Showing items 1 - 20 of 178

A case for causal loop diagrams to model electronic health records ecosystems

- Hashmi, Mustafa, McInnes, Angelique, Sahama, Tony, Stranieri, Andrew

  • Authors: Hashmi, Mustafa , McInnes, Angelique , Sahama, Tony , Stranieri, Andrew
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 2023 Australasian Computer Science Week, ACSW 2023, Melbourne, Australia, 31 January-3 February 2023, ACSW '23: Proceedings of the 2023 Australasian Computer Science Week p. 238-239
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  • Description: Causal loop diagrams (CLD) that emerged from systems thinking disciplines have been used to simulate complex inter-dependencies between causal factors in diverse phenomena. This paper highlights a process for generating a casual loop diagrams to represent the quality of electronic health record (EHR) ecosystem in a medical context. The quality inherent in the use of electronic health records for specific clinical purposes is taken to depend on factors including data integrity, reliability, relevance, timeliness and completeness. By improving the electronic health record ecosystem quality, health care providers can enhance their data sharing practices, and personalised patient care, while reducing the probabilities of medical errors. Ultimately the CLD can be used to run multiple simulations for several clinical case scenarios to understand the impact of various case phenomena on the quality of the electronic health record ecosystem. © 2023 ACM.

A case for the re-use of community reasoning

- Stranieri, Andrew, Yearwood, John

  • Authors: Stranieri, Andrew , Yearwood, John
  • Date: 2011
  • Type: Text , Book chapter
  • Relation: Technologies for supporting reasoning communities and collaborative decision making: Cooperative approaches p.
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  • Description: In software engineering, the re-use concept is a design principle that improves efficency, quality and maintainability by ensuring that software artifacts are developed once and re-used may times. In an analogous way, a group's reasoning can be imagined to be re-used by that or another group to enhance efficiency, transparency and consistency in decison-making. However, the re-use of reasoning is difficult to achieve because group reasoning cannot easily be captured and the way in which a group reasoning artifact is subsequently used is not obvious. This chapter explores the case for the re-use of community reasoning and concludes that individuals can benefit from a representation of a previous groups's coalesced reasoning to be modeled and the scheme to represent the reasoning have been selected to suit the task. The authors contend that specifying the future community like to re-use the reasoning, called the intended audience, informs a decision regarding whether an exercise aimed at coalescing a group's reasoning is best performed verbally, in writing or with the use of more structured schemes such as Argument visualization.

A classification algorithm that derives weighted sum scores for insight into disease

- Quinn, Anthony, Stranieri, Andrew, Yearwood, John, Hafen, Gaudenz

  • Authors: Quinn, Anthony , Stranieri, Andrew , Yearwood, John , Hafen, Gaudenz
  • Date: 2009
  • Type: Text , Conference paper
  • Relation: Paper presented at Third Australasian Workshop on Health Informatics and Knowledge Management (HIKM 2009), Wellington, New Zealand : Vol. 97, p. 13-17
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  • Description: Data mining is often performed with datasets associated with diseases in order to increase insights that can ultimately lead to improved prevention or treatment. Classification algorithms can achieve high levels of predictive accuracy but have limited application for facilitating the insight that leads to deeper understanding of aspects of the disease. This is because the representation of knowledge that arises from classification algorithms is too opaque, too complex or too sparse to facilitate insight. Clustering, association and visualisation approaches enable greater scope for clinicians to be engaged in a way that leads to insight, however predictive accuracy is compromised or non-existent. This research investigates the practical applications of Automated Weighted Sum, (AWSum), a classification algorithm that provides accuracy comparable to other techniques whilst providing some insight into the data. This is achieved by calculating a weight for each feature value that represents its influence on the class value. Clinicians are very familiar with weighted sum scoring scales so the internal representation is intuitive and easily understood. This paper presents results from the use of the AWSum approach with data from patients suffering from Cystic Fibrosis.

A comparison of machine learning algorithms for multilabel classification of CAN

- Kelarev, Andrei, Stranieri, Andrew, Yearwood, John, Jelinek, Herbert

  • Authors: Kelarev, Andrei , Stranieri, Andrew , Yearwood, John , Jelinek, Herbert
  • Date: 2012
  • Type: Text , Journal article
  • Relation: Advances in Computer Science and Engineering Vol. 9, no. 1 (2012), p. 1-4
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  • Description: This article is devoted to the investigation and comparison of several important machine learning algorithms in their ability to obtain multilabel classifications of the stages of cardiac autonomic neuropathy (CAN). Data was collected by the Diabetes Complications Screening Research Initiative at Charles Sturt University. Our experiments have achieved better results than those published previously in the literature for similar CAN identification tasks.

A count data model for heart rate variability forecasting and premature ventricular contraction detection

- Allami, Ragheed, Stranieri, Andrew, Balasubramanian, Venki, Jelinek, Herbert

  • Authors: Allami, Ragheed , Stranieri, Andrew , Balasubramanian, Venki , Jelinek, Herbert
  • Date: 2017
  • Type: Text , Journal article
  • Relation: Signal Image and Video Processing Vol. 11, no. 8 (2017), p. 1427-1435
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  • Description: Heart rate variability (HRV) measures including the standard deviation of inter-beat variations (SDNN) require at least 5 min of ECG recordings to accurately measure HRV. In this paper, we predict, using counts data derived from a 3-min ECG recording, the 5-min SDNN and also detect premature ventricular contraction (PVC) beats with a high degree of accuracy. The approach uses counts data combined with a Poisson-generated function that requires minimal computational resources and is well suited to remote patient monitoring with wearable sensors that have limited power, storage and processing capacity. The ease of use and accuracy of the algorithm provide opportunity for accurate assessment of HRV and reduce the time taken to review patients in real time. The PVC beat detection is implemented using the same count data model together with knowledge-based rules derived from clinical knowledge.

A Decentralized Patient Agent Controlled Blockchain for Remote Patient Monitoring

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

  • Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
  • Date: 2019
  • Type: Text , Conference proceedings
  • Relation: 15th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2019 Vol. 2019-October, p. 207-214
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  • Description: Blockchain emerging for healthcare provides a secure, decentralized and patient driven record management system. However, the storage of data generated from IoT devices in remote patient management applications requires a fast consensus mechanism. In this paper, we propose a lightweight consensus mechanism and a decentralized patient software agent to control a remote patient monitoring (RPM) system. The decentralized RPM architecture includes devices at three levels; 1) Body Area Sensor Network-medical sensors typically on or in patient's body transmitting data to a Smartphone, 2) Fog/Edge, and 3) Cloud. We propose that a Patient Agent(PA) software replicated on the Smartphone, Fog and Cloud servers processes medical data to ensure reliable, secure and private communication. Performance analysis has been conducted to demonstrate the feasibility of the proposed Blockchain leveraged, distributed Patient Agent controlled remote patient monitoring system. © 2019 IEEE.
  • Description: E1

A genetic algorithm-neural network wrapper approach for bundle branch block detection

- Allami, Ragheed, Stranieri, Andrew, Balasubramanian, Venki

  • Authors: Allami, Ragheed , Stranieri, Andrew , Balasubramanian, Venki
  • Date: 2016
  • Type: Text , Conference paper
  • Relation: Computing in Cardiology Conference (CinC), 2016; Vancouver, BC ;11-14 Sept. 2016, published in Computing in Cardiology p. 461-464
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  • Description: An Electrocardiogram (ECG) records the electrical impulses of the heart and indicates rhythm anomalies for diagnostic purposes [1], [2]. A typical ECG tracing of the cardiac cycle consists of a P wave, QRS complex, and T wave [3]. Good performance of an ECG analyzing system depends heavily upon the accurate and reliable detection of the QRS complex, as well as the T and P waves [4]. A Bundle Branch Block (BBB) is a delay or obstruction along electrical impulse pathways of the heart manifesting in a prolonged QRS interval usually greater than 120ms. The automated detection and classification of a BBB is important for prompt, accurate diagnosis and treatment to reduce morbidity and mortality.

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

A heuristic gene regulatory networks model for cardiac function and pathology

- Zarnegar, Armita, Vamplew, Peter, Stranieri, Andrew, Jelinek, Herbert

  • Authors: Zarnegar, Armita , Vamplew, Peter , Stranieri, Andrew , Jelinek, Herbert
  • Date: 2016
  • Type: Text , Conference proceedings
  • Relation: 2016 Computing in Cardiology Conference (CinC); Vancouver; 11-14th Sept, 2016
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  • Description: Genome-wide association studies (GWAS) and next-generation sequencing (NGS) has led to an increase in information about the human genome and cardiovascular disease. Understanding the role of genes in cardiac function and pathology requires modeling gene interactions and identification of regulatory genes as part of a gene regulatory network (GRN). Feature selection and data reduction not sufficient and require domain knowledge to deal with large data. We propose three novel innovations in constructing a GRN based on heuristics. A 2D Visualised Co-regulation function. Post-processing to identify gene-gene interactions. Finally a threshold algorithm is applied to identify the hub genes that provide the backbone of the GRN. The 2D Visualized Co-regulation function performed significantly better compared to the Pearson's correlation for measuring pairwise associations (t=3.46, df=5, p=0.018). The F-measure, improved from 0.11 to 0.12. The hub network provided a 60% improvement to that reported in the literature. The performance of the hub network was then also compared against ARACNe and performed significantly better (p=0.024). We conclude that a heuristics approach in developing GRNs has potential to improve our understanding of gene regulation and interaction in diverse biological function and disease.

A lightweight blockchain based framework for underwater ioT

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

  • Authors: Uddin, Md Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
  • Date: 2019
  • Type: Text , Journal article
  • Relation: Electronics (Switzerland) Vol. 8, no. 12 (2019), p.
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  • Description: The Internet of Things (IoT) has facilitated services without human intervention for a wide range of applications, including underwater monitoring, where sensors are located at various depths, and data must be transmitted to surface base stations for storage and processing. Ensuring that data transmitted across hierarchical sensor networks are kept secure and private without high computational cost remains a challenge. In this paper, we propose a multilevel sensor monitoring architecture. Our proposal includes a layer-based architecture consisting of Fog and Cloud elements to process and store and process the Internet of Underwater Things (IoUT) data securely with customized Blockchain technology. The secure routing of IoUT data through the hierarchical topology ensures the legitimacy of data sources. A security and performance analysis was performed to show that the architecture can collect data from IoUT devices in the monitoring region efficiently and securely. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

A model for the introduction of Ayurvedic and Allopathic Electronic Health Records in Sri Lanka

- Stranieri, Andrew, Sahama, Tony, Butler-Henderson, Kerryn, Perera, Kamal

  • Authors: Stranieri, Andrew , Sahama, Tony , Butler-Henderson, Kerryn , Perera, Kamal
  • Date: 2016
  • Type: Text , Conference proceedings
  • Relation: 2016 IEEE International Symposium on Technology and Society; Trivandrum, Kerala, India; 20th-22nd October 2016 p. 56-61
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  • Description: Fully integrated electronic health records (EHR) provide healthcare providers and patients access to records across a health care system and promise efficient and effective provision of health care. However, fully integrated records have proven to be very expensive and difficult to establish. Currently. EHR's have been developed largely to accommodate Western medicine events. These barriers impact on the introduction of EHR's in Sri Lanka, where health budgets are already stretched and Ayurvedic medicine is routinely practiced alongside Allopathic medicine. This article identifies requirements for EHR in the Sri Lankan context and advances a model for the introduction of EHR's that suits that context. The model is justified by drawing on insights and experiences with EHR in Western nations.

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 patient agent to manage blockchains for remote patient monitoring

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

  • Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
  • Date: 2018
  • Type: Text , Conference proceedings
  • Relation: 7th International Conference on Global Telehealth, GT 2018; Colombo, Sri Lanka; 10th-11th October 2018; published in Studies in Health Technology and Informatics Vol. 254, p. 105-115
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  • Description: Continuous monitoring of patient's physiological signs has the potential to augment traditional medical practice, particularly in developing countries that have a shortage of healthcare professionals. However, continuously streamed data presents additional security, storage and retrieval challenges and further inhibits initiatives to integrate data to form electronic health record systems. Blockchain technologies enable data to be stored securely and inexpensively without recourse to a trusted authority. Blockchain technologies also promise to provide architectures for electronic health records that do not require huge government expenditure that challenge developing nations. However, Blockchain deployment, particularly with streamed data challenges existing Blockchain algorithms that take too long to place data in a block, and have no mechanism to determine whether every data point in every stream should be stored in such a secure way. This article presents an architecture that involves a Patient Agent, coordinating the insertion of continuous data streams into Blockchains to form an electronic health record.
  • Description: Studies in Health Technology and Informatics

A process-oriented framework for regulating artificial intelligence systems

- Stranieri, Andrew, Sun, Zhaohao

  • Authors: Stranieri, Andrew , Sun, Zhaohao
  • Date: 2022
  • Type: Text , Book chapter
  • Relation: Handbook of Research on Foundations and Applications of Intelligent Business Analytics p. 96-112
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  • Description: Intelligent business analytics is an emerging technology that has become a mainstream market adopted broadly across industries, organizations, and geographic regions. Intelligent business analytics is a current focus for research and development across academia and industries and must be examined and considered thoroughly so businesses can apply the technology appropriately. The Handbook of Research on Foundations and Applications of Intelligent Business Analytics examines the technologies and applications of intelligent business analytics and discusses the foundations of intelligent analytics such as intelligent mining, intelligent statistical modeling, and machine learning. Covering topics such as augmented analytics and artificial intelligence systems, this major reference work is ideal for scholars, engineers, professors, practitioners, researchers, industry professionals, academicians, and students. Intelligent business analytics is an emerging technology that has become a mainstream market adopted broadly across industries, organizations, and geographic regions. Intelligent business analytics is a current focus for research and development across academia and industries and must be examined and considered thoroughly so businesses can apply the technology appropriately. The Handbook of Research on Foundations and Applications of Intelligent Business Analytics examines the technologies and applications of intelligent business analytics and discusses the foundations of intelligent analytics such as intelligent mining, intelligent statistical modeling, and machine learning. Covering topics such as augmented analytics and artificial intelligence systems, this major reference work is ideal for scholars, engineers, professors, practitioners, researchers, industry professionals, academicians, and students.

A reasoning community perspective on deliberate democracy

- Yearwood, John, Stranieri, Andrew

  • Authors: Yearwood, John , Stranieri, Andrew
  • Date: 2011
  • Type: Text , Book chapter
  • Relation: Technologies for supporting reasoning communities and collaborative decision making: Cooperative approaches p.237-246
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  • Description: This chapter describes some of the current approaches to delibertative democracy and the considers them from the perspective of a reasoning community framework. This approach highlights important tasks, process and structures that can be used to enhance the process of groups engaging in deliberative democracy approaches. In particular it focuses attention on the potential for technologies to support groups in achieving broad agreed structured reasoning bases that capture the scope of an issue from multiple perspectives.

A reasoning framework for decision making in water allocation: a tree for water

- Graymore, Michelle, Mays, Heather, Stranieri, Andrew, Lehmann, La Vergne, McRae-Williams, Pamela, Thoms, Gavin, Yearwood, John

A rule based inference model to establish strategy-process relationship

- Dinh, Loan, Karmakar, Gour, Kamruzzaman, Joarder, Stranieri, Andrew

  • Authors: Dinh, Loan , Karmakar, Gour , Kamruzzaman, Joarder , Stranieri, Andrew
  • Date: 2017
  • Type: Text , Conference proceedings
  • Relation: 30th International Business Information Management Association Conference - Vision 2020: Sustainable Economic development, Innovation Management, and Global Growth, IBIMA 2017; Madrid, Spain; 8th-9th November 2017 Vol. 2017-January, p. 4544-4556
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  • Description: An effective relationship between business processes and their relevant strategies helps enterprises achieve their goals. As a business organisation changes quickly, business processes implement their relevant business operations for efficiency. It is important to know which business process achieves which business strategies dynamically. To the best of our knowledge, there exists a framework which aims to automatically determine the strategy-process relationship (Morrison et al. 2011). However, this framework can only work when the effect of the business process is known, but it is difficult to determine such effect accurately. Moreover, by optimising business processes to satisfy business strategies, higher efficiency may be achieved but there is a high chance of losing discriminative information. It therefore creates certain level of uncertainty in achieving accurate strategy-process relationship. To reduce this uncertainty and determine the relationship accurately between business processes and their relevant strategies as defined by business domain experts, in this paper, we introduce a rule-based inference model. This model not only helps business organisations realize which business processes need to be involved for the organisation to achieve their goals when strategies are made, but also reduces the possibility of losing important details from business process optimisation. We have developed a business case to validate our proposed model and the results show that our model can infer the relation accurately for each rule defined for the related business case.

A scalable cloud Platform for Active healthcare monitoring applications

- Balasubramanian, Venki, Stranieri, Andrew

  • Authors: Balasubramanian, Venki , Stranieri, Andrew
  • Date: 2015
  • Type: Text , Conference paper
  • Relation: 2014 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2014; Melbourne, Australia; 10th-12th December 2014 p. 93-98
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  • Description: Continuous, remote monitoring of patients using wearable sensors can facilitate early detection of many conditions and can help to manage the growing healthcare crisis worldwide. A remote patient monitoring application consists of many emerging services such as wireless wearable sensor configuration, patient registration and authentication, collaborative consultation of doctors, storage and maintenance of electronic health record. The provision of these services requires the development and maintenance of a remote healthcare monitoring application (HMA) that includes a body area wireless sensor network (BASWN) and Health Applications (HA) to detect specific health issues. In addition, the deployment of HMAs for different hospitals is not easily scalable owing to the heterogeneous nature of hardware and software involved. Cloud computing overcomes this aspect by allowing simple and easy maintenance of ICT infrastructure. In this work, we report a real-time-like cloud based architecture known as Assistive Patient monitoring cloud Platform for Active healthcare applications (AppA) using a delegate pattern. The built AppA is highly scalable and capable of spawning new instances based on monitoring requirements from the health care providers, and are aligned with scalable economic models. © 2014 IEEE.

A scenario-based learning environment for critical care nursing

- Yearwood, John, Stranieri, Andrew

  • Authors: Yearwood, John , Stranieri, Andrew
  • Date: 2005
  • Type: Text , Conference paper
  • Relation: Paper presented at HIC 2005: Thirteenth National Health Informatic Conference, 31 July-2 August 2005, Melbourne, Australia, Melbourne : 31st July, 2005
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  • Description: Narrative or story telling has long been used to structure and organise human experience. In contrast to logical models of reasoning, narrative models enable complex situations to be understood and recalled by humans readily. In this work a narrative model is integrated into a logical reasoning model for the purpose of advancing a learning environment that promises to be engaging and effective. The narrative model includes a representation of the point of a story and a simple story grammar. The logical reasoning model is based on an argumentation model. The learning environment is designed to enable the automated generation of plausible scenarios that involves an intensive care unit nurse responding to a low oxygen level alarm.
  • Description: E1
  • Description: 2003001434

A secured real-time IoMT application for monitoring isolated COVID-19 patients using edge computing

- Balasubramanian, Venki, Sulthana, Rehena, Stranieri, Andrew, Manoharan, G., Arora, Teena, Srinivasan, Ram, Mahalakshmi, K., Menon, Varun