Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

Mental health outcomes for unemployed individuals who attend occupational skills / personal development training programs

Four studies report on outcomes for long-term unemployed individuals who attend occupational skills/personal development training courses in Australia. Levels of distress, depression, guilt, anger, helplessness, positive and negative affect, life satisfaction and self esteem were used as measures of well-being. Employment value, employment expectations and employment commitment were used as measures of work attitude. Social support, financial strain, and use of community resources were used as measures of life situation. Other variables investigated were causal attribution, unemployment blame, levels of coping, self efficacy, the personality variable of neuroticism, the psycho-social climate of the training course, and changes to occupational status. Training courses were (a) government funded occupational skills-based programs which included some components of personal development training, and (b) a specially developed course which focused exclusively on improving well-being, and which utilised the cognitive-behavioural therapy (CBT) approach. Data for all studies were collected longitudinally by having subjects complete questionnaires pre-course, post-course, and (for 3 of the 4 studies) at 3 months follow-up, in order to investigate long-term effects. One of the studies utilised the case-study methodology and was designed to be illustrative and assist in interpreting the quantitative data from the other 3 evaluations. The outcomes for participants were contrasted with control subjects who met the same sel~tion criteria for training. Results confirmed earlier findings that the experiences of unemployment were negative. Immediate effects of the courses were to improve well-being. Improvements were greater for those who attended courses with higher levels of personal development input, and the best results were obtained from the specially developed CBT program. Participants who had lower levels of well-being at the beginning of the courses did better as a result of training than those who were already functioning at higher levels. Course participants gained only marginal advantages over control subjects in relation to improving their occupational status. Many of the short term well-being gains made as a result of attending the courses were still evident at 3 months follow-up. Best results were achieved for the specially designed CBT program. Results were discussed in the context of prevailing theories of Ynemployment (Fryer, 1986,1988; Jahoda, 1981, 1982; Warr, 1987a, 1987b).

The impact of outcomes-driven curriculum reform on teacher perspectives of professional practice

This thesis is the result of an investigation of a Queensland example of curriculum reform based on outcomes, a type of reform common to many parts of the world during the last decade. The purpose of the investigation was to determine the impact of outcomes on teacher perspectives of professional practice. The focus was chosen to permit investigation not only of changes in behaviour resulting from the reform but also of teachers' attitudes and beliefs developed during implementation. The study is based on qualitative methodology, chosen because of its suitability for the investigation of attitudes and perspectives. The study exploits the researcher's opportunities for prolonged, direct contact with groups of teachers through the selection of an over-arching ethnography approach, an approach designed to capture the holistic nature of the reform and to contextualise the data within a broad perspective. The selection of grounded theory as a basis for data analysis reflects the open nature of this inquiry and demonstrates the study's constructivist assumptions about the production of knowledge. The study also constitutes a multi-site case study by virtue of the choice of three individual school sites as objects to be studied and to form the basis of the report. Three primary school sites administered by Brisbane Catholic Education were chosen as the focus of data collection. Data were collected from three school sites as teachers engaged in the first year of implementation of Student Performance Standards, the Queensland version of English outcomes based on the current English syllabus. Teachers' experience of outcomes-driven curriculum reform was studied by means of group interviews conducted at individual school sites over a period of fourteen months, researcher observations and the collection of artefacts such as report cards. Analysis of data followed grounded theory guidelines based on a system of coding. Though classification systems were not generated prior to data analysis, the labelling of categories called on standard, non-idiosyncratic terminology and analytic frames and concepts from existing literature wherever practicable in order to permit possible comparisons with other related research. Data from school sites were examined individually and then combined to determine teacher understandings of the reform, changes that have been made to practice and teacher responses to these changes in terms of their perspectives of professionalism. Teachers in the study understood the reform as primarily an accountability mechanism. Though teachers demonstrated some acceptance of the intentions of the reform, their responses to its conceptualisation, supporting documentation and implications for changing work practices were generally characterised by reduced confidence, anger and frustration. Though the impact of outcomes-based curriculum reform must be interpreted through the inter-relationships of a broad range of elements which comprise teachers' work and their attitudes towards their work, it is proposed that the substantive findings of the study can be understood in terms of four broad themes. First, when the conceptual design of outcomes did not serve teachers' accountability requirements and outcomes were perceived to be expressed in unfamiliar technical language, most teachers in the study lost faith in the value of the reform and lost confidence in their own abilities to understand or implement it. Second, this reduction of confidence was intensified when the scope of outcomes was outside the scope of the teachers' existing curriculum and assessment planning and teachers were confronted with the necessity to include aspects of syllabuses or school programs which they had previously omitted because of a lack of understanding or appreciation. The corollary was that outcomes promoted greater syllabus fidelity when frameworks were closely aligned. Third, other benefits the teachers associated with outcomes included the development of whole school curriculum resources and greater opportunity for teacher collaboration, particularly among schools. The teachers, however, considered a wide range of factors when determining the overall impact of the reform, and perceived a number of them in terms of the costs of implementation. These included the emergence of ethical dilemmas concerning relationships with students, colleagues and parents, reduced individual autonomy, particularly with regard to the selection of valued curriculum content and intensification of workload with the capacity to erode the relationships with students which teachers strongly associated with the rewards of their profession. Finally, in banding together at the school level to resist aspects of implementation, some teachers showed growing awareness of a collective authority capable of being exercised in response to top-down reform. These findings imply that Student Performance Standards require review and, additional implementation resourcing to support teachers through times of reduced confidence in their own abilities. Outcomes prove an effective means of high-fidelity syllabus implementation, and, provided they are expressed in an accessible way and aligned with syllabus frameworks and terminology, should be considered for inclusion in future syllabuses across a range of learning areas. The study also identifies a range of unintended consequences of outcomes-based curriculum and acknowledges the complexity of relationships among all the aspects of teachers' work. It also notes that the impact of reform on teacher perspectives of professional practice may alter teacher-teacher and school-system relationships in ways that have the potential to influence the effectiveness of future curriculum reform.

Constructing the hallucinations of psychosis in Virtual Reality

Schizophrenia is a mental disorder affecting 1-2% of the population and it is estimated 12-16% of hospital beds in Australia are occupied by patients with psychosis. The suicide rate for patients with this diagnosis is higher than that of the general population. Any technique which enhances training and treatment of this disorder will have a significant societal and economic impact. A significant research project using Virtual Reality (VR), in which both visual and auditory hallucinations are simulated, is currently being undertaken at the University of Queensland. The virtual environments created by the new software are expected to enhance the experiential learning outcomes of medical students by enabling them to experience the inner world of a patient with psychosis. In addition the Virtual Environment has the potential to provide a technologically advanced therapeutic setting where behavioral, exposure therapies can be conducted with exactly controlled exposure stimuli with an expected reduction in risk of harm. This paper reports on the current work of the project, previous stages of software development and future educational and clinical applications of the Virtual Environments. (C) 2004 Elsevier Ltd. All rights reserved.