Injury narrative text classification : a preliminary study

Description of a patient's injuries is recorded in narrative text form by hospital emergency departments. For statistical reporting, this text data needs to be mapped to pre-defined codes. Existing research in this field uses the Naïve Bayes probabilistic method to build classifiers for mapping. In this paper, we focus on providing guidance on the selection of a classification method. We build a number of classifiers belonging to different classification families such as decision tree, probabilistic, neural networks, and instance-based, ensemble-based and kernel-based linear classifiers. An extensive pre-processing is carried out to ensure the quality of data and, in hence, the quality classification outcome. The records with a null entry in injury description are removed. The misspelling correction process is carried out by finding and replacing the misspelt word with a soundlike word. Meaningful phrases have been identified and kept, instead of removing the part of phrase as a stop word. The abbreviations appearing in many forms of entry are manually identified and only one form of abbreviations is used. Clustering is utilised to discriminate between non-frequent and frequent terms. This process reduced the number of text features dramatically from about 28,000 to 5000. The medical narrative text injury dataset, under consideration, is composed of many short documents. The data can be characterized as high-dimensional and sparse, i.e., few features are irrelevant but features are correlated with one another. Therefore, Matrix factorization techniques such as Singular Value Decomposition (SVD) and Non Negative Matrix Factorization (NNMF) have been used to map the processed feature space to a lower-dimensional feature space. Classifiers with these reduced feature space have been built. In experiments, a set of tests are conducted to reflect which classification method is best for the medical text classification. The Non Negative Matrix Factorization with Support Vector Machine method can achieve 93% precision which is higher than all the tested traditional classifiers. We also found that TF/IDF weighting which works well for long text classification is inferior to binary weighting in short document classification. Another finding is that the Top-n terms should be removed in consultation with medical experts, as it affects the classification performance.

Causes of rigidity in SMEs' export commencement decision

Today, small-medium sized enterprises (SMEs) collectively contribute to the largest percentage of job creation in OECD countries. SMEs have become increasingly international since the turn of the century despite being smaller in size in comparison to large multinational firms, and notably, exporting is the most favoured mode of international market entry utilised by SMEs in their internationalisation strategy. Governments around the world have acknowledged the importance of export promotion and have employed policies that are targeted at increasing the export activity of SMEs. However, in many countries, the involvement of SMEs in export operations remains rather low. Within Australia, for example, only about one-third of local SMEs are exporting and this raises an important question as to why there is such a huge percentage of non-exporters. Much scholarly research that focuses on this problem has concentrated on the broad concept of 'export barriers' that act as obstacles to a firm's export development. This paper takes a different approach to previous studies and proposes that a firm's resistance to commence exporting can be better understood through an analysis of the behavioural decision process during its pre-export state. Using a sample of Australian SMEs, the factors that are important in preventing a firm’s initial export commencement decision are categorised and discussed through the use of factor analysis.

Understanding Uncertainties in Non-Linear Population Trajectories: A Bayesian Semi-Parametric Hierarchical Approach to Large-Scale Surveys of Coral Cover

Recently, attempts to improve decision making in species management have focussed on uncertainties associated with modelling temporal fluctuations in populations. Reducing model uncertainty is challenging; while larger samples improve estimation of species trajectories and reduce statistical errors, they typically amplify variability in observed trajectories. In particular, traditional modelling approaches aimed at estimating population trajectories usually do not account well for nonlinearities and uncertainties associated with multi-scale observations characteristic of large spatio-temporal surveys. We present a Bayesian semi-parametric hierarchical model for simultaneously quantifying uncertainties associated with model structure and parameters, and scale-specific variability over time. We estimate uncertainty across a four-tiered spatial hierarchy of coral cover from the Great Barrier Reef. Coral variability is well described; however, our results show that, in the absence of additional model specifications, conclusions regarding coral trajectories become highly uncertain when considering multiple reefs, suggesting that management should focus more at the scale of individual reefs. The approach presented facilitates the description and estimation of population trajectories and associated uncertainties when variability cannot be attributed to specific causes and origins. We argue that our model can unlock value contained in large-scale datasets, provide guidance for understanding sources of uncertainty, and support better informed decision making

The decision-making processes of early childhood teachers when working with children experiencing parental separation and divorce

In this study, the pedagogical decision-making processes of 21 Australian early childhood teachers working with children experiencing parental separation and divorce were examined. Transcripts from interviews and a focus group with teachers were analysed using grounded theory methodology. The findings showed that as teachers interacted with young children experiencing parental separation and divorce, they reported using strategic, reflexive pedagogical decision-making processes. These processes comprised five stages: (1) teachers constructing their knowledge; (2) teachers thinking about their knowledge; (3) teachers using decision-making schemas; (4) teachers taking action, and; (5) teachers monitoring action and evaluating. This understanding of teachers’ reflexive pedagogical decision-making is useful for identifying how teachers and educational leaders can support children experiencing parental separation and divorce or other life challenges.

Reinforcing guardianship regimes through assisted decision-making : a Scottish perspective

Guardianship laws which provide legal mechanisms for decision-making on behalf of adults with limited or impaired capacity to make decisions have become a feature of most Western jurisdictions. In the UK, the Adults with Incapacity (Scotland) Act 2000 and the English and Welsh Mental Capacity Act 2005 have established a comprehensive framework for authorising medical, financial and welfare decisions to be taken on behalf of adults who are unable to take any or all such decisions themselves. One feature of guardianship regimes has been their tendency to evolve over time. Legal reforms have been instigated by the changes in philosophical views about how society should engage with adults with a decision-making impairment and also modern views about the role human rights play in guardianships regimes. The latest ideas in guardianship regimes around the Western world centre on the possibility of legal recognition of the concept of assisted decision-making...

Decision support for the efficient annotation of bioacoustic events

Acoustic sensors allow scientists to scale environmental monitoring over large spatiotemporal scales. The faunal vocalisations captured by these sensors can answer ecological questions, however, identifying these vocalisations within recorded audio is difficult: automatic recognition is currently intractable and manual recognition is slow and error prone. In this paper, a semi-automated approach to call recognition is presented. An automated decision support tool is tested that assists users in the manual annotation process. The respective strengths of human and computer analysis are used to complement one another. The tool recommends the species of an unknown vocalisation and thereby minimises the need for the memorization of a large corpus of vocalisations. In the case of a folksonomic tagging system, recommending species tags also minimises the proliferation of redundant tag categories. We describe two algorithms: (1) a “naïve” decision support tool (16%–64% sensitivity) with efficiency of O(n) but which becomes unscalable as more data is added and (2) a scalable alternative with 48% sensitivity and an efficiency ofO(log n). The improved algorithm was also tested in a HTML-based annotation prototype. The result of this work is a decision support tool for annotating faunal acoustic events that may be utilised by other bioacoustics projects.

Statistical methods for electromyography data and associated problems

This thesis proposes three novel models which extend the statistical methodology for motor unit number estimation, a clinical neurology technique. Motor unit number estimation is important in the treatment of degenerative muscular diseases and, potentially, spinal injury. Additionally, a recent and untested statistic to enable statistical model choice is found to be a practical alternative for larger datasets. The existing methods for dose finding in dual-agent clinical trials are found to be suitable only for designs of modest dimensions. The model choice case-study is the first of its kind containing interesting results using so-called unit information prior distributions.

Interventions to control nosocomial infections : study designs and statistical issues

There is a wide range of potential study designs for intervention studies to decrease nosocomial infections in hospitals. The analysis is complex due to competing events, clustering, multiple timescales and time-dependent period and intervention variables. This review considers the popular pre-post quasi-experimental design and compares it with randomized designs. Randomization can be done in several ways: randomization of the cluster [intensive care unit (ICU) or hospital] in a parallel design; randomization of the sequence in a cross-over design; and randomization of the time of intervention in a stepped-wedge design. We introduce each design in the context of nosocomial infections and discuss the designs with respect to the following key points: bias, control for nonintervention factors, and generalizability. Statistical issues are discussed. A pre-post-intervention design is often the only choice that will be informative for a retrospective analysis of an outbreak setting. It can be seen as a pilot study with further, more rigorous designs needed to establish causality. To yield internally valid results, randomization is needed. Generally, the first choice in terms of the internal validity should be a parallel cluster randomized trial. However, generalizability might be stronger in a stepped-wedge design because a wider range of ICU clinicians may be convinced to participate, especially if there are pilot studies with promising results. For analysis, the use of extended competing risk models is recommended.

Non-monetised multi-objective decision making system for road management

This paper presents a performance-based optimisation approach for conducting trade-off analysis between safety (roads) and condition (bridges and roads). Safety was based on potential for improvement (PFI). Road condition was based on surface distresses and bridge condition was based on apparent age per subcomponent. The analysis uses a non-monetised optimisation that expanded upon classical Pareto optimality by observing performance across time. It was found that achievement of good results was conditioned by the availability of early age treatments and impacted by a frontier effect preventing the optimisation algorithm from realising of the long-term benefits of deploying actions when approaching the end of the analysis period. A disaggregated bridge condition index proved capable of improving levels of service in bridge subcomponents.

Pollen image classification using the classifynder system: Algorithm comparison and a case study on New Zealand honey

We describe an investigation into how Massey University’s Pollen Classifynder can accelerate the understanding of pollen and its role in nature. The Classifynder is an imaging microscopy system that can locate, image and classify slide based pollen samples. Given the laboriousness of purely manual image acquisition and identification it is vital to exploit assistive technologies like the Classifynder to enable acquisition and analysis of pollen samples. It is also vital that we understand the strengths and limitations of automated systems so that they can be used (and improved) to compliment the strengths and weaknesses of human analysts to the greatest extent possible. This article reviews some of our experiences with the Classifynder system and our exploration of alternative classifier models to enhance both accuracy and interpretability. Our experiments in the pollen analysis problem domain have been based on samples from the Australian National University’s pollen reference collection (2,890 grains, 15 species) and images bundled with the Classifynder system (400 grains, 4 species). These samples have been represented using the Classifynder image feature set.We additionally work through a real world case study where we assess the ability of the system to determine the pollen make-up of samples of New Zealand honey. In addition to the Classifynder’s native neural network classifier, we have evaluated linear discriminant, support vector machine, decision tree and random forest classifiers on these data with encouraging results. Our hope is that our findings will help enhance the performance of future releases of the Classifynder and other systems for accelerating the acquisition and analysis of pollen samples.

A new approach to estimating trends in chlamydia incidence

Objectives Directly measuring disease incidence in a population is difficult and not feasible to do routinely. We describe the development and application of a new method of estimating at a population level the number of incident genital chlamydia infections, and the corresponding incidence rates, by age and sex using routine surveillance data. Methods A Bayesian statistical approach was developed to calibrate the parameters of a decision-pathway tree against national data on numbers of notifications and tests conducted (2001-2013). Independent beta probability density functions were adopted for priors on the time-independent parameters; the shape parameters of these beta distributions were chosen to match prior estimates sourced from peer-reviewed literature or expert opinion. To best facilitate the calibration, multivariate Gaussian priors on (the logistic transforms of) the time-dependent parameters were adopted, using the Matérn covariance function to favour changes over consecutive years and across adjacent age cohorts. The model outcomes were validated by comparing them with other independent empirical epidemiological measures i.e. prevalence and incidence as reported by other studies. Results Model-based estimates suggest that the total number of people acquiring chlamydia per year in Australia has increased by ~120% over 12 years. Nationally, an estimated 356,000 people acquired chlamydia in 2013, which is 4.3 times the number of reported diagnoses. This corresponded to a chlamydia annual incidence estimate of 1.54% in 2013, increased from 0.81% in 2001 (~90% increase). Conclusions We developed a statistical method which uses routine surveillance (notifications and testing) data to produce estimates of the extent and trends in chlamydia incidence.

Hydrochemical evolution and groundwater flow processes in the Galilee and Eromanga basins, Great Artesian Basin, Australia: A multivariate statistical approach

The Galilee and Eromanga basins are sub-basins of the Great Artesian Basin (GAB). In this study, a multivariate statistical approach (hierarchical cluster analysis, principal component analysis and factor analysis) is carried out to identify hydrochemical patterns and assess the processes that control hydrochemical evolution within key aquifers of the GAB in these basins. The results of the hydrochemical assessment are integrated into a 3D geological model (previously developed) to support the analysis of spatial patterns of hydrochemistry, and to identify the hydrochemical and hydrological processes that control hydrochemical variability. In this area of the GAB, the hydrochemical evolution of groundwater is dominated by evapotranspiration near the recharge area resulting in a dominance of the Na–Cl water types. This is shown conceptually using two selected cross-sections which represent discrete groundwater flow paths from the recharge areas to the deeper parts of the basins. With increasing distance from the recharge area, a shift towards a dominance of carbonate (e.g. Na–HCO3 water type) has been observed. The assessment of hydrochemical changes along groundwater flow paths highlights how aquifers are separated in some areas, and how mixing between groundwater from different aquifers occurs elsewhere controlled by geological structures, including between GAB aquifers and coal bearing strata of the Galilee Basin. The results of this study suggest that distinct hydrochemical differences can be observed within the previously defined Early Cretaceous–Jurassic aquifer sequence of the GAB. A revision of the two previously recognised hydrochemical sequences is being proposed, resulting in three hydrochemical sequences based on systematic differences in hydrochemistry, salinity and dominant hydrochemical processes. The integrated approach presented in this study which combines different complementary multivariate statistical techniques with a detailed assessment of the geological framework of these sedimentary basins, can be adopted in other complex multi-aquifer systems to assess hydrochemical evolution and its geological controls.

A framework for model integration and holistic modelling of socio-technical systems

This paper presents a layered framework for the purposes of integrating different Socio-Technical Systems (STS) models and perspectives into a whole-of-systems model. Holistic modelling plays a critical role in the engineering of STS due to the interplay between social and technical elements within these systems and resulting emergent behaviour. The framework decomposes STS models into components, where each component is either a static object, dynamic object or behavioural object. Based on existing literature, a classification of the different elements that make up STS, whether it be a social, technical or a natural environment element, is developed; each object can in turn be classified according to the STS elements it represents. Using the proposed framework, it is possible to systematically decompose models to an extent such that points of interface can be identified and the contextual factors required in transforming the component of one model to interface into another is obtained. Using an airport inbound passenger facilitation process as a case study socio-technical system, three different models are analysed: a Business Process Modelling Notation (BPMN) model, Hybrid Queue-based Bayesian Network (HQBN) model and an Agent Based Model (ABM). It is found that the framework enables the modeller to identify non-trivial interface points such as between the spatial interactions of an ABM and the causal reasoning of a HQBN, and between the process activity representation of a BPMN and simulated behavioural performance in a HQBN. Such a framework is a necessary enabler in order to integrate different modelling approaches in understanding and managing STS.

Supporting the needs of public health decision-makers and review authors in the UK

Introduction Systematic reviews, through the synthesis of multiple primary research studies, can be powerful tools in enabling evidence-informed public health policy debate, development and action. In seeking to optimize the utility of these reviews, it is important to understand the needs of those using them. Previous work has emphasized that researchers should adopt methods that are appropriate to the problems that public health decision-makers are grappling with, as well as to the policy context in which they operate.1,2 Meeting these demands poses significant methodological challenges for review authors and prompts a reconsideration of the resources, training and support structures available to facilitate the efficient and timely production of useful, comprehensive reviews. The Cochrane Public Health Group (CPHG) was formed in 2008 to support reviews of complex, upstream public health topics. The majority of CPHG authors are from the UK, which has historically been at the forefront of efforts to promote the production and use of systematic reviews of research relevant to public health decision-makers. The UK therefore provides a suitably mature national context in which to examine (i) the current and future demands of decision-makers to increase the use, value and impact of evidence syntheses; (ii) the implications this has for the scope and methods of reviews and (iii) the required action to build and support capacity to conduct such reviews.

Establishing statistical foundations early : data modeling with young learners

This paper addresses research from a three-year longitudinal study that engaged children in data modeling experiences from the beginning school year through to third year (6-8 years). A data modeling approach to statistical development differs in several ways from what is typically done in early classroom experiences with data. In particular, data modeling immerses children in problems that evolve from their own questions and reasoning, with core statistical foundations established early. These foundations include a focus on posing and refining statistical questions within and across contexts, structuring and representing data, making informal inferences, and developing conceptual, representational, and metarepresentational competence. Examples are presented of how young learners developed and sustained informal inferential reasoning and metarepresentational competence across the study to become “sophisticated statisticians”.