Tackling the burden of injury in Australasia : developing a binational trauma registry

Existing trauma registries in Australia and New Zealand play an important role in monitoring the management of injured patients. Over the past decade, such monitoring has been translated into changes in clinical processes and practices. Monitoring and changes have been ad hoc, as there are currently no Australasian benchmarks for “optimal” injury management. A binational trauma registry is urgently needed to benchmark injury management to improve outcomes for injured patients.

Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform

Spatial information captured from optical remote sensors on board unmanned aerial vehicles (UAVs) has great potential in automatic surveillance of electrical infrastructure. For an automatic vision-based power line inspection system, detecting power lines from a cluttered background is one of the most important and challenging tasks. In this paper, a novel method is proposed, specifically for power line detection from aerial images. A pulse coupled neural filter is developed to remove background noise and generate an edge map prior to the Hough transform being employed to detect straight lines. An improved Hough transform is used by performing knowledge-based line clustering in Hough space to refine the detection results. The experiment on real image data captured from a UAV platform demonstrates that the proposed approach is effective for automatic power line detection.

Predicting project performance through neural networks

Successful project delivery of construction projects depends on many factors. With regard to the construction of a facility, selecting a competent contractor for the job is paramount. As such, various approaches have been advanced to facilitate tender award decisions. Essentially, this type of decision involves the prediction of a bidderÕs performance based on information available at the tender stage. A neural network based prediction model was developed and presented in this paper. Project data for the study were obtained from the Hong Kong Housing Department. Information from the tender reports was used as input variables and performance records of the successful bidder during construction were used as output variables. It was found that the networks for the prediction of performance scores for Works gave the highest hit rate. In addition, the two most sensitive input variables toward such prediction are ‘‘Difference between Estimate’’ and ‘‘Difference between the next closest bid’’. Both input variables are price related, thus suggesting the importance of tender sufficiency for the assurance of quality production.

Cognitive assessment in closed head injury : stability, validity and parallel forms for two neuropsychological measures of recovery

Investigated the psychometric properties of the original and alternate sets of the Trail Making Test (TMT) and the Controlled Oral Word Association Test (COWAT; A. L. Benton and D. Hamsher, 1978) in 50 orthopedic and 15 closed head injured (1 yr after trauma) patients (aged 15–59 yrs). Although the alternate forms of both measures proved to be stable and consistent with each other in both groups, only the parallel sets of TMT reliably discriminated the clinical group from controls. Practice effects in the head injured were significant only for Trail B of TMT. Factor analysis of the control group's results identified Verbal Knowledge as a major contributor to performance on COWAT, whereas TMT was more dependent on Rapid Visual Search and Visuomotor Sequencing.

The use of narrative text for injury surveillance research : a systematic review

Objective: To summarise the extent to which narrative text fields in administrative health data are used to gather information about the event resulting in presentation to a health care provider for treatment of an injury, and to highlight best practise approaches to conducting narrative text interrogation for injury surveillance purposes.----- Design: Systematic review----- Data sources: Electronic databases searched included CINAHL, Google Scholar, Medline, Proquest, PubMed and PubMed Central.. Snowballing strategies were employed by searching the bibliographies of retrieved references to identify relevant associated articles.----- Selection criteria: Papers were selected if the study used a health-related database and if the study objectives were to a) use text field to identify injury cases or use text fields to extract additional information on injury circumstances not available from coded data or b) use text fields to assess accuracy of coded data fields for injury-related cases or c) describe methods/approaches for extracting injury information from text fields.----- Methods: The papers identified through the search were independently screened by two authors for inclusion, resulting in 41 papers selected for review. Due to heterogeneity between studies metaanalysis was not performed.----- Results: The majority of papers reviewed focused on describing injury epidemiology trends using coded data and text fields to supplement coded data (28 papers), with these studies demonstrating the value of text data for providing more specific information beyond what had been coded to enable case selection or provide circumstantial information. Caveats were expressed in terms of the consistency and completeness of recording of text information resulting in underestimates when using these data. Four coding validation papers were reviewed with these studies showing the utility of text data for validating and checking the accuracy of coded data. Seven studies (9 papers) described methods for interrogating injury text fields for systematic extraction of information, with a combination of manual and semi-automated methods used to refine and develop algorithms for extraction and classification of coded data from text. Quality assurance approaches to assessing the robustness of the methods for extracting text data was only discussed in 8 of the epidemiology papers, and 1 of the coding validation papers. All of the text interrogation methodology papers described systematic approaches to ensuring the quality of the approach.----- Conclusions: Manual review and coding approaches, text search methods, and statistical tools have been utilised to extract data from narrative text and translate it into useable, detailed injury event information. These techniques can and have been applied to administrative datasets to identify specific injury types and add value to previously coded injury datasets. Only a few studies thoroughly described the methods which were used for text mining and less than half of the studies which were reviewed used/described quality assurance methods for ensuring the robustness of the approach. New techniques utilising semi-automated computerised approaches and Bayesian/clustering statistical methods offer the potential to further develop and standardise the analysis of narrative text for injury surveillance.

Injury bulletin 105 : skateboard injury

Summary of findings: QISU estimates that approximately 1,000 skateboard-associated injuries are seen at emergency departments each year in Queensland. 10-14 year old males are the most likely group to present to a QISU ED with a skateboard related injury. The peak time for skateboard injuries to occur is on the weekend and in the late afternoon. Only 19% of skateboard injuries occur at skate park facilities, with the remainder oc-curring in non-skate parks, on roads and footpaths. Risk factors associated with more severe injuries are, age less than 10 years and involvement in a motor vehicle crash The most common types of injuries are fractures and sprains of the upper limbs. Isolated head injuries represent approximately 5% of skateboarding injuries, but 60% of serious injuries requiring resuscitation. These injuries may be minimized or pre-vented with helmet use.

Pram and stroller related injury in Queensland children under 5 years of age

An estimated 200 Queensland children under 5 years of age are injured every year in incidents involving prams or strollers. The majority of injuries are due to falls from or falls with the pram or stroller Nineteen children were identified as having been caught in the pram or stroller mechanism (13 sustained finger injuries). Stairs and escalators were a factor in nearly 10 percent of pram or stroller fall injuries, with children being tipped out of the pram or stroller, or rolling down the stairs in the device. Roll away injuries accounted for eight percent of all pram or stroller fall injuries (some also involving stairs) Roll away injuries could be prevented by a default brake system similar to airport trolleys. Pram or stroller failure was identified in 2% of injuries

A fuzzy neural network approach for contractor pre-qualification

Nonlinearity, uncertainty and subjectivity are the three predominant characteristics of contractors prequalification which cause the process more of an art than a scientific evaluation. A fuzzy neural network (FNN) model, amalgamating both the fuzzy set and neural network theories, has been developed aiming to improve the objectiveness of contractor prequalification. Through the FNN theory, the fuzzy rules as used by the prequalifiers can be identified and the corresponding membership functions can be transformed. Eighty-five cases with detailed decision criteria and rules for prequalifying Hong Kong civil engineering contractors were collected. These cases were used for training (calibrating) and testing the FNN model. The performance of the FNN model was compared with the original results produced by the prequalifiers and those generated by the general feedforward neural network (GFNN, i.e. a crisp neural network) approach. Contractor’s ranking orders, the model efficiency (R2) and the mean absolute percentage error (MAPE) were examined during the testing phase. These results indicate the applicability of the neural network approach for contractor prequalification and the benefits of the FNN model over the GFNN model. The FNN is a practical approach for modelling contractor prequalification.

Decision support system for contractor pre-qualification : artificial neural network model

The selection criteria for contractor pre-qualification are characterized by the co-existence of both quantitative and qualitative data. The qualitative data is non-linear, uncertain and imprecise. An ideal decision support system for contractor pre-qualification should have the ability of handling both quantitative and qualitative data, and of mapping the complicated nonlinear relationship of the selection criteria, such that rational and consistent decisions can be made. In this research paper, an artificial neural network model was developed to assist public clients identifying suitable contractors for tendering. The pre-qualification criteria (variables) were identified for the model. One hundred and twelve real pre-qualification cases were collected from civil engineering projects in Hong Kong, and eighty-eight hypothetical pre-qualification cases were also generated according to the “If-then” rules used by professionals in the pre-qualification process. The results of the analysis totally comply with current practice (public developers in Hong Kong). Each pre-qualification case consisted of input ratings for candidate contractors’ attributes and their corresponding pre-qualification decisions. The training of the neural network model was accomplished by using the developed program, in which a conjugate gradient descent algorithm was incorporated for improving the learning performance of the network. Cross-validation was applied to estimate the generalization errors based on the “re-sampling” of training pairs. The case studies show that the artificial neural network model is suitable for mapping the complicated nonlinear relationship between contractors’ attributes and their corresponding pre-qualification (disqualification) decisions. The artificial neural network model can be concluded as an ideal alternative for performing the contractor pre-qualification task.

Generating rules with predicates, terms and variables from the pruned neural networks

Artificial neural networks (ANN) have demonstrated good predictive performance in a wide range of applications. They are, however, not considered sufficient for knowledge representation because of their inability to represent the reasoning process succinctly. This paper proposes a novel methodology Gyan that represents the knowledge of a trained network in the form of restricted first-order predicate rules. The empirical results demonstrate that an equivalent symbolic interpretation in the form of rules with predicates, terms and variables can be derived describing the overall behaviour of the trained ANN with improved comprehensibility while maintaining the accuracy and fidelity of the propositional rules.

Identification of factors predicting clickthrough in Web searching using neural network analysis

In this research, we aim to identify factors that significantly affect the clickthrough of Web searchers. Our underlying goal is determine more efficient methods to optimize the clickthrough rate. We devise a clickthrough metric for measuring customer satisfaction of search engine results using the number of links visited, number of queries a user submits, and rank of clicked links. We use a neural network to detect the significant influence of searching characteristics on future user clickthrough. Our results show that high occurrences of query reformulation, lengthy searching duration, longer query length, and the higher ranking of prior clicked links correlate positively with future clickthrough. We provide recommendations for leveraging these findings for improving the performance of search engine retrieval and result ranking, along with implications for search engine marketing

An adolescent injury intervention : selecting targeted behaviours with implications for program design and evaluation

Objectives: This paper sought to identify the behaviour change targets for an injury prevention program; Skills for Preventing Injury in Youth, SPIY. The aim was to explore how such behaviours could subsequently be implemented and evaluated in the program. Methods and Design: The quantitative procedure involved a survey with 267 Year 8 and 9 students (mean age 13.23 years) regarding their engagement in risk-taking behaviours that may lead to injury. The qualitative study involved 30 students aged 14 to 17 years reporting their experiences of injury and risk-taking. Results: Injury risk behaviours co-occurred among three-quarters of those who reported engaging in any alcohol use or transport or violence related injury risk behaviour. Students described in detail some of these experiences. Conclusions: The selection process of identifying target behaviours for change for an injury prevention program is described. Adolescents’ description of such risk behaviours can inform the process of operationalising and contextualising program content and deciding on evaluation methodology. The design of an effective injury prevention program involves considerable preparatory work and this paper was able to describe the process of identifying the behavioural targets for change that can be operationalised and evaluated in the injury prevention program, SPIY.

A short-term evaluation of a school-based early adolescent injury prevention program

This paper reports on the development of a school-based intervention to reduce risk-taking and associated injuries. There is limited but important evidence that intervention design should ensure participation does not lead to an increase in target risk behaviors with some studies in alcohol and drug prevention finding unexpected negative effects. The short-term evaluation of Skills for Preventing Injury in Youth (SPIY) examined change in interpersonal violence, alcohol and transport-related risks. Intervention (n = 360) and comparison (n = 180) students were surveyed pre/post-intervention. A qualitative analysis based on focus groups (70 students) explored experiences of change. Findings indicate significant positive changes reinforced by students’ reports. A decrease in reported risk-taking for the intervention group and an increase in the comparison group were observed. These findings endorse SPIY as a useful curriculum approach to reducing injuries and lend support to the future conduct of a long-term outcome evaluation.