Identification of alcohol involvement in injury-related hospitalisations using routine data compared to medical record review

Objective: To quantify the extent to which alcohol related injuries are adequately identified in hospitalisation data using ICD-10-AM codes indicative of alcohol involvement. Method: A random sample of 4373 injury-related hospital separations from 1 July 2002 to 30 June 2004 were obtained from a stratified random sample of 50 hospitals across 4 states in Australia. From this sample, cases were identified as involving alcohol if they contained an ICD-10-AM diagnosis or external cause code referring to alcohol, or if the text description extracted from the medical records mentioned alcohol involvement. Results: Overall, identification of alcohol involvement using ICD codes detected 38% of the alcohol-related sample, whilst almost 94% of alcohol-related cases were identified through a search of the text extracted from the medical records. The resultant estimate of alcohol involvement in injury-related hospitalisations in this sample was 10%. Emergency department records were the most likely to identify whether the injury was alcohol-related with almost three-quarters of alcohol-related cases mentioning alcohol in the text abstracted from these records. Conclusions and Implications: The current best estimates of the frequency of hospital admissions where alcohol is involved prior to the injury underestimate the burden by around 62%. This is a substantial underestimate that has major implications for public policy, and highlights the need for further work on improving the quality and completeness of routine administrative data sources for identification of alcohol-related injuries.

A preliminary investigation of the role of the Phenylalynine: Tyrosine Ratio in children with early and continuously treated phenylketonuria : toward identification of “safe” levels

Children with early and continuously treated phenylketonuria (ECT-PKU) remain at risk of developing executive function (EF) deficits. There is some evidence that a high phenylalanine to tyrosine ratio (phe:tyr) is more strongly associated with impaired EF development than high phenylalanine alone. This study examined EF in a sample of 11 adolescents against concurrent and historical levels of phenylalanine, phe:tyr, and tyrosine. Lifetime measures of phe:tyr were more strongly associated with EF than phenylalanine-only measures. Children with a lifetime phe:tyr less than 6 demonstrated normal EF, whereas children who had a lifetime phe:tyr above 6, on average, demonstrated clinically impaired EF.

System identification and control of an aerobot drive system

Fast thrust changes are important for authoritive control of VTOL micro air vehicles. Fixed-pitch rotors that alter thrust by varying rotor speed require high-bandwidth control systems to provide adequate performace. We develop a feedback compensator for a brushless hobby motor driving a custom rotor suitable for UAVs. The system plant is identified using step excitation experiments. The aerodynamic operating conditions of these rotors are unusual and so experiments are performed to characterise expected load disturbances. The plant and load models lead to a proportional controller design capable of significantly decreasing rise-time and propagation of disturbances, subject to bus voltage constraints.

Damage detection for truss bridge structures using correlation-based structural modal strain energy

This paper presents the feasibility of using structural modal strain energy as a parameter employed in correlation- based damage detection method for truss bridge structures. It is an extension of the damage detection method adopting multiple damage location assurance criterion. In this paper, the sensitivity of modal strain energy to damage obtained from the analytical model is incorporated into the correlation objective function. Firstly, the sensitivity matrix of modal strain energy to damage is conducted offline, and for an arbitrary damage case, the correlation coefficient (objective function) is calculated by multiplying the sensitivity matrix and damage vector. Then, a genetic algorithm is used to iteratively search the damage vector maximising the correlation between the corresponding modal strain energy change (hypothesised) and its counterpart in measurement. The proposed method is simulated and compared with the conventional methods, e.g. frequency-error method, coordinate modal assurance criterion and multiple damage location assurance criterion using mode shapes on a numerical truss bridge structure. The result demonstrates the modal strain energy correlation method is able to yield acceptable damage detection outcomes with less computing efforts, even in a noise contaminated condition.

Identification and specification of relationships as the foundation for service bundling

Service bundling can be regarded as an option for service providers to strengthen their competitive advantages, cope with dynamic market conditions and heterogeneous consumer demand. Despite these positive effects, actual guidance for the identification of service bundles and the act of bundling itself can be regarded as a gap. Previous research has resulted in a conceptualization of a service bundling method relying on a structured service description in order to fill this gap. This method addresses the reasoning about the suitability of services to be part of a bundle based on analyzing existing relationships between services captured by a description language. This paper extends the aforementioned research by presenting an initial set of empirically derived relationships between services in existing bundles that can subsequently be utilized to identify potential new bundles. Additionally, a gap analysis points out to what extent prominent ontologies and service description languages accommodate for the identified relationships.

Service identification through value chain analysis and prioritization

In a resource constrained business world, strategic choices must be made on process improvement and service delivery. There are calls for more agile forms of enterprises and much effort is being directed at moving organizations from a complex landscape of disparate application systems to that of an integrated and flexible enterprise accessing complex systems landscapes through service oriented architecture (SOA). This paper describes the deconstruction of an enterprise into business services using value chain analysis as each element in the value chain can be rendered as a business service in the SOA. These business services are explicitly linked to the attainment of specific organizational strategies and their contribution to the attainment of strategy is assessed and recorded. This contribution is then used to provide a rank order of business service to strategy. This information facilitates executive decision making on which business service to develop into the SOA. The paper describes an application of this Critical Service Identification Methodology (CSIM) to a case study.

An identification of factors influencing police workplace motivation

Police work tasks are diverse and require the ability to take command, demonstrate leadership, make serious decisions and be self directed (Beck, 1999; Brunetto & Farr-Wharton, 2002; Howard, Donofrio & Boles, 2002). This work is usually performed in pairs or sometimes by an officer working alone. Operational police work is seldom performed under the watchful eyes of a supervisor and a great amount of reliance is placed on the high levels of motivation and professionalism of individual officers. Research has shown that highly motivated workers produce better outcomes (Whisenand & Rush, 1998; Herzberg, 2003). It is therefore important that Queensland police officers are highly motivated to provide a quality service to the Queensland community. This research aims to identify factors which motivate Queensland police to perform quality work. Researchers acknowledge that there is a lack of research and knowledge in regard to the factors which motivate police (Beck, 1999; Bragg, 1998; Howard, Donofrio & Boles, 2002; McHugh & Verner, 1998). The motivational factors were identified in regard to the demographic variables of; age, sex, rank, tenure and education. The model for this research is Herzberg’s two-factor theory of workplace motivation (1959). Herzberg found that there are two broad types of workplace motivational factors; those driven by a need to prevent loss or harm and those driven by a need to gain personal satisfaction or achievement. His study identified 16 basic sub-factors that operate in the workplace. The research utilised a questionnaire instrument based on the sub-factors identified by Herzberg (1959). The questionnaire format consists of an initial section which sought demographic information about the participant and is followed by 51 Likert scale questions. The instrument is an expanded version of an instrument previously used in doctoral studies to identify sources of police motivation (Holden, 1980; Chiou, 2004). The questionnaire was forwarded to approximately 960 police in the Brisbane, Metropolitan North Region. The data were analysed using Factor Analysis, MANOVAs, ANOVAs and multiple regression analysis to identify the key sources of police motivation and to determine the relationships between demographic variables such as: age, rank, educational level, tenure, generation cohort and motivational factors. A total of 484 officers responded to the questionnaire from the sample population of 960. Factor analysis revealed five broad Prime Motivational Factors that motivate police in their work. The Prime Motivational Factors are: Feeling Valued, Achievement, Workplace Relationships, the Work Itself and Pay and Conditions. The factor Feeling Valued highlighted the importance of positive supportive leaders in motivating officers. Many officers commented that supervisors who only provided negative feedback diminished their sense of feeling valued and were a key source of de-motivation. Officers also frequently commented that they were motivated by operational police work itself whilst demonstrating a strong sense of identity with their team and colleagues. The study showed a general need for acceptance by peers and an idealistic motivation to assist members of the community in need and protect victims of crime. Generational cohorts were not found to exert a significant influence on police motivation. The demographic variable with the single greatest influence on police motivation was tenure. Motivation levels were found to drop dramatically during the first two years of an officer’s service and generally not improve significantly until near retirement age. The findings of this research provide the foundation of a number of recommendations in regard to police retirement, training and work allocation that are aimed to improve police motivation levels. The five Prime Motivational Factor model developed in this study is recommended for use as a planning tool by police leaders to improve motivational and job-satisfaction components of police Service policies. The findings of this study also provide a better understanding of the current sources of police motivation. They are expected to have valuable application for Queensland police human resource management when considering policies and procedures in the areas of motivation, stress reduction and attracting suitable staff to specific areas of responsibility.

Alternative analytical methods for the identification of cancer-related symptom clusters

Advances in symptom management strategies through a better understanding of cancer symptom clusters depend on the identification of symptom clusters that are valid and reliable. The purpose of this exploratory research was to investigate alternative analytical approaches to identify symptom clusters for patients with cancer, using readily accessible statistical methods, and to justify which methods of identification may be appropriate for this context. Three studies were undertaken: (1) a systematic review of the literature, to identify analytical methods commonly used for symptom cluster identification for cancer patients; (2) a secondary data analysis to identify symptom clusters and compare alternative methods, as a guide to best practice approaches in cross-sectional studies; and (3) a secondary data analysis to investigate the stability of symptom clusters over time. The systematic literature review identified, in 10 years prior to March 2007, 13 cross-sectional studies implementing multivariate methods to identify cancer related symptom clusters. The methods commonly used to group symptoms were exploratory factor analysis, hierarchical cluster analysis and principal components analysis. Common factor analysis methods were recommended as the best practice cross-sectional methods for cancer symptom cluster identification. A comparison of alternative common factor analysis methods was conducted, in a secondary analysis of a sample of 219 ambulatory cancer patients with mixed diagnoses, assessed within one month of commencing chemotherapy treatment. Principal axis factoring, unweighted least squares and image factor analysis identified five consistent symptom clusters, based on patient self-reported distress ratings of 42 physical symptoms. Extraction of an additional cluster was necessary when using alpha factor analysis to determine clinically relevant symptom clusters. The recommended approaches for symptom cluster identification using nonmultivariate normal data were: principal axis factoring or unweighted least squares for factor extraction, followed by oblique rotation; and use of the scree plot and Minimum Average Partial procedure to determine the number of factors. In contrast to other studies which typically interpret pattern coefficients alone, in these studies symptom clusters were determined on the basis of structure coefficients. This approach was adopted for the stability of the results as structure coefficients are correlations between factors and symptoms unaffected by the correlations between factors. Symptoms could be associated with multiple clusters as a foundation for investigating potential interventions. The stability of these five symptom clusters was investigated in separate common factor analyses, 6 and 12 months after chemotherapy commenced. Five qualitatively consistent symptom clusters were identified over time (Musculoskeletal-discomforts/lethargy, Oral-discomforts, Gastrointestinaldiscomforts, Vasomotor-symptoms, Gastrointestinal-toxicities), but at 12 months two additional clusters were determined (Lethargy and Gastrointestinal/digestive symptoms). Future studies should include physical, psychological, and cognitive symptoms. Further investigation of the identified symptom clusters is required for validation, to examine causality, and potentially to suggest interventions for symptom management. Future studies should use longitudinal analyses to investigate change in symptom clusters, the influence of patient related factors, and the impact on outcomes (e.g., daily functioning) over time.

Fingerprint analysis using wavelet transform with application to compression and feature extraction

The main goal of this research is to design an efficient compression al~ gorithm for fingerprint images. The wavelet transform technique is the principal tool used to reduce interpixel redundancies and to obtain a parsimonious representation for these images. A specific fixed decomposition structure is designed to be used by the wavelet packet in order to save on the computation, transmission, and storage costs. This decomposition structure is based on analysis of information packing performance of several decompositions, two-dimensional power spectral density, effect of each frequency band on the reconstructed image, and the human visual sensitivities. This fixed structure is found to provide the "most" suitable representation for fingerprints, according to the chosen criteria. Different compression techniques are used for different subbands, based on their observed statistics. The decision is based on the effect of each subband on the reconstructed image according to the mean square criteria as well as the sensitivities in human vision. To design an efficient quantization algorithm, a precise model for distribution of the wavelet coefficients is developed. The model is based on the generalized Gaussian distribution. A least squares algorithm on a nonlinear function of the distribution model shape parameter is formulated to estimate the model parameters. A noise shaping bit allocation procedure is then used to assign the bit rate among subbands. To obtain high compression ratios, vector quantization is used. In this work, the lattice vector quantization (LVQ) is chosen because of its superior performance over other types of vector quantizers. The structure of a lattice quantizer is determined by its parameters known as truncation level and scaling factor. In lattice-based compression algorithms reported in the literature the lattice structure is commonly predetermined leading to a nonoptimized quantization approach. In this research, a new technique for determining the lattice parameters is proposed. In the lattice structure design, no assumption about the lattice parameters is made and no training and multi-quantizing is required. The design is based on minimizing the quantization distortion by adapting to the statistical characteristics of the source in each subimage. 11 Abstract Abstract Since LVQ is a multidimensional generalization of uniform quantizers, it produces minimum distortion for inputs with uniform distributions. In order to take advantage of the properties of LVQ and its fast implementation, while considering the i.i.d. nonuniform distribution of wavelet coefficients, the piecewise-uniform pyramid LVQ algorithm is proposed. The proposed algorithm quantizes almost all of source vectors without the need to project these on the lattice outermost shell, while it properly maintains a small codebook size. It also resolves the wedge region problem commonly encountered with sharply distributed random sources. These represent some of the drawbacks of the algorithm proposed by Barlaud [26). The proposed algorithm handles all types of lattices, not only the cubic lattices, as opposed to the algorithms developed by Fischer [29) and Jeong [42). Furthermore, no training and multiquantizing (to determine lattice parameters) is required, as opposed to Powell's algorithm [78). For coefficients with high-frequency content, the positive-negative mean algorithm is proposed to improve the resolution of reconstructed images. For coefficients with low-frequency content, a lossless predictive compression scheme is used to preserve the quality of reconstructed images. A method to reduce bit requirements of necessary side information is also introduced. Lossless entropy coding techniques are subsequently used to remove coding redundancy. The algorithms result in high quality reconstructed images with better compression ratios than other available algorithms. To evaluate the proposed algorithms their objective and subjective performance comparisons with other available techniques are presented. The quality of the reconstructed images is important for a reliable identification. Enhancement and feature extraction on the reconstructed images are also investigated in this research. A structural-based feature extraction algorithm is proposed in which the unique properties of fingerprint textures are used to enhance the images and improve the fidelity of their characteristic features. The ridges are extracted from enhanced grey-level foreground areas based on the local ridge dominant directions. The proposed ridge extraction algorithm, properly preserves the natural shape of grey-level ridges as well as precise locations of the features, as opposed to the ridge extraction algorithm in [81). Furthermore, it is fast and operates only on foreground regions, as opposed to the adaptive floating average thresholding process in [68). Spurious features are subsequently eliminated using the proposed post-processing scheme.

Spectral coding methods for speech compression and speaker identification

This thesis investigates aspects of encoding the speech spectrum at low bit rates, with extensions to the effect of such coding on automatic speaker identification. Vector quantization (VQ) is a technique for jointly quantizing a block of samples at once, in order to reduce the bit rate of a coding system. The major drawback in using VQ is the complexity of the encoder. Recent research has indicated the potential applicability of the VQ method to speech when product code vector quantization (PCVQ) techniques are utilized. The focus of this research is the efficient representation, calculation and utilization of the speech model as stored in the PCVQ codebook. In this thesis, several VQ approaches are evaluated, and the efficacy of two training algorithms is compared experimentally. It is then shown that these productcode vector quantization algorithms may be augmented with lossless compression algorithms, thus yielding an improved overall compression rate. An approach using a statistical model for the vector codebook indices for subsequent lossless compression is introduced. This coupling of lossy compression and lossless compression enables further compression gain. It is demonstrated that this approach is able to reduce the bit rate requirement from the current 24 bits per 20 millisecond frame to below 20, using a standard spectral distortion metric for comparison. Several fast-search VQ methods for use in speech spectrum coding have been evaluated. The usefulness of fast-search algorithms is highly dependent upon the source characteristics and, although previous research has been undertaken for coding of images using VQ codebooks trained with the source samples directly, the product-code structured codebooks for speech spectrum quantization place new constraints on the search methodology. The second major focus of the research is an investigation of the effect of lowrate spectral compression methods on the task of automatic speaker identification. The motivation for this aspect of the research arose from a need to simultaneously preserve the speech quality and intelligibility and to provide for machine-based automatic speaker recognition using the compressed speech. This is important because there are several emerging applications of speaker identification where compressed speech is involved. Examples include mobile communications where the speech has been highly compressed, or where a database of speech material has been assembled and stored in compressed form. Although these two application areas have the same objective - that of maximizing the identification rate - the starting points are quite different. On the one hand, the speech material used for training the identification algorithm may or may not be available in compressed form. On the other hand, the new test material on which identification is to be based may only be available in compressed form. Using the spectral parameters which have been stored in compressed form, two main classes of speaker identification algorithm are examined. Some studies have been conducted in the past on bandwidth-limited speaker identification, but the use of short-term spectral compression deserves separate investigation. Combining the major aspects of the research, some important design guidelines for the construction of an identification model when based on the use of compressed speech are put forward.