Gestalt based evaluation of health information diagrams
- Authors: Sharma, Vishakha , Stranieri, Andrew , Burstein, Frada , Warren, Jim , Firmin, Sally
- Date: 2020
- Type: Text , Conference paper
- Relation: 24th International Conference Information Visualisation, IV 2020 Vol. 2020-September, p. 195-201
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- Description: Diagrams for four different health care settings have been proposed: Snapshot Diagram, Diagnosis Diagram, Strength of Evidence Diagram and Patient Pathway Diagram The availability of large amount of digital health care data and potential to utilize its benefits led to the development of these diagrams. This paper presents an analysis of the diagrams based on the selection of a subset of Gestalt principles deemed relevant for each diagram. Although Gestalt and human-computer interaction principles are advanced to apply to all diagrams or user interfaces, in practice a sub-set of principles must be selected to evaluate a diagram or interface The selection of a subset of principles to use on a diagram has not been widely studied. This paper presents an approach for identifying a subset of relevant Gestalt principles tailored for each of the four diagrams advanced for health care settings. © 2020 IEEE.
Grid-based information retrieval for the aggregation of legal datasets in online dispute resolution
- Authors: Saeed, Ather , Stranieri, Andrew , Dazeley, Richard , Ma, Liping
- Date: 2009
- Type: Text , Journal article
- Relation: Communications of SIWN Vol. 6, no. April (2009), p. 16-22
- Full Text: false
- Description: The Web is a stateless and complex environment when it comes to the retrieval of information from millions of computers connected to the Internet via WWW servers. Information Retrieval (IR) from heterogeneous data sources poses a great challenge as the information of interest is stored in a variety of different formats. Answering an enormous amount of queries is a resource and computational intensive task in ODR (Online Dispute Resolution). Information availability also poses a challenge when it comes to the mediation and arbitration processes in resolving eCommerce and legal disputes. A new Grid-based information retrieval model is proposed for the aggregation and replication of legal datasets from remote machines with indexed-based search facility. Datasets of interests will be indexed with a slight modification to the existing indexing scheme. A new strategy is proposed to deal with similar queries posted over and over again and how the commonality among the XML query trees are exploited and merged for the efficient retrieval of information.
Group decision making in health care : A case study of multidisciplinary meetings
- Authors: Sharma, Vishakha , Stranieri, Andrew , Burstein, Frada , Warren, Jim , Daly, Sharon , Patterson, Louise , Yearwood, John , Wolff, Alan
- Date: 2016
- Type: Text , Journal article
- Relation: Journal of Decision Systems Vol. 25, no. (2016), p. 476-485
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- Description: Abstract: Recent studies have demonstrated that Multi-Disciplinary Meetings (MDM) practiced in some medical contexts can contribute to positive health care outcomes. The group reasoning and decision-making in MDMs has been found to be most effective when deliberations revolve around the patient’s needs, comprehensive information is available during the meeting, core members attend and the MDM is effectively facilitated. This article presents a case study of the MDMs in cancer care in a region of Australia. The case study draws on a group reasoning model called the Reasoning Community model to analyse MDM deliberations to illustrate that many factors are important to support group reasoning, not solely the provision of pertinent information. The case study has implications for the use of data analytics in any group reasoning context. © 2016 Informa UK Limited, trading as Taylor & Francis Group.
Group structured reasoning for coalescing group decisions
- Authors: Yearwood, John , Stranieri, Andrew
- Date: 2009
- Type: Text , Journal article
- Relation: Group Decision and Negotiation Vol. , no. (2009), p. 1-29
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- Description: In this paper we present the notion of structured reasoning through a model, called the Generic/Actual Argument Model (GAAM). The model which has been used as a computational representation for machine modelling of reasoning and for hybrid combinations of human and machine reasoning can be used as a coalescent framework for decision making. Whilst the notion of structuring reasoning is not new, structured reasoning is advanced as a technique where group consensus on reasoning structures at various levels can be used to facilitate the comprehension of complex reasoning particularly where there are multiple perspectives. For an issue, the approach provides a scaffolding structure for cognitive co-operation and a normative reasoning structure against which group participants can identify points of difference and points in common as well as the nature of the differences and similarities. Intra-group transparency characterized by the ability to recognise points in common and understand the nature of differences is important to the process of coalescing group decisions that carry maximum group support. © 2009 Springer Science+Business Media B.V.
High definition 3D telemedicine: The next frontier?
- Authors: Stranieri, Andrew , Collmann, Richard , Borda, Ann
- Date: 2012
- Type: Text , Conference proceedings
- Relation: Studies in Health Technology and Informatics, 182, p.133-41.
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- Description: Evidence from the literature indicates that the degree of immersion often referred to as the "sense of being there" experienced by clinicians and patients is a factor in the success of tele-health installations. High definition and 3D telemedicine offers a compelling mechanism to achieve a sense of immersion and contribute to an enhanced quality of use. This article surveys HD3D trials in tele-health and concludes that the way HD3D is integrated into telemedicine depends on the clinical, organisational and technological context. In some settings real time HD3D is not so desirable whereas asynchronous transmission of HD3D images and videos is highly desirable. © 2012 The authors and IOS Press.
Hybrid wrapper-filter approaches for input feature selection using maximum relevance and Artificial Neural Network Input Gain Measurement Approximation (ANNIGMA)
- Authors: Huda, Shamsul , Yearwood, John , Stranieri, Andrew
- Date: 2010
- Type: Text , Conference proceedings
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- Description: Feature selection is an important research problem in machine learning and data mining applications. This paper proposes a hybrid wrapper and filter feature selection algorithm by introducing the filter's feature ranking score in the wrapper stage to speed up the search process for wrapper and thereby finding a more compact feature subset. The approach hybridizes a Mutual Information (MI) based Maximum Relevance (MR) filter ranking heuristic with an Artificial Neural Network (ANN) based wrapper approach where Artificial Neural Network Input Gain Measurement Approximation (ANNIGMA) has been combined with MR (MR-ANNIGMA) to guide the search process in the wrapper. The novelty of our approach is that we use hybrid of wrapper and filter methods that combines filter's ranking score with the wrapper-heuristic's score to take advantages of both filter and wrapper heuristics. Performance of the proposed MRANNIGMA has been verified using bench mark data sets and compared to both independent filter and wrapper based approaches. Experimental results show that MR-ANNIGMA achieves more compact feature sets and higher accuracies than both filter and wrapper approaches alone. © 2010 IEEE.
Improving classifications for cardiac autonomic neuropathy using multi-level ensemble classifiers and feature selection based on random forest
- Authors: Kelarev, Andrei , Stranieri, Andrew , Abawajy, Jemal , Yearwood, John , Jelinek, Herbert
- Date: 2012
- Type: Text , Conference paper
- Relation: Tenth Australasian Data Mining Conference Vol. 134, p. 93-101
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- Description: This paper is devoted to empirical investigation of novel multi-level ensemble meta classifiers for the detection and monitoring of progression of cardiac autonomic neuropathy, CAN, in diabetes patients. Our experiments relied on an extensive database and concentrated on ensembles of ensembles, or multi-level meta classifiers, for the classification of cardiac autonomic neuropathy progression. First, we carried out a thorough investigation comparing the performance of various base classifiers for several known sets of the most essential features in this database and determined that Random Forest significantly and consistently outperforms all other base classifiers in this new application. Second, we used feature selection and ranking implemented in Random Forest. It was able to identify a new set of features, which has turned out better than all other sets considered for this large and well-known database previously. Random Forest remained the very best classifier for the new set of features too. Third, we investigated meta classifiers and new multi-level meta classifiers based on Random Forest, which have improved its performance. The results obtained show that novel multi-level meta classifiers achieved further improvement and obtained new outcomes that are significantly better compared with the outcomes published in the literature previously for cardiac autonomic neuropathy.
Inference of gene expression networks using memetic gene expression programming
- Authors: Zarnegar, Armita , Vamplew, Peter , Stranieri, Andrew
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at Thirty-Second Australasian Computer Science Conference (ACSC 2009), Wellington, New Zealand : Vol. 91, p. 17-23
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- Description: In this paper we aim to infer a model of genetic networks from time series data of gene expression profiles by using a new gene expression programming algorithm. Gene expression networks are modelled by differential equations which represent temporal gene expression relations. Gene Expression Programming is a new extension of genetic programming. Here we combine a local search method with gene expression programming to form a memetic algorithm in order to find not only the system of differential equations but also fine tune its constant parameters. The effectiveness of the proposed method is justified by comparing its performance with that of conventional genetic programming applied to this problem in previous studies.
Informatics to support patient choice between diverse medical systems C3 - 2014 IEEE 16th International Conference on e-Health Networking, Applications and Services, Healthcom 2014
- Authors: Golden, Isaac , Stranieri, Andrew , Sahama, Tony , Pilapitiya, Senaka , Siribaddana, Sisira , Vaughan, Stephen
- Date: 2014
- Type: Text , Conference proceedings
- Full Text: false
- Description: Culturally, philosophically and religiously diverse medical systems including Western medicine, Traditional Chinese Medicine, Ayurvedic Medicine and Homeopathic Medicine, once situated in places and times relatively unconnected from each other, currently co-exist to a point where patients must choose which system to consult. These decisions require comparative analyses, yet the divergence in key underpinning assumptions is so great that comparisons cannot easily be made. However, diverse medical systems can be meaningfully juxtaposed for the purpose of making practical decisions if relevant information is presented appropriately. Information regarding privacy provisions inherent in the typical practice of each medical system is an important element in this juxtaposition. In this paper the information needs of patients making decisions regarding the selection of a medical system, are examined.
Insights from jurisprudence for machine learning in law
- Authors: Stranieri, Andrew , Zeleznikow, John
- Date: 2012
- Type: Text , Book chapter
- Relation: Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques p. 85-98
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- Description: The central theme of this chapter is that the application of machine learning to data in the legal domain involves considerations that derive from jurisprudential assumptions about the nature of legal reasoning. Jurisprudence provides a unique resource for machine learning in that, for over one hundred years, significant thinkers have advanced concepts including open texture and discretion. These concepts inform and guide applications of machine learning to law.
Instructors’ perceptions of the development of work-readiness through simulations
- Authors: Faisal, Nadia , Chadhar, Mehmood , Goriss-Hunter, Anitra , Stranieri, Andrew
- Date: 2022
- Type: Text , Conference paper
- Relation: 33rd Australasian Conference on Information Systems: The Changing Face of IS, ACIS 2022, Melbourne, 4-7 December 2022, ACIS 2022 - Australasian Conference on Information Systems, Proceedings
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- Description: The global ERP software market is expected to reach $117.09 billion by 2030 (Biel, July 12, 2022). To boost graduate work-readiness, Australian institutions are adopting new pedagogical strategies by familiarising Information systems (IS) students with this highly sought-after software. One of these techniques is simulation games that provide students with a risk-free, real-world simulation of popular software to develop soft and hard skills needed by the IS industry. This exploratory study employed the Grounded Theory approach to evaluate instructors' perceptions of the influence of simulation games on the work-readiness of information systems students. We conducted semi-structured interviews with (Enterprise Resource Planning Simulation) ERPsim game laboratory instructors. The authors utilised Work Readiness Integrated Competency Model to map the three learning outcomes from the interviews’ analysis: abilities, knowledge, and attitudes. The mapping demonstrated that simulation games could support the development of specific skills and attitudes needed by the information systems sector. Copyright © 2022 Faisal, Chadhar, Goriss-Hunter & Stranieri.
Integrating biological heuristics and gene expression data for gene regulatory network inference
- Authors: Zarnegar, Armita , Jelinek, Herbert , Vamplew, Peter , Stranieri, Andrew
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 2019 Australasian Computer Science Week Multiconference, ACSW 2019; Sydney, Australia; 29th-31st January 2019 p. 1-10
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- Description: Gene Regulatory Networks (GRNs) offer enhanced insight into the biological functions and biochemical pathways of cells associated with gene regulatory mechanisms. However, obtaining accurate GRNs that explain gene expressions and functional associations remains a difficult task. Only a few studies have incorporated heuristics into a GRN discovery process. Doing so has the potential to improve accuracy and reduce the search space and computational time. A technique for GRN discovery that integrates heuristic information into the discovery process is advanced. The approach incorporates three elements: 1) a novel 2D visualized coexpression function that measures the association between genes; 2) a post-processing step that improves detection of up, down and self-regulation and 3) the application of heuristics to generate a Hub network as the backbone of the GRN. Using available microarray and next generation sequencing data from Escherichia coli, six synthetic benchmark GRN datasets were generated with the neighborhood addition and cluster addition methods available in SynTReN. Results of the novel 2D-visualization co-expression function were compared with results obtained using Pearson's correlation and mutual information. The performance of the biological genetics-based heuristics consisting of the 2D-Visualized Co-expression function, post-processing and Hub network was then evaluated by comparing the performance to the GRNs obtained by ARACNe and CLR. The 2D-Visualized Co-expression function significantly improved gene-gene association matching compared to Pearson's correlation coefficient (t = 3.46, df = 5, p = 0.02) and Mutual Information (t = 4.42, df = 5, p = 0.007). The heuristics model gave a 60% improvement against ARACNe (p = 0.02) and CLR (p = 0.019). Analysis of Escherichia coli data suggests that the GRN discovery technique proposed is capable of identifying significant transcriptional regulatory interactions and the corresponding regulatory networks.
Knowledge based regulation of statistical databases
- Authors: Mishra, Vivek , Stranieri, Andrew , Miller, Mirka , Ryan, Joe
- Date: 2006
- Type: Text , Journal article
- Relation: WSEAS Transactions on Information Science and Applications Vol. 3, no. 2 (2006), p. 239-244
- Full Text: false
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- Description: A statistical database system is a system that contains information about individuals, companies or organisations that enables authorized users to retrieve aggregate statistics such as mean and count. The regulation of a statistical database involves limiting the use of the database so that no sequence of queries is sufficient to infer protected information about an individual. The database is said to be compromised when individual confidential information is obtained as a result of a statistical query. Devices to protect against compromise include adding noise to the data or restricting a query. While effective, these techniques are sometimes too strong in that legitimate compromises for reasons of public safety are always blocked. Further, a statistical database can be often be compromised with some knowledge about the database attributes (working knowledge), the real world (supplementary knowledge) or the legal system (legal knowledge). In this paper we illustrate that a knowledge based system that represents working, supplementary and legal knowledge can contribute to the regulation of a statistical database.
- Description: C1
- Description: 2003001608
Knowledge discovery from legal databases
- Authors: Stranieri, Andrew , Zeleznikow, John
- Date: 2005
- Type: Text , Book
- Full Text: false
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- Description: A1
- Description: 2003000833
Medical system choice: Information that affects the selection of healthcare provider in Australia?
- Authors: Sahama, Tony , Stranieri, Andrew , Butler-Henderson, Kerryn , Golden, Isaac
- Date: 2018
- Type: Text , Conference paper
- Relation: 40th Medical Informatics in Europe Conference MIE 2018 Vol. 247, p. 596
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- Description: Many complementary and alternative medical practices (CAM) are readily assessable in Australia alongside Allopathic practitioners. Although CAM practices are prevalent, little is known about how patients seek and use information when deciding which system to consult. We report some preliminary findings of a longitudinal study, designed to solicit factors that influence the Australian public when selecting from diverse medical systems. Fifty-four general public participants, willing to provide their confidential and anonymous opinion were included. The magnitudes of importance, critical in influencing factors, were screened. Results indicated a medical system was selected for its effectiveness, safety, credentials and care (p<0.001). Consultation time, convenience, cost, empowerment and rapport were less important factors (p<0.001) influencing selection of a medical system. The level of choices by participants [χ2 (1, N=54) = 53.445, p<0.001] follow similar trends found for those in conventional medical systems. This contrasts with findings in other locations, where cost and time were major contributing factors when selecting medical systems.
Melanoma classification using efficientnets and ensemble of models with different input resolution
- Authors: Karki, Sagar , Kulkarni, Pradnya , Stranieri, Andrew
- Date: 2021
- Type: Text , Conference paper
- Relation: 2021 Australasian Computer Science Week Multiconference, ACSW 2021, Virtual, Online, 1-5 February 2021, ACM International Conference Proceeding Series
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- Description: Early and accurate detection of melanoma with data analytics can make treatment more effective. This paper proposes a method to classify melanoma cases using deep learning on dermoscopic images. The method demonstrates that heavy augmentation during training and testing produces promising results and warrants further research. The proposed method has been evaluated on the SIIM-ISIC Melanoma Classification 2020 dataset and the best ensemble model achieved 0.9411 area under the ROC curve on hold out test data. © 2021 ACM.
Missing data imputation for individualised CVD diagnostic and treatment
- Authors: Venkatraman, Sitalakshmi , Yatsko, Andrew , Stranieri, Andrew , Jelinek, Herbert
- Date: 2016
- Type: Text , Conference paper
- Relation: Computing in Cardiology, 2016 Vol. 43 I E E E Computer Society
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- Description: Cardiac health screening standards require increasingly more clinical tests consisting of blood, urine and anthropometric measures as well as an extensive clinical and medication history. To ensure optimal screening referrals, diagnostic determinants need to be highly accurate to reduce false positives and ensuing stress to individual patients. However, the data from individual patients partaking in population screening is often incomplete. The current study provides an imputation algorithm that has been applied to patientcentered cardiac health screening. Missing values are iteratively imputed in conjunction with combinations of values on subsets of selected features. The approach was evaluated on the DiabHealth dataset containing 2800 records with over 180 attributes. The results for predicting CVD after data completion showed sensitivity and specificity of 94% and 99% respectively. Removing variables that define cardiac events and associated conditions directly, left ‘age’ followed by ‘use’ of antihypertensive and anti-cholesterol medication, especially statins among the best predictors.
Missing health data pattern matching technique for continuous remote patient monitoring
- Authors: Arora, Teena , Balasubramanian, Venki , Stranieri, Andrew
- Date: 2023
- Type: Text , Conference paper
- Relation: 20th International Conference on Smart Living and Public Health, ICOST 2023, Wonju, Korea, 7-8 July 2023, Digital Health Transformation, Smart Ageing, and Managing Disability, 20th International Conference, ICOST 2023, Wonju, South Korea, July 7–8, 2023, Proceedings Vol. 14237 LNCS, p. 130-143
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- Description: Remote patient monitoring (RPM) has been gaining popularity recently. However, health data acquisition is a significant challenge associated with patient monitoring. In continuous RPM, health data acquisition may miss health data during transmission. Missing data compromises the quality and reliability of patient risk assessment. Several studies suggested techniques for analyzing missing data; however, many are unsuitable for RPM. These techniques neglect the variability of missing data and provide biased results with imputation. Therefore, a holistic approach must consider the correlation and variability of the various vitals and avoid biased imputation. This paper proposes a coherent computation pattern-matching technique to identify and predict missing data patterns. The performance of the proposed approach is evaluated using data collected from a field trial. Results show that the technique can effectively identify and predict missing patterns. © 2023, The Author(s).
Multivariate data-driven decision guidance for clinical scientists
- Authors: Burstein, Frada , De Silva, Daswin , Jelinek, Herbert , Stranieri, Andrew
- Date: 2013
- Type: Text , Conference paper
- Relation: 29th International Conference on Data Engineering Workshops, ICDEW 2013; Proceedings - International Conference on Data Engineering p. 193-199
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- Description: Clinical decision-support is gaining widespread attention as medical institutions and governing bodies turn towards utilising better information management for effective and efficient healthcare delivery and quality assured outcomes. Amass of data across all stages, from disease diagnosis to palliative care, is further indication of the opportunities and challenges created for effective data management, analysis, prediction and optimization techniques as parts of knowledge management in clinical environments. A Data-driven Decision Guidance Management System (DD-DGMS) architecture can encompass solutions into a single closed-loop integrated platform to empower clinical scientists to seamlessly explore a multivariate data space in search of novel patterns and correlations to inform their research and practice. The paper describes the components of such an architecture, which includes a robust data warehouse as an infrastructure for comprehensive clinical knowledge management. The proposed DD-DGMS architecture incorporates the dynamic dimensional data model as its elemental core. Given the heterogeneous nature of clinical contexts and corresponding data, the dimensional data model presents itself as an adaptive model that facilitates knowledge discovery, distribution and application, which is essential for clinical decision support. The paper reports on a trial of the DD-DGMS system prototype conducted on diabetes screening data which further establishes the relevance of the proposed architecture to a clinical context.
- Description: E1
Narrative-based interactive learning environments from modelling reasoning
- Authors: Yearwood, John , Stranieri, Andrew
- Date: 2007
- Type: Text , Journal article
- Relation: Educational Technology and Society Vol. 10, no. 3 (2007), p. 192-208
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- Description: Narrative and story telling has a long history of use in structuring, organising and communicating human experience. This paper describes a narrative based interactive intelligent learning environment which aims to elucidate practical reasoning using interactive emergent narratives that can be used in training novices in decision making. Its design is based on an approach to generating narrative from knowledge that has been modelled in specific decision/reasoning domains. The approach uses a narrative model that is guided partially by inference and contextual information contained in the particular knowledge representation used, the Generic/Actual argument model of structured reasoning. The approach is described with examples in the area of critical care nursing training and positive learning outcomes are reported. © International Forum of Educational Technology & Society (IFETS).
- Description: C1
- Description: 2003002522