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As one of the most vocal marsupials in Australia, conservation of the yellow-bellied glider (Petaurus australis brevirostrum) (YBG) could benefit from the extraction of more information from their calls during acoustic monitoring. This pilot study investigates the variability of syllable components of full calls and identifies acoustic parameters that underlie variability in recorded calls from wild YBGs in North Queensland, Australia. Full calls with three syllables and with two syllables were distinguished. Using batch correlations and principal component analyses (PCAs), it was found that syllable two and syllable three show high variability in their duration and that maximum frequency is the strongest contributing sound parameter to the observed variability. However, because data collection was done in wild populations, the identity of call emitters remained unknown during call analyses, demanding further studies. Further subdivision of calls, and investigation into further acoustic parameters, could aid in decoding sexual, seasonal and individual-based meanings of calls. In Far North Queensland, passive acoustic monitoring has been used to study the yellow-bellied glider, a species threatened by habitat loss and fragmentation. However, because this species is very vocal, it is likely that more information – related to gender, season or individual-specific parameters – can be extracted from its calls. We present an attempt to analyse acoustic parameters and their variability in components of full calls of this species as a basis for more meaningful acoustic monitoring. [ABSTRACT FROM AUTHOR]

Objectives: To examine the scale of private equity investment in Australian health care delivery assets (clinics, hospitals, imaging facilities, other doctor‐led health care services). Study design, setting: Extraction of information about private equity acquisitions of hospitals, clinics, imaging centres and in vitro fertilisation facilities in Australia, 2008–2022, from a commercial database (PitchBook), supplemented by information from publicly available online media sources. Main outcome measures: Number and value of private equity acquisitions of health care assets, 2008–2022; numbers of clinic parent company and clinic acquisitions, 2017–2022. Results: A total of 75 private equity acquisitions of health care delivery assets in Australia during 2008–2022 were identified; the annual number rose from three acquisitions in 2008 to eighteen in 2022. During 2008–2010, five of seven acquisitions were of in vitro fertilisation providers; during 2020–2022, 22 of 39 acquisitions were of clinics or clinic groups, including eleven of eighteen in 2022. The total value of the 39 acquisitions for which purchase price could be ascertained (52%) was $24.1 billion. During 2017–2022, the clinic specialty with the greatest number of private equity acquisitions was general practice (256 of 446 clinics purchased within acquisitions). Seven companies owning ophthalmology clinics (24 clinics) were acquired by private equity. Four private equity acquisitions during 2017–2022 included 60 oncology clinics, all related to a single clinic group. Conclusions: The number of private equity acquisitions of Australian health care delivery assets increased during 2008–2022. Doctors should be aware of the motivations and dynamics of private equity companies, as they are increasingly likely to interact with these firms and assets owned by these firms. [ABSTRACT FROM AUTHOR]

The objectives of the study were: 1) Describe the thematic structure and evolution of the field of physical therapy; 2) identify the main research producers (i.e. countries and institutions); and 3) compare their research output and citation impact. Papers related to physical therapy indexed in Web of Science (2000–2018) were identified to delineate the field, using keywords, journals, and citation networks. VOSviewer software, advanced bibliometric text mining, and visualization techniques were used to evaluate the thematic structure. We collected data about the country and institutional affiliation of all the authors and calculated production and citation impact indicators. 85,697 papers were analyzed. Eleven thematic clusters were identified: 1) "health care and education"; 2) "biomechanics"; 3) "psychosocial, chronic pain and quality of life outcomes"; 4) "evidence-based physical therapy research methods"; 5) "traumatology and orthopedics"; 6) "neurological rehabilitation"; 7) "psychometrics and cross-cultural adaptation"; 8) "gait-balance analysis and Parkinson's disease"; 9) "exercise"; 10) "respiratory physical therapy"; and 11) "back pain." The United States, the United Kingdom, and Australia were the most productive countries. Netherlands, Norway, and Sweden had the highest citation impact. Our bibliometric visualization approach makes it possible to comprehensively study the thematic structure of physical therapy. The ranking of producers has evolved and now includes China and Brazil. High research production does not imply a high citation impact. [ABSTRACT FROM AUTHOR]

This study introduces data mining methods to accounting education scholarship to explore the relationship between accounting students' current academic performance (grades), demographic information, pre-university entrance scores and predicted academic performance. It adopts a C4.5 classification algorithm based on decision-tree analysis to examine 640 accounting students enrolled in an undergraduate accounting program at an Australian university. A significant contribution of this study is improved prediction of academic performance and identification of characteristics of students deemed to be at risk. By partitioning students into sub-groups based on tertiary entrance scores and employing clustering of study units, this study facilitates a more nuanced understanding of predictor attributes. Key findings were the dominance of a cluster of second year units in predicting students' later academic performance; that gender did not influence performance; and that performance in first year at university, rather than secondary school grades, was the most important predictor of subsequent academic performance. [ABSTRACT FROM AUTHOR]

Given that metals, minerals and energy resources extracted through mining are fundamental to human society, it follows that accurate data describing mine production are equally important. Although there are often national statistical sources, this typically includes data for metals (e.g., gold), minerals (e.g., iron ore) or energy resources (e.g., coal). No such study has ever compiled a national mine production data set which includes basic mining data such as ore processed, grades, extracted products (e.g., metals, concentrates, saleable ore) and waste rock. These data are crucial for geological assessments of mineable resources, environmental impacts, material flows (including losses during mining, smelting-refining, use and disposal or recycling) as well as facilitating more quantitative assessments of critical mineral potential (including possible extraction from tailings and/or waste rock left by mining). This data set achieves these needs for Australia, providing a world-first and comprehensive review of a national mining industry and an exemplar of what can be achieved for other countries with mining industry sectors. [ABSTRACT FROM AUTHOR]

Restoration of natural vegetation is affected by many factors. Success in restoration is increasingly linked with knowledge sharing on restoration planning, preparation, implementation, and maintenance. This trend is reflected in the emergence of regional and international databases that collate and store information on restoration projects and allow the extraction of this information by practitioners. Here we describe how workshops with restoration stakeholders and educational institutions identified ways to collate project-specific spatial and non-spatial data on large-scale tropical rainforest restoration projects in Far North Queensland, Australia. We show how this data is stored in a regional knowledge sharing restoration platform that facilitates the planning and improved implementation of future restoration projects, and the identification of site-specific restoration inhibiting and facilitating factors. We provide examples on how the database promotes regional partnerships in conservation and restoration efforts, and how it can be made easily accessible to practitioners and researchers. We demonstrate how the longevity of this database can be ensured by its integration into a curriculum of a tertiary educational institution and by its potential linkage with existing global databases. [ABSTRACT FROM AUTHOR]

Annual turnover of home care workers represents a huge loss of revenue and is a key source of inefficiency in the home health care industry. In this article, we propose a data-driven approach to monitor employee churn and to capture the evolution of employee intent to leave. Unlike most papers in the literature, we use machine learning techniques to analyze over 2 million visits in the US, Canada, and Australia between 2016 and 2019. Results show that the gap between the number of hours worked and in the contract is the most important factor to predict employee intent to leave, which means an employee should be given as many hours as requested in the contract to improve retention. Secondary results show that having diverse shift lengths and continuity in services and patients seem to be associated with less turnover. [ABSTRACT FROM AUTHOR]

Simple Summary: In radiotherapy treatment, organs at risk and target volumes are contoured by the clinicians to prepare a dosimetry plan. In retrospective data, these structures are not often standardised to universal names across the patients plans, which is required to enable data mining and analysis. In this paper, a new method was proposed and evaluated to automatically standardise radiotherapy structures names using machine learning algorithms. The proposed approach was deployed over a dataset with 1613 patients collected from Liverpool & Macarthur Cancer Therapy Centres, New South Wales, Australia. It was concluded that machine learning techniques can standardise the dosimetry plan structures, taking into consideration the integration of multiple modalities representing each structure during the training process. In progressing the use of big data in health systems, standardised nomenclature is required to enable data pooling and analyses. In many radiotherapy planning systems and their data archives, target volumes (TV) and organ-at-risk (OAR) structure nomenclature has not been standardised. Machine learning (ML) has been utilised to standardise volumes nomenclature in retrospective datasets. However, only subsets of the structures have been targeted. Within this paper, we proposed a new approach for standardising all the structures nomenclature by using multi-modal artificial neural networks. A cohort consisting of 1613 breast cancer patients treated with radiotherapy was identified from Liverpool & Macarthur Cancer Therapy Centres, NSW, Australia. Four types of volume characteristics were generated to represent each target and OAR volume: textual features, geometric features, dosimetry features, and imaging data. Five datasets were created from the original cohort, the first four represented different subsets of volumes and the last one represented the whole list of volumes. For each dataset, 15 sets of combinations of features were generated to investigate the effect of using different characteristics on the standardisation performance. The best model reported 99.416% classification accuracy over the hold-out sample when used to standardise all the nomenclatures in a breast cancer radiotherapy plan into 21 classes. Our results showed that ML based automation methods can be used for standardising naming conventions in a radiotherapy plan taking into consideration the inclusion of multiple modalities to better represent each volume. [ABSTRACT FROM AUTHOR]

Lakes commonly form in mine pits following the end of mining. A good understanding of the pit lake water balance over future decades to centuries is essential to understand and manage environmental risks from the lake. Evaporation is often the major or only outflow from the lake, thus being an important determinant of equilibrium lake level and environmental risks. A general lack of in situ measurements of pit lake evaporation has meant that estimates have usually been based on pan coefficients derived for other contexts or on alternative unvalidated evaporation models. Our research used data from an evaporation pan and weather station that were floated on a pit lake in semi-arid central Queensland, Australia. A deterministic aerodynamic evaporation model was developed from these data to infill missing values, and an adjusted aerodynamic model was used to reconstruct long-term historical daily evaporation data. With an average bias of 6.5% during the measurement period, this long-term model was found to be more accurate than alternative simple models (e.g., using the commonly used pan coefficient of 0.7 gave a bias of 45%). The reconstructed data were then used to fit and assess a stochastic model for the generation of future evaporation and rainfall realisations, assuming a stationary climate. Fitting stochastic models at a monthly time step was found to accurately represent the monthly evaporation statistics. For example, the cross-correlation between historical rainfall and evaporation was within the 25 and 75 percentiles of the modelled values in 11 of 12 months and always within the 2.5 and 97.5 percentiles. However, the stationary nature of the model presented limitations in capturing interannual anomalies, with continuous periods of up to 6 years, where the modelled annual rainfall was consistently lower and modelled annual evaporation consistently higher than the historical values. Fitting stochastic models at a daily time step had problems capturing a range of statistics of both rainfall and evaporation. For example, in 6 of the 12 months, the cross-correlation between historical rainfall and evaporation was outside the modelled 2.5 and 97.5 percentiles. This likely arises from the complex patterns in transitions from wet to dry days in the semi-arid climate of the case study. While the long-term model and monthly stochastic model are promising, further work is needed to understand the significance of the observed errors and refine the models. [ABSTRACT FROM AUTHOR]

Australia is one of the most bushfire-prone countries. Prediction and management of bushfires in bushfire-susceptible areas can reduce the negative impacts of bushfires. The generation of bushfire susceptibility maps can help improve the prediction of bushfires. The main aim of this study was to use single gene expression programming (GEP) and ensemble of GEP with well-known data mining to generate bushfire susceptibility maps for New South Wales, Australia, as a case study. We used eight methods for bushfire susceptibility mapping: GEP, random forest (RF), support vector machine (SVM), frequency ratio (FR), ensemble techniques of GEP and FR (GEPFR), RF and FR (RFFR), SVM and FR (SVMFR), and logistic regression (LR) and FR (LRFR). Areas under the curve (AUCs) of the receiver operating characteristic were used to evaluate the proposed methods. GEPFR exhibited the best performance for bushfire susceptibility mapping based on the AUC (0.892 for training, 0.890 for testing), while RFFR had the highest accuracy (95.29% for training, 94.70% for testing) among the proposed methods. GEPFR is an ensemble method that uses features from the evolutionary algorithm and the statistical FR method, which results in a better AUC for the bushfire susceptibility maps. Single GEP showed AUC of 0.884 for training and 0.882 for testing. RF also showed AUC of 0.902 and 0.876 for training and testing, respectively. SVM had 0.868 for training and 0.781 for testing for bushfire susceptibility mapping. The ensemble methods had better performances than those of the single methods. [ABSTRACT FROM AUTHOR]

Social workers provide services in many cancer settings however there has been limited research into Australian oncology social work practice. This multisite study investigated the reasons those affected by a cancer diagnosis were referred to social workers and the types of intervention undertaken. Clinical data-mining methodology was used to complete a retrospective review of 250 patient medical records. Qualitative and descriptive analyses were undertaken with comparisons to existing classification systems. Findings of significance were that residential location, socio-economic status, gender, relationship status, responsibilities for dependent children, and other complex individual circumstances were key reasons for referral to social workers. Interventions included psychosocial assessment, financial management, counselling, service coordination, and advocacy across multiple systems. Complex circumstances reflecting systemic social inequalities led to higher levels of social worker engagement. The study outcomes indicate that social workers have a significant role in the provision of effective psychosocial cancer care. Poorer cancer outcomes are linked to socio-economic, cultural, and systemic disadvantage, all of which are domains of social work practice. Working across multiple systems, social workers provide a range of skilled interventions to patients, families, carers, and others in complex social situations exacerbated by a diagnosis of cancer. Improving availability and access to social workers will address many of the social circumstances that have a detrimental impact on those affected by cancer. [ABSTRACT FROM AUTHOR]

Assessing OpenStreetMap (OSM) data quality against authoritative data sources may not always be viable. This is primarily because of the multi-dimensional nature and heterogeneity of the maps, yet the activity is pivotal for targeted data cleansing and quality enhancement undertakings in these data sets. A salient facet of OSM, allowing contributors to flag potential problems encountered during the mapping process, is the FIXME tag. In this article, we examine and discuss OSM data quality through the vast expanse of issues (knowledge) documented via FIXME. We present a classification and analysis of these quality issues, exposed as topic models and grounded in the ISO-19157 standard, across USA and Australia. Regional distributions of these topics are further qualitatively analyzed to ascertain the variation of key issues in OSM. We also present a comparison of the intrinsic issue classification against those identified in an issue corpus of an authoritative map data source. Due to the considerable heterogeneity in user mapping and reporting, OSM issue detection and classification remains problematic. This research presents a flexible and intrinsic data-mining approach, linking established ISO data quality standards to OSM issue categorization. Our work, thus informs the development of automated error correction methods for VGI datasets. [ABSTRACT FROM AUTHOR]

Objective: Epilepsy surgery is known to be underutilized. Machine learning-natural language processing (ML-NLP) may be able to assist with identifying patients suitable for referral for epilepsy surgery evaluation., Methods: Data were collected from two tertiary hospitals for patients seen in neurology outpatients for whom the diagnosis of "epilepsy" was mentioned. Individual case note review was undertaken to characterize the nature of the diagnoses discussed in these notes, and whether those with epilepsy fulfilled prespecified criteria for epilepsy surgery workup (namely focal drug refractory epilepsy without contraindications). ML-NLP algorithms were then developed using fivefold cross-validation on the first free-text clinic note for each patient to identify these criteria., Results: There were 457 notes included in the study, of which 250 patients had epilepsy. There were 37 (14.8%) individuals who fulfilled the prespecified criteria for epilepsy surgery referral without described contraindications, 32 (12.8%) of whom were not referred for epilepsy surgical evaluation in the given clinic visit. In the prediction of suitability for epilepsy surgery workup using the prespecified criteria, the tested models performed similarly. For example, the random forest model returned an area under the receiver operator characteristic curve of 0.97 (95% confidence interval 0.93-1.0) for this task, sensitivity of 1.0, and specificity of 0.93., Significance: This study has shown that there are patients in tertiary hospitals in South Australia who fulfill prespecified criteria for epilepsy surgery evaluation who may not have been referred for such evaluation. ML-NLP may assist with the identification of patients suitable for such referral., Plain Language Summary: Epilepsy surgery is a beneficial treatment for selected individuals with drug-resistant epilepsy. However, it is vastly underutilized. One reason for this underutilization is a lack of prompt referral of possible epilepsy surgery candidates to comprehensive epilepsy centers. Natural language processing, coupled with machine learning, may be able to identify possible epilepsy surgery candidates through the analysis of unstructured clinic notes. This study, conducted in two tertiary hospitals in South Australia, demonstrated that there are individuals who fulfill criteria for epilepsy surgery evaluation referral but have not yet been referred. Machine learning-natural language processing demonstrates promising results in assisting with the identification of such suitable candidates in Australia., (© 2024 The Authors. Epilepsia Open published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.)

Faults represent important analytical targets for the identification of perceptual ground motions and associated seismic hazards. In particular, during oil production, important data such as the path and flow rate of fluid flows can be obtained from information on fault location and their connectivity. Seismic attributes are conventional methods used for fault detection, whereby information obtained from seismic data are analyzed using various property processing methods. The analyzed data eventually provide information on fault properties and imaging of fault surfaces. In this study, we propose an efficient workflow for fault detection and extraction of requisite information to construct a fault surface model using 3D seismic cubes. This workflow not only improves the ability to detect faults but also distinguishes the edges of a fault more clearly, even with the application of fewer attributes compared to conventional workflows. Thus, the computing time of attribute processing is reduced, and fault surface cubes are generated more rapidly. In addition, the reduction in input variables reduces the effect of the interpreter's subjective intervention on the results. Furthermore, the clustering method can be applied to the azimuth and dip of the fault to be extracted from the complexly intertwined fault faces and subsequently imaged. The application of the proposed workflow to field data obtained from the Vincentian oil field in Australia resulted in a significant reduction in noise compared to conventional methods. It also led to clearer and continuous edge detection and extraction. [ABSTRACT FROM AUTHOR]

Family presence in paediatric resuscitation settings is now accepted practice. However, there is limited evidence to guide psychosocial care of these families. This study aimed to increase the understanding of family demographics and needs for psychosocial care at the Royal Children's Hospital, Melbourne, Australia and to identify the factors that impact on families at this time of emotional and social distress. A retrospective clinical data-mining methodology was utilised (n = 1123). Descriptive and inferential data analysis was undertaken using STATA software to identify population demographics, patterns in service usage and family experience, and to identify factors associated with parents' emotional responses. Four areas were identified that support evidence-informed psychosocial care for families in paediatric resuscitation contexts: 1) family and parent dynamics 2) families' experiences of distress in relation to patient diagnostic category 3) systemic nature of family response, and 4) the identification and analysis of family emotional support needs, which is central to the psychosocial response. [ABSTRACT FROM AUTHOR]

Background: Implementation research has delved into barriers to implementing change and interventions for the implementation of innovation in practice. There remains a gap, however, that fails to connect implementation barriers to the most effective implementation strategies and provide a more tailored approach during implementation. This study aimed to explore barriers for the implementation of professional services in community pharmacies and to predict the effectiveness of facilitation strategies to overcome implementation barriers using machine learning techniques.Methods: Six change facilitators facilitated a 2-year change programme aimed at implementing professional services across community pharmacies in Australia. A mixed methods approach was used where barriers were identified by change facilitators during the implementation study. Change facilitators trialled and recorded tailored facilitation strategies delivered to overcome identified barriers. Barriers were coded according to implementation factors derived from the Consolidated Framework for Implementation Research and the Theoretical Domains Framework. Tailored facilitation strategies were coded into 16 facilitation categories. To predict the effectiveness of these strategies, data mining with random forest was used to provide the highest level of accuracy. A predictive resolution percentage was established for each implementation strategy in relation to the barriers that were resolved by that particular strategy.Results: During the 2-year programme, 1131 barriers and facilitation strategies were recorded by change facilitators. The most frequently identified barriers were a 'lack of ability to plan for change', 'lack of internal supporters for the change', 'lack of knowledge and experience', 'lack of monitoring and feedback', 'lack of individual alignment with the change', 'undefined change objectives', 'lack of objective feedback' and 'lack of time'. The random forest algorithm used was able to provide 96.9% prediction accuracy. The strategy category with the highest predicted resolution rate across the most number of implementation barriers was 'to empower stakeholders to develop objectives and solve problems'.Conclusions: Results from this study have provided a better understanding of implementation barriers in community pharmacy and how data-driven approaches can be used to predict the effectiveness of facilitation strategies to overcome implementation barriers. Tailored facilitation strategies such as these can increase the rate of real-time implementation of innovations in healthcare, leading to an industry that can confidently and efficiently adapt to continuous change. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) power has attracted widespread attention from many countries around the world due to its clean and renewable characteristics. To ensure the stable operation of the power system, accurate PV power forecasting has become a mandatory and challenging task. Currently, deep learning methods have become a vital approach in the field of PV power forecasting. In this work, a multistage attention neural network based on neural ordinary differential equation (MANODE) is proposed to address the main limitations of previous deep learning methods applied to PV power forecasting. Based on the neural ordinary differential equation (NODE), MANODE optimizes the long short-term memory network (LSTM) and temporal convolutional neural network (TCN), and combines the attention mechanism to achieve fine-grained spatio-temporal information extraction of PV series. In addition, the proposed MANODE model is applied to three different PV series collected from the Alice Springs meteorological station. Compared to previous state-of-the-art methods, the proposed method reduces the PV power forecasting error by at least 12.05%, 13.15%, and 9.71% on three different PV datasets, in terms of mean absolute error metric. The average errors of the MANODE method in four-hour-ahead PV power forecasting on the three datasets are 0.321, 0.350, and 0.567. • A novel neural network method called MANODE is applied in PV power forecasting. • MANODE addresses drawbacks of previous PV forecasting methods using LSTMs or CNNs. • Three multivariate datasets located in Australia are adopted in this work. • MANODE outperforms previous state-of-the-art neural network method in PV forecasting. [ABSTRACT FROM AUTHOR]

• Bus crashes are investigated by crash dataset of the State of Victoria, Australia. • Data mining used to discover chain of factors that lead to fatality in bus crashes. • Bus crashes with non-motor vehicles and on weekends increase fatality risk. • Old driver, intersection and vulnerable user increase fatality in non-motor crashes. • Fatal crash on weekend is linked to darkness, speed, pedestrian and female driver. Introduction: Buses are different vehicles in terms of dimensions, maneuverability, and driver's vision. Although bus traveling is a safe mode to travel, the number of annual bus crashes cannot be neglected. Moreover, limited studies have been conducted on the bus involved in fatal crashes. Therefore, identification of the contributing factors in the bus involved fatal crashes can reduce the risk of fatality. Method: Data set of bus involved crashes in the State of Victoria, Australia was analyzed over the period of 2006–2019. Clustering of crash data was accomplished by dividing them into homogeneous categories, and by implementing association rules discovery on the clusters, the factors affecting fatality in bus involved crashes were extracted. Results: Clustering results show bus crashes with all vehicles except motor vehicles and weekend crashes have a high rate of fatality. According to the association rule discovery findings, the factors that increase the risk of bus crashes with non-motor vehicles are: old bus driver, collision with pedestrians at signalized intersections, and the presence of vulnerable road users. Likewise, factors that increase the risk of fatality in bus involved crashes on weekends are: darkness of roads in high-speed zones, pedestrian presence at highways, bus crashes with passenger car by a female bus driver, and the occurrence of multi-vehicle crashes in high-speed zones. Practical Applications: The study provides a sequential pattern of factors, named rules that lead to fatality in bus involved crashes. By eliminating or improving one or all of the factors involved in rules, fatal bus crashes may be prevented. The recommendations to reduce fatality in bus crashes are: observing safe distances with the buses, using road safety campaigns to reduce pedestrians' distracted behavior, improving the lighting conditions, implementing speed bumps and rumble strips in high-speed zones, installing pedestrian detection systems on buses and setting special bus lanes in crowded areas. [ABSTRACT FROM AUTHOR]

• This paper is dedicated to vehicle-bicycle crash severity modeling. • The class-imbalanced issue in vehicle-bicycle crash severity analysis is handled with the imbalanced data resampling process. • To address the complexity of crash dataset, the learning-based feature extraction process is adopted in an iterative manner, such that the the most significant contributing factors to the severity of vehicle-bicycle crashes are determined and the trade-off between computation time and model performance is catered for. • The vehicle-bicycle crash dataset in this paper contains a large number of discrete variables. The gradient boosting algorithm is applied to handle the large number of categories and does not rely on strict statistical assumptions. • The impact of the most significant contributing factors on the severity of vehiclebicycle crashes are explained with the marginal effect analysis. • The result can provide some implications for policies and counter-measures for fatal and serious vehicle-bicycle crashes. Introduction: Although cycling is increasingly being promoted for transportation, the safety concern of bicyclists is one of the major impediments to their adoption. A thorough investigation on the contributing factors to fatalities and injuries involving bicyclist. Method: This paper designs an integrated data mining framework to determine the significant factors that contribute to the severity of vehicle-bicycle crashes based on the crash dataset of Victorian, Australia (2013–2018). The framework integrates imbalanced data resampling, learning-based feature extraction with gradient boosting algorithm and marginal effect analysis. The top 10 significant predictors of the severity of vehicle-bicycle crashes are extracted, which gives an area under ROC curve (AUC) value of 0.8236 and computing time as 37.8 s. Results: The findings provide insights for understanding and developing countermeasures or policy initiatives to reduce severe vehicle-bicycle crashes. [ABSTRACT FROM AUTHOR]

A foundation for digitally enabling healthier living is the safe development and use of technology. The practice of digital health safety has emerged from patient harm attributed to failing technologies. The study aimed to investigate how to adopt and implement digital health safety guidelines at scale. Data was collected through an online survey, semi-structured interviews, focus groups, document review, and data mining of artefacts. The findings of this study capture the emerging practice from Australia in a way that offers insights into the problem of practice, patient safety practice, safety culture, and socio-technical factors. The research findings contribute to better understanding of the complexities of balancing digital innovation with patient safety. The four recommendations from the study and the provision of a logic model will support the audience to implement actions toward a safer digital health ecology.

Identifying potentially fraudulent or wasteful medical insurance claims can be difficult due to the large amounts of data and human effort involved. We applied unsupervised machine learning to construct interpretable models which rank variations in medical provider claiming behaviour in the domain of unilateral joint replacement surgery, using data from the Australian Medicare Benefits Schedule. For each of three surgical procedures reference models of claims for each procedure were constructed and compared analytically to models of individual provider claims. Providers were ranked using a score based on fees for typical claims made in addition to those in the reference model. Evaluation of the results indicated that the top-ranked providers were likely to be unusual in their claiming patterns, with typical claims from outlying providers adding up to 192% to the cost of a procedure. The method is efficient, generalizable to other procedures and, being interpretable, integrates well into existing workflows.

Identifying and determining the multitude of reasons behind school absences of students is often challenging. This study aims to uncover the hidden reasons for school absence in children and adolescents. The analysis is conducted on a national survey that includes 2967 Australian children and adolescents aged 11-17. The Apriori association rule generator of machine learning techniques and binary logistic regression are used to identify the significant predictors of school absences. Out of 2484, 83.7% (n = 2079) aged (11-17) years children and adolescents have missed school for various reasons, 42.28% (n = 879) are (11-15) years old, 24.52% (n = 609) and 16.9% (n = 420) are 16- and 17-years old adolescents respectively. A considerable proportion of adolescents, specifically 16.4% (n = 407) and 23.4% (n = 486) of 16 and 17 years old, respectively, have selected 'refused to say' as their reason for not attending school. It also highlights the negative outcomes associated with undisclosed reasons for school absence, such as bullying, excessive internet/gaming, reduced family involvement, suicide attempts, and existential hopelessness. The findings of the national survey underscore the importance of addressing these undisclosed reasons for school absence to improve the overall well-being and educational outcomes of children and adolescents., (© 2024. The Author(s).)

The lack of personal and economic attributes in emerging public transit big data (such as smart card data) is a general issue that needs to be addressed. Passengers in the public transit network are from different socioeconomic classes, and their trip attributes usually depend on their personal and economic attributes. For instance, age as a demographic attribute plays an important role in trip attributes; adolescent passengers travel to school, young professionals travel to work, and old passengers travel to medical facilities more often. Relations between the socioeconomic and trip attributes of the passengers can be examined by developing a Bayesian network that represents the relations between the attributes by directed acyclic graphs, and calculating the joint and conditional probability values in the graph. This study infers the socioeconomic attributes of the public transit passengers from the trip attributes through developing a Bayesian network. Considered socioeconomic attributes are age, gender, and income; considered trip attributes are start time and duration of the trip, stay duration, and available origin and destination land use types. First, potential structures of the Bayesian network are examined by comparing network scores and arc strength test. After learning the network's parameters, the reasoning is done through both prediction and diagnosis in the network. Also, the most likely combinations of the socioeconomic and trip attributes are discovered. The case study for developing the Bayesian network is a Household Travel Survey dataset from Queensland, Australia, that contains both socioeconomic and trip attributes. Results clearly show how the socioeconomic attributes can be inferred from the trip attributes. Discovered probability distributions can be used to enrich the smart card datasets with the socioeconomic attributes. Moreover, the Bayesian classifier is applied to the dataset to validate the capability of the model in predicting the socioeconomic attributes. In the end, the developed network is implemented on a set of smart card records to discuss the potential applications. [ABSTRACT FROM AUTHOR]

Graphs are commonly used to represent objects, such as images and text, for pattern classification. In a dynamic world, an object may continuously evolve over time, and so does the graph extracted from the underlying object. These changes in graph structure with respect to the temporal order present a new representation of the graph, in which an object corresponds to a set of time-variant graphs. In this paper, we formulate a novel time-variant graph classification task and propose a new graph feature, called a graph-shapelet pattern, for learning and classifying time-variant graphs. Graph-shapelet patterns are compact and discriminative graph transformation subsequences. A graph-shapelet pattern can be regarded as a graphical extension of a shapelet—a class of discriminative features designed for vector-based temporal data classification. To discover graph-shapelet patterns, we propose to convert a time-variant graph sequence into time-series data and use the discovered shapelets to find graph transformation subsequences as graph-shapelet patterns. By converting each graph-shapelet pattern into a unique tokenized graph transformation sequence, we can measure the similarity between two graph-shapelet patterns and therefore classify time-variant graphs. Experiments on both synthetic and real-world data demonstrate the superior performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This article explores the links between older people's homelessness and family relationships and aims to inform social work practice frameworks. Whilst breakdown in family relationships is widely recognised as linked to being at risk of homelessness, there is less understanding of the interplay of family, both positive and negative, with older people's homelessness. Drawing on a study incorporating data mining of service records, this article aims to provide clarity on supportive and troubled family relationships and their links to housing crises as experienced by older Australians. The findings highlight a number of domains for social work practice including undertaking skilled assessments to understand the strengths and constraints experienced by families. Assessments will then inform intervention to support and provide resources to some families to prevent their older family members' homelessness and to intervene in both a preventative and empowering way to address elder abuse. The implications for policy, in particular, the need for sectors of housing, aged care and health to intersect, are discussed. [ABSTRACT FROM AUTHOR]

Introduction: Psychosocial risk and protective factors specific to acute burn patients have been shown to impact on longer-term health outcomes. Yet the nature and impact of such factors throughout the acute hospital admission phase have not been extensively examined to date. This study analysed the psychosocial pre-admission, hospitalisation and discharge factors for a sample of burn patients at an Australian specialist acute burns unit (The Alfred) and the relationship with their admission length.Methods: A retrospective audit was undertaken of psychosocial factors documented in patient medical records, Victorian Adult Burns Service registry data, and social work files (January-December 2014).Results: Two hundred and forty-nine patients were identified, with a mean age of 42.83 years and a preponderance of men (73.9%). The majority (71%) had a low burn severity (<10%), and an average of eight psychosocial factors. Independent of the severity of the burn, two psychosocial domains were strongly predictive of length of stay - coping with treatment and discharge planning - along with pre-existing psychosocial factors and family support.Conclusion: A diversity of psychosocial factors was identified, highlighting the clinical complexity of this patient group. Given three domains of admission-related psychosocial factors were predictive of the length of patient stay, more targeted psychosocial interventions in these areas may ensure patients and their families are supported more effectively throughout an admission, and length of stay costs may also be reduced. [ABSTRACT FROM AUTHOR]

Group recommendation has become highly demanded when users communicate in the forms of group activities in online sharing communities. These group activities include student group study, family TV program watching, friends travel decision, etc. Existing group recommendation techniques mainly focus on the small user groups. However, online sharing communities have enabled group activities among thousands of users. Accordingly, recommendation over large groups has become urgent. In this paper, we propose a new framework to accomplish this goal by exploring the group interests and the connections between group users. We first divide a big group into different interest subgroups, each of which contains users closely connected with each other and sharing the similar interests. Then, for each interest subgroup, our framework exploits the connections between group users to collect a comparably compact potential candidate set of media-user pairs, on which the collaborative filtering is performed to generate an interest subgroup-based recommendation list. After that, a novel aggregation function is proposed to integrate the recommended media lists of all interest subgroups as the final group recommendation results. Extensive experiments have been conducted on two real social media datasets to demonstrate the effectiveness and efficiency of our proposed approach. [ABSTRACT FROM AUTHOR]

Background: Individuals experiencing severe and persistent mental illness report a desire to gain and sustain work. Individual Placement and Support (IPS) is an evidence-based approach to vocational rehabilitation to support competitive employment outcomes. Aim/Objective: This study aimed to evaluate whether a joint-governance management partnership, between a clinical adult mental health and an employment service, could deliver a sustained IPS program in Australia. Materials and Method: The methodology entailed a Clinical Data Mining approach, to examine records from seven years of implementation of IPS in one setting within an Australian public mental health service context. Results/Findings: Despite the prevalence of schizophrenia spectrum diagnoses and an older mean age (39 years), indicating that a large proportion of the cohort had experienced serious mental illness for over twenty years, findings were that 46.3% of participants achieved employment. Conclusions: This is an excellent result and is comparable to the only randomised control trial, with adult services, in the Australian context, which found a 42.5% employment rate possible under IPS compared with just 23.5% with referral to external employment services. Significance: More extensive trialling of IPS across clinical services is required, in Australia and internationally, including fidelity protocols, for knowledge translation to be achieved. [ABSTRACT FROM AUTHOR]

Text and Data Mining (hereinafter, TDM) issue for the purpose of scientific research or for any other purpose which is included in the provisions of the new EU Directive on Copyright in the Digital Single Market (hereinafter, DSM). TDM is a term that includes Web harvesting and Web Archiving activities. Web harvesting and archiving pertains to the processes of collecting from the web and archiving of works that reside on the Web. In the following analysis we will elaborate briefly upon provisions in EU Copyright law which were discussed during the proposal for a new Directive on Copyright in the DSM as well as provisions which are included in the text of art.3 and art.4 of the new Directive 2019/790/EU per TDM. In addition, the following analysis presents legislation in very few EU Member States which pertains to TDM and preceded the rulings of Directive 2019/790/EU. Digital legal deposit remarkable examples from EU Member States are also presented in this paper. The example of Australia is also presented below hereto because it is one of the oldest and most successful worldwide. The National Library of Australia's digital legal deposit is state-of-the-art. [ABSTRACT FROM AUTHOR]

Knowledge on the determinants and patterns of water demand for different consumers supports the design of customized demand management strategies. Smart meters coupled with big data analytics tools create a unique opportunity to support such strategies. Yet, at present, the information content of smart meter data is not fully mined and usually needs to be complemented with water fixture inventory and survey data to achieve detailed customer segmentation based on end use water usage. In this paper, we developed a data‐driven approach that extracts information on heterogeneous water end use routines, main end use components, and temporal characteristics, only via data mining existing smart meter readings at the scale of individual households. We tested our approach on data from 327 households in Australia, each monitored with smart meters logging water use readings every 5 s. As part of the approach, we first disaggregated the household‐level water use time series into different end uses via Autoflow. We then adapted a customer segmentation based on eigenbehavior analysis to discriminate among heterogeneous water end use routines and identify clusters of consumers presenting similar routines. Results revealed three main water end use profile clusters, each characterized by a primary end use: shower, clothes washing, and irrigation. Time‐of‐use and intensity‐of‐use differences exist within each class, as well as different characteristics of regularity and periodicity over time. Our customer segmentation analysis approach provides utilities with a concise snapshot of recurrent water use routines from smart meter data and can be used to support customized demand management strategies. Key Points: We investigate which water use behaviors can be identified from nonintrusive, single‐point, smart meter dataWe identify and cluster primary water use behaviors of single‐family households from disaggregated end use dataWe reveal the main water end uses driving different behaviors, usage patterns, and regularity, to support customized demand management [ABSTRACT FROM AUTHOR]

Secondary fracture prevention programs mostly identify patients with symptomatic non-vertebral fractures, whereas asymptomatic vertebral fractures are usually missed. We here describe the development and validation of a simple method to systematically identify patients with radiographic vertebral fractures using simple text-based searching of free-text radiology reports. The study consisted of two phases. In the development phase (DP), twelve search terms were used to identify vertebral fractures in all X-ray and CT reports issued over a period of 6 months. Positive reports were manually reviewed to confirm whether or not a vertebral fracture had in fact been reported. The three search terms most effective in detecting vertebral fractures during the DP were then applied during the implementation phase (IP) over several weeks to test their ability to identify patients with vertebral fractures. The search terms 'Loss of Height' (LoH), 'Compression Fracture' (CoF) and 'Crush Fracture' (CrF) identified the highest number of imaging reports with a confirmed vertebral fracture. These three search terms identified 581 of 689 (84%) of all true vertebral fractures with a positive predictive value of 76%. Using these three terms in the IP, 126 reports were identified of which 100 (79%) had a vertebral fracture confirmed on manual review. Amongst a sample of 587 reports in week 1 of the IP, 7 (1.2%) were false negatives. Many patients with vertebral fractures can be identified via a simple text-based search of electronic radiology reports. This method may be utilised by secondary fracture prevention programs to narrow the 'care gap' in osteoporosis management. [ABSTRACT FROM AUTHOR]

This research-on-research study describes efforts to develop non-Cochrane systematic reviews (SRs) by analyzing demographical and time-course collaborations between international institutions using protocols registered in the International Prospective Register of Systematic Reviews (PROSPERO) or published in scientific journals. We have published an a priori protocol to develop this study. Protocols published in scientific journals were searched using the MEDLINE and Embase databases; the query terms “Systematic review” [Title] AND “protocol” [Title] were searched from February 2011 to December 2017. Protocols registered at PROSPERO during the same period were obtained by web scraping all non-Cochrane records with a Python script. After excluding protocols that had a fulfillment or duplication rate of less than 90%, they were classified as published “only in PROSPERO”, “only in journals”, or in “journals and PROSPERO”. Results of data and metadata extraction using text mining processes were curated by two reviewers. These Datasets and R scripts are freely available to facilitate reproducibility. We obtained 20,814 protocols of non-Cochrane SRs. While “unique protocols” by reviewers’ institutions from 60 countries were the most frequent, a median of 6 (2-150) institutions from 130 different countries were involved in the preparation of “collaborative protocols”. The highest Ranked countries involved in overall protocol production were the UK, the U.S., Australia, Brazil, China, Canada, the Netherlands, Germany, and Italy. Most protocols were registered only in PROSPERO. However, the number of protocols published in scientific journals (924) or in both PROSPERO and journals (807) has increased over the last three years. Syst Rev and BMJ Open published more than half of the total protocols. While the more productive countries were involved in “unique” and “collaborative protocols”, less productive countries only participated in “collaborative protocols” that were mainly published in PROSPERO. Our results suggest that, although most countries were involved in solitary production of protocols for non-Cochrane SRs during the study period, it would be useful to develop new strategies to promote international collaborations, especially with less productive countries. [ABSTRACT FROM AUTHOR]

Background: Bali, Indonesia, presents significant infectious and non‐infectious health risks for Australian travellers. Understanding this spectrum of illnesses has the potential to assist clinicians in evaluating unwell returning travellers and guide provision of pre‐travel advice. Aim: To describe the spectrum of illnesses seen in returned travellers from Bali. Methods: Using a novel text mining approach, we performed a retrospective, observational study of all adult emergency department presentations to a metropolitan health service in Melbourne, from 2011 to 2015. Outcome measures included demographic, clinical and laboratory features of travel‐related illnesses. Results: A total of 464 patients met inclusion criteria. Gastroenteritis (119/464, 26%), systemic febrile illness (88/464, 19%) and respiratory tract infection (51/464, 11%) were the most common diagnoses. Dengue was the most common laboratory‐confirmed diagnosis (25/464, 5%). No cases of malaria were identified. Common non‐infectious presentations included traumatic injury (47/464, 10%) and animal bites requiring rabies post‐exposure prophylaxis (29/464, 6%). A total of 110 patients (24%) was admitted to the hospital; those presenting with systemic febrile illness were more likely to be admitted compared to those presenting with other illnesses (odds ratio 3.42, 95% confidence interval 2.02–5.75, P < 0.001). Conclusion: This is the first study to use a text mining approach to identify and describe emergency department presentations related to diseases acquired in Bali by Australian travellers. Although infections are important causes of illness, trauma and animal bites account for a significant number of hospital presentations. Our findings contribute to the knowledge on the health risks for travellers to Bali, and will assist clinicians in relevant pre‐ and post‐travel evaluations. [ABSTRACT FROM AUTHOR]

The Great Resignation has brought significant challenges to the recovery of the hospitality industry from the depression caused by the coronavirus pandemic (COVID-19). Prior studies have revealed that the leading cause of the Great Resignation is negative employee experience. However, few empirical studies have been conducted to obtain deep insights into the negative experiences of hospitality employees. Hotel managers still lack the knowledge to help them resolve the workforce problem and maintain competitiveness during the pandemic. This study proposes a novel framework, named HENEX, that uses data-mining technologies and employees' online reviews about hotels to identify the factors that lead to hospitality employees' negative experiences and changes in these factors caused by COVID-19. We demonstrate the effectiveness of HENEX through a case study that involves major hotels in Australia. The findings could help hotel managers develop strategies to resolve the workforce problem and maintain competitiveness during the Great Resignation period. • A framework that could understand employee experience is proposed. • The aspects that lead to negative employee experience are identified. • The changes in factors that lead to negative employee experiences are revealed. • The effectiveness of the framework is demonstrated by data from Glassdoor. • Knowledge that could help hoteliers respond to the Great Resignation is discovered. [ABSTRACT FROM AUTHOR]

Because of the inefficiency in analyzing the comprehensive travel data, tourism managers are facing the challenge of gaining insights into travelers’ behavior and preferences. In most cases, existing techniques are incapable of capturing the sequential patterns hidden in travel data. To address these issues, this article proposes to analyze the travelers’ behavior through geotagged photos and sequential rule mining. Travel diaries, constructed from the photo sequences, can capture comprehensive travel information, and then sequential patterns can be discovered to infer the potential destinations. The effectiveness of the proposed framework is demonstrated in a case study of Australian outbound tourism, using a data set of more than 890,000 photos from 3,623 travelers. The introduced framework has the potential to benefit tourism researchers and practitioners from capturing and understanding the behaviors and preferences of travelers. The findings can support destination-marketing organizations (DMOs) in promoting appropriate destinations to prospective travelers. [ABSTRACT FROM AUTHOR]

Background: The Sexual Assault Resource Center (SARC) in Perth, Western Australia provides free 24-hour medical, forensic, and counseling services to persons aged over 13 years following sexual assault. Objective: The aim of this research was to design a data management system that maintains accurate quality information on all sexual assault cases referred to SARC, facilitating audit and peer-reviewed research. Methods: The work to develop SARC Medical Services Clinical Information System (SARC-MSCIS) took place during 2007–2009 as a collaboration between SARC and Curtin University, Perth, Western Australia. Patient demographics, assault details, including injury documentation, and counseling sessions were identified as core data sections. A user authentication system was set up for data security. Data quality checks were incorporated to ensure high-quality data. Results: An SARC-MSCIS was developed containing three core data sections having 427 data elements to capture patient’s data. Development of the SARC-MSCIS has resulted in comprehensive capacity to support sexual assault research. Four additional projects are underway to explore both the public health and criminal justice considerations in responding to sexual violence. The data showed that 1,933 sexual assault episodes had occurred among 1881 patients between January 1, 2009 and December 31, 2015. Sexual assault patients knew the assailant as a friend, carer, acquaintance, relative, partner, or ex-partner in 70% of cases, with 16% assailants being a stranger to the patient. Conclusion: This project has resulted in the development of a high-quality data management system to maintain information for medical and forensic services offered by SARC. This system has also proven to be a reliable resource enabling research in the area of sexual violence. [ABSTRACT FROM AUTHOR]

Effective and accurate use of routinely collected health data to produce Key Performance Indicator reporting is dependent on the underlying data quality. In this research, Process Mining methodology and tools were leveraged to assess the data quality of time-based Emergency Department data sourced from electronic health records. This research was done working closely with the domain experts to validate the process models. The hospital patient journey model was used to assess flow abnormalities which resulted from incorrect timestamp data used in time-based performance metrics. The research demonstrated process mining as a feasible methodology to assess data quality of time-based hospital performance metrics. The insight gained from this research enabled appropriate corrective actions to be put in place to address the data quality issues. [ABSTRACT FROM AUTHOR]

Today's information systems log vast amounts of data. These collections of data (implicitly) describe events (e.g. placing an order or taking a blood test) and, hence, provide information on the actual execution of business processes. The analysis of such data provides an excellent starting point for business process improvement. This is the realm of process mining, an area which has provided a repertoire of many analysis techniques. Despite the impressive capabilities of existing process mining algorithms, dealing with the abundance of data recorded by contemporary systems and devices remains a challenge. Of particular importance is the capability to guide the meaningful interpretation of 'oceans of data' by process analysts. To this end, insights from the field of visual analytics can be leveraged. This article proposes an approach where process states are reconstructed from event logs and visualised in succession, leading to an animated history of a process. This approach is customisable in how a process state, partially defined through a collection of activity instances, is visualised: one can select a map and specify a projection of events on this map based on the properties of the events. This paper describes a comprehensive implementation of the proposal. It was realised using the open-source process mining framework ProM. Moreover, this paper also reports on an evaluation of the approach conducted with Suncorp, one of Australia's largest insurance companies. [ABSTRACT FROM AUTHOR]