Applying anatomical therapeutic chemical (ATC) and critical term ontologies to Australian drug safety data for association rules and adverse event signalling
- Saunders, Gary, Ivkovic, Sasha, Ghosh, Ranadhir, Yearwood, John
- Authors: Saunders, Gary , Ivkovic, Sasha , Ghosh, Ranadhir , Yearwood, John
- Date: 2005
- Type: Text , Journal article
- Relation: Conferences in Research and Practice in Information Technology, Advances in Ontologies 2005: Proceedings of the Australasian Ontology Workshop AOW 2005 Vol. 58, no. (2005), p. 93-98
- Full Text:
- Reviewed:
- Description: C1
- Description: 2003001450
- Authors: Saunders, Gary , Ivkovic, Sasha , Ghosh, Ranadhir , Yearwood, John
- Date: 2005
- Type: Text , Journal article
- Relation: Conferences in Research and Practice in Information Technology, Advances in Ontologies 2005: Proceedings of the Australasian Ontology Workshop AOW 2005 Vol. 58, no. (2005), p. 93-98
- Full Text:
- Reviewed:
- Description: C1
- Description: 2003001450
Data mining with combined use of optimization techniques and self-organizing maps for improving risk grouping rules : Application to prostate cancer patients
- Churilov, Leonid, Bagirov, Adil, Schwartz, Daniel, Smith, Kate, Dally, Michael
- Authors: Churilov, Leonid , Bagirov, Adil , Schwartz, Daniel , Smith, Kate , Dally, Michael
- Date: 2005
- Type: Text , Journal article
- Relation: Journal of Management Information Systems Vol. 21, no. 4 (2005), p. 85-100
- Full Text:
- Reviewed:
- Description: Data mining techniques provide a popular and powerful tool set to generate various data-driven classification systems. In this paper, we investigate the combined use of self-organizing maps (SOM) and nonsmooth nonconvex optimization techniques in order to produce a working case of a data-driven risk classification system. The optimization approach strengthens the validity of SOM results, and the improved classification system increases both the quality of prediction and the homogeneity within the risk groups. Accurate classification of prostate cancer patients into risk groups is important to assist in the identification of appropriate treatment paths. We start with the existing rules and aim to improve classification accuracy by identifying inconsistencies utilizing self-organizing maps as a data visualization tool. Then, we progress to the study of assigning prostate cancer patients into homogenous groups with the aim to support future clinical treatment decisions. Using the case of prostate cancer patients grouping, we demonstrate strong potential of data-driven risk classification schemes for addressing the risk grouping issues in more general organizational settings. © 2005 M.E. Sharpe, Inc.
- Description: C1
- Description: 2003001265
- Authors: Churilov, Leonid , Bagirov, Adil , Schwartz, Daniel , Smith, Kate , Dally, Michael
- Date: 2005
- Type: Text , Journal article
- Relation: Journal of Management Information Systems Vol. 21, no. 4 (2005), p. 85-100
- Full Text:
- Reviewed:
- Description: Data mining techniques provide a popular and powerful tool set to generate various data-driven classification systems. In this paper, we investigate the combined use of self-organizing maps (SOM) and nonsmooth nonconvex optimization techniques in order to produce a working case of a data-driven risk classification system. The optimization approach strengthens the validity of SOM results, and the improved classification system increases both the quality of prediction and the homogeneity within the risk groups. Accurate classification of prostate cancer patients into risk groups is important to assist in the identification of appropriate treatment paths. We start with the existing rules and aim to improve classification accuracy by identifying inconsistencies utilizing self-organizing maps as a data visualization tool. Then, we progress to the study of assigning prostate cancer patients into homogenous groups with the aim to support future clinical treatment decisions. Using the case of prostate cancer patients grouping, we demonstrate strong potential of data-driven risk classification schemes for addressing the risk grouping issues in more general organizational settings. © 2005 M.E. Sharpe, Inc.
- Description: C1
- Description: 2003001265
A fuzzy-neural approach for interpretation and fusion of colour and texture features for CBIR systems
- Verma, Brijesh, Kulkarni, Siddhivinayak
- Authors: Verma, Brijesh , Kulkarni, Siddhivinayak
- Date: 2004
- Type: Text , Journal article
- Relation: Journal of Applied Soft Computing Vol. 5, no. 1 (2004), p. 119-130
- Full Text:
- Reviewed:
- Description: This paper presents a fuzzy-neural approach for interpretation and fusion of colour and texture features for CBIR systems. The presented approach uses fuzzy logic to interpret queries expressed in natural language such as mostly red, many green, few red for colour feature. Tamura feature is used to represent the texture of an image in the database. A term set on each Tamura feature is generated using a fuzzy clustering algorithm to pose a query in terms of natural language. The query can be expressed as a logic combination of natural language terms and Tamura feature values. A fusion of multiple queries is incorporated into the proposed approach. The performance of the technique was evaluated on Brodatz texture benchmark database and it was noticed that there was a prominent increase in the confidence factor for the images. Fusion experiments were conducted using neurofuzzy, fuzzy AND and binary AND techniques. A comparative analysis showed that fuzzy-neural approach has significantly improved the performance of CBIR system.
- Description: C1
- Description: 2003002798
- Authors: Verma, Brijesh , Kulkarni, Siddhivinayak
- Date: 2004
- Type: Text , Journal article
- Relation: Journal of Applied Soft Computing Vol. 5, no. 1 (2004), p. 119-130
- Full Text:
- Reviewed:
- Description: This paper presents a fuzzy-neural approach for interpretation and fusion of colour and texture features for CBIR systems. The presented approach uses fuzzy logic to interpret queries expressed in natural language such as mostly red, many green, few red for colour feature. Tamura feature is used to represent the texture of an image in the database. A term set on each Tamura feature is generated using a fuzzy clustering algorithm to pose a query in terms of natural language. The query can be expressed as a logic combination of natural language terms and Tamura feature values. A fusion of multiple queries is incorporated into the proposed approach. The performance of the technique was evaluated on Brodatz texture benchmark database and it was noticed that there was a prominent increase in the confidence factor for the images. Fusion experiments were conducted using neurofuzzy, fuzzy AND and binary AND techniques. A comparative analysis showed that fuzzy-neural approach has significantly improved the performance of CBIR system.
- Description: C1
- Description: 2003002798
Networking tourism SMEs : E-commerce and e-marketing issues in regional Australia
- Authors: Braun, Patrice
- Date: 2002
- Type: Text , Journal article
- Relation: Information Technology and Tourism Vol. 5, no. 1 (2002), p. 13-23
- Full Text:
- Reviewed:
- Description: Networks, knowledge, and relationships have become crucial assets to business survival in the new economy. Research indicates that network building is a major new source of competitive advantage and an essential regional and indeed global management requirement. Because regional policies encourage interfirm alliances and the development of regional economic communities, the fostering of a culture of connectivity, networking, learning, and trust between regional Australian small and medium- size tourism enterprises (SMTEs) may offer a potential solution to the possible loss of competitive advantage for Australian tourism enterprises. It is suggested that SMTEs would benefit from increased information flow through regional networking and cooperative e-marketing campaigns to enhance market visibility, global positioning, and strategic leverage in the new economy.
- Description: C1
- Description: 2003000256
- Authors: Braun, Patrice
- Date: 2002
- Type: Text , Journal article
- Relation: Information Technology and Tourism Vol. 5, no. 1 (2002), p. 13-23
- Full Text:
- Reviewed:
- Description: Networks, knowledge, and relationships have become crucial assets to business survival in the new economy. Research indicates that network building is a major new source of competitive advantage and an essential regional and indeed global management requirement. Because regional policies encourage interfirm alliances and the development of regional economic communities, the fostering of a culture of connectivity, networking, learning, and trust between regional Australian small and medium- size tourism enterprises (SMTEs) may offer a potential solution to the possible loss of competitive advantage for Australian tourism enterprises. It is suggested that SMTEs would benefit from increased information flow through regional networking and cooperative e-marketing campaigns to enhance market visibility, global positioning, and strategic leverage in the new economy.
- Description: C1
- Description: 2003000256
Exploring ways in which social networkers contribute to online groups : A case study of one facebook group's discussion of australian broadcaster channel 9 during the 2010 winter olympic games
- Scott, Olan, Bradshaw, Ryan, Larkin, Paul
- Authors: Scott, Olan , Bradshaw, Ryan , Larkin, Paul
- Date: 2013
- Type: Text , Journal article
- Relation: First Monday Vol. 18, no. 4 (2013), p.
- Full Text:
- Reviewed:
- Description: The advent of the Internet has allowed consumers more opportunities to communicate than ever before. The emergence of the Internet has decreased the gate-keeping role of the media in that Internet users can interact with media outlets and other users in real time. This study breaks ground in the analysis of media messages as one Facebook group is studied to uncover how Internet users virtually protested media outlets on social networking Web sites. © First Monday. 2013, Olan Kees Martin Scott, Ryan Bradshaw, and Paul Larkin.
- Description: 2003011106
- Authors: Scott, Olan , Bradshaw, Ryan , Larkin, Paul
- Date: 2013
- Type: Text , Journal article
- Relation: First Monday Vol. 18, no. 4 (2013), p.
- Full Text:
- Reviewed:
- Description: The advent of the Internet has allowed consumers more opportunities to communicate than ever before. The emergence of the Internet has decreased the gate-keeping role of the media in that Internet users can interact with media outlets and other users in real time. This study breaks ground in the analysis of media messages as one Facebook group is studied to uncover how Internet users virtually protested media outlets on social networking Web sites. © First Monday. 2013, Olan Kees Martin Scott, Ryan Bradshaw, and Paul Larkin.
- Description: 2003011106
Understanding personal use of the Internet at work: An integrated model of neutralization techniques and general deterrence theory
- Cheng, Lijiao, Li, Wenli, Zhai, Qingguo, Smyth, Russell
- Authors: Cheng, Lijiao , Li, Wenli , Zhai, Qingguo , Smyth, Russell
- Date: 2014
- Type: Text , Journal article
- Relation: Computers in Human Behavior Vol. 38, no. (September 2014 2014), p. 220-228
- Full Text:
- Reviewed:
- Description: This paper examines the influence of neutralization techniques, perceived sanction severity, perceived detection certainty and perceived benefits of using the Internet for personal purposes on intention to use the Internet at work for personal use. To do so, we draw on a conceptual framework integrating neutralization theory and general deterrence theory. The study finds that both neutralization techniques and perceived benefits have a positive effect on personal use of the Internet. Perceived detection certainty is found to have a negative effect on personal use of the Internet, while the effect of perceived sanctions severity on personal use of the Internet is not significant. The effect of neutralization and perceived benefits are much stronger than perceived detection certainty. The findings suggest that people may think more about neutralization and perceived benefits than they do about costs, when deciding whether to use the Internet at work for personal purposes.
- Description: C1
- Authors: Cheng, Lijiao , Li, Wenli , Zhai, Qingguo , Smyth, Russell
- Date: 2014
- Type: Text , Journal article
- Relation: Computers in Human Behavior Vol. 38, no. (September 2014 2014), p. 220-228
- Full Text:
- Reviewed:
- Description: This paper examines the influence of neutralization techniques, perceived sanction severity, perceived detection certainty and perceived benefits of using the Internet for personal purposes on intention to use the Internet at work for personal use. To do so, we draw on a conceptual framework integrating neutralization theory and general deterrence theory. The study finds that both neutralization techniques and perceived benefits have a positive effect on personal use of the Internet. Perceived detection certainty is found to have a negative effect on personal use of the Internet, while the effect of perceived sanctions severity on personal use of the Internet is not significant. The effect of neutralization and perceived benefits are much stronger than perceived detection certainty. The findings suggest that people may think more about neutralization and perceived benefits than they do about costs, when deciding whether to use the Internet at work for personal purposes.
- Description: C1
Group decision making in health care : A case study of multidisciplinary meetings
- Sharma, Vishakha, Stranieri, Andrew, Burstein, Frada, Warren, Jim, Daly, Sharon, Patterson, Louise, Yearwood, John, Wolff, Alan
- 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
- Full Text:
- Reviewed:
- 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.
- 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
- Full Text:
- Reviewed:
- 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.
A simulated annealing-based maximum-margin clustering algorithm
- Seifollahi, Sattar, Bagirov, Adil, Borzeshi, Ehsan, Piccardi, Massimo
- Authors: Seifollahi, Sattar , Bagirov, Adil , Borzeshi, Ehsan , Piccardi, Massimo
- Date: 2019
- Type: Text , Journal article
- Relation: Computational Intelligence Vol. 35, no. 1 (2019), p. 23-41
- Full Text:
- Reviewed:
- Description: Maximum-margin clustering is an extension of the support vector machine (SVM) to clustering. It partitions a set of unlabeled data into multiple groups by finding hyperplanes with the largest margins. Although existing algorithms have shown promising results, there is no guarantee of convergence of these algorithms to global solutions due to the nonconvexity of the optimization problem. In this paper, we propose a simulated annealing-based algorithm that is able to mitigate the issue of local minima in the maximum-margin clustering problem. The novelty of our algorithm is twofold, ie, (i) it comprises a comprehensive cluster modification scheme based on simulated annealing, and (ii) it introduces a new approach based on the combination of k-means++ and SVM at each step of the annealing process. More precisely, k-means++ is initially applied to extract subsets of the data points. Then, an unsupervised SVM is applied to improve the clustering results. Experimental results on various benchmark data sets (of up to over a million points) give evidence that the proposed algorithm is more effective at solving the clustering problem than a number of popular clustering algorithms.
- Authors: Seifollahi, Sattar , Bagirov, Adil , Borzeshi, Ehsan , Piccardi, Massimo
- Date: 2019
- Type: Text , Journal article
- Relation: Computational Intelligence Vol. 35, no. 1 (2019), p. 23-41
- Full Text:
- Reviewed:
- Description: Maximum-margin clustering is an extension of the support vector machine (SVM) to clustering. It partitions a set of unlabeled data into multiple groups by finding hyperplanes with the largest margins. Although existing algorithms have shown promising results, there is no guarantee of convergence of these algorithms to global solutions due to the nonconvexity of the optimization problem. In this paper, we propose a simulated annealing-based algorithm that is able to mitigate the issue of local minima in the maximum-margin clustering problem. The novelty of our algorithm is twofold, ie, (i) it comprises a comprehensive cluster modification scheme based on simulated annealing, and (ii) it introduces a new approach based on the combination of k-means++ and SVM at each step of the annealing process. More precisely, k-means++ is initially applied to extract subsets of the data points. Then, an unsupervised SVM is applied to improve the clustering results. Experimental results on various benchmark data sets (of up to over a million points) give evidence that the proposed algorithm is more effective at solving the clustering problem than a number of popular clustering algorithms.
Applying Turner's three-process theory of power to the study of power relations in a troubled information systems implementation
- Ye, Michelle, de Salas, Kristy, Ollington, Nadia, McKay, Judy
- Authors: Ye, Michelle , de Salas, Kristy , Ollington, Nadia , McKay, Judy
- Date: 2017
- Type: Text , Journal article
- Relation: Australasian Journal of Information Systems Vol. 21, no. (2017), p. 1-25
- Full Text:
- Reviewed:
- Description: This paper explores the nature and exercise of power in an interpretive case study of a troubled information systems (IS) implementation in a university in the Asia Pacific region using Turner's Three-Process Theory of Power based on Social Identity Theory and Self-Categorisation Theory. The findings demonstrate the value of Turner's theoretical lens as well as its insufficiency for explaining all power related activities. This research has led to the development of an extended Three-Process Theory of Power by adding the alternative components that emerged from the data in the case study in relation to the nature and exercises of power. Based on the findings, we further recommend specific guidelines for IS theoreticians and practitioners including advice to project managers on a range of key issues. Thus, this paper contributes theorising the sources of power and tactical applications of power in given situations, particularly in IS implementation projects. © 2017 Ye, de Salas, Ollington & McKay.
- Authors: Ye, Michelle , de Salas, Kristy , Ollington, Nadia , McKay, Judy
- Date: 2017
- Type: Text , Journal article
- Relation: Australasian Journal of Information Systems Vol. 21, no. (2017), p. 1-25
- Full Text:
- Reviewed:
- Description: This paper explores the nature and exercise of power in an interpretive case study of a troubled information systems (IS) implementation in a university in the Asia Pacific region using Turner's Three-Process Theory of Power based on Social Identity Theory and Self-Categorisation Theory. The findings demonstrate the value of Turner's theoretical lens as well as its insufficiency for explaining all power related activities. This research has led to the development of an extended Three-Process Theory of Power by adding the alternative components that emerged from the data in the case study in relation to the nature and exercises of power. Based on the findings, we further recommend specific guidelines for IS theoreticians and practitioners including advice to project managers on a range of key issues. Thus, this paper contributes theorising the sources of power and tactical applications of power in given situations, particularly in IS implementation projects. © 2017 Ye, de Salas, Ollington & McKay.
Video coding using arbitrarily shaped block partitions in globally optimal perspective
- Paul, Manoranjan, Murshed, Manzur
- Authors: Paul, Manoranjan , Murshed, Manzur
- Date: 2011
- Type: Text , Journal article
- Relation: EURASIP Journal on Advances in Signal Processing Vol. 16, no. (2011), p.
- Full Text:
- Reviewed:
- Description: Algorithms using content-based patterns to segment moving regions at the macroblock (MB) level have exhibited good potential for improved coding efficiency when embedded into the H.264 standard as an extra mode. The content-based pattern generation (CPG) algorithm provides local optimal result as only one pattern can be optimally generated from a given set of moving regions. But, it failed to provide optimal results for multiple patterns from entire sets. Obviously, a global optimal solution for clustering the set and then generation of multiple patterns enhances the performance farther. But a global optimal solution is not achievable due to the non-polynomial nature of the clustering problem. In this paper, we propose a near-optimal content-based pattern generation (OCPG) algorithm which outperforms the existing approach. Coupling OCPG, generating a set of patterns after clustering the MBs into several disjoint sets, with a direct pattern selection algorithm by allowing all the MBs in multiple pattern modes outperforms the existing pattern-based coding when embedded into the H.264.
- Authors: Paul, Manoranjan , Murshed, Manzur
- Date: 2011
- Type: Text , Journal article
- Relation: EURASIP Journal on Advances in Signal Processing Vol. 16, no. (2011), p.
- Full Text:
- Reviewed:
- Description: Algorithms using content-based patterns to segment moving regions at the macroblock (MB) level have exhibited good potential for improved coding efficiency when embedded into the H.264 standard as an extra mode. The content-based pattern generation (CPG) algorithm provides local optimal result as only one pattern can be optimally generated from a given set of moving regions. But, it failed to provide optimal results for multiple patterns from entire sets. Obviously, a global optimal solution for clustering the set and then generation of multiple patterns enhances the performance farther. But a global optimal solution is not achievable due to the non-polynomial nature of the clustering problem. In this paper, we propose a near-optimal content-based pattern generation (OCPG) algorithm which outperforms the existing approach. Coupling OCPG, generating a set of patterns after clustering the MBs into several disjoint sets, with a direct pattern selection algorithm by allowing all the MBs in multiple pattern modes outperforms the existing pattern-based coding when embedded into the H.264.
Addressing the complexities of big data analytics in healthcare : The diabetes screening case
- De Silva, Daswin, Burstein, Frada, Jelinek, Herbert, Stranieri, Andrew
- Authors: De Silva, Daswin , Burstein, Frada , Jelinek, Herbert , Stranieri, Andrew
- Date: 2015
- Type: Text , Journal article
- Relation: Australasian Journal of Information Systems Vol. 19, no. (2015), p. S99-S115
- Full Text:
- Reviewed:
- Description: The healthcare industry generates a high throughput of medical, clinical and omics data of varying complexity and features. Clinical decision-support is gaining widespread attention as medical institutions and governing bodies turn towards better management of this data 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 to effective data management, analysis, prediction and optimization techniques as parts of knowledge management in clinical environments. Big Data analytics (BDA) presents the potential to advance this industry with reforms in clinical decision-support and translational research. However, adoption of big data analytics has been slow due to complexities posed by the nature of healthcare data. The success of these systems is hard to predict, so further research is needed to provide a robust framework to ensure investment in BDA is justified. In this paper we investigate these complexities from the perspective of updated Information Systems (IS) participation theory. We present a case study on a large diabetes screening project to integrate, converge and derive expedient insights from such an accumulation of data and make recommendations for a successful BDA implementation grounded in a participatory framework and the specificities of big data in healthcare context. © 2015 De Silva, Burstein, Jelinek, Stranieri.
- Authors: De Silva, Daswin , Burstein, Frada , Jelinek, Herbert , Stranieri, Andrew
- Date: 2015
- Type: Text , Journal article
- Relation: Australasian Journal of Information Systems Vol. 19, no. (2015), p. S99-S115
- Full Text:
- Reviewed:
- Description: The healthcare industry generates a high throughput of medical, clinical and omics data of varying complexity and features. Clinical decision-support is gaining widespread attention as medical institutions and governing bodies turn towards better management of this data 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 to effective data management, analysis, prediction and optimization techniques as parts of knowledge management in clinical environments. Big Data analytics (BDA) presents the potential to advance this industry with reforms in clinical decision-support and translational research. However, adoption of big data analytics has been slow due to complexities posed by the nature of healthcare data. The success of these systems is hard to predict, so further research is needed to provide a robust framework to ensure investment in BDA is justified. In this paper we investigate these complexities from the perspective of updated Information Systems (IS) participation theory. We present a case study on a large diabetes screening project to integrate, converge and derive expedient insights from such an accumulation of data and make recommendations for a successful BDA implementation grounded in a participatory framework and the specificities of big data in healthcare context. © 2015 De Silva, Burstein, Jelinek, Stranieri.
Business analytics-based enterprise information systems
- Sun, Zhaohao, Strang, Kenneth, Firmin, Sally
- Authors: Sun, Zhaohao , Strang, Kenneth , Firmin, Sally
- Date: 2017
- Type: Text , Journal article
- Relation: Journal of Computer Information Systems Vol. 57, no. 2 (2017), p. 169-178
- Full Text:
- Reviewed:
- Description: Big data analytics and business analytics are a disruptive technology and innovative solution for enterprise development. However, what is the relationship between business analytics, big data analytics, and enterprise information systems (EIS)? How can business analytics enhance the development of EIS? How can analytics be incorporated into EIS? These are still big issues. This article addresses these three issues by proposing ontology of business analytics, presenting an analytics service-oriented architecture (ASOA) and applying ASOA to EIS, where our surveyed data analysis showed that the proposed ASOA is viable for developing EIS. This article then examines incorporation of business analytics into EIS through proposing a model for business analytics service-based EIS, or ASEIS for short. The proposed approach in this article might facilitate the research and development of EIS, business analytics, big data analytics, and business intelligence.
- Authors: Sun, Zhaohao , Strang, Kenneth , Firmin, Sally
- Date: 2017
- Type: Text , Journal article
- Relation: Journal of Computer Information Systems Vol. 57, no. 2 (2017), p. 169-178
- Full Text:
- Reviewed:
- Description: Big data analytics and business analytics are a disruptive technology and innovative solution for enterprise development. However, what is the relationship between business analytics, big data analytics, and enterprise information systems (EIS)? How can business analytics enhance the development of EIS? How can analytics be incorporated into EIS? These are still big issues. This article addresses these three issues by proposing ontology of business analytics, presenting an analytics service-oriented architecture (ASOA) and applying ASOA to EIS, where our surveyed data analysis showed that the proposed ASOA is viable for developing EIS. This article then examines incorporation of business analytics into EIS through proposing a model for business analytics service-based EIS, or ASEIS for short. The proposed approach in this article might facilitate the research and development of EIS, business analytics, big data analytics, and business intelligence.
An empirical evaluation of the potential of public e-procurement to reduce corruption
- Neupane, Arjun, Soar, Jeffrey, Vaidya, Kishor
- Authors: Neupane, Arjun , Soar, Jeffrey , Vaidya, Kishor
- Date: 2014
- Type: Text , Journal article
- Relation: Australasian Journal of Information Systems Vol. 18, no. 2 (2014), p. 21-44
- Full Text:
- Reviewed:
- Description: One of the significant potential benefits of e-procurement technology is reducing opportunities for corruption in public procurement processes. The authors identified anticorruption capabilities of e-procurement through an extensive literature review and a theoretical model representing the impact of three latent variables: monopoly of power, information asymmetry, and transparency and accountability upon the dependent variable, the intent-to-adopt e-procurement. This research was guided by the Principal- Agent theory and collected the perceptions of 46 government officers of the potential of public e-procurement to reduce corruption in public procurement processes. Results were analysed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. The findings suggest that the intent-to-adopt e-procurement has a positive and significant relationship with the independent variables that might inform developing countries in strategies to combat corruption in public procurement.
- Authors: Neupane, Arjun , Soar, Jeffrey , Vaidya, Kishor
- Date: 2014
- Type: Text , Journal article
- Relation: Australasian Journal of Information Systems Vol. 18, no. 2 (2014), p. 21-44
- Full Text:
- Reviewed:
- Description: One of the significant potential benefits of e-procurement technology is reducing opportunities for corruption in public procurement processes. The authors identified anticorruption capabilities of e-procurement through an extensive literature review and a theoretical model representing the impact of three latent variables: monopoly of power, information asymmetry, and transparency and accountability upon the dependent variable, the intent-to-adopt e-procurement. This research was guided by the Principal- Agent theory and collected the perceptions of 46 government officers of the potential of public e-procurement to reduce corruption in public procurement processes. Results were analysed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. The findings suggest that the intent-to-adopt e-procurement has a positive and significant relationship with the independent variables that might inform developing countries in strategies to combat corruption in public procurement.
Current programs and future needs in health literacy for older people : a literature review
- Lê, Quynh, Terry, Daniel, Woodroffe, Jess
- Authors: Lê, Quynh , Terry, Daniel , Woodroffe, Jess
- Date: 2013
- Type: Text , Journal article , Review
- Relation: Journal of Consumer Health on the Internet Vol. 17, no. 4 (2013), p. 369-388
- Full Text:
- Reviewed:
- Description: Inadequate health literacy occurs more amongst older adults and can result in difficulties synthesising information and communicating with health professionals, increased emergency visits and hospitalizations, poor uptake of preventative interventions, increased mortality, and ultimately greater health care costs. A literature review was conducted that identified 12 articles that discussed and examined health literacy interventions among older adults. It revealed few papers exist which highlight programs that examine health literacy outcomes for older adults. The review identified evidence-based best-practice models of health literacy interventions need to be further developed to meet the health literacy needs of aging population. © 2013 Copyright Quynh Le, Daniel R. Terry, and Jess Woodroffe.
- Authors: Lê, Quynh , Terry, Daniel , Woodroffe, Jess
- Date: 2013
- Type: Text , Journal article , Review
- Relation: Journal of Consumer Health on the Internet Vol. 17, no. 4 (2013), p. 369-388
- Full Text:
- Reviewed:
- Description: Inadequate health literacy occurs more amongst older adults and can result in difficulties synthesising information and communicating with health professionals, increased emergency visits and hospitalizations, poor uptake of preventative interventions, increased mortality, and ultimately greater health care costs. A literature review was conducted that identified 12 articles that discussed and examined health literacy interventions among older adults. It revealed few papers exist which highlight programs that examine health literacy outcomes for older adults. The review identified evidence-based best-practice models of health literacy interventions need to be further developed to meet the health literacy needs of aging population. © 2013 Copyright Quynh Le, Daniel R. Terry, and Jess Woodroffe.
Rapid health data repository allocation using predictive machine learning
- Uddin, Ashraf, Stranieri, Andrew, Gondal, Iqbal, Balasubramanian, Venki
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2020
- Type: Text , Journal article
- Relation: Health Informatics Journal Vol. 26, no. 4 (2020), p. 3009-3036
- Full Text:
- Reviewed:
- Description: Health-related data is stored in a number of repositories that are managed and controlled by different entities. For instance, Electronic Health Records are usually administered by governments. Electronic Medical Records are typically controlled by health care providers, whereas Personal Health Records are managed directly by patients. Recently, Blockchain-based health record systems largely regulated by technology have emerged as another type of repository. Repositories for storing health data differ from one another based on cost, level of security and quality of performance. Not only has the type of repositories increased in recent years, but the quantum of health data to be stored has increased. For instance, the advent of wearable sensors that capture physiological signs has resulted in an exponential growth in digital health data. The increase in the types of repository and amount of data has driven a need for intelligent processes to select appropriate repositories as data is collected. However, the storage allocation decision is complex and nuanced. The challenges are exacerbated when health data are continuously streamed, as is the case with wearable sensors. Although patients are not always solely responsible for determining which repository should be used, they typically have some input into this decision. Patients can be expected to have idiosyncratic preferences regarding storage decisions depending on their unique contexts. In this paper, we propose a predictive model for the storage of health data that can meet patient needs and make storage decisions rapidly, in real-time, even with data streaming from wearable sensors. The model is built with a machine learning classifier that learns the mapping between characteristics of health data and features of storage repositories from a training set generated synthetically from correlations evident from small samples of experts. Results from the evaluation demonstrate the viability of the machine learning technique used. © The Author(s) 2020.
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2020
- Type: Text , Journal article
- Relation: Health Informatics Journal Vol. 26, no. 4 (2020), p. 3009-3036
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- Description: Health-related data is stored in a number of repositories that are managed and controlled by different entities. For instance, Electronic Health Records are usually administered by governments. Electronic Medical Records are typically controlled by health care providers, whereas Personal Health Records are managed directly by patients. Recently, Blockchain-based health record systems largely regulated by technology have emerged as another type of repository. Repositories for storing health data differ from one another based on cost, level of security and quality of performance. Not only has the type of repositories increased in recent years, but the quantum of health data to be stored has increased. For instance, the advent of wearable sensors that capture physiological signs has resulted in an exponential growth in digital health data. The increase in the types of repository and amount of data has driven a need for intelligent processes to select appropriate repositories as data is collected. However, the storage allocation decision is complex and nuanced. The challenges are exacerbated when health data are continuously streamed, as is the case with wearable sensors. Although patients are not always solely responsible for determining which repository should be used, they typically have some input into this decision. Patients can be expected to have idiosyncratic preferences regarding storage decisions depending on their unique contexts. In this paper, we propose a predictive model for the storage of health data that can meet patient needs and make storage decisions rapidly, in real-time, even with data streaming from wearable sensors. The model is built with a machine learning classifier that learns the mapping between characteristics of health data and features of storage repositories from a training set generated synthetically from correlations evident from small samples of experts. Results from the evaluation demonstrate the viability of the machine learning technique used. © The Author(s) 2020.
Using radar plots for performance benchmarking at patient and hospital levels using an Australian orthopaedics dataset
- Morales-Silva, Daniel, McPherson, Cameron, Pineda-Villavicencio, Guillermo, Atchison, Rory
- Authors: Morales-Silva, Daniel , McPherson, Cameron , Pineda-Villavicencio, Guillermo , Atchison, Rory
- Date: 2020
- Type: Text , Journal article
- Relation: Health Informatics Journal Vol. 26, no. 3 (2020), p. 2119-2137
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- Description: This study will highlight the diagnostic potential that radar plots display for reporting on performance benchmarking from patient admissions to hospital for surgical procedures. Two drawbacks of radar plots – the presence of missing information and ordering of indicators – are addressed. Ten different orthopaedic surgery procedures were considered in this study. Moreover, twelve outcome indicators were provided for each of the 10 surgeries of interest. These indicators were displayed using a radar plot, which we call a scorecard. At the hospital level, we propose a facile process by which to consolidate our 10 scorecards into one. We addressed the ordering of indicators in our scorecards by considering the national median of the indicators as a benchmark. Furthermore, our the consolidated scorecard facilitates concise visualisation and dissemination of complex data. It also enables the classification of providers into potential low and high performers that warrant further investigation. In conclusion, radar plots provide a clear and effective comparative tool for discerning multiple outcome indicators against the benchmarks of patient admission. A case study between two top and bottom performers on a consolidated scorecard (at hospital level) showed that medical provider charges varied more than other outcome indicators. © The Author(s) 2020.
- Authors: Morales-Silva, Daniel , McPherson, Cameron , Pineda-Villavicencio, Guillermo , Atchison, Rory
- Date: 2020
- Type: Text , Journal article
- Relation: Health Informatics Journal Vol. 26, no. 3 (2020), p. 2119-2137
- Full Text:
- Reviewed:
- Description: This study will highlight the diagnostic potential that radar plots display for reporting on performance benchmarking from patient admissions to hospital for surgical procedures. Two drawbacks of radar plots – the presence of missing information and ordering of indicators – are addressed. Ten different orthopaedic surgery procedures were considered in this study. Moreover, twelve outcome indicators were provided for each of the 10 surgeries of interest. These indicators were displayed using a radar plot, which we call a scorecard. At the hospital level, we propose a facile process by which to consolidate our 10 scorecards into one. We addressed the ordering of indicators in our scorecards by considering the national median of the indicators as a benchmark. Furthermore, our the consolidated scorecard facilitates concise visualisation and dissemination of complex data. It also enables the classification of providers into potential low and high performers that warrant further investigation. In conclusion, radar plots provide a clear and effective comparative tool for discerning multiple outcome indicators against the benchmarks of patient admission. A case study between two top and bottom performers on a consolidated scorecard (at hospital level) showed that medical provider charges varied more than other outcome indicators. © The Author(s) 2020.
Local contrast as an effective means to robust clustering against varying densities
- Chen, Bo, Ting, Kaiming, Washio, Takashi, Zhu, Ye
- Authors: Chen, Bo , Ting, Kaiming , Washio, Takashi , Zhu, Ye
- Date: 2018
- Type: Text , Journal article
- Relation: Machine Learning Vol. 107, no. 8-10 (2018), p. 1621-1645
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- Description: Most density-based clustering methods have difficulties detecting clusters of hugely different densities in a dataset. A recent density-based clustering CFSFDP appears to have mitigated the issue. However, through formalising the condition under which it fails, we reveal that CFSFDP still has the same issue. To address this issue, we propose a new measure called Local Contrast, as an alternative to density, to find cluster centers and detect clusters. We then apply Local Contrast to CFSFDP, and create a new clustering method called LC-CFSFDP which is robust in the presence of varying densities. Our empirical evaluation shows that LC-CFSFDP outperforms CFSFDP and three other state-of-the-art variants of CFSFDP. © 2018, The Author(s).
- Authors: Chen, Bo , Ting, Kaiming , Washio, Takashi , Zhu, Ye
- Date: 2018
- Type: Text , Journal article
- Relation: Machine Learning Vol. 107, no. 8-10 (2018), p. 1621-1645
- Full Text:
- Reviewed:
- Description: Most density-based clustering methods have difficulties detecting clusters of hugely different densities in a dataset. A recent density-based clustering CFSFDP appears to have mitigated the issue. However, through formalising the condition under which it fails, we reveal that CFSFDP still has the same issue. To address this issue, we propose a new measure called Local Contrast, as an alternative to density, to find cluster centers and detect clusters. We then apply Local Contrast to CFSFDP, and create a new clustering method called LC-CFSFDP which is robust in the presence of varying densities. Our empirical evaluation shows that LC-CFSFDP outperforms CFSFDP and three other state-of-the-art variants of CFSFDP. © 2018, The Author(s).
A framework for ERP post-implementation amendments : A literature analysis
- Oseni, Taiwo, Foster, Susan, Rahim, Mahbubur, Smith, Stephen Patrick
- Authors: Oseni, Taiwo , Foster, Susan , Rahim, Mahbubur , Smith, Stephen Patrick
- Date: 2017
- Type: Text , Journal article
- Relation: Australasian Journal of Information Systems Vol. 21, no. (2017), p.
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- Description: Post-implementation amendments to ERP systems (ERP-PIA) are of importance for advancing ERP research, but more importantly essential if ERP systems are to be used as a strategic and competitive business tool. For ease of clarity, we have adopted the term “amendments” to encompass the main forms of post implementation activities: maintenance, enhancements and upgrades. The term “amendments” is used to counteract one of the major findings from this research - the inconsistency of terms used by many authors to explain post implementation activities. This paper presents a review of the ERP post-implementation amendment literature in order to provide answers to two specific questions: first, what is the current state of research in the field of ERP-PIA; and second, what are the future research directions that need to be explored in the field of ERP-PIA. From the review, we develop a framework to identify: (a) major themes concerning ERP post-implementation amendments, (b) inherent gaps in the post-implementation amendments literature, and (c) specific areas that require further research attention influencing the uptake of amendments. Suggestions on empirical evaluation of research directions and their relevance in the extension of existing literature is presented.
- Authors: Oseni, Taiwo , Foster, Susan , Rahim, Mahbubur , Smith, Stephen Patrick
- Date: 2017
- Type: Text , Journal article
- Relation: Australasian Journal of Information Systems Vol. 21, no. (2017), p.
- Full Text:
- Reviewed:
- Description: Post-implementation amendments to ERP systems (ERP-PIA) are of importance for advancing ERP research, but more importantly essential if ERP systems are to be used as a strategic and competitive business tool. For ease of clarity, we have adopted the term “amendments” to encompass the main forms of post implementation activities: maintenance, enhancements and upgrades. The term “amendments” is used to counteract one of the major findings from this research - the inconsistency of terms used by many authors to explain post implementation activities. This paper presents a review of the ERP post-implementation amendment literature in order to provide answers to two specific questions: first, what is the current state of research in the field of ERP-PIA; and second, what are the future research directions that need to be explored in the field of ERP-PIA. From the review, we develop a framework to identify: (a) major themes concerning ERP post-implementation amendments, (b) inherent gaps in the post-implementation amendments literature, and (c) specific areas that require further research attention influencing the uptake of amendments. Suggestions on empirical evaluation of research directions and their relevance in the extension of existing literature is presented.
An evaluation methodology for interactive reinforcement learning with simulated users
- Bignold, Adam, Cruz, Francisco, Dazeley, Richard, Vamplew, Peter, Foale, Cameron
- Authors: Bignold, Adam , Cruz, Francisco , Dazeley, Richard , Vamplew, Peter , Foale, Cameron
- Date: 2021
- Type: Text , Journal article
- Relation: Biomimetics Vol. 6, no. 1 (2021), p. 1-15
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- Description: Interactive reinforcement learning methods utilise an external information source to evaluate decisions and accelerate learning. Previous work has shown that human advice could significantly improve learning agents’ performance. When evaluating reinforcement learning algorithms, it is common to repeat experiments as parameters are altered or to gain a sufficient sample size. In this regard, to require human interaction every time an experiment is restarted is undesirable, particularly when the expense in doing so can be considerable. Additionally, reusing the same people for the experiment introduces bias, as they will learn the behaviour of the agent and the dynamics of the environment. This paper presents a methodology for evaluating interactive reinforcement learning agents by employing simulated users. Simulated users allow human knowledge, bias, and interaction to be simulated. The use of simulated users allows the development and testing of reinforcement learning agents, and can provide indicative results of agent performance under defined human constraints. While simulated users are no replacement for actual humans, they do offer an affordable and fast alternative for evaluative assisted agents. We introduce a method for performing a preliminary evaluation utilising simulated users to show how performance changes depending on the type of user assisting the agent. Moreover, we describe how human interaction may be simulated, and present an experiment illustrating the applicability of simulating users in evaluating agent performance when assisted by different types of trainers. Experimental results show that the use of this methodology allows for greater insight into the performance of interactive reinforcement learning agents when advised by different users. The use of simulated users with varying characteristics allows for evaluation of the impact of those characteristics on the behaviour of the learning agent. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Bignold, Adam , Cruz, Francisco , Dazeley, Richard , Vamplew, Peter , Foale, Cameron
- Date: 2021
- Type: Text , Journal article
- Relation: Biomimetics Vol. 6, no. 1 (2021), p. 1-15
- Full Text:
- Reviewed:
- Description: Interactive reinforcement learning methods utilise an external information source to evaluate decisions and accelerate learning. Previous work has shown that human advice could significantly improve learning agents’ performance. When evaluating reinforcement learning algorithms, it is common to repeat experiments as parameters are altered or to gain a sufficient sample size. In this regard, to require human interaction every time an experiment is restarted is undesirable, particularly when the expense in doing so can be considerable. Additionally, reusing the same people for the experiment introduces bias, as they will learn the behaviour of the agent and the dynamics of the environment. This paper presents a methodology for evaluating interactive reinforcement learning agents by employing simulated users. Simulated users allow human knowledge, bias, and interaction to be simulated. The use of simulated users allows the development and testing of reinforcement learning agents, and can provide indicative results of agent performance under defined human constraints. While simulated users are no replacement for actual humans, they do offer an affordable and fast alternative for evaluative assisted agents. We introduce a method for performing a preliminary evaluation utilising simulated users to show how performance changes depending on the type of user assisting the agent. Moreover, we describe how human interaction may be simulated, and present an experiment illustrating the applicability of simulating users in evaluating agent performance when assisted by different types of trainers. Experimental results show that the use of this methodology allows for greater insight into the performance of interactive reinforcement learning agents when advised by different users. The use of simulated users with varying characteristics allows for evaluation of the impact of those characteristics on the behaviour of the learning agent. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Diarrhoeal disease surveillance in Papua New Guinea : findings and challenges
- Abdad, Mohammad, Soli, Kevin, Pham, Bang, Bande, Grace, Maure, Tobias, Jonduo, Marinjo, Kisa, Debbie, Rai, Glennis, Phuanukoonnon, Suparat, Siba, Peter, Horwood, Paul, Greenhill, Andrew
- Authors: Abdad, Mohammad , Soli, Kevin , Pham, Bang , Bande, Grace , Maure, Tobias , Jonduo, Marinjo , Kisa, Debbie , Rai, Glennis , Phuanukoonnon, Suparat , Siba, Peter , Horwood, Paul , Greenhill, Andrew
- Date: 2020
- Type: Text , Journal article
- Relation: Western Pacific Surveillance and Response Vol. 11, no. 1 (Jan-Mar 2020), p. 6
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- Description: Diarrhoeal diseases are among the leading causes of morbidity and mortality in the Western Pacific Region. However, data on the major causes of infectious diarrhoea are limited in many countries within the Region, including Papua New Guinea. In 2013-2014, we conducted surveillance for acute diarrhoeal illness in four provinces in Papua New Guinea. One rural health clinic from each province participated in the surveillance activity. Samples were sent to central laboratories and batch analysed for bacterial and viral gastrointestinal pathogens that are commonly associated with diarrhoea. Across the four sites, the most commonly detected pathogens were Shigella spp., Campylobacter spp. and rotavirus. In this paper, we report the results of the surveillance activity and the challenges that we faced. The lessons learnt may be applicable to other parts of the Region with a similar socioeconomic status.
- Authors: Abdad, Mohammad , Soli, Kevin , Pham, Bang , Bande, Grace , Maure, Tobias , Jonduo, Marinjo , Kisa, Debbie , Rai, Glennis , Phuanukoonnon, Suparat , Siba, Peter , Horwood, Paul , Greenhill, Andrew
- Date: 2020
- Type: Text , Journal article
- Relation: Western Pacific Surveillance and Response Vol. 11, no. 1 (Jan-Mar 2020), p. 6
- Full Text:
- Reviewed:
- Description: Diarrhoeal diseases are among the leading causes of morbidity and mortality in the Western Pacific Region. However, data on the major causes of infectious diarrhoea are limited in many countries within the Region, including Papua New Guinea. In 2013-2014, we conducted surveillance for acute diarrhoeal illness in four provinces in Papua New Guinea. One rural health clinic from each province participated in the surveillance activity. Samples were sent to central laboratories and batch analysed for bacterial and viral gastrointestinal pathogens that are commonly associated with diarrhoea. Across the four sites, the most commonly detected pathogens were Shigella spp., Campylobacter spp. and rotavirus. In this paper, we report the results of the surveillance activity and the challenges that we faced. The lessons learnt may be applicable to other parts of the Region with a similar socioeconomic status.