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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. Source

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. Source

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. Source

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. Source

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. Source

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