Using MediaWiki as an efficient data repository and ubiquitous learning tool : an Australian example

The functionality of MediaWiki ensures it is a valuable learning repository for sharing and storing information. Constructivist learning can be promoted alongside a wiki repository and various wireless u-learning tools such as mobile phones and digital cameras, to encourage students to gather and share a range of primary and secondary information in a variety of subject areas. This paper outlines one initiative adopted at an Australian University specialising in distance education, which uses a MediaWiki as the primary method for content delivery. Over a period of three-years, the Drugs, Crime and Society wiki has evolved into an organic information repository for storing and accessing current research, press and drug agency material that supplements core themes examined in each topic of the curriculum. A constructivist approach has been employed to encourage students to engage in a range of assessable and non-assessable information sharing activities. The paper also demonstrates how the Drugs, Crime and Society wiki can be accessed through various wireless u-learning technologies, which enables students undertaking field placements to add and share primary information with other students and practitioners working in the drugs field.

The summer games

As part of a nationally funded project, we have developed and used 'games' as student centred teaching resources to enrich the capacity for design in beginning students in architecture, landscape architecture and urban design. Students are encouraged to learn inter-actively in a milieu characterised by self-directed play in a low-risk computer modelling environment. Recently thirteen upper year design students, six from Adelaide University (Adelaide, South Australia, Australia), five from Deakin University (Geelong, Victoria, Australia), and two from Victoria University, (Wellington, New Zealand) were commissioned over a ten-week period of the 2000-2001 Australian summer to construct a new series of games. This paper discusses the process behind constructing these games.

This paper discusses six topical areas:

– what is a game;
– specific goals of the summer games;
– the structure of a game;
– the game-making process;
– key findings from the production unit; and
– future directions.

Radiation exposure and the justification of computed tomography scanning in an Australian hospital emergency department

In an emergency department (ED), computed tomography (CT) is particularly beneficial in the investigation of high-speed trauma patients. With the advent of multidetector CT (MDCT) scanners, it is becoming faster and easier to conduct scans. In recent years, this has become evident with an increasing number of CT requests. Patients who have multiple CT scans during their hospital stay can receive radiation doses that have an increased theoretical risk of induction of cancer. It is essential that the clinical justification for each CT scan be considered on an individual basis and that due consideration is given to the radiation risk and possible diagnostic benefit. The current lack of a central State or Commonwealth data repository for medical images is a contributing factor to excessive radiation dosage to the population. The principles of justification and radiation risks are discussed in this study.

The post-fracture care gap among Canadian First Nations peoples : a retrospective cohort study

Summary Despite targeted attempts to reduce post-fracture care gaps, we hypothesized that a larger care gap would be experienced by First Nations compared to non-First Nations people. First Nations peoples were eight times less likely to receive post-fracture care compared to non-First Nations peoples, representing a clinically significant ethnic difference in post-fracture care.

Introduction First Nations peoples are the largest group of aboriginal (indigenous or native) peoples in Canada. Canadian First Nations peoples have a greater risk of fracture compared to non-First Nations peoples. We hypothesized that ethnicity might be associated with a larger gap in post-fracture care.

Methods Non-traumatic major osteoporotic fractures for First Nations and non-First Nations peoples aged ≥50 years were identified from a population-based data repository for Manitoba, Canada between April 1996 and March 2002. Logistic regression analysis was used to examine the probability of receiving a BMD test, a diagnosis of osteoporosis, or beginning an osteoporosis-related drug in the 6 months post-fracture.

Results A total of 11,234 major osteoporotic fractures were identified; 502 occurred in First Nations peoples. After adjustment for confounding covariates, First Nations peoples were less likely to receive a BMD test [odds ratio (OR) 0.1, 95% confidence interval (CI), 0.0–0.5], osteoporosis-related drug treatment (OR, 0.5; 95% CI, 0.3–0.7), or a diagnosis of osteoporosis (OR, 0.5; 95% CI, 0.3–0.7) following a fracture compared to non-First Nations peoples. Females were more likely to have a BMD test (OR, 5.0; 95% CI, 2.6–9.3), to be diagnosed with osteoporosis (OR, 1.7; 95% CI, 1.5–2.0), and to begin drug treatment (OR, 4.1; 95% CI, 2.7–6.4) compared to males.

Conclusions An ethnicity difference in post-fracture care was observed. Further work is needed to elucidate underlying mechanisms for this difference and to determine whether failure to initiate treatment originates with the medical practitioner, the patient, or a combination of both. It is imperative that all residents of Manitoba receive efficacious and equal care post-fracture, regardless of ethnicity.

IPMA : Indirect patterns mining algorithm

Indirect pattern is considered as valuable and hidden information in transactional database. It represents the property of high dependencies between two items that are rarely occurred together but indirectly appeared via another items. Indirect pattern mining is very important because it can reveal a new knowledge in certain domain applications. Therefore, we propose an Indirect Pattern Mining Algorithm (IPMA) in an attempt to mine the indirect patterns from data repository. IPMA embeds with a measure called Critical Relative Support (CRS) measure rather than the common interesting measures. The result shows that IPMA is successful in generating the indirect patterns with the various threshold values.

Fracture prediction from repeat BMD measurements in clinical practice

Summary: We investigated whether repeat BMD measurements in clinical populations are useful for fracture risk assessment. We report that repeat BMD measurements are a robust predictor of fracture in clinical populations; this is not affected by preceding BMD change or recent osteoporosis therapy. Introduction: In clinical practice, many patients selectively undergo repeat bone mineral density (BMD) measurements. We investigated whether repeat BMD measurements in clinical populations are useful for fracture risk assessment and whether this is affected by preceding change in BMD or recent osteoporosis therapy. Methods: We identified women and men aged ≥50 years who had a BMD measurement during 1990–2009 from a large clinical BMD database for Manitoba, Canada (n = 50,215). Patient subgroups aged ≥50 years at baseline with repeat BMD measures were identified. Data were linked to an administrative data repository, from which osteoporosis therapy, fracture outcomes, and covariates were extracted. Using Cox proportional hazards models, we assessed covariate-adjusted risk for major osteoporotic fracture (MOF) and hip fracture according to BMD (total hip, lumbar spine, femoral neck) at different time points. Results: Prevalence of osteoporosis therapy increased from 18 % at baseline to 55 % by the fourth measurement. Total hip BMD was predictive of MOF at each time point. In the patient subgroup with two repeat BMD measurements (n = 13,481), MOF prediction with the first and second measurements was similar: adjusted-hazard ratio (HR) per SD 1.45 (95 % CI 1.34–1.56) vs. 1.64 (95 % CI 1.48–1.81), respectively. No differences were seen when the second measurement results were stratified by preceding change in BMD or osteoporosis therapy (both p-interactions >0.2). Similar results were seen for hip fracture prediction and when spine and femoral neck BMD were analyzed. Conclusion: Repeat BMD measurements are a robust predictor of fracture in clinical populations; this is not affected by preceding BMD change or recent osteoporosis therapy.

Meta-learning for data summarization based on instance selection method

The purpose of instance selection is to identify which instances (examples, patterns) in a large dataset should be selected as representatives of the entire dataset, without significant loss of information. When a machine learning method is applied to the reduced dataset, the accuracy of the model should not be significantly worse than if the same method were applied to the entire dataset. The reducibility of any dataset, and hence the success of instance selection methods, surely depends on the characteristics of the dataset, as well as the machine learning method. This paper adopts a meta-learning approach, via an empirical study of 112 classification datasets from the UCI Repository [1], to explore the relationship between data characteristics, machine learning methods, and the success of instance selection method.

Classification of healthcare data using genetic fuzzy logic system and wavelets

Healthcare plays an important role in promoting the general health and well-being of people around the world. The difficulty in healthcare data classification arises from the uncertainty and the high-dimensional nature of the medical data collected. This paper proposes an integration of fuzzy standard additive model (SAM) with genetic algorithm (GA), called GSAM, to deal with uncertainty and computational challenges. GSAM learning process comprises three continual steps: rule initialization by unsupervised learning using the adaptive vector quantization clustering, evolutionary rule optimization by GA and parameter tuning by the gradient descent supervised learning. Wavelet transformation is employed to extract discriminative features for high-dimensional datasets. GSAM becomes highly capable when deployed with small number of wavelet features as its computational burden is remarkably reduced. The proposed method is evaluated using two frequently-used medical datasets: the Wisconsin breast cancer and Cleveland heart disease from the UCI Repository for machine learning. Experiments are organized with a five-fold cross validation and performance of classification techniques are measured by a number of important metrics: accuracy, F-measure, mutual information and area under the receiver operating characteristic curve. Results demonstrate the superiority of the GSAM compared to other machine learning methods including probabilistic neural network, support vector machine, fuzzy ARTMAP, and adaptive neuro-fuzzy inference system. The proposed approach is thus helpful as a decision support system for medical practitioners in the healthcare practice.

A hybrid FAM–CART model and its application to medical data classification

In this paper, a hybrid model consisting of the fuzzy ARTMAP (FAM) neural network and the classification and regression tree (CART) is formulated. FAM is useful for tackling the stability–plasticity dilemma pertaining to data-based learning systems, while CART is useful for depicting its learned knowledge explicitly in a tree structure. By combining the benefits of both models, FAM–CART is capable of learning data samples stably and, at the same time, explaining its predictions with a set of decision rules. In other words, FAM–CART possesses two important properties of an intelligent system, i.e., learning in a stable manner (by overcoming the stability–plasticity dilemma) and extracting useful explanatory rules (by overcoming the opaqueness issue). To evaluate the usefulness of FAM–CART, six benchmark medical data sets from the UCI repository of machine learning and a real-world medical data classification problem are used for evaluation. For performance comparison, a number of performance metrics which include accuracy, specificity, sensitivity, and the area under the receiver operation characteristic curve are computed. The results are quantified with statistical indicators and compared with those reported in the literature. The outcomes positively indicate that FAM–CART is effective for undertaking data classification tasks. In addition to producing good results, it provides justifications of the predictions in the form of a decision tree so that domain users can easily understand the predictions, therefore making it a useful decision support tool.

Medical data classification using interval type-2 fuzzy logic system and wavelets

This paper introduces an automated medical data classification method using wavelet transformation (WT) and interval type-2 fuzzy logic system (IT2FLS). Wavelet coefficients, which serve as inputs to the IT2FLS, are a compact form of original data but they exhibits highly discriminative features. The integration between WT and IT2FLS aims to cope with both high-dimensional data challenge and uncertainty. IT2FLS utilizes a hybrid learning process comprising unsupervised structure learning by the fuzzy c-means (FCM) clustering and supervised parameter tuning by genetic algorithm. This learning process is computationally expensive, especially when employed with high-dimensional data. The application of WT therefore reduces computational burden and enhances performance of IT2FLS. Experiments are implemented with two frequently used medical datasets from the UCI Repository for machine learning: the Wisconsin breast cancer and Cleveland heart disease. A number of important metrics are computed to measure the performance of the classification. They consist of accuracy, sensitivity, specificity and area under the receiver operating characteristic curve. Results demonstrate a significant dominance of the wavelet-IT2FLS approach compared to other machine learning methods including probabilistic neural network, support vector machine, fuzzy ARTMAP, and adaptive neuro-fuzzy inference system. The proposed approach is thus useful as a decision support system for clinicians and practitioners in the medical practice. copy; 2015 Elsevier B.V. All rights reserved.