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
- Subjects: 0807 Library and Information Studies , 1117 Public Health and Health Services , Blockchain , Electronic Health Record , Mulit-Level Storage , Multiple Blockchain , Patient Agent , Remote Patient Monitoring
- Type: Text , Conference proceedings , Conference Paper
- Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/166746 , vital:13463 , https://doi.org/10.3233/978-1-61499-914-0-105 , ISBN:09269630 (ISSN); 9781614999133 (ISBN)
- 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. , Studies in Health Technology and Informatics
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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
- Subjects: 08 Information and Computing Sciences , 09 Engineering , 10 Technology , Blockchain , Body area sensor network , Dynamically generated session key , Healthcare , Internet of Things , Patient centric agent , Patient record encryption key , Proof of work , Remote patient monitoring , Streamed data
- Type: Text , Journal article
- Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/165503 , vital:13337 , https://doi.org/10.1109/ACCESS.2018.2846779 , ISBN:2169-3536
- 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.
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- Reviewed:
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2018
- Subjects: 08 Information and Computing Sciences , 09 Engineering , 10 Technology , Blockchain , Body area sensor network , Dynamically generated session key , Healthcare , Internet of Things , Patient centric agent , Patient record encryption key , Proof of work , Remote patient monitoring , Streamed data
- Type: Text , Journal article
- Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/165503 , vital:13337 , https://doi.org/10.1109/ACCESS.2018.2846779 , ISBN:2169-3536
- 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.
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- Klein, Britt, Clinnick, Lisa, Chesler, Jessica, Stranieri, Andrew, Bignold, Adam, Dazeley, Richard, McLaren, Suzanne, Lauder, Sue, Balasubramanian, Venki
- Authors: Klein, Britt , Clinnick, Lisa , Chesler, Jessica , Stranieri, Andrew , Bignold, Adam , Dazeley, Richard , McLaren, Suzanne , Lauder, Sue , Balasubramanian, Venki
- Date: 2018
- Subjects: 0807 Library and Information Studies , 1117 Public Health and Health Services , Behavioural intervention , Dementia , EHealth , Psychotropic medication , Residential aged care
- Type: Text , Conference paper
- Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/164680 , vital:13101 , https://doi.org/10.3233/978-1-61499-845-7-24 , 9781614998440 (ISBN)
- Description: Background: This regional pilot site ‘end-user attitudes’ study explored nurses’ experiences and impressions of using the Nurses’ Behavioural Assistant (NBA) (a knowledge-based, interactive ehealth system) to assist them to better respond to behavioural and psychological symptoms of dementia (BPSD) and will be reported here. Methods: Focus groups were conducted, followed by a four-week pilot site ‘end-user attitudes’ trial of the NBA at a regional aged care residential facility (ACRF). Brief interviews were conducted with consenting nursing staff. Results: Focus group feedback (N = 10) required only minor cosmetic changes to the NBA prototype. Post pilot site end-user interview data (N = 10) indicated that the regional ACRF nurses were positive and enthusiastic about the NBA, however several issues were also identified. Conclusions: Overall the results supported the utility of the NBA to promote a person centred care approach to managing BPSD. Slight modifications may be required to maximise its uptake across all ACRF nursing staff.
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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
- Subjects: 0801 Artificial Intelligence and Image Processing , 0906 Electrical and Electronic Engineering , Heart rate variability forecasting , RR interval , SDNN , Premature ventricular contraction
- Type: Text , Journal article
- Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/165240 , vital:13196 , https://doi.org/10.1007/s11760-017-1103-x , ISBN:1863-1703
- 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.
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- Reviewed:
- Authors: Allami, Ragheed , Stranieri, Andrew , Balasubramanian, Venki , Jelinek, Herbert
- Date: 2017
- Subjects: 0801 Artificial Intelligence and Image Processing , 0906 Electrical and Electronic Engineering , Heart rate variability forecasting , RR interval , SDNN , Premature ventricular contraction
- Type: Text , Journal article
- Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/165240 , vital:13196 , https://doi.org/10.1007/s11760-017-1103-x , ISBN:1863-1703
- 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.
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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
- Subjects: Business process , Business strategy , Inference model , Rule base
- Type: Text , Conference proceedings , Conference Paper
- Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/165438 , vital:13284 , ISBN:9780986041990 (ISBN) , https://ibima.org/accepted-paper/a-rule-based-inference-model-to-establish-strategy-process-relationship/
- 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.
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- Reviewed:
Efficient route selection in ad hoc on-demand distance vector routing
- Uddin, Ashraf, Akther, Arnisha, Parvez, Shamima, Stranieri, Andrew
- Authors: Uddin, Ashraf , Akther, Arnisha , Parvez, Shamima , Stranieri, Andrew
- Date: 2017
- Subjects: Covariance , Quality of service , Link stability , Link breakage
- Type: Text , Conference paper
- Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/165138 , vital:13263 , https://doi.org/10.1109/ICCITECHN.2017.8281807 , ISBN:978-1-5386-1150-0
- Description: The protocol diversities of mobile ad hoc have already got hold of the field to a peak of a matured and developed area. Still, the restraint of delay and bandwidth of mobile ad hoc network have kept a little room to draft a routing protocol for the pursuit of providing quality of service. In the paper, we proposed protocol namely Efficient Route Selection in Ad Hoc On-Demand Distance Vector Routing. We select the best path among multiple paths from source to destination using covariance and delay. We consider the delay, link stability and energy to devise a covariance-based metric to discover the most balanced path. We also propose a metric for the selection of a node that acts as a local backup node for the most vulnerable nodes on the selected path. We accomplish our implementation in NS3and it shows the more reliable path and less end to end delay than other counterpart protocols.
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- Authors: Uddin, Ashraf , Akther, Arnisha , Parvez, Shamima , Stranieri, Andrew
- Date: 2017
- Subjects: Covariance , Quality of service , Link stability , Link breakage
- Type: Text , Conference paper
- Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/165138 , vital:13263 , https://doi.org/10.1109/ICCITECHN.2017.8281807 , ISBN:978-1-5386-1150-0
- Description: The protocol diversities of mobile ad hoc have already got hold of the field to a peak of a matured and developed area. Still, the restraint of delay and bandwidth of mobile ad hoc network have kept a little room to draft a routing protocol for the pursuit of providing quality of service. In the paper, we proposed protocol namely Efficient Route Selection in Ad Hoc On-Demand Distance Vector Routing. We select the best path among multiple paths from source to destination using covariance and delay. We consider the delay, link stability and energy to devise a covariance-based metric to discover the most balanced path. We also propose a metric for the selection of a node that acts as a local backup node for the most vulnerable nodes on the selected path. We accomplish our implementation in NS3and it shows the more reliable path and less end to end delay than other counterpart protocols.
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Significance level of a query for enterprise data
- Thi Ngoc Dinh, Loan, Karmakar, Gour, Kamruzzaman, Joarder, Stranieri, Andrew, Das, Rajkumar
- Authors: Thi Ngoc Dinh, Loan , Karmakar, Gour , Kamruzzaman, Joarder , Stranieri, Andrew , Das, Rajkumar
- Date: 2017
- Subjects: Business process , Data analysis , Data collection , Query processing , Semantic similarity
- Type: Text , Conference proceedings , Conference Paper
- Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/165451 , vital:13335 , ISBN:9780986041990 (ISBN)
- Description: To operate enterprise activities, a large number of queries need to be processed every day through an enterprise system. Consequently, such a system frequently faces hugely overloaded information and incurs high delay in producing query responses for big data. This is because, traditional queries are normally treated with equal importance. With the advent of big data and its use in enterprise systems and the growth of process complexity, the traditional approach of query processing is no more suitable as it does not consider semantic information and captures all data irrespective of their relevance to a business organization, which eventually increases the computational time in both big data collection and analysis. The significance level of a query can make a trade-off between query response delay and the extent of data collection and analysis. This motivates us to concentrate on determining the significance level of a query considering its importance to an enterprise system. To our knowledge, no such approach is available in the literature. To bridge this research gap, this paper, for the first time, proposes an approach to determine the significance level of a query to prioritize them with the relevance to a business organization. As business processes play key roles in any enterprise system and all business processes are not equally important, this is done by determining the semantic similarity between a query and the processes of a business organization and the importance of a business process to that organization. With a case study on an enterprise system of a retail company, the results produced by our proposed approach have shown that significance level is higher for more important queries compared to the less important ones.
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- Reviewed:
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
- Subjects: Electrocardiography , Feature extraction , Genetic algorithms , Optimization , Artificial neural networks , Sensitivity
- Type: Text , Conference paper
- Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/161606 , vital:12511 , ISBN: 2325-887X
- 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.
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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
- Subjects: Electronic Health Record , Ayurvedic medicine
- Type: Text , Conference proceedings
- Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/154478 , vital:11173 , http://doi.org/10.1109/ISTAS.2016.7764050 , ISBN:ISBN 978-1-5090-2498-8; ISSN 2158-3412
- 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.
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- Authors: Stranieri, Andrew , Sahama, Tony , Butler-Henderson, Kerryn , Perera, Kamal
- Date: 2016
- Subjects: Electronic Health Record , Ayurvedic medicine
- Type: Text , Conference proceedings
- Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/154478 , vital:11173 , http://doi.org/10.1109/ISTAS.2016.7764050 , ISBN:ISBN 978-1-5090-2498-8; ISSN 2158-3412
- 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.
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- Reviewed:
- Edirisinghe, Ruwini, Stranieri, Andrew, Wickramasinghe, Nilmini
- Authors: Edirisinghe, Ruwini , Stranieri, Andrew , Wickramasinghe, Nilmini
- Date: 2016
- Type: Text , Book chapter
- Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/158929 , vital:11915 , https:/dx.doi.org/10.4018/978-1-5225-0920-2.ch036 , ISBN: 9781522509202
- Description: Recently, we are witnessing an exponential growth in remote monitoring and mobile applications for healthcare. These solutions are all designed to ultimately enable the consumer to enjoy better healthcare delivery and /or wellness. In order to understand this growing area, we believe it is necessary to develop a framework to analyse and evaluate these solutions. The purpose of this chapter then is to offer a suitable taxonomy to systematically analyse and evaluate the existing solutions based on number of dimensions including technological, clinical, social, and economic.
- Full Text: false
- Reviewed:
Cost-analysis of teledentistry in residential aged care facilities
- Mariño, Rodrigo, Tonmukayakul, Utsana, Manton, David, Stranieri, Andrew, Clarke, Ken
- Authors: Mariño, Rodrigo , Tonmukayakul, Utsana , Manton, David , Stranieri, Andrew , Clarke, Ken
- Date: 2016
- Subjects: Economic evaluation , Cost-analysis , Teledentistry , Oral health , Older adults , 1105 Dentistry
- Type: Text , Journal article
- Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/101912 , vital:10726 , http://dx.doi.org/10.1177/1357633X15608991 , ISSN:1357-633X
- Description: Introduction: The purpose of this research was to conduct a cost-analysis, from a public healthcare perspective, comparing the cost and benefits of face-to-face patient examination assessments conducted by a dentist at a residential aged care facility (RACF) situated in rural areas of the Australian state of Victoria, with two teledentistry approaches utilizing virtual oral examination. Methods: The costs associated with implementing and operating the teledentistry approach were identified and measured using 2014 prices in Australian dollars. Costs were measured as direct intervention costs and programme costs. A population of 100 RACF residents was used as a basis to estimate the cost of oral examination and treatment plan development for the traditional face-to-face model vs. two teledentistry models: an asynchronous review and treatment plan preparation; and realtime communication with a remotely located oral health professional. Results: It was estimated that if 100 residents received an asynchronous oral health assessment and treatment plan, the net cost from a healthcare perspective would be AU$32.35 (AU$27.19–AU$38.49) per resident. The total cost of the conventional face-to-face examinations by a dentist would be AU$36.59 ($30.67–AU$42.98) per resident using realistic assumptions. Meanwhile, the total cost of real-time remote oral examination would be AU$41.28 (AU$34.30–AU$48.87) per resident. Discussion: Teledental asynchronous patient assessments were the lowest cost service model. Access to oral health professionals is generally low in RACFs; however, the real-time consultation could potentially achieve better outcomes due to twoway communication between the nurse and a remote oral health professional via health promotion/disease prevention delivered in conjunction with the oral examination
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- Reviewed:
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
- Subjects: 08 Information and Computing Sciences , 09 Engineering , 11 Medical and Health Sciences , Data analytics , Glycated haemoglobin , Type 2 diabetes mellitus
- Type: Text , Journal article
- Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/162752 , vital:12717 , https://doi.org/10.1016/j.compbiomed.2016.05.005 , ISBN:0010-4825
- 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.
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- Reviewed:
ECG reduction for wearable sensor
- Allami, Ragheed, Stranieri, Andrew, Balasubramanian, Venki, Jelinek, Herbert
- Authors: Allami, Ragheed , Stranieri, Andrew , Balasubramanian, Venki , Jelinek, Herbert
- Date: 2016
- Subjects: ECG intervals , Compression ratio , Data reduction , Real-time , Energy consumption
- Type: Text , Conference proceedings , Proceedings Paper
- Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/165215 , vital:13195 , https://doi.org/10.1109/sitis.2016.88 , ISBN:978-1-5090-5698-9
- Description: The transmission, storage and analysis of electrocardiogram (ECG) data in real-time is essential for remote patient monitoring with wearable ECG devices and mobile ECG contexts. However, this remains a challenge to achieve within the processing power and the storage capacity of mobile devices. ECG reduction algorithms have an important role to play in reducing the processing requirements for mobile devices, however many existing ECG reduction and compression algorithms are computationally expensive to execute in mobile devices and have not been designed for real-time computation and incremental data arrival. In this paper, we describe a computationally naive, yet effective, algorithm that achieves high ECG reduction rates while maintaining key diagnostic features including PR, QRS, ST, QT and RR intervals. While reduction does not enable ECG waves to be reproduced, the ability to transmit key indicators (diagnostic features) using minimal computational resources, is particularly useful in mobile health contexts involving power constrained sensors and devices. Results of the proposed reduction algorithm indicate that the proposed algorithm outperforms other ECG reduction algorithms at a reduction/compression ratio (CR) of 5:1. If power or processing capacity is low, the algorithm can readily switch to a compression ratio of up to 10: 1 while still maintaining an error rate below 10%.
- Full Text:
- Reviewed:
- Authors: Allami, Ragheed , Stranieri, Andrew , Balasubramanian, Venki , Jelinek, Herbert
- Date: 2016
- Subjects: ECG intervals , Compression ratio , Data reduction , Real-time , Energy consumption
- Type: Text , Conference proceedings , Proceedings Paper
- Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/165215 , vital:13195 , https://doi.org/10.1109/sitis.2016.88 , ISBN:978-1-5090-5698-9
- Description: The transmission, storage and analysis of electrocardiogram (ECG) data in real-time is essential for remote patient monitoring with wearable ECG devices and mobile ECG contexts. However, this remains a challenge to achieve within the processing power and the storage capacity of mobile devices. ECG reduction algorithms have an important role to play in reducing the processing requirements for mobile devices, however many existing ECG reduction and compression algorithms are computationally expensive to execute in mobile devices and have not been designed for real-time computation and incremental data arrival. In this paper, we describe a computationally naive, yet effective, algorithm that achieves high ECG reduction rates while maintaining key diagnostic features including PR, QRS, ST, QT and RR intervals. While reduction does not enable ECG waves to be reproduced, the ability to transmit key indicators (diagnostic features) using minimal computational resources, is particularly useful in mobile health contexts involving power constrained sensors and devices. Results of the proposed reduction algorithm indicate that the proposed algorithm outperforms other ECG reduction algorithms at a reduction/compression ratio (CR) of 5:1. If power or processing capacity is low, the algorithm can readily switch to a compression ratio of up to 10: 1 while still maintaining an error rate below 10%.
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- Reviewed:
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
- Subjects: 0806 Information Systems , 1702 Cognitive Science , 1503 Business and Management , Group reasoning , Healthcare , Multi-disciplinary meetings , Reasoning community
- Type: Text , Journal article
- Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/102231 , vital:10776 , https://doi.org/10.1080/12460125.2016.1187388 , ISSN:12460125
- 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.
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- Reviewed:
- Authors: Sharma, Vishakha , Stranieri, Andrew , Burstein, Frada , Warren, Jim , Daly, Sharon , Patterson, Louise , Yearwood, John , Wolff, Alan
- Date: 2016
- Subjects: 0806 Information Systems , 1702 Cognitive Science , 1503 Business and Management , Group reasoning , Healthcare , Multi-disciplinary meetings , Reasoning community
- Type: Text , Journal article
- Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/102231 , vital:10776 , https://doi.org/10.1080/12460125.2016.1187388 , ISSN:12460125
- 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.
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Missing data imputation for individualised CVD diagnostic and treatment
- Venkatraman, Sitalakshmi, Yatsko, Andrew, Stranieri, Andrew, Jelinek, Herbert
- Authors: Venkatraman, Sitalakshmi , Yatsko, Andrew , Stranieri, Andrew , Jelinek, Herbert
- Date: 2016
- Type: Text , Conference paper
- Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/161600 , vital:12538 , http://dx.doi.org/10.22489/CinC.2016.100-179 , ISBN:2325-887X
- Description: Cardiac health screening standards require increasingly more clinical tests consisting of blood, urine and anthropometric measures as well as an extensive clinical and medication history. To ensure optimal screening referrals, diagnostic determinants need to be highly accurate to reduce false positives and ensuing stress to individual patients. However, the data from individual patients partaking in population screening is often incomplete. The current study provides an imputation algorithm that has been applied to patientcentered cardiac health screening. Missing values are iteratively imputed in conjunction with combinations of values on subsets of selected features. The approach was evaluated on the DiabHealth dataset containing 2800 records with over 180 attributes. The results for predicting CVD after data completion showed sensitivity and specificity of 94% and 99% respectively. Removing variables that define cardiac events and associated conditions directly, left ‘age’ followed by ‘use’ of antihypertensive and anti-cholesterol medication, especially statins among the best predictors.
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Remote monitoring and mobile Apps
- Stranieri, Andrew, Edirisinghe, Ruwini, Wickramasinghe, Nilmini
- Authors: Stranieri, Andrew , Edirisinghe, Ruwini , Wickramasinghe, Nilmini
- Date: 2016
- Subjects: Mobile , Smartphone , Mobile app , Sensors , Remote monitoring
- Type: Text , Book chapter
- Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/161281 , vital:12426 , ISBN:978-3-319-25973-4
- Description: Recently, we have been witnessing an exponential growth in mobile health (mHealth) for health care. These solutions are all designed ultimate to enable the consumer to enjoy better health-care delivery and/or wellness. In order to understand this growing area, we believe it is necessary to develop a framework to analyse and evaluate these solutions. The purpose of this chapter is to proffer a suitable taxonomy to do this.
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A scalable cloud Platform for Active healthcare monitoring applications
- Balasubramanian, Venki, Stranieri, Andrew
- Authors: Balasubramanian, Venki , Stranieri, Andrew
- Date: 2015
- Subjects: BAWSN , cloud based computing , early detection , health condition , E-learning , Health care , Maintenance , Monitoring , Patient monitoring , Wearable sensors , Wearable technology , Wireless sensor networks , Cloud based computing , Cloud-based architectures , Electronic health record , Hardware and software , Health care application , Health condition , Healthcare monitoring , Remote patient monitoring
- Type: Text , Conference paper
- Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/89627 , vital:9276 , http://doi.org/10.1109/IC3e.2014.7081248 , ISBN:9781479971770 (ISBN)
- 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.
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- Authors: Balasubramanian, Venki , Stranieri, Andrew
- Date: 2015
- Subjects: BAWSN , cloud based computing , early detection , health condition , E-learning , Health care , Maintenance , Monitoring , Patient monitoring , Wearable sensors , Wearable technology , Wireless sensor networks , Cloud based computing , Cloud-based architectures , Electronic health record , Hardware and software , Health care application , Health condition , Healthcare monitoring , Remote patient monitoring
- Type: Text , Conference paper
- Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/89627 , vital:9276 , http://doi.org/10.1109/IC3e.2014.7081248 , ISBN:9781479971770 (ISBN)
- 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.
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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
- Subjects: 0806 Information Systems , 1503 Business and Management , Big data analytics , Business analytics , Clinical decision support , Health informatics , Information fusion , Translational research
- Type: Text , Journal article
- Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/160771 , vital:12261 , https://doi.org/10.3127/ajis.v19i0.1183 , ISBN:1449-8618
- 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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- Authors: De Silva, Daswin , Burstein, Frada , Jelinek, Herbert , Stranieri, Andrew
- Date: 2015
- Subjects: 0806 Information Systems , 1503 Business and Management , Big data analytics , Business analytics , Clinical decision support , Health informatics , Information fusion , Translational research
- Type: Text , Journal article
- Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/160771 , vital:12261 , https://doi.org/10.3127/ajis.v19i0.1183 , ISBN:1449-8618
- 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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Analysis and comparison of co-occurrence matrix and pixel n-gram features for mammographic images
- Kulkarni, Pradnya, Stranieri, Andrew, Kulkarni, Sid, Ugon, Julien, Mittal, Manish
- Authors: Kulkarni, Pradnya , Stranieri, Andrew , Kulkarni, Sid , Ugon, Julien , Mittal, Manish
- Date: 2015
- Subjects: Mammogram classification , N-grams , Co-occurrence matrix , Multilayer perceptron , Computational time
- Type: Text , Conference paper
- Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/162479 , vital:12639
- Description: Mammography is a proven way of detecting breast cancer at an early stage. Various feature extraction techniques such as histograms, co-occurrence matrix, local binary patterns, Gabor filters, wavelet transforms are used for analysing mammograms. The novel pixel N-gram feature extraction technique has been inspired from the character N-gram concept of text retrieval. In this paper, we have compared the novel N-gram feature extraction technique with the co-occurrence matrix feature extraction technique. The experiments were conducted on the benchmark miniMIAS mammography database. Classification of mammograms into normal and abnormal category using N-gram features showed promising results with greater classification accuracy, sensitivity and specificity compared to classification using co-occurrence matrix features. Moreover, N-gram features computation are found to be considerably faster than co-occurrence matrix feature computation
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AppA : Assistive patient monitoring cloud platform for active healthcare applications
- Balasubramanian, Venki, Stranieri, Andrew, Kaur, Ranjit
- Authors: Balasubramanian, Venki , Stranieri, Andrew , Kaur, Ranjit
- Date: 2015
- Subjects: BAWSN , Cloud based computing , Early detection , Health condition , Health care , Information management , Maintenance , Monitoring , Patient monitoring , Wearable sensors , Wearable technology , Wireless sensor networks , Cloud-based architectures , Electronic health record , Hardware and software , Health care application , Sensor configurations , Remote patient monitoring
- Type: Text , Conference paper
- Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/81528 , vital:8237 , http://doi.org/10.1145/2701126.2701224 , ISBN:9781450333771 (ISBN)
- 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 realtime- 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 the monitoring requirements from the health care providers, and is aligned with scalable economic models.
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- Reviewed:
- Authors: Balasubramanian, Venki , Stranieri, Andrew , Kaur, Ranjit
- Date: 2015
- Subjects: BAWSN , Cloud based computing , Early detection , Health condition , Health care , Information management , Maintenance , Monitoring , Patient monitoring , Wearable sensors , Wearable technology , Wireless sensor networks , Cloud-based architectures , Electronic health record , Hardware and software , Health care application , Sensor configurations , Remote patient monitoring
- Type: Text , Conference paper
- Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/81528 , vital:8237 , http://doi.org/10.1145/2701126.2701224 , ISBN:9781450333771 (ISBN)
- 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 realtime- 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 the monitoring requirements from the health care providers, and is aligned with scalable economic models.
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