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Narrative-based interactive learning environments from modelling reasoning

- Yearwood, John, Stranieri, Andrew

  • 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

Data analytics identify glycated haemoglobin co-markers for type 2 diabetes mellitus diagnosis

- Jelinek, Herbert, Stranieri, Andrew, Yatsko, Andrew, Venkatraman, Sitalakshmi

  • Authors: Jelinek, Herbert , Stranieri, Andrew , Yatsko, Andrew , Venkatraman, Sitalakshmi
  • Date: 2016
  • Type: Text , Journal article
  • Relation: Computers in Biology and Medicine Vol. 75, no. (2016), p. 90-97
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  • Description: Glycated haemoglobin (HbA1c) is being more commonly used as an alternative test for the identification of type 2 diabetes mellitus (T2DM) or to add to fasting blood glucose level and oral glucose tolerance test results, because it is easily obtained using point-of-care technology and represents long-term blood sugar levels. HbA1c cut-off values of 6.5% or above have been recommended for clinical use based on the presence of diabetic comorbidities from population studies. However, outcomes of large trials with a HbA1c of 6.5% as a cut-off have been inconsistent for a diagnosis of T2DM. This suggests that a HbA1c cut-off of 6.5% as a single marker may not be sensitive enough or be too simple and miss individuals at risk or with already overt, undiagnosed diabetes. In this study, data mining algorithms have been applied on a large clinical dataset to identify an optimal cut-off value for HbA1c and to identify whether additional biomarkers can be used together with HbA1c to enhance diagnostic accuracy of T2DM. T2DM classification accuracy increased if 8-hydroxy-2-deoxyguanosine (8-OhdG), an oxidative stress marker, was included in the algorithm from 78.71% for HbA1c at 6.5% to 86.64%. A similar result was obtained when interleukin-6 (IL-6) was included (accuracy=85.63%) but with a lower optimal HbA1c range between 5.73 and 6.22%. The application of data analytics to medical records from the Diabetes Screening programme demonstrates that data analytics, combined with large clinical datasets can be used to identify clinically appropriate cut-off values and identify novel biomarkers that when included improve the accuracy of T2DM diagnosis even when HbA1c levels are below or equal to the current cut-off of 6.5%. © 2016 Elsevier Ltd.

Knowledge based regulation of statistical databases

- Mishra, Vivek, Stranieri, Andrew, Miller, Mirka, Ryan, Joe

  • 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
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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

An argumentation shell for supporting the development and drafting of legal arguments

- Yearwood, John, Stranieri, Andrew

  • Authors: Yearwood, John , Stranieri, Andrew
  • Date: 2002
  • Type: Text , Journal article
  • Relation: Information and Communication Technology Law Vol. 11, no. 1 (2002), p. 75-86
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  • Description: This article describes an argumentation shell to support the formulation, representation and drafting of legal arguments. The shell can be used to capture generic arguments in many legal domains as well as to assist decision-makers in constructing their own actual arguments . The shell demonstrates that knowledge represented using the generic/actual argument model (GAAM) (a variant of Toulmin's argument structure) can be used to: (a) support the development of complex arguments, (b) add context and increase specificity for the retrieval of relevant documents, (c) incorporate background knowledge, (d) assist in the drafting of documents that represent arguments made, and (e) provide a structure for complex inferences requiring a range of mechanisms. The shell can be used to support decision making in a range of legal domains, including discretionary domains.
  • Description: C1
  • Description: 2003000141

Continuous patient monitoring with a patient centric agent : A block architecture

- Uddin, Ashraf, Stranieri, Andrew, Gondal, Iqbal, Balasubramanian, Venki

  • Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
  • Date: 2018
  • Type: Text , Journal article
  • Relation: IEEE Access Vol. 6, no. (2018), p. 32700-32726
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  • Description: The Internet of Things (IoT) has facilitated services without human intervention for a wide range of applications, including continuous remote patient monitoring (RPM). However, the complexity of RPM architectures, the size of data sets generated and limited power capacity of devices make RPM challenging. In this paper, we propose a tier-based End to End architecture for continuous patient monitoring that has a patient centric agent (PCA) as its center piece. The PCA manages a blockchain component to preserve privacy when data streaming from body area sensors needs to be stored securely. The PCA based architecture includes a lightweight communication protocol to enforce security of data through different segments of a continuous, real time patient monitoring architecture. The architecture includes the insertion of data into a personal blockchain to facilitate data sharing amongst healthcare professionals and integration into electronic health records while ensuring privacy is maintained. The blockchain is customized for RPM with modifications that include having the PCA select a Miner to reduce computational effort, enabling the PCA to manage multiple blockchains for the same patient, and the modification of each block with a prefix tree to minimize energy consumption and incorporate secure transaction payments. Simulation results demonstrate that security and privacy can be enhanced in RPM with the PCA based End to End architecture.

An approach for Ewing test selection to support the clinical assessment of cardiac autonomic neuropathy

- Stranieri, Andrew, Abawajy, Jemal, Kelarev, Andrei, Huda, Shamsul, Chowdhury, Morshed, Jelinek, Herbert

  • Authors: Stranieri, Andrew , Abawajy, Jemal , Kelarev, Andrei , Huda, Shamsul , Chowdhury, Morshed , Jelinek, Herbert
  • Date: 2013
  • Type: Text , Journal article
  • Relation: Artificial Intelligence in Medicine Vol. 58, no. 3 (2013), p. 185-193
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  • Description: Objective: This article addresses the problem of determining optimal sequences of tests for the clinical assessment of cardiac autonomic neuropathy (CAN) We investigate the accuracy of using only one of the recommended Ewing tests to classify CAN and the additional accuracy obtained by adding the remaining tests of the Ewing battery This is important as not all five Ewing tests can always be applied in each situation in practice Methods and material: We used new and unique database of the diabetes screening research initiative project, which is more than ten times larger than the data set used by Ewing in his original investigation of CAN We utilized decision trees and the optimal decision path finder (ODPF) procedure for identifying optimal sequences of tests Results: We present experimental results on the accuracy of using each one of the recommended Ewing tests to classify CAN and the additional accuracy that can be achieved by adding the remaining tests of the Ewing battery We found the best sequences of tests for cost-function equal to the number of tests The accuracies achieved by the initial segments of the optimal sequences for 2, 3 and 4 categories of CAN are 80.80, 91.33, 93.97 and 94.14, and respectively, 79.86, 89.29, 91.16 and 91.76, and 78.90, 86.21, 88.15 and 88.93 They show significant improvement compared to the sequence considered previously in the literature and the mathematical expectations of the accuracies of a random sequence of tests The complete outcomes obtained for all subsets of the Ewing features are required for determining optimal sequences of tests for any cost-function with the use of the ODPF procedure We have also found two most significant additional features that can increase the accuracy when some of the Ewing attributes cannot be obtained Conclusions: The outcomes obtained can be used to determine the optimal sequences of tests for each individual cost-function by following the ODPF procedure The results show that the best single Ewing test for diagnosing CAN is the deep breathing heart rate variation test Optimal sequences found for the cost-function equal to the number of tests guarantee that the best accuracy is achieved after any number of tests and provide an improvement in comparison with the previous ordering of tests or a random sequence © 2013 Elsevier B.V.
  • Description: 2003011130

Predicting cardiac autonomic neuropathy category for diabetic data with missing values

- Abawajy, Jemal, Kelarev, Andrei, Chowdhury, Morshed, Stranieri, Andrew, Jelinek, Herbert

  • Authors: Abawajy, Jemal , Kelarev, Andrei , Chowdhury, Morshed , Stranieri, Andrew , Jelinek, Herbert
  • Date: 2013
  • Type: Text , Journal article
  • Relation: Computers in Biology and Medicine Vol. 43, no. 10 (2013), p. 1328-1333
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  • Description: Cardiovascular autonomic neuropathy (CAN) is a serious and well known complication of diabetes. Previous articles circumvented the problem of missing values in CAN data by deleting all records and fields with missing values and applying classifiers trained on different sets of features that were complete. Most of them also added alternative features to compensate for the deleted ones. Here we introduce and investigate a new method for classifying CAN data with missing values. In contrast to all previous papers, our new method does not delete attributes with missing values, does not use classifiers, and does not add features. Instead it is based on regression and meta-regression combined with the Ewing formula for identifying the classes of CAN. This is the first article using the Ewing formula and regression to classify CAN. We carried out extensive experiments to determine the best combination of regression and meta-regression techniques for classifying CAN data with missing values. The best outcomes have been obtained by the additive regression meta-learner based on M5Rules and combined with the Ewing formula. It has achieved the best accuracy of 99.78% for two classes of CAN, and 98.98% for three classes of CAN. These outcomes are substantially better than previous results obtained in the literature by deleting all missing attributes and applying traditional classifiers to different sets of features without regression. Another advantage of our method is that it does not require practitioners to perform more tests collecting additional alternative features. © 2013 Elsevier Ltd.
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