Medical Informatics

Information technology has been revolutionizing the everyday life of the common man, while medical science has been making rapid strides in understanding disease mechanisms, developing diagnostic techniques and effecting successful treatment regimen, even for those cases which would have been classified as a poor prognosis a decade earlier. The confluence of information technology and biomedicine has brought into its ambit additional dimensions of computerized databases for patient conditions, revolutionizing the way health care and patient information is recorded, processed, interpreted and utilized for improving the quality of life. This book consists of seven chapters dealing with the three primary issues of medical information acquisition from a patient's and health care professional's perspective, translational approaches from a researcher's point of view, and finally the application potential as required by the clinicians/physician. The book covers modern issues in Information Technology, Bioinformatics Methods and Clinical Applications. The chapters describe the basic process of acquisition of information in a health system, recent technological developments in biomedicine and the realistic evaluation of medical informatics

Enhancing home based care for HIV patients using an advisory expert system

South Africa has one of the highest Human Immunodeficiency Virus (HIV) prevalence rates in the world. People living with HIV/AIDS experience many unrelieved symptoms. Nutritional care and support are important in preventing development of nutritional deficiencies. Home remedies can extend and improve the quality of their lives. Home remedies treatment involves eating healthy food, avoiding certain types of foods, psychological and emotional support and practicing hygiene to avoid skin infections (Sizani, Bandile; Nikiwe 2012). HIV/AIDS treatment and management strategies require ongoing management and support. In this research, we work with people from a clinic in Gugulethu Township in Western Cape, South Africa. The area has high prevalence of HIV (Ministry of health South Africa 2011). Most of the HIV patients in this area access medical information by walking long distances to the clinic. Most of these patients are poor and sometimes cannot afford to visit the clinic regularly for medical advice. In this township there is scarcity of health care workers (HCWs). The HCWs toil on many fronts to meet the enormous demand for the HIV/AIDS services but they are not able to meet the patients' needs. The aim of this research is to empower HIV-patients to self-manage the HIV-related symptoms which they experience. We investigated the way in which the HCWs deliver information to the patients. We interviewed the patients to understand what measures they take to manage the symptoms which they experienced. Consequently, we developed an advisory expert system to enhance Home-Based Care for HIV patients. An advisory expert system is defined as a computing system which is capable of representing and reasoning about some knowledge–rich domain, with a view to solving problems and giving advice (Gustafson et al. 1994). Since South Africa has high mobile phone penetration and most of the patients own them, we opted to use mobile phone as a tool to access the information provided by the advisory expert system. The system was then deployed at the clinic. We trained both HCWs and patients how to use the system. The findings were captured and reported after a six month deployment of the system. The results show that our system can be used as an effective tool to disseminate nutritional and psychological support information to HIV- patients in Gugulethu. The system is simple, yet practical. It helps the patients to self-manage the HIV-related symptoms which they experienced and at the same time, saves time and cost for both HCWs and the patients

Probabilistic modelling of oil rig drilling operations for business decision support: a real world application of Bayesian networks and computational intelligence.

This work investigates the use of evolved Bayesian networks learning algorithms based on computational intelligence meta-heuristic algorithms. These algorithms are applied to a new domain provided by the exclusive data, available to this project from an industry partnership with ODS-Petrodata, a business intelligence company in Aberdeen, Scotland. This research proposes statistical models that serve as a foundation for building a novel operational tool for forecasting the performance of rig drilling operations. A prototype for a tool able to forecast the future performance of a drilling operation is created using the obtained data, the statistical model and the experts' domain knowledge. This work makes the following contributions: applying K2GA and Bayesian networks to a real-world industry problem; developing a well-performing and adaptive solution to forecast oil drilling rig performance; using the knowledge of industry experts to guide the creation of competitive models; creating models able to forecast oil drilling rig performance consistently with nearly 80% forecast accuracy, using either logistic regression or Bayesian network learning using genetic algorithms; introducing the node juxtaposition analysis graph, which allows the visualisation of the frequency of nodes links appearing in a set of orderings, thereby providing new insights when analysing node ordering landscapes; exploring the correlation factors between model score and model predictive accuracy, and showing that the model score does not correlate with the predictive accuracy of the model; exploring a method for feature selection using multiple algorithms and drastically reducing the modelling time by multiple factors; proposing new fixed structure Bayesian network learning algorithms for node ordering search-space exploration. Finally, this work proposes real-world applications for the models based on current industry needs, such as recommender systems, an oil drilling rig selection tool, a user-ready rig performance forecasting software and rig scheduling tools

Problem dependent metaheuristic performance in Bayesian network structure learning.

Bayesian network (BN) structure learning from data has been an active research area in the machine learning field in recent decades. Much of the research has considered BN structure learning as an optimization problem. However, the finding of optimal BN from data is NP-hard. This fact has driven the use of heuristic algorithms for solving this kind of problem. Amajor recent focus in BN structure learning is on search and score algorithms. In these algorithms, a scoring function is introduced and a heuristic search algorithm is used to evaluate each network with respect to the training data. The optimal network is produced according to the best score evaluated. This thesis investigates a range of search and score algorithms to understand the relationship between technique performance and structure features of the problems. The main contributions of this thesis include (a) Two novel Ant Colony Optimization based search and score algorithms for BN structure learning; (b) Node juxtaposition distribution for studying the relationship between the best node ordering and the optimal BN structure; (c) Fitness landscape analysis for investigating the di erent performances of both chain score function and the CH score function; (d) A classifier method is constructed by utilizing receiver operating characteristic curve with the results on fitness landscape analysis; and finally (e) a selective o -line hyperheuristic algorithm is built for unseen BN structure learning with search and score algorithms. In this thesis, we also construct a new algorithm for producing BN benchmark structures and apply our novel approaches to a range of benchmark problems and real world problem

Regularized model learning in EDAs for continuous and multi-objective optimization

Probabilistic modeling is the de�ning characteristic of estimation of distribution algorithms (EDAs) which determines their behavior and performance in optimization. Regularization is a well-known statistical technique used for obtaining an improved model by reducing the generalization error of estimation, especially in high-dimensional problems. `1-regularization is a type of this technique with the appealing variable selection property which results in sparse model estimations. In this thesis, we study the use of regularization techniques for model learning in EDAs. Several methods for regularized model estimation in continuous domains based on a Gaussian distribution assumption are presented, and analyzed from di�erent aspects when used for optimization in a high-dimensional setting, where the population size of EDA has a logarithmic scale with respect to the number of variables. The optimization results obtained for a number of continuous problems with an increasing number of variables show that the proposed EDA based on regularized model estimation performs a more robust optimization, and is able to achieve signi�cantly better results for larger dimensions than other Gaussian-based EDAs. We also propose a method for learning a marginally factorized Gaussian Markov random �eld model using regularization techniques and a clustering algorithm. The experimental results show notable optimization performance on continuous additively decomposable problems when using this model estimation method. Our study also covers multi-objective optimization and we propose joint probabilistic modeling of variables and objectives in EDAs based on Bayesian networks, speci�cally models inspired from multi-dimensional Bayesian network classi�ers. It is shown that with this approach to modeling, two new types of relationships are encoded in the estimated models in addition to the variable relationships captured in other EDAs: objectivevariable and objective-objective relationships. An extensive experimental study shows the e�ectiveness of this approach for multi- and many-objective optimization. With the proposed joint variable-objective modeling, in addition to the Pareto set approximation, the algorithm is also able to obtain an estimation of the multi-objective problem structure. Finally, the study of multi-objective optimization based on joint probabilistic modeling is extended to noisy domains, where the noise in objective values is represented by intervals. A new version of the Pareto dominance relation for ordering the solutions in these problems, namely �-degree Pareto dominance, is introduced and its properties are analyzed. We show that the ranking methods based on this dominance relation can result in competitive performance of EDAs with respect to the quality of the approximated Pareto sets. This dominance relation is then used together with a method for joint probabilistic modeling based on `1-regularization for multi-objective feature subset selection in classi�cation, where six di�erent measures of accuracy are considered as objectives with interval values. The individual assessment of the proposed joint probabilistic modeling and solution ranking methods on datasets with small-medium dimensionality, when using two di�erent Bayesian classi�ers, shows that comparable or better Pareto sets of feature subsets are approximated in comparison to standard methods

Investigating Solutions to Minimise Participation Bias in Case-Control Studies

Case-control studies are used in epidemiology to try to determine variables associated with a disease, by comparing those with the disease (cases) against those without (controls). Participation rates in epidemiology studies have declined over recent years, particularly in the control group where there is less motivation to participate. Non-participation can lead to bias and this can result in the findings differing from the truth. A literature review of the last nine years shows that non-participation occurred in published studies as recently as 2015, and an assessment of articles from three high impact factor epidemiology journals concludes that participation bias is a possibility which is not always controlled for. Methods to reduce bias resulting from non-participation are provided, which suit different data structures and purposes. A guidance tool is subsequently developed to aid the selection of a suitable approach. Many of these methods rely on the assumption that the data are missing at random. Therefore, a new solution is developed which utilises population data in place of the control data, which recovers the true odds ratio even when data are missing not at random. Chain event graphs are a graphical representation of a statistical model which are used for the first time to draw conclusions about the missingness mechanisms resulting from non-participation in case-control data. These graphs are also adapted specifically to further investigate non-participation in case-control studies. Throughout, in addition to hypothetical examples and simulated data, a diabetes dataset is used to demonstrate the methods. Critical comparisons are drawn between existing methods and the new methods developed here, and discussion provided for when each method is suitable. Identification of factors associated with a disease are crucial for improved patient care, and accurate analyses of case-control data, with minimal biases, are one way in which this can be achieved

Infections

• Infections with viruses, bacteria, and macroparasites have been identified as strong risk factors for specific cancers. • Overall, about 2 million (16%) of the total of 12.7 million new cancer cases in 2008 are attributable to infections. This fraction varies 10-fold by region; it is lowest in North America, Australia, and New Zealand (≤ 4%) and highest in sub-Saharan Africa (33%). • Helicobacter pylori, hepatitis B and C viruses, and human papillomaviruses are responsible for 1.9 million cancer cases globally, including mainly gastric, liver, and cervical cancer, respectively. • Infection with HIV substantially increases the risk of virusassociated cancers, through immunosuppression. • Application of existing methods for infection prevention, such as vaccination, safe injection practices, and safe sexual behaviour, or antimicrobial and antiparasite treatments could have a major impact on the future burden of cancer worldwide

Developing tissue proteomics: Differential in gel electrophoresis in biomarker discovery and proteomic degradation

The field of proteomics and functional genomics has developed steadily since the completion of the human genome project. The wealth of genomic information and the pace at which it was compiled was astounding. Proteomics, despite considerable effort, on the other hand has not seen quite the same pace of development. The progress being considerably hindered by the lack of an amplification process and the relative complexity of the proteome in comparison to the genome. These intrinsic difficulties have led to the sensitivity of proteomic techniques being pushed closer to physical limits. There is therefore a further need to re-evaluated techniques such as sample preparation and integrity, analytical methods and collaborative strategies to maximise the effectiveness and quality of data collected. The importance of tissue in scientific and clinical research is unequivocal. However, tissue is difficult to collect, store and work with due to issues with proteomic degradation and storage. Good lab practices can minimise the effect of degradation but degradation of proteins can be rapid. Strategies to minimise degradation include freezing, formalin fixing and microwave treatment which all have their relative advantages and disadvantages. The importance of sample preparation as being the top of the workflow is often acknowledged but improvements are not well described in the literature. The main aim of this thesis is to present investigative studies into the mitigation of some of the limitations in tissue sample degradation, analytical approaches in differential in gel electrophoresis and accessing DiGE spot and tissue profile data. Presented is the evaluation of the effectiveness of rapid and controlled heating of intact tissue to inactivate native enzymatic activity and to aid in the cessation of proteomic degradation. A multifaceted analytical approach of differential in Gel electrophoresis spot data is assessed, giving proteomic profiles of mouse brain tissue. Preliminary data is presented showing that the process of heat-treatment has had a predominantly beneficial effect on mouse brain tissue, with a higher percentage of spots stabilised in heat-treated samples compared to snap-frozen samples. However, stabilisation did occur in snap-frozen samples for different protein spot so the appropriateness of using heat-treatment is as yet not fully determined and requires further analysis. In addition, the variation in tissue profiles of WKY, SP.WKYGla.2a and SHRSP rat model for hypertension is investigated with the future prospect of providing that vital connection between genomic and proteomic data and link phenotype and genotype preliminary investigated. A number of putative markers were identified and quantified using DiGE analysis. In order for these markers to be accepted as biomarkers, more downstream validation is required, however this study provides a good spring board as a proof of concept in using DiGE as an global putative biomarker discovery platform