier and help us make the right choices on time. There exists a growing need for quality products that help us in our day to day activities. Easy-to-use computer-based decision support systems apply all available and applicable data with the correct model, knowledge and skill of decision makers to support the user to choose the best solution. It is therefore important to develop decision support systems correctly to be of value to the user. Looking at other information system developments, the author tries to suggest ways to develop decision support systems. System development methodologies are investigated to determine if they are able to address the development of the very important decision support system components. Five methodologies were discussed and researched for their theoretical suitability to address the development of decision support systems. The author performed qualitative research using case studies and semi-structured interviews to assess the use or non-use of system development methodologies in the development of decision support systems in a South African context. Content and cross-case analyses were used to achieve results that are discussed to broaden the knowledge on the development of decision support systems. The author provides some explanations to why system development methodologies were not used in the development of the case studies. This research not only contributes to the academic body of knowledge about using system development methodologies in the development of decision support systems, but could also be useful to developers embarking on a new decision support system development. / Thesis (MSc (Computer Science))--North-West University, Potchefstroom Campus, 2011.

Current monitoring tools in the intensive care units focus on displaying physiologically monitored parameters (e.g. vital signs such as heart rate, respiratory rate and blood pressure) at the present moment. Added clinical utility can be found by analyzing how the conditions of a patient evolve with time, and automatically relating that dynamics to population trends. Variability analysis consists of monitoring patterns of variation over intervals in time of physiological signals such as heart rate and respiratory rate. Given that illness has been associated in multiple studies with altered variability, most commonly lack of variation, variability monitoring represents a tool whose contribution at the bedside still needs to be explored. With the long term objective of improving care, this thesis promotes the use of variability analysis through three distinct types of analysis: facing the technical challenges involved with the dimensionality of variability analysis, enhancing the physiological understanding of variability, and showing its utility in real world clinical applications. In particular, the contributions of this thesis include: the review and classification into domains of a large array of measures of variability; the design of system and methods to integrate multiple measures of variability into a unique score, called composite measure, bringing relevant information to specific clinical problems; the comparison of patterns of heart rate variability during exercise and sepsis development, showing the inability of single measures of variability to discriminate between the two kinds of stressors; the analysis of variability produced from a physiologically-based model of the cardiovascular system, showing that each single measure of variability is an unspecific sensor of the body, thereby promoting multivariate analysis to the only means of understanding the physiology underlying variability; the study of heart rate variability in a population at high risk of sepsis development, showing the ability of variability to predict the occurrence of sepsis more than 48 hours in advance respect to the time of diagnosis of the clinical team; the study of heart and respiratory rate variability in intubated intensive care unit patients, showing how variability can provide a better way of assessing extubation readiness respect to commonly used clinical parameters. Overall, it is hoped that these novel contributions will help promoting bedside applications of variability monitoring to improve patient care.

This research addresses the design and development of a decision support tool to provide healthcare policy makers with insights and feedback when evaluating proposed patient flow and infection mitigation and control strategies in the emergency department (ED). An agent-based modeling (ABM) approach was used to simulate EDs, designed to be tuneable to specific parameters related to specification of topography, agent characteristics and behaviours, and the application in question. In this way, it allows for the user to simulate various ‘what-if’ scenarios related to infection spread and patient flow, where such policy questions may otherwise be left “best intent open loop” in practice. Infection spread modeling and patient flow modeling have been addressed by mathematical and queueing models in the past; however, the application of an ABM approach at the level of an institution is novel. A conjecture of this thesis is that such a tool should be augmented with Machine Learning (ML) technology to assist in performing optimization or search in which patient flow and infection spread are signals or variables of interest. Therefore this work seeks to design and demonstrate a decision support tool with ML capability for optimizing ED processes. The primary contribution of this thesis is the development of a novel, flexible, and tuneable framework for spatial, human-scale ABM in the context of a decision support tool for healthcare policy relating to infection spread and patient flow within EDs . The secondary contribution is the demonstration of the utility of ML for automatic policy generation with respect to the ABM tool. The application of ML to automatically generate healthcare policy in concert with an ABM is believed to be novel and of emerging practical importance. The tertiary contribution is the development and testing of a novel heuristic specific to the ML paradigm used: Genetic Programming (GP). This heuristic aids learning tasks performed in conjunction with ABMs for healthcare policy. The primary contribution is clearly demonstrated within this thesis. The others are of a more difficult nature; the groundwork has been laid for further work in these areas that are likely to remain open for the foreseeable future.

Research has shown that data warehouses potentially offer great investment opportunities to business. To benefit from this, business needs to invest large sums of money. Such investments are very risky, as no guarantee of the success of these ventures can be given. Object-oriented development has proved successful for developing operational systems in industry. This study researches object-oriented techniques to discover whether these techniques could be used successfully in data warehousing. A literature study focuses on the definition of an information systems development methodology and defines the components of such methodology. A further literature study on four popular object-oriented methodologies determines the commonalities of these methodologies. In conclusion, a literature study on data warehouse methodologies is done to discover the phases and techniques used in developing data warehouses. Based on the literature, a method is proposed to build a data warehouse harnessing object-oriented phases and techniques. The proposed method is applied as an interpretive experiment, followed by an evaluation of the data warehouse implemented. / Thesis (M.Sc. (Computer Science))--North-West University, Potchefstroom Campus, 2009.

The world we live in today demands systems that make our lives easier and help us make the right choices on time. There exists a growing need for quality products that help us in our day to day activities. Easy-to-use computer-based decision support systems apply all available and applicable data with the correct model, knowledge and skill of decision makers to support the user to choose the best solution. It is therefore important to develop decision support systems correctly to be of value to the user. Looking at other information system developments, the author tries to suggest ways to develop decision support systems. System development methodologies are investigated to determine if they are able to address the development of the very important decision support system components. Five methodologies were discussed and researched for their theoretical suitability to address the development of decision support systems. The author performed qualitative research using case studies and semi-structured interviews to assess the use or non-use of system development methodologies in the development of decision support systems in a South African context. Content and cross-case analyses were used to achieve results that are discussed to broaden the knowledge on the development of decision support systems. The author provides some explanations to why system development methodologies were not used in the development of the case studies. This research not only contributes to the academic body of knowledge about using system development methodologies in the development of decision support systems, but could also be useful to developers embarking on a new decision support system development. / Thesis (MSc (Computer Science))--North-West University, Potchefstroom Campus, 2011.

As part of the Accelerated Shared Growth Initiative of South Africa (AsgiSA), the South African government is focusing on improving the country's export performance (Department of Trade and Industry, 2006a). One of the objectives of the Department of Trade and Industry (DTI) is to increase the level of South African exports through the development and implementation of strategies for targeted markets. The DTI commissioned a study to scientifically identify priority products and markets in order to justify its export promotion activities and to ensure that government resources are effectively allocated. Tills led to the development of the Decision Support Model (DSM) by Viviers and Pearson (2007) as an instrument to identify realistic export opportunities for South Africa. The Viviers and Pearson (2007) study adapted and refined the methodology of Cuyvers et al. (1995) to the South African circumstances and used a sequential filtering process to identify realistic product/market export combinations for South Africa. The outcome of the DSM for South Africa was 9690 SITC product/market opportunities in seventy-four countries, clustered in twelve geographic regions. One of these regions was the Middle East. The DTI indicated the need for a study on South Africa's export opportunities to the Middle East as a result of their prioritisation of regions for export promotion. This need is therefore the rationale of this study. The research objectives were to analyse the identified Middle East countries and determine the product with the most realistic export opportunities to two of the Middle East countries and lastly, to develop a market profile to assist the DTI in promoting the exports of this product to these countries. In this study, several methods of product and market selection were investigated in order to determine the most suitable method to identify the product/market opportunities from the DSM to the Middle East. The product selection method selected involved a three-phase filtering process to determine the product with the highest export potential to the Middle East. The analysis was based on a cluster-selection process. It was determined that fruit juice was the product with the highest export potential to the region. The market selection process used a composite market potential index to determine which country in the Middle East had the highest potential for exports of fruit juice from South Africa. The two countries in the Middle East with the highest potential to import fruit juice from South Africa were Oman and Qatar. A market profile for the export of fruit juice to Oman and Qatar was developed. The market profile provided an economic overview of the two markets, analysed the market potential of fruit juice and provided technical information regarding the requirements for the export of fruit juice by South Africa to Oman and Qatar. The results of this study firstly indicate to the DTI and exporters that South Africa should export fruit juice to Oman and Qatar and secondly presents a market profile with detailed information of the process to follow in exporting fruit juice to these countries. / Thesis (M.Com. (International Commerce))--North-West University, Potchefstroom Campus, 2010.

The aim of the thesis was to develop and evaluate a Computerised Decision Support System (CDSS) for use in pre-hospital care. The thesis was guided by a theoretical framework for developing and evaluating a complex intervention. The four studies used different designs and methods. The first study was a systematic review of randomised controlled trials. The second and the last studies had experimental and quasi-experimental designs, where the CDSS was evaluated in a simulation setting and in a clinical setting. The third study included in the thesis had a qualitative case study design. The main findings from the studies in the thesis were that there is a weak evidence base for the use of CDSS in pre-hospital care. No studies have previously evaluated the effect of CDSS in pre-hospital care. Due to the context, pre-hospital care is dependent on protocol-based care to be able to deliver safe, high-quality care. The physical format of the current paper based guidelines and protocols are the main obstacle to their use. There is a request for guidelines and protocols in an electronic format among both clinicians and leaders of the ambulance organisations. The use of CDSS in the pre-hospital setting has a positive effect on compliance with pre-hospital guidelines. The largest effect is in the primary survey and in the anamnesis of the patient. The CDSS also increases the amount of information collected in the basic pre-hospital assessment process. The evaluated CDSS had a limited effect on on-the-scene time. The developed and evaluated CDSS has the ability to increase pre-hospital patient safety by reducing the risks of cognitive bias. Standardising the assessment process, enabling explicit decision support in the form of checklists, assessment rules, differential diagnosis lists and rule out worst-case scenario strategies, reduces the risk of premature closure in the assessment of the pre-hospital patient.

Clinicians and hospital administrators are increasingly challenged to achieve efficient evidence-based care. Clinical decision support (CDS) tools are being introduced into the clinical setting to facilitate the bridging of knowledge gaps at the point of care. Order sets are one of the tools used to facilitate this knowledge translation. Using the realist review methodology and a focus group of interview participants, this thesis explored retrospectively some of the causal relationships that lead to effective and successful order set adoption. Findings demonstrate the need for in-depth and regular review of context and order set adoption. Technology can offer some enhancements in the form of delivery tools, but it also introduces new and complex challenges for development and implementation. Ongoing software development is needed to improve delivery formats as well as incorporate effective tools to allow for efficient continuous quality improvement supports. / Graduate / 0769 / 0566 / hallsm.77@gmail.com

This dissertation focuses on developing imaging informatics algorithms for clinical decision support systems (CDSSs) based on histopathological whole-slide images (WSIs). Currently, histopathological analysis is a common clinical procedure for diagnosing cancer presence, type, and progression. While diagnosing patients using biopsy slides, pathologists manually assess nuclear morphology. However, making decisions manually from a slide with millions of nuclei can be time-consuming and subjective. Researchers have proposed CDSSs that help in decision making but they have limited reproducibility. The development of robust CDSSs for WSIs faces several informatics challenges: (1) Lack of robust segmentation methods for histopathological images, (2) Semantic gap between quantitative information and pathologist’s knowledge, (3) Lack of batch-invariant imaging informatics methods, (4) Lack of knowledge models for capturing informative patterns in large WSIs, and (5) Lack of guidelines for optimizing and validating diagnostic models. I conducted advanced imaging informatics research to overcome these challenges and developed novel methods to extract information from WSIs, to model knowledge embedded in large histopathological datasets, such as The Cancer Genome Atlas (TCGA), and to assist decision making with biological and clinical validation. I validated my methods for two applications: (1) diagnosis of histopathology-based endpoints such as subtype and grade and (2) prediction of clinical endpoints such as metastasis, stage, lymphnode spread, and survival. The statistically emergent feature subsets in the diagnostic models for histopathology-based endpoints were concordant with pathologists’ knowledge.