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Hosein A.N.,University of Queensland | Hosein A.N.,Queensland Institute of Medical Research Berghofer Medical Research Institute | Lim Y.C.,Queensland Institute of Medical Research Berghofer Medical Research Institute | Day B.,Queensland Institute of Medical Research Berghofer Medical Research Institute | And 8 more authors.
Journal of Neuro-Oncology | Year: 2015

Glioblastoma multiforme (GBM) has nearly uniformly fatal with a median survival of less than 2 years. While there have not been any novel anti-GBM therapeutics approved for many years, there has been the gradual accumulation of clinical data suggesting that the widely used anti-convulsant agent, valproic acid (VPA) may significantly prolong survival in GBM patients. This pre-clinical study aimed to determine the potential clinical utility of VPA in the treatment of GBM. Primary GBM cells were treated with VPA as a monotherapy and in combination with temozolomide and irradiation. At clinically achievable concentrations, VPA was shown to be effective as a monotherapy agent in the five primary lines tested. VPA was then used as a sensitizing agent to in vitro radiation and showed significant augmentation of in vitro irradiation therapy. In addition, when VPA, radiation and temozolomide were combined an additive, rather than synergistic effect was noted. Gene expression profiling demonstrated close clustering of triple treated cells with VPA mono-treated cells while untreated cells clustered closer with TMZ-irradiation dual treated cells. These microarray data suggest a dominant role of VPA at the gene expression level when combining these different treatment options. Moreover, in an in vivo tumor transplantation model, we were able to demonstrate an increase in animal survival when cells were pre-treated with irradiation-VPA and when triple treated. These findings provide a significant rationale for the investigation of VPA in the treatment of GBM patients. © 2015, Springer Science+Business Media New York.


Xie Q.,Queensland University of Technology | Huang Z.,Queensland University of Technology | Huang Z.,Queensland Research Laboratory | Shen H.T.,Queensland University of Technology | And 3 more authors.
World Wide Web | Year: 2012

Online video stream data are surging to an unprecedented level. Massive video publishing and sharing impose heavy demands on continuous video near-duplicate detection for many novel video applications. This paper presents an accurate and accelerated system for video near-duplicate detection over continuous video streams. We propose to transform a high-dimensional video stream into a one-dimensional Video Trend Stream (VTS) to monitor the continuous luminance changes of consecutive frames, based on which video similarity is derived. In order to do fast comparison and effective early pruning, a compact auxiliary signature named CutSig is proposed to approximate the video structure. CutSig explores cut distribution feature of the video structure and contributes to filter candidates quickly. To scan along a video stream in a rapid way, shot cuts with local maximum AI (average information value) in a query video are used as reference cuts, and a skipping approach based on reference cut alignment is embedded for efficient acceleration. Extensive experimental results on detecting diverse near-duplicates in real video streams show the effectiveness and efficiency of our method. © 2011 Springer Science+Business Media, LLC.


Butt L.,The Australian e Health Research Center | Zuccon G.,The Australian e Health Research Center | Nguyen A.,The Australian e Health Research Center | Bergheim A.,Cancer Institute NSW | Grayson N.,Cancer Institute NSW
Australasian Medical Journal | Year: 2013

Background Cancer monitoring and prevention relies on the critical aspect of timely notification of cancer cases. However, the abstraction and classification of cancer from the free-text of pathology reports and other relevant documents, such as death certificates, exist as complex and time-consuming activities. Aims In this paper, approaches for the automatic detection of notifiable cancer cases as the cause of death from free-text death certificates supplied to Cancer Registries are investigated. Method A number of machine learning classifiers were studied. Features were extracted using natural language techniques and the Medtex toolkit. The numerous features encompassed stemmed words, bi-grams, and concepts from the SNOMED CT medical terminology. The baseline consisted of a keyword spotter using keywords extracted from the long description of ICD-10 cancer related codes. Results Death certificates with notifiable cancer listed as the cause of death can be effectively identified with the methods studied in this paper. A Support Vector Machine (SVM) classifier achieved best performance with an overall F-measure of 0.9866 when evaluated on a set of 5,000 free-text death certificates using the token stem feature set. The SNOMED CT concept plus token stem feature set reached the lowest variance (0.0032) and false negative rate (0.0297) while achieving an F-measure of 0.9864. The SVM classifier accounts for the first 18 of the top 40 evaluated runs, and entails the most robust classifier with a variance of 0.001141, half the variance of the other classifiers. Conclusion The selection of features significantly produced the most influences on the performance of the classifiers, although the type of classifier employed also affects performance. In contrast, the feature weighting schema created a negligible effect on performance. Specifically, it is found that stemmed tokens with or without SNOMED CT concepts create the most effective feature when combined with an SVM classifier.


Kargar Z.S.,Griffith University | Kargar Z.S.,The Australian e Health Research Center | Khanna S.,Griffith University | Khanna S.,The Australian e Health Research Center | Sattar A.,Griffith University
Australasian Medical Journal | Year: 2013

Background: An ageing population and higher rates of chronic disease increase the demand on health services. The Australian Institute of Health and Welfare reports a 3.6% per year increase in total elective surgery admissions over the past four years.1 The newly introduced National Elective Surgery Target (NEST) stresses the need for efficiency and necessitates the development of improved planning and scheduling systems in hospitals. Aims: To provide an overview of the challenges of elective surgery scheduling and develop a prediction based methodology to drive optimal management of scheduling processes. Method: Our proposed two stage methodology initially employs historic utilisation data and current waiting list information to manage case mix distribution. A novel algorithm uses current and past perioperative information to accurately predict surgery duration. A NEST-compliance guided optimisation algorithm is then used to drive allocation of patients to the theatre schedule. Results: It is expected that the resulting improvement in scheduling processes will lead to more efficient use of surgical suites, higher productivity, and lower labour costs, and ultimately improve patient outcomes. Conclusion: Accurate prediction of workload and surgery duration, retrospective and current waitlist as well as perioperative information, and NEST-compliance driven allocation of patients are employed by our proposed methodology in order to deliver further improvement to hospital operating facilities.


Hassanzadeh H.,University of Queensland | Hassanzadeh H.,The Australian e Health Research Center | Groza T.,University of Queensland | Nguyen A.,The Australian e Health Research Center | Hunter J.,University of Queensland
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

Data skewness is a challenge encountered, in particular, when applying supervised machine learning approaches in various domains, such as in healthcare and biomedical information engineering. Evidence Based Medicine (EBM) is a clinical strategy for prescribing treatment based on current best evidence for individual patients. Clinicians need to query publication repositories in order to find the best evidence to support their decision-making processes. This sophisticated information is materialised in the form of scientific artefacts in scholarly publications and the automatic extraction of these artefacts is a technical challenge for current generic search engines. Many classification approaches have been proposed for identifying key scientific artefacts in EBM, however their performance is affected by the imbalanced characteristic of data in this domain. In this paper, we present four data balancing approaches applied in a binary ensemble classifier framework for classifying scientific artefacts in the EBM domain. Our balancing approaches improve the ensemble classifier’s F-score by up to 15% for classes of scientific artefacts with xtremely low coverage in the domain. In addition, we propose a classifier selection method for choosing the best classifier based on the distributional feature of classes. The resulting classifiers show improved classification performances when compared to state of the art approaches. Springer International Publishing Switzerland 2014.


Wagholikar A.S.,The Australian e Health Research Center
AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium | Year: 2011

Patients presenting to Emergency Departments may be categorised into different symptom groups for the purpose of research and quality improvement. The grouping is challenging due to the variability in the way presenting complaints are recorded by clinical staff. This work proposes analysis of the presenting complaint free-text using the semantics encoded in the SNOMED CT ontology. This work demonstrates a validated prototype system that can classify unstructured free-text narratives into patient's symptom group. A rule-based mechanism was developed using variety of keywords to identify the patient's symptom group. The system was validated against the manual identification of the symptom groups by two expert clinical research nurses on 794 patient presentations from six participating hospitals. The comparison of system results with one clinical research nurse showed 99.3% sensitivity; 80.0% specificity and 0.9 F-score for identifying "chest pain" symptom group.


PubMed | The Australian e Health Research Center
Type: | Journal: AMIA ... Annual Symposium proceedings. AMIA Symposium | Year: 2011

Patients presenting to Emergency Departments may be categorised into different symptom groups for the purpose of research and quality improvement. The grouping is challenging due to the variability in the way presenting complaints are recorded by clinical staff. This work proposes analysis of the presenting complaint free-text using the semantics encoded in the SNOMED CT ontology. This work demonstrates a validated prototype system that can classify unstructured free-text narratives into patients symptom group. A rule-based mechanism was developed using variety of keywords to identify the patients symptom group. The system was validated against the manual identification of the symptom groups by two expert clinical research nurses on 794 patient presentations from six participating hospitals. The comparison of system results with one clinical research nurse showed 99.3% sensitivity; 80.0% specificity and 0.9 F-score for identifying chest pain symptom group.


PubMed | The Australian e Health Research Center
Type: | Journal: AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science | Year: 2013

To develop and evaluate machine learning techniques that identify limb fractures and other abnormalities (e.g. dislocations) from radiology reports.99 free-text reports of limb radiology examinations were acquired from an Australian public hospital. Two clinicians were employed to identify fractures and abnormalities from the reports; a third senior clinician resolved disagreements. These assessors found that, of the 99 reports, 48 referred to fractures or abnormalities of limb structures. Automated methods were then used to extract features from these reports that could be useful for their automatic classification. The Naive Bayes classification algorithm and two implementations of the support vector machine algorithm were formally evaluated using cross-fold validation over the 99 reports.Results show that the Naive Bayes classifier accurately identifies fractures and other abnormalities from the radiology reports. These results were achieved when extracting stemmed token bigram and negation features, as well as using these features in combination with SNOMED CT concepts related to abnormalities and disorders. The latter feature has not been used in previous works that attempted classifying free-text radiology reports.Automated classification methods have proven effective at identifying fractures and other abnormalities from radiology reports (F-Measure up to 92.31%). Key to the success of these techniques are features such as stemmed token bigrams, negations, and SNOMED CT concepts associated with morphologic abnormalities and disorders.This investigation shows early promising results and future work will further validate and strengthen the proposed approaches.


Dowson N.,The Australian e Health Research Center
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention | Year: 2010

Kinetic analysis is an essential tool of Positron Emission Tomography image analysis. However it requires a pure tissue time activity curve (TAC) in order to calculate the system parameters. Pure tissue TACs are particularly difficult to obtain in the brain as the low resolution of PET means almost all voxels are a mixture of tissues. Factor analysis explicitly accounts for mixing but is an underdetermined problem that can give arbitrary results. A joint factor and kinetic analysis is proposed whereby factor analysis explicitly accounts for mixing of tissues. Hence, more meaningful parameters are obtained by the kinetic models, which also ensure a less ambiguous solution to the factor analysis. The method was tested using a cylindrical phantom and the 18F-DOPA data of a brain cancer patient.


PubMed | The Australian e Health Research Center
Type: Journal Article | Journal: The Australasian medical journal | Year: 2013

Cancer monitoring and prevention relies on the critical aspect of timely notification of cancer cases. However, the abstraction and classification of cancer from the free-text of pathology reports and other relevant documents, such as death certificates, exist as complex and time-consuming activities.In this paper, approaches for the automatic detection of notifiable cancer cases as the cause of death from free-text death certificates supplied to Cancer Registries are investigated.A number of machine learning classifiers were studied. Features were extracted using natural language techniques and the Medtex toolkit. The numerous features encompassed stemmed words, bi-grams, and concepts from the SNOMED CT medical terminology. The baseline consisted of a keyword spotter using keywords extracted from the long description of ICD-10 cancer related codes.Death certificates with notifiable cancer listed as the cause of death can be effectively identified with the methods studied in this paper. A Support Vector Machine (SVM) classifier achieved best performance with an overall Fmeasure of 0.9866 when evaluated on a set of 5,000 freetext death certificates using the token stem feature set. The SNOMED CT concept plus token stem feature set reached the lowest variance (0.0032) and false negative rate (0.0297) while achieving an F-measure of 0.9864. The SVM classifier accounts for the first 18 of the top 40 evaluated runs, and entails the most robust classifier with a variance of 0.001141, half the variance of the other classifiers.The selection of features significantly produced the most influences on the performance of the classifiers, although the type of classifier employed also affects performance. In contrast, the feature weighting schema created a negligible effect on performance. Specifically, it is found that stemmed tokens with or without SNOMED CT concepts create the most effective feature when combined with an SVM classifier.

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