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Introduction/Purpose: Ultrasound picture archiving and communication system (PACS) databases are useful for quality improvement and clinical research but frequently contain free text that is not easily readable. Here, we present a method to extract and clean a semi‐structured echocardiography (cardiac ultrasound) PACS database. Methods: Echocardiography studies between 1 January 2010 and 31 December 2018 were extracted using a data mining tool. Numeric variables were recoded with extreme values excluded. Analysis of free text, including descriptions of the heart valves and right and left ventricular size and function, was performed using a rule‐based system. Different levels of free text variables were initially identified using commonly used phrases and then iteratively developed. Randomly selected sets of 100 studies were compared to the electronic health record to validate the data cleaning process. Results: The data validation step was performed three times in total, with Cohen's kappa ranging between 0.88 and 1.00 for the final set of data validation across all measures. Conclusion: Free text cleaning of semi‐structured PACS databases is possible using freely available open‐source software. The accuracy of this method is high, and the resulting dataset can be linked to administrative data to answer research questions. We present a method that could be used to answer clinical questions or to develop quality improvement initiatives. [ABSTRACT FROM AUTHOR]

Purpose: Understanding the nature of wellbeing as multidimensional and complex provides a policy window to generate a strengths-based policy orientation to promote wellbeing in education settings. The purpose of this exploratory paper is to map how wellbeing is interpreted across public education policy documents in Aotearoa New Zealand. Method: To explore the narrative that this group of documents weave, we draw on models of holistic wellbeing, ecological systems and appreciative inquiry. Policy documents were analysed using text mining software to track notions of wellbeing; their occurrence and co-occurrence with related concepts. Results: Key findings include the predominance of wellbeing, the interrelatedness of wellbeing with relationships, and the predominance of student wellbeing over the wellbeing of other stakeholders, highlighting that current education policy does not interpret wellbeing as relational, complex or contextual. Conclusion: We argue that interpreting such documents through a wellbeing lens demonstrates the complexity and disparity of the conceptualization and contextualization. We assert that it is critical to explore possibilities for deliberate and ecological wellbeing connections within educational policy and practice for the good of all stakeholders. [ABSTRACT FROM AUTHOR]

In this introduction, we first summarize the Fourth International Workshop on Semantics-Powered Data Mining and Analytics (SEPDA 2019) held on October 26, 2019 in conjunction with the 18th International Semantic Web Conference (ISWC 2019) in Auckland, New Zealand, and then briefly introduce seven research articles included in this supplement issue, covering the topics on Knowledge Graph, Ontology-Powered Analytics, and Deep Learning. [ABSTRACT FROM AUTHOR]

This article reports on a sample of young New Zealand children under two years of age entering care in 2005 (n = 228) and follows their progress over a five-year period. The study, the first of its kind in New Zealand, used a clinical data mining method to focus particularly on issues of stability, continuity and permanency. In these areas the research findings were generally positive for this cohort of children. Most children had only one or two caregivers. Almost all were ethnically matched with their caregivers in both kinship and foster care, and permanency, or a stable, permanent living situation, was achieved for the majority of the children. [ABSTRACT FROM AUTHOR]

Data mining algorithms were used to develop models to forecast the outcome of leafroller pest monitoring decisions on ‘Hayward’ kiwifruit crops in New Zealand. Using industry spray diary and pest monitoring data gathered at an orchard block level for compliance purposes, 80 attributes (independent variables) were created in three categories from the spray diary data: (1) individual insecticide applications applied during 2-week time windows, (2) groups of insecticide applications within time periods prior to or after fruit set and (3) orchard management attributes. Five machine learning algorithms (Decision Tree, Naïve Bayes, Random Forest, AdaBoost, Support Vector Machine) and one statistical method (Logistic regression) (classifiers) were used to develop models to forecast insecticide application decisions for leafroller control, by predicting whether pest monitoring results were above or below a spray threshold. Models to forecast 2011 spraying decisions were trained on 2008 and 2009 data and tested on 2010 data. Forecasts were made for spray and no-spray decisions based upon pre-determined acceptable rates of precision (proportion of correct decisions in test results). Orchard blocks in which a forecast could not be made to a prescribed degree of precision were recommended to be monitored, which is the normal practice. Spray decisions could not be forecast to an acceptable degree of precision, but decisions not to spray were successfully forecast for 49% of the blocks to a precision of 98% (AdaBoost) and 70% of the blocks to a precision of 95% (Naïve Bayes). Models with as few as four attributes gave useful forecasts, and orchard management attributes were the most important determinants of model forecasting accuracy. The potential for this methodology to assist with pest spray forecasting using customised data sets is discussed. [ABSTRACT FROM AUTHOR]

Objectives: Online Cessation Support Networks (OCSNs) are associated with increased quit success rates, but few studies have examined their use over time. We identified usage patterns in New Zealand's largest OCSN over two years and explored implications for OCSN intervention design and evaluation. Methods: We analysed metadata relating to 133,096 OCSN interactions during 2011 and 2012. Metrics covered aggregate network activity, user posting activity and longevity, and between-user commenting. Binary logistic regression models were estimated to investigate the feasibility of predicting low user engagement using early interaction data. Results: Repeating periodic peaks and troughs in aggregate activity related not only to seasonality (e.g., New Year), but also to day of the week. Out of 2,062 unique users, 69 Highly Engaged Users (180+ interactions each) contributed 69% of all OCSN interactions in 2012 compared to 1.3% contributed by 864 Minimally Engaged Users (< = 2 items each). The proportion of Highly Engaged Users increased with network growth between 2011 and 2012 (with marginal significance), but the proportion of Minimally Engaged Users did not decline substantively. First week interaction data enabled identification of Minimally Engaged Users with high specificity and sensitivity (AUROC  = 0.94). Implications: Results suggest future research should develop and test interventions that promote activity, and hence cessation support, amongst specific user groups or at key time points. For example, early usage information could help identify Minimally Engaged Users for tests of targeted messaging designed to improve their integration into, or re-engagement with, the OCSN. Furthermore, although we observed strong growth over time on varied metrics including posts and comments, this change did not coincide with large gains in first-time user persistence. Researchers assessing intervention effects should therefore examine multiple measures when evaluating changes in network dynamics over time. [ABSTRACT FROM AUTHOR]

The location of the author of a social media message is not invariably the same as the location that the author writes about in the message. In applications that mine these messages for information such as tracking news, political events or responding to disasters, it is the geographic content of the message rather than the location of the author that is important. To this end, we present a method to geo-parse the short, informal messages known as microtext. Our preliminary investigation has shown that many microtext messages contain place references that are abbreviated, misspelled, or highly localized. These references are missed by standard geo-parsers. Our geo-parser is built to find such references. It uses Natural Language Processing methods to identify references to streets and addresses, buildings and urban spaces, and toponyms, and place acronyms and abbreviations. It combines heuristics, open-source Named Entity Recognition software, and machine learning techniques. Our primary data consisted of Twitter messages sent immediately following the February 2011 earthquake in Christchurch, New Zealand. The algorithm identified location in the data sample, Twitter messages, giving an F statistic of 0.85 for streets, 0.86 for buildings, 0.96 for toponyms, and 0.88 for place abbreviations, with a combined average F of 0.90 for identifying places. The same data run through a geo-parsing standard, Yahoo! Placemaker, yielded an F statistic of zero for streets and buildings (because Placemaker is designed to find neither streets nor buildings), and an F of 0.67 for toponyms. [ABSTRACT FROM AUTHOR]

Many governments are seeking to 'mature' social science through research capability. The Building Research Capability in the Social Sciences (BRCSS) experiment gave New Zealand's social science community a chance to mature itself through capability building. We examine the emergence of BRCSS's `sustainability' research theme. Selected as one of five key research themes because of its public good investment priority and the level of social science interest, the meanings and purpose of the theme emerged in practice. We present the theme as a series of performances through the life of BRCSS, itself an emergent assemblage of changing theoretical projects, research and policy priorities, and research capability. Reading the experiment reflexively from the inside, we consolidate the experience of performing new research practices into existence to reconceptualise and theorise research capability. We conclude that any fresh engagement in substantive issues must confront how knowledge production might be done differently, and that experimentation around capacities and capabilities for knowledge production begins to open up substantive concerns in fresh and generative ways. [ABSTRACT FROM AUTHOR]

Purpose – The introduction of competitive tendering and contracting and a lack of transparent funding processes has seen a move towards greater contestability of services in the nongovernment organization (NGO) sector. To ameliorate this situation requires a sound understanding of knowledge management (KM) practices. However, not all NGOs have been equally successful at embracing KM principles. The purpose of this paper, therefore, is to explore the KM challenges faced by New Zealand NGOs in the health and disability sector.Design/methodology/approach – Using qualitative research methods, specifically interpretive case study research, the authors studied the KM practices of nine NGOs in the health and disability sector in New Zealand. Qualitative data were obtained from documents and semi-structured interviews following a dramaturgical approach.Findings – The findings suggest many barriers to successful implementation of KM in NGOs; some of the most important ones being related to organizational structure and culture. Specifically, tensions between local and national organizational branches, difficulty integrating volunteers and complex funding arrangements are key challenges faced by NGOs in the health and disability sector.Originality/value – Existing literature suggests that volunteer turnover is a serious impediment to successful KM implementation. The authors' research suggests that volunteer integration, more so than turnover, is an issue. The research also reports on an underlying tension between commercialization and the inherent service culture of the sector. This paper makes recommendations for improved volunteer management and suggests that there is a clear opportunity for better KM systems and practices in the NGO sector. [ABSTRACT FROM AUTHOR]

Abstract: This research combines automated Geographical Information Systems (GIS) and iterative logistic regression scripting to data-mine for terrain variables that predict stoat (Mustela erminea) visitation to tracking-stations in a New Zealand indigenous forest and explore the impact of spatial analysis scale on modelling outcomes. Variables such as curvature and density of tracks are dependent on the scale of the analysis window used to compute the values. With automated GIS analysis it is possible to derive a large number of terrain parameters that vary in computational scale. It is common for analysts to make nominal choices regarding the size and type of analysis windows when calculating these derived variables, without testing the statistical validity of that choice. Stoats are a significant pest in New Zealand and threaten extinction for a number of vulnerable native species. Field data on stoat activity, based on footprint tracking tunnels, were used to develop an Akaike Information Criterion (AIC) optimised predictive model applying stepwise model selection. Once the optimal model was determined, the sensitivity of the model to different terrain parameters was tested by systematically substituting each variable and calculating the difference this made to the model equation. The most dominant terrain predictors influencing stoat visitation were proximity to tracks, altitude, northerly and easterly aspect, mean curvature, and topographical position and slope. Proximity to tracks and mean curvature were the most sensitive variables to analysis scale. This paper demonstrates the importance of considering scale in developing predictive models and the need to test many ecologically sensible analysis scales in order to find the best predictive variables. The paper concludes that GIS-based spatial data extraction, combined with automated statistical data mining methods, has an important role in developing accurate animal activity models. [Copyright &y& Elsevier]

Abstract: Spatial interpolation is frequently used to predict values across a landscape enabling the spatial variation and patterns of a property to be quantified. Inverse distance weighting (IDW), ordinary kriging (OK), regression kriging (RK), and partial least squares (PLS) regression are interpolation techniques typically used where the region of interest''s spatial extent is relatively small and observations are numerous and regularly spaced. In the current era of data ‘mining’ and utilisation of sparse data, the above criteria are not always fully met, increasing model uncertainties. Furthermore, regression modelling and kriging techniques require good judgement, experience, and expertise by the practitioner compared with IDW with its more rudimentary approach. In this study we compared spatial predictions derived from IDW, PLS, RK, and OK for Pinus radiata volume mean annual increment (referred to as 300 Index) and mean top height at age twenty (referred to as Site Index) across New Zealand using cross-validation techniques. Validation statistics (RMSE, ME, and R 2) show that RK, OK, and IDW provided predictions that were less biased and of greater accuracy than PLS predictions. Standard deviation of rank (SDR) and mean rank (MR) validation statistics showed similar results with OK the most consistent (SDR) predictor, whereas RK had the lowest mean rank (MR), closely followed by IDW. However, the mean performance rankings for validation observations classified according to their distance to the nearest model data point indicate that although PLS provided the poorest predictions at relatively close separation distances (<2km), in the medium range (∼4–8km) performance was of similar ranking to that of the other techniques, and at greater separation distances PLS outperformed the other techniques. Maps illustrating the spatial variation of P. radiata forest productivity are provided. [Copyright &y& Elsevier]

The article focuses on the use of Web 2.0 technology to assess the risk of depression in young people in Australia, Great Britain and New Zealand. It discusses the use of data warehousing technologies and text mining in determining and monitoring depressed bloggers who are prone to suicide risk. It provides a literature review on the affective and emotional content text mining as well as suicide research. Studies show that text mining technologies can be used in determining depressed bloggers. It points out that identifying at-risk bloggers and offering appropriate interventions can prevent tragic consequences.

1. Ecologists use statistical models for both explanation and prediction, and need techniques that are flexible enough to express typical features of their data, such as nonlinearities and interactions. 2. This study provides a working guide to boosted regression trees (BRT), an ensemble method for fitting statistical models that differs fundamentally from conventional techniques that aim to fit a single parsimonious model. Boosted regression trees combine the strengths of two algorithms: regression trees (models that relate a response to their predictors by recursive binary splits) and boosting (an adaptive method for combining many simple models to give improved predictive performance). The final BRT model can be understood as an additive regression model in which individual terms are simple trees, fitted in a forward, stagewise fashion. 3. Boosted regression trees incorporate important advantages of tree-based methods, handling different types of predictor variables and accommodating missing data. They have no need for prior data transformation or elimination of outliers, can fit complex nonlinear relationships, and automatically handle interaction effects between predictors. Fitting multiple trees in BRT overcomes the biggest drawback of single tree models: their relatively poor predictive performance. Although BRT models are complex, they can be summarized in ways that give powerful ecological insight, and their predictive performance is superior to most traditional modelling methods. 4. The unique features of BRT raise a number of practical issues in model fitting. We demonstrate the practicalities and advantages of using BRT through a distributional analysis of the short-finned eel ( Anguilla australis Richardson), a native freshwater fish of New Zealand. We use a data set of over 13 000 sites to illustrate effects of several settings, and then fit and interpret a model using a subset of the data. We provide code and a tutorial to enable the wider use of BRT by ecologists. [ABSTRACT FROM AUTHOR]

Discusses the technological innovations that have improved businesses' customer services of call centers or contact centers. Data mining product of Datamine; Role of the business in the goals of a contact center; Impact of modern technology on contact centers; Qualities of contact centers in New Zealand. INSET: CD CALLING CARD.

The article reports on the top 10 business trends for New Zealand in 2015. Among the trends include the digital marketing, the construction industry, and the data mining technology. Also mentioned that there will be an increase in the demand for skilled professionals in all areas of labour and trades such as in welding, carpentry, and flooring.

The article reports that the New Zealand computer software company Patent Analysis offers free international patent searching and specializes in data mining and search management. The company also provides intellectual property reports and summaries of patent classes that enable companies and organizations to analyze international patent trends.