Integrating ontologies and argumentation for decision-making in breast cancer

This thesis describes some of the problems in providing care for patients with breast cancer. These are then used to motivate the development of an extension to an existing theory of argumentation, which I call the Ontology-based Argumentation Formalism (OAF). The work is assessed in both theoretical and empirical ways. From a clinical perspective, there is a problem with the provision of care. Numerous reports have noted the failure to provide uniformly high quality care, as well as the number of deaths caused by medical care. The medical profession has responded in various ways, but one of these has been the development of Decision Support Systems (DSS). The evidence for the effectiveness of such systems is mixed, and the technical basis of such systems remains open to debate. However, one basis that has been used is argumentation. An important aspect of clinical practice is the use of the evidence from clinical trials, but these trials are based on the results in defined groups of patients. Thus when we use the results of clinical trials to reason about treatments, there are two forms of information we are interested in - the evidence from trials and the relationships between groups of patients and treatments. The relational information can be captured in an ontology about the groups of patients and treatments, and the information from the trials captured as a set of defeasible rules. OAF is an extension of an existing argumentation system, and provides the basis for an argumentation-based Knowledge Representation system which could serve as the basis for future DSS. In OAF, the ontology provides a repository of facts, both asserted and inferred on the basis of formulae in the ontology, as well as defining the language of the defeasible rules. The defeasible rules are used in a process of defeasible reasoning, where monotonic consistent chains of reasoning are used to draw plausible conclusions. This defeasible reasoning is used to generate arguments and counter-arguments. Conflict between arguments is defined in terms of inconsistent formulae in the ontology, and by using existing proposals for ontology languages we are able to make use of existing proposals and technologies for ontological reasoning. There are three substantial areas of novel work: I develop an extension to an existing argumentation formalism, and prove some simple properties of the formalism. I also provide a novel formalism of the practical syllogism and related hypothetical reasoning, and compare my approach to two other proposals in the literature. I conclude with a substantial case study based on a breast cancer guideline, and in order to do so I describe a methodology for comparing formal and informal arguments, and use the results of this to discuss the strengths and weaknesses of OAF. In order to develop the case study, I provide a prototype implementation. The prototype uses a novel incremental algorithm to construct arguments and I give soundness, completeness and time-complexity results. The final chapter of the thesis discusses some general lessons from the development of OAF and gives ideas for future work

An OLAP-GIS System for Numerical-Spatial Problem Solving in Community Health Assessment Analysis

Community health assessment (CHA) professionals who use information technology need a complete system that is capable of supporting numerical-spatial problem solving. On-Line Analytical Processing (OLAP) is a multidimensional data warehouse technique that is commonly used as a decision support system in standard industry. Coupling OLAP with Geospatial Information System (GIS) offers the potential for a very powerful system. For this work, OLAP and GIS were combined to develop the Spatial OLAP Visualization and Analysis Tool (SOVAT) for numerical-spatial problem solving. In addition to the development of this system, this dissertation describes three studies in relation to this work: a usability study, a CHA survey, and a summative evaluation.The purpose of the usability study was to identify human-computer interaction issues. Fifteen participants took part in the study. Three participants per round used the system to complete typical numerical-spatial tasks. Objective and subjective results were analyzed after each round and system modifications were implemented. The result of this study was a novel OLAP-GIS system streamlined for the purposes of numerical-spatial problem solving.The online CHA survey aimed to identify the information technology currently used for numerical-spatial problem solving. The survey was sent to CHA professionals and allowed for them to record the individual technologies they used during specific steps of a numerical-spatial routine. In total, 27 participants completed the survey. Results favored SPSS for numerical-related steps and GIS for spatial-related steps.Next, a summative within-subjects crossover design compared SOVAT to the combined use of SPSS and GIS (termed SPSS-GIS) for numerical-spatial problem solving. Twelve individuals from the health sciences at the University of Pittsburgh participated. Half were randomly selected to use SOVAT first, while the other half used SPSS-GIS first. In the second session, they used the alternate application. Objective and subjective results favored SOVAT over SPSS-GIS. Inferential statistics were analyzed using linear mixed model analysis. At the .01 level, SOVAT was statistically significant from SPSS-GIS for satisfaction and time (p < .002).The results demonstrate the potential for OLAP-GIS in CHA analysis. Future work will explore the impact of an OLAP-GIS system in other areas of public health

Computer aided identification of biological specimens using self-organizing maps

For scientific or socio-economic reasons it is often necessary or desirable that biological material be identified. Given that there are an estimated 10 million living organisms on Earth, the identification of biological material can be problematic. Consequently the services of taxonomist specialists are often required. However, if such expertise is not readily available it is necessary to attempt an identification using an alternative method. Some of these alternative methods are unsatisfactory or can lead to a wrong identification. One of the most common problems encountered when identifying specimens is that important diagnostic features are often not easily observed, or may even be completely absent. A number of techniques can be used to try to overcome this problem, one of which, the Self Organizing Map (or SOM), is a particularly appealing technique because of its ability to handle missing data. This thesis explores the use of SOMs as a technique for the identification of indigenous trees of the Acacia species in KwaZulu-Natal, South Africa. The ability of the SOM technique to perform exploratory data analysis through data clustering is utilized and assessed, as is its usefulness for visualizing the results of the analysis of numerical, multivariate botanical data sets. The SOM’s ability to investigate, discover and interpret relationships within these data sets is examined, and the technique’s ability to identify tree species successfully is tested. These data sets are also tested using the C5 and CN2 classification techniques. Results from both these techniques are compared with the results obtained by using a SOM commercial package. These results indicate that the application of the SOM to the problem of biological identification could provide the start of the long-awaited breakthrough in computerized identification that biologists have eagerly been seeking.Dissertation (MSc)--University of Pretoria, 2011.Computer Scienceunrestricte