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Using meta-regression data mining to improve predictions of performance based on heart rate dynamics for Australian football

- Jelinek, Herbert, Kelarev, Andrei, Robinson, Dean, Stranieri, Andrew, Cornforth, David

  • Authors: Jelinek, Herbert , Kelarev, Andrei , Robinson, Dean , Stranieri, Andrew , Cornforth, David
  • Date: 2014
  • Type: Text , Journal article
  • Relation: Applied Soft Computing Vol. 14, no. PART A (2014), p. 81-87
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  • Description: This work investigates the effectiveness of using computer-based machine learning regression algorithms and meta-regression methods to predict performance data for Australian football players based on parameters collected during daily physiological tests. Three experiments are described. The first uses all available data with a variety of regression techniques. The second uses a subset of features selected from the available data using the Random Forest method. The third used meta-regression with the selected feature subset. Our experiments demonstrate that feature selection and meta-regression methods improve the accuracy of predictions for match performance of Australian football players based on daily data of medical tests, compared to regression methods alone. Meta-regression methods and feature selection were able to obtain performance prediction outcomes with significant correlation coefficients. The best results were obtained by the additive regression based on isotonic regression for a set of most influential features selected by Random Forest. This model was able to predict athlete performance data with a correlation coefficient of 0.86 (p < 0.05). © 2013 Published by Elsevier B.V. All rights reserved.
  • Description: C1

A participatory information management framework for patient centred care of autism spectrum disorder

- De Silva, Daswin, Burstein, Frada, Stranieri, Andrew, Williams, Katrina, Rinehart, Nicole

A global optimisation approach to classification in medical diagnosis and prognosis

- Bagirov, Adil, Rubinov, Alex, Yearwood, John, Stranieri, Andrew

  • Authors: Bagirov, Adil , Rubinov, Alex , Yearwood, John , Stranieri, Andrew
  • Date: 2001
  • Type: Text , Conference paper
  • Relation: Paper presented at 34th Hawaii International Conference on System Sciences, HICSS-34, Maui, Hawaii, USA : 3rd-6th January 2001
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  • Description: In this paper global optimisation-based techniques are studied in order to increase the accuracy of medical diagnosis and prognosis with FNA image data from the Wisconsin Diagnostic and Prognostic Breast Cancer databases. First we discuss the problem of determining the most informative features for the classification of cancerous cases in the databases under consideration. Then we apply a technique based on convex and global optimisation to breast cancer diagnosis. It allows the classification of benign cases and malignant ones and the subsequent diagnosis of patients with very high accuracy. The third application of this technique is a method that calculates centres of clusters to predict when breast cancer is likely to recur in patients for which cancer has been removed. The technique achieves higher accuracy with these databases than reported elsewhere in the literature.
  • Description: 2003003950

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
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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.

An intelligent learning environment for traditional Chinese medicine practitioners and students

- Jia, Long, Stranieri, Andrew, Shen, J

  • Authors: Jia, Long , Stranieri, Andrew , Shen, J
  • Date: 2008
  • Type: Text , Conference paper
  • Relation: Paper presented at HIC 2008 Australia's Health Informatics Conference; The Person in the Centre, Brunswick East, Victoria : 31st August - 2nd September 2008
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  • Description: Objectives: This study aims to support the training of Traditional Chinese Medicine practitioners by embedding an expert diagnostic model for arthritis into an Intelligent Interactive Learning Environment (IILE). Background: The increasing prevalence of Traditional Chinese Medicine (TCM) outside China is characterised by the emergence of university level practitioner training and stringent regulatory requirements. TCM differential diagnosis is a difficult task that was traditionally taught by exposure to large numbers of patients in a master-apprentice context. In university degree programs, students and novice diagnosticians cannot have the exposure to cases possible in the traditional context. An online system that engages students in the interactive construction of a virtual case and provides immediate feedback on the appropriateness of student actions and the accuracy of diagnostic conclusions can enhance student learning. The system, an Intelligent Interactive Learning Environment (IILE) is based on an approach that has been shown to improve learning outcomes in intensive care nurse training. Methods: An expert model of diagnostic reasoning elicited from TCM expert practitioners lies at the core of the IILE. The knowledge acquisition is performed using an argumentation tree representation that has been shown to be effective in structuring complex knowledge and facilitating engineer - expert interactions. Problems associated with keeping knowledge bases up to date are mitigated with the use of a knowledge model known as ripple down rules permits dynamic updating of knowledge so that knowledge bases evolve over time. A simple narrative model builds up the virtual case study as user interaction proceeds. Results and discussion: This article reports preliminary results in the study that includes an overview of TCM differential diagnosis, the argument tree, the ripple down rule representation and the narrative based IILE. Segments of the knowledge model based solely on TCM literature are illustrated.
  • Description: 2003006755

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
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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.

Re-consider : The integration of online dispute resolution and decision support systems

- Muecke, Nial, Stranieri, Andrew, Miller, Charlynn

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
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  • 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.
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