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Wang Y.,University of New South Wales | Lin X.,University of New South Wales | Wu L.,University of Adelaide | Zhang W.,University of New South Wales | And 2 more authors.
IEEE Transactions on Image Processing | Year: 2015

More often than not, a multimedia data described by multiple features, such as color and shape features, can be naturally decomposed of multi-views. Since multi-views provide complementary information to each other, great endeavors have been dedicated by leveraging multiple views instead of a single view to achieve the better clustering performance. To effectively exploit data correlation consensus among multi-views, in this paper, we study subspace clustering for multi-view data while keeping individual views well encapsulated. For characterizing data correlations, we generate a similarity matrix in a way that high affinity values are assigned to data objects within the same subspace across views, while the correlations among data objects from distinct subspaces are minimized. Before generating this matrix, however, we should consider that multi-view data in practice might be corrupted by noise. The corrupted data will significantly downgrade clustering results. We first present a novel objective function coupled with an angular based regularizer. By minimizing this function, multiple sparse vectors are obtained for each data object as its multiple representations. In fact, these sparse vectors result from reaching data correlation consensus on all views. For tackling noise corruption, we present a sparsity-based approach that refines the angular-based data correlation. Using this approach, a more ideal data similarity matrix is generated for multi-view data. Spectral clustering is then applied to the similarity matrix to obtain the final subspace clustering. Extensive experiments have been conducted to validate the effectiveness of our proposed approach. © 1992-2012 IEEE.


Rose S.,University of Queensland | Rose S.,Australian e Health Research Center | Rose S.,Royal Brisbane and Womens Hospital | Fay M.,CSIRO | And 7 more authors.
American Journal of Neuroradiology | Year: 2013

BACKGROUND AND PURPOSE: There is significant interest in whether diffusion-weighted MR imaging indices, such as the minimum apparent diffusion coefficient, may be useful clinically for preoperative tumor grading and treatment planning. To help establish the pathologic correlate of minimum ADC, we undertook a study investigating the relationship between minimum ADC and maximum FDOPA PET uptake in patients with newly diagnosed glioblastoma multiforme. MATERIALS AND METHODS: MR imaging and FDOPA PET data were acquired preoperatively from 15 patients who were subsequently diagnosed with high-grade brain tumor (WHO grade III or IV) by histopathologic analysis. ADC and SUVR normalized FDOPA PET maps were registered to the corresponding CE MR imaging. Regions of minimum ADC within the FDOPA-defined tumor volume were anatomically correlated with areas of maximum FDOPA SUVR uptake. RESULTS: Minimal anatomic overlap was found between regions exhibiting minimum ADC (a putative marker of tumor cellularity) and maximum FDOPA SUVR uptake (a marker of tumor infiltration and proliferation). FDOPA SUVR measures for tumoral regions exhibiting minimum ADC (1.36 ± 0.22) were significantly reduced compared with those with maximum FDOPA uptake (2.45 ± 0.88, P = .0001). CONCLUSIONS: There was a poor correlation between minimum ADC and the most viable/aggressive component of high-grade gliomas. This study suggests that other factors, such as tissue compression and ischemia, may be contributing to restricted diffusion in GBM. Caution should be exercised in the clinical use of minimum ADC as a marker of tumor grade and the use of this index for guiding tumor biopsies preoperatively.


PubMed | Royal Brisbane and Womens Hospital, Murdoch Childrens Research Institute, University of Queensland, Australian e Health Research Center and 3 more.
Type: | Journal: Journal of medical imaging and radiation oncology | Year: 2016

Patients presenting with clinically isolated syndrome (CIS) may proceed to clinically definite multiple sclerosis (CDMS). Midsagittal corpus callosum area (CCA) is a surrogate marker for callosal atrophy, and can be obtained from a standard MRI study. This study explores the relationship between CCA measured at CIS presentation (baseline) and at 5years post presentation, with conversion from CIS to CDMS. The association between CCA and markers of disability progression is explored.Corpus callosum area was measured on MRI scans at presentation and 5-year review following diagnosis of a first demyelinating event, or evidence of progressive MS, in 143 participants in the Ausimmune/AusLong Study. Relationships between CCA (at baseline and follow-up) and clinical outcomes were assessed.Mean CCA at baseline study was 6.63cmBaseline CCA obtained from standard MRI protocols may be compared with subsequent MRI examinations as a surrogate for neurodegeneration and cerebral atrophy in patients with MS. This study demonstrates an association between CCA and disability in individuals presenting with CIS who convert to MS.


Zhang Q.,Australian e Health Research Center | Zhang Q.,University of New South Wales | Ye P.,Australian e Health Research Center | Ye P.,University of New South Wales | And 2 more authors.
ACM International Conference Proceeding Series | Year: 2013

Skyline analysis is a key in a wide spectrum of real applications involving multi-criteria optimal decision making. In recent years, a considerable amount of research has been contributed on efficient computation of skyline probabilities over uncertain environment. Most studies if not all, assume uncertainty lies only in attribute values. To the extent of our knowledge, only one study addresses the skyline probability computation problem in scenarios where uncertainty resides in attribute preferences, instead of values. However this study takes a problematic approach by assuming independent object dominance, which we find is not always true in uncertain preference scenarios. In fact this assumption has already been shown to be not necessarily true in uncertain value scenarios. Motivated by this, we revisit the skyline probability computation over uncertain preferences in this paper. We first show that the problem of skyline probability computation over uncertain preferences is #P-complete. Then we propose efficient exact and approximate algorithms to tackle this problem. While the exact algorithm remains exponential in the worst case, our experiments demonstrate its efficiency in practice. The approximate algorithm achieves ε-approximation by the confidence (1 - Δ) with time complexity O(dn 1/ε2 ln 1/Δ), where n is the number of objects and d is the dimensionality. The efficiency and effectiveness of our methods are verified by extensive experimental results on real and synthetic data sets. © 2013 ACM.


Zhang Q.,Australian e Health Research Center | Pang C.,Australian e Health Research Center | Mcbride S.,Australian e Health Research Center | Hansen D.,Australian e Health Research Center | And 2 more authors.
2010 IEEE/ICME International Conference on Complex Medical Engineering, CME2010 | Year: 2010

Data streams, or data sets which continuously and rapidly grow over time, are a prominent form of clinical data generated during the monitoring and treatment of patients in the health care industry. We propose the name Health Data Stream Analytics (HDSA) to the application of stream data processing to clinical data. Our work in this area is demonstrating the useful role Health Data Stream Analytics can play in clinical decision support, patient safety improvement and early detection of adverse patient outcomes. Two major challenges in applying stream data processing to heath care are tailoring query support for the clinical context and dealing with the clinical requirement of online query processing. In this paper, we propose the Anaesthetic Data Analyser (ADA) as a Health Data Stream Analytics System for the anaesthetics specialty and describe how it addresses these challenges. ADA differentiates from current approaches by looking at trends in the data stream rather than a single data value against a preset threshold. The trend analysis supported by ADA is a novel application in this area, and enables support for adverse symptoms monitoring in physiological stream data, alerting clinicians when a pre-defined adverse data pattern is detected in the physiological signals. This paper also describes an online query processing algorithm and the results of experiments on "real world" physiological steam data which indicate the algorithms has sub-second response times for trend queries. © 2010 IEEE.


Khanna S.,Griffith University | Khanna S.,Australian e Health Research Center | Cleaver T.,Griffith University | Sattar A.,Griffith University | And 2 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012

Scheduling of patients, staff, and resources for elective surgery in an under-resourced and overburdened public health system represents an inherently distributed class of problems. The complexity and dynamics of interacting factors demand a flexible, reactive and timely solution, in order to achieve a high level of utilization. In this paper, we present an Automated Scheduler for Elective Surgery (ASES) wherein we model the problem using the multiagent systems paradigm. ASES is designed to reflect and complement the existing manual methods of elective surgery scheduling, while offering efficient mechanisms for negotiation and optimization. Inter-agent negotiation in ASES is powered by a distributed constraint optimization algorithm. This strategy provides hospital departments with control over their individual schedules while ensuring conflict free optimal scheduling. We evaluate ASES to demonstrate the feasibility of our approach and demonstrate the effect of fluctuation in staffing levels on theatre utilization. We also discuss ongoing development of the system, mapping key challenges in the journey towards deployment. © Springer-Verlag Berlin Heidelberg 2012.


Khanna S.,Australian e Health Research Center | Khanna S.,Griffith University | Sattar A.,Griffith University | Boyle J.,Australian e Health Research Center | And 2 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012

The Multiagent Systems paradigm offers expressively rich and natural fit mechanisms for modeling and negotiation for solving distributed problems. Solving complex and distributed real world problems in dynamic domains however presents a significant challenge and requires the integration of technology innovation and domain expertise to create intelligent solutions. Scheduling of patients, staff, and resources for elective surgery in an under-resourced and overburdened public health system presents an excellent example of this class of problems. In this paper, we discuss the research challenges presented by the problem and outline our efforts of applying distributed constraint optimization, intelligent decision support, and prediction based theater allocation to address these challenges. We also discuss how these technologies can be used to drive better planning and change management in the context of surgery scheduling. © Springer-Verlag Berlin Heidelberg 2012.


Rembach A.,University of Melbourne | Ryan T.M.,University of Melbourne | Roberts B.R.,University of Melbourne | Doecke J.D.,Australian e Health Research Center | And 5 more authors.
Biomarkers in Medicine | Year: 2013

Alzheimer's disease (AD) is the most common cause of dementia in the elderly population and attempts to develop therapies have been unsuccessful because there is no means to target an effective therapeutic window. CNS biomarkers are insightful but impractical for high-throughput population-based screening. Therefore, a peripheral, blood-based biomarker for AD would significantly improve early diagnosis, potentially enable presymptomatic detection and facilitate effective targeting of disease-modifying treatments. The various constituents of blood, including plasma, platelets and cellular fractions, are now being systematically explored as a pool of putative peripheral biomarkers for AD. In this review we cover some less known peripheral biomarkers and highlight the latest developments for their clinical application. © 2013 Future Medicine Ltd.


Ding H.,Australian e Health Research Center
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference | Year: 2012

Chronic Obstructive Pulmonary Disease (COPD) is a major cause of morbidity and mortality in Australia and globally, and leads to a substantial burden on healthcare services. Effective and timely management of patients with COPD has been essential to alleviate COPD exacerbation, improve the quality of life, and consequently reduce the economic burden. To achieve this, a mobile and internet technologies assisted home care model (M-COPD) was developed to assist clinicians to remotely monitor and manage COPD conditions and events. This paper will focus on the technical aspect of M-COPD system by describing its setup and discussing how the M-COPD could address the clinical needs in monitoring and managing COPD conditions of patients at home.


Good N.,Australian e Health Research Center | Bain C.,Alfred Health | Hansen D.,Australian e Health Research Center | Gibson S.,Australian e Health Research Center
CEUR Workshop Proceedings | Year: 2014

This project was designed to integrate, analyse, synthesise and present essential health and hospital information in a highly accessible, agile and visual form - because pictures are worth a thousand words. We developed a prototype software tool that is; capable of drawing on standardised data files that replicate known industry standard, or are easily derivable from such standards provides the user (analyst, operational manager, financial manger, executive) with a customisable view of the relative outcomes of, and resources used in, care in a number of dimensions- clinical (LOS, number of adverse events, number of drug doses, attending doctor etc) and financial (surgical, pharmacy, nursing etc) - in one setting and identify outliers using advanced statistical modelling techniques. This tool will generate immediate value for a hospitals' endeavour in continuous operational improvement and will be of particular interest to potential customers throughout Australia given the move to nationally provided Activity Based Funding for hospital services. The tool is a useful way to harness the power of "big data" through advanced analytics. Copyright © 2014 for the individual papers by the papers' authors.

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