Criteria to measure social media value in health care settings : narrative literature review
- Ukoha, Chukwuma, Stranieri, Andrew
- Authors: Ukoha, Chukwuma , Stranieri, Andrew
- Date: 2019
- Type: Text , Journal article , Review
- Relation: Journal of Medical Internet Research Vol. 21, no. 12 (2019), p.
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- Description: Background: With the growing use of social media in health care settings, there is a need to measure outcomes resulting from its use to ensure continuous performance improvement. Despite the need for measurement, a unified approach for measuring the value of social media used in health care remains elusive. Objective: This study aimed to elucidate how the value of social media in health care settings can be ascertained and to taxonomically identify steps and techniques in social media measurement from a review of relevant literature. Methods: A total of 65 relevant articles drawn from 341 articles on the subject of measuring social media in health care settings were qualitatively analyzed and synthesized. The articles were selected from the literature from diverse disciplines including business, information systems, medical informatics, and medicine. Results: The review of the literature showed different levels and focus of analysis when measuring the value of social media in health care settings. It equally showed that there are various metrics for measurement, levels of measurement, approaches to measurement, and scales of measurement. Each may be relevant, depending on the use case of social media in health care. Conclusions: A comprehensive yardstick is required to simplify the measurement of outcomes resulting from the use of social media in health care. At the moment, there is neither a consensus on what indicators to measure nor on how to measure them. We hope that this review is used as a starting point to create a comprehensive measurement criterion for social media used in health care. © 2019 Chukwuma Ukoha, Andrew Stranieri.
- Authors: Ukoha, Chukwuma , Stranieri, Andrew
- Date: 2019
- Type: Text , Journal article , Review
- Relation: Journal of Medical Internet Research Vol. 21, no. 12 (2019), p.
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- Description: Background: With the growing use of social media in health care settings, there is a need to measure outcomes resulting from its use to ensure continuous performance improvement. Despite the need for measurement, a unified approach for measuring the value of social media used in health care remains elusive. Objective: This study aimed to elucidate how the value of social media in health care settings can be ascertained and to taxonomically identify steps and techniques in social media measurement from a review of relevant literature. Methods: A total of 65 relevant articles drawn from 341 articles on the subject of measuring social media in health care settings were qualitatively analyzed and synthesized. The articles were selected from the literature from diverse disciplines including business, information systems, medical informatics, and medicine. Results: The review of the literature showed different levels and focus of analysis when measuring the value of social media in health care settings. It equally showed that there are various metrics for measurement, levels of measurement, approaches to measurement, and scales of measurement. Each may be relevant, depending on the use case of social media in health care. Conclusions: A comprehensive yardstick is required to simplify the measurement of outcomes resulting from the use of social media in health care. At the moment, there is neither a consensus on what indicators to measure nor on how to measure them. We hope that this review is used as a starting point to create a comprehensive measurement criterion for social media used in health care. © 2019 Chukwuma Ukoha, Andrew Stranieri.
Patient-empowered electronic health records
- Sahama, Tony, Stranieri, Andrew, Butler-Henderson, Kerryn
- Authors: Sahama, Tony , Stranieri, Andrew , Butler-Henderson, Kerryn
- Date: 2019
- Type: Text , Conference proceedings
- Relation: MEDINFO 2019: Health and Wellbeing e-Networks for All Vol. 264, p. 1765
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- Description: Electronic Health Records (EHRs) constitute evidence of online health information management. Critical healthcare information technology (HIT) infrastructure facilitates health information exchange of 'modern' health systems. The growth and implementation of EHRs are progressing in many countries while the adoption rate is lagging and lacking momentum amidst privacy and security concerns. This paper uses an interrupted time series (ITS) analysis of OECD data related to EHRs from many countries to make predictions about EHR adoption. The ITS model can be used to explore the impact of various HIT on adoption. Assumptions about the impact of Information Accountability are entered into the model to generate projections if information accountability technologies are developed. In this way, the OECD data and ITS analysis can be used to perform simulations for improving EHR adoption.
- Authors: Sahama, Tony , Stranieri, Andrew , Butler-Henderson, Kerryn
- Date: 2019
- Type: Text , Conference proceedings
- Relation: MEDINFO 2019: Health and Wellbeing e-Networks for All Vol. 264, p. 1765
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- Description: Electronic Health Records (EHRs) constitute evidence of online health information management. Critical healthcare information technology (HIT) infrastructure facilitates health information exchange of 'modern' health systems. The growth and implementation of EHRs are progressing in many countries while the adoption rate is lagging and lacking momentum amidst privacy and security concerns. This paper uses an interrupted time series (ITS) analysis of OECD data related to EHRs from many countries to make predictions about EHR adoption. The ITS model can be used to explore the impact of various HIT on adoption. Assumptions about the impact of Information Accountability are entered into the model to generate projections if information accountability technologies are developed. In this way, the OECD data and ITS analysis can be used to perform simulations for improving EHR adoption.
Personalised measures of obesity using waist to height ratios from an Australian health screening program
- Jelinek, Herbert, Stranieri, Andrew, Yatsko, Anderw, Venkatraman, Sitalakshmi
- Authors: Jelinek, Herbert , Stranieri, Andrew , Yatsko, Anderw , Venkatraman, Sitalakshmi
- Date: 2019
- Type: Text , Journal article
- Relation: Digital Health Vol. 5, no. (2019), p. 1-8
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- Description: Objectives The aim of the current study is to generate waist circumference to height ratio cut-off values for obesity categories from a model of the relationship between body mass index and waist circumference to height ratio. We compare the waist circumference to height ratio discovered in this way with cut-off values currently prevalent in practice that were originally derived using pragmatic criteria. Method Personalized data including age, gender, height, weight, waist circumference and presence of diabetes, hypertension and cardiovascular disease for 847 participants over eight years were assembled from participants attending a rural Australian health review clinic (DiabHealth). Obesity was classified based on the conventional body mass index measure (weight/height(2)) and compared to the waist circumference to height ratio. Correlations between the measures were evaluated on the screening data, and independently on data from the National Health and Nutrition Examination Survey that included age categories. Results This article recommends waist circumference to height ratio cut-off values based on an Australian rural sample and verified using the National Health and Nutrition Examination Survey database that facilitates the classification of obesity in clinical practice. Gender independent cut-off values are provided for waist circumference to height ratio that identify healthy (waist circumference to height ratio >= 0.45), overweight (0.53) and the three obese (0.60, 0.68, 0.75) categories verified on the National Health and Nutrition Examination Survey dataset. A strong linearity between the waist circumference to height ratio and the body mass index measure is demonstrated. Conclusion The recommended waist circumference to height ratio cut-off values provided a useful index for assessing stages of obesity and risk of chronic disease for improved healthcare in clinical practice.
- Authors: Jelinek, Herbert , Stranieri, Andrew , Yatsko, Anderw , Venkatraman, Sitalakshmi
- Date: 2019
- Type: Text , Journal article
- Relation: Digital Health Vol. 5, no. (2019), p. 1-8
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- Description: Objectives The aim of the current study is to generate waist circumference to height ratio cut-off values for obesity categories from a model of the relationship between body mass index and waist circumference to height ratio. We compare the waist circumference to height ratio discovered in this way with cut-off values currently prevalent in practice that were originally derived using pragmatic criteria. Method Personalized data including age, gender, height, weight, waist circumference and presence of diabetes, hypertension and cardiovascular disease for 847 participants over eight years were assembled from participants attending a rural Australian health review clinic (DiabHealth). Obesity was classified based on the conventional body mass index measure (weight/height(2)) and compared to the waist circumference to height ratio. Correlations between the measures were evaluated on the screening data, and independently on data from the National Health and Nutrition Examination Survey that included age categories. Results This article recommends waist circumference to height ratio cut-off values based on an Australian rural sample and verified using the National Health and Nutrition Examination Survey database that facilitates the classification of obesity in clinical practice. Gender independent cut-off values are provided for waist circumference to height ratio that identify healthy (waist circumference to height ratio >= 0.45), overweight (0.53) and the three obese (0.60, 0.68, 0.75) categories verified on the National Health and Nutrition Examination Survey dataset. A strong linearity between the waist circumference to height ratio and the body mass index measure is demonstrated. Conclusion The recommended waist circumference to height ratio cut-off values provided a useful index for assessing stages of obesity and risk of chronic disease for improved healthcare in clinical practice.
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.
- 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.
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.
- 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.
Deriving value from health 2.0 : a study of social media use in australian healthcare organizations
- Ukoha, Chukwuma, Stranieri, Andrew, Chadhar, Mehmood
- Authors: Ukoha, Chukwuma , Stranieri, Andrew , Chadhar, Mehmood
- Date: 2017
- Type: Text , Conference paper
- Relation: 21st Pacific Asia Conference on Information Systems: Societal Transformation Through IS/IT, PACIS 2017, Langkawi Island, Malaysia
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- Description: Health 2.0 is becoming increasingly ubiquitous. The features and functionalities of social media make it suitable for health-related communication. Many healthcare organizations use social media however, the value that they derive from it is unclear. At the moment, there is no consensus on how best the value derived from Health 2.0 can be measured. In order to address this problem, this study explores how Australian healthcare organizations derive value from Health 2.0, and how the derived value can be measured. It is expected that this study will make significant contributions to both theory and practice. The study will put forward a Health 2.0 value-evaluation framework, based on both the research findings, and IS literature. The outcome of this study would help healthcare organizations to understand how value is derived from Health 2.0 and how to measure it. The result of this study will also provide digital health leaders with relevant information that would enable them to make better investment decisions. Overall, the findings of this study will help healthcare organizations to design social media strategies that can yield tangible value. © PACIS 2017.
- Authors: Ukoha, Chukwuma , Stranieri, Andrew , Chadhar, Mehmood
- Date: 2017
- Type: Text , Conference paper
- Relation: 21st Pacific Asia Conference on Information Systems: Societal Transformation Through IS/IT, PACIS 2017, Langkawi Island, Malaysia
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- Description: Health 2.0 is becoming increasingly ubiquitous. The features and functionalities of social media make it suitable for health-related communication. Many healthcare organizations use social media however, the value that they derive from it is unclear. At the moment, there is no consensus on how best the value derived from Health 2.0 can be measured. In order to address this problem, this study explores how Australian healthcare organizations derive value from Health 2.0, and how the derived value can be measured. It is expected that this study will make significant contributions to both theory and practice. The study will put forward a Health 2.0 value-evaluation framework, based on both the research findings, and IS literature. The outcome of this study would help healthcare organizations to understand how value is derived from Health 2.0 and how to measure it. The result of this study will also provide digital health leaders with relevant information that would enable them to make better investment decisions. Overall, the findings of this study will help healthcare organizations to design social media strategies that can yield tangible value. © PACIS 2017.
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
- Type: Text , Conference paper
- Relation: 20th International Conference of Computer and Information, IICIT 2017; Dhaka, Bangladesh; 22nd-24th December 2017 p. 1-6
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- 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.
- Authors: Uddin, Ashraf , Akther, Arnisha , Parvez, Shamima , Stranieri, Andrew
- Date: 2017
- Type: Text , Conference paper
- Relation: 20th International Conference of Computer and Information, IICIT 2017; Dhaka, Bangladesh; 22nd-24th December 2017 p. 1-6
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- 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.
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.
- 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.
ECG reduction for wearable sensor
- Allami, Ragheed, Stranieri, Andrew, Balasubramanian, Venki, Jelinek, Herbert
- Authors: Allami, Ragheed , Stranieri, Andrew , Balasubramanian, Venki , Jelinek, Herbert
- Date: 2016
- Type: Text , Conference proceedings
- Relation: 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS); Naples, Italy; 28th November-1st December 2016 p. 520-525
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- 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%.
- Authors: Allami, Ragheed , Stranieri, Andrew , Balasubramanian, Venki , Jelinek, Herbert
- Date: 2016
- Type: Text , Conference proceedings
- Relation: 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS); Naples, Italy; 28th November-1st December 2016 p. 520-525
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- 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%.
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.
- 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.
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.
- 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
- Full Text:
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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.
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.
- 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.
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
- Type: Text , Conference paper
- Relation: 9th International Conference on Ubiquitous Information Management and Communication, ACM IMCOM 2015; Bali, Indonesia; 8th-10th January 2015
- Full Text:
- Reviewed:
- 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.
- Authors: Balasubramanian, Venki , Stranieri, Andrew , Kaur, Ranjit
- Date: 2015
- Type: Text , Conference paper
- Relation: 9th International Conference on Ubiquitous Information Management and Communication, ACM IMCOM 2015; Bali, Indonesia; 8th-10th January 2015
- Full Text:
- Reviewed:
- 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.
Data-analytically derived flexible HbA1c thresholds for type 2 diabetes mellitus diagnostic
- Stranieri, Andrew, Yatsko, Andrew, Jelinek, Herbert, Venkatraman, Sitalakshmi
- Authors: Stranieri, Andrew , Yatsko, Andrew , Jelinek, Herbert , Venkatraman, Sitalakshmi
- Date: 2015
- Type: Text , Journal article
- Relation: Artificial Intelligence Research Vol. 5, no. 1 (2015), p. 111-134
- Full Text:
- Reviewed:
- Description: Glycated haemoglobin (HbA1c) is now more commonly used as an alternative test to the fasting plasma glucose and oral glucose tolerance tests for the identification of Type 2 Diabetes Mellitus (T2DM) because it is easily obtained using the point-of-care technology and represents long-term blood sugar levels. According to WHO guidelines, HbA1c values of 6.5% or above are required for a diagnosis of T2DM. However outcomes of a large number of trials with HbA1c have been inconsistent across the clinical spectrum and further research is required to determine the efficacy of HbA1c testing in identification of T2DM. Medical records from a diabetes screening program in Australia illustrate that many patients could be classified as diabetics if other clinical indicators are included, even though the HbA1c result does not exceed 6.5%. This suggests that a cutoff for the general population of 6.5% may be too simple and miss individuals at risk or with already overt, undiagnosed diabetes. In this study, data mining algorithms have been applied to identify markers that can be used with HbA1c. The results indicate that T2DM is best classified by HbA1c at 6.2% - a cutoff level lower than the currently recommended one, which can be even less, having assumed the threshold flexibility, if additionally to HbA1c being high the rule is conditioned on oxidative stress or inflammation being present, atherogenicity or adiposity being high, or hypertension being diagnosed, etc.
- Authors: Stranieri, Andrew , Yatsko, Andrew , Jelinek, Herbert , Venkatraman, Sitalakshmi
- Date: 2015
- Type: Text , Journal article
- Relation: Artificial Intelligence Research Vol. 5, no. 1 (2015), p. 111-134
- Full Text:
- Reviewed:
- Description: Glycated haemoglobin (HbA1c) is now more commonly used as an alternative test to the fasting plasma glucose and oral glucose tolerance tests for the identification of Type 2 Diabetes Mellitus (T2DM) because it is easily obtained using the point-of-care technology and represents long-term blood sugar levels. According to WHO guidelines, HbA1c values of 6.5% or above are required for a diagnosis of T2DM. However outcomes of a large number of trials with HbA1c have been inconsistent across the clinical spectrum and further research is required to determine the efficacy of HbA1c testing in identification of T2DM. Medical records from a diabetes screening program in Australia illustrate that many patients could be classified as diabetics if other clinical indicators are included, even though the HbA1c result does not exceed 6.5%. This suggests that a cutoff for the general population of 6.5% may be too simple and miss individuals at risk or with already overt, undiagnosed diabetes. In this study, data mining algorithms have been applied to identify markers that can be used with HbA1c. The results indicate that T2DM is best classified by HbA1c at 6.2% - a cutoff level lower than the currently recommended one, which can be even less, having assumed the threshold flexibility, if additionally to HbA1c being high the rule is conditioned on oxidative stress or inflammation being present, atherogenicity or adiposity being high, or hypertension being diagnosed, etc.
Diagnostic with incomplete nominal/discrete data
- Jelinek, Herbert, Yatsko, Andrew, Stranieri, Andrew, Venkatraman, Sitalakshmi, Bagirov, Adil
- Authors: Jelinek, Herbert , Yatsko, Andrew , Stranieri, Andrew , Venkatraman, Sitalakshmi , Bagirov, Adil
- Date: 2015
- Type: Text , Journal article
- Relation: Artificial Intelligence Research Vol. 4, no. 1 (2015), p. 22-35
- Full Text:
- Reviewed:
- Description: Missing values may be present in data without undermining its use for diagnostic / classification purposes but compromise application of readily available software. Surrogate entries can remedy the situation, although the outcome is generally unknown. Discretization of continuous attributes renders all data nominal and is helpful in dealing with missing values; particularly, no special handling is required for different attribute types. A number of classifiers exist or can be reformulated for this representation. Some classifiers can be reinvented as data completion methods. In this work the Decision Tree, Nearest Neighbour, and Naive Bayesian methods are demonstrated to have the required aptness. An approach is implemented whereby the entered missing values are not necessarily a close match of the true data; however, they intend to cause the least hindrance for classification. The proposed techniques find their application particularly in medical diagnostics. Where clinical data represents a number of related conditions, taking Cartesian product of class values of the underlying sub-problems allows narrowing down of the selection of missing value substitutes. Real-world data examples, some publically available, are enlisted for testing. The proposed and benchmark methods are compared by classifying the data before and after missing value imputation, indicating a significant improvement.
- Authors: Jelinek, Herbert , Yatsko, Andrew , Stranieri, Andrew , Venkatraman, Sitalakshmi , Bagirov, Adil
- Date: 2015
- Type: Text , Journal article
- Relation: Artificial Intelligence Research Vol. 4, no. 1 (2015), p. 22-35
- Full Text:
- Reviewed:
- Description: Missing values may be present in data without undermining its use for diagnostic / classification purposes but compromise application of readily available software. Surrogate entries can remedy the situation, although the outcome is generally unknown. Discretization of continuous attributes renders all data nominal and is helpful in dealing with missing values; particularly, no special handling is required for different attribute types. A number of classifiers exist or can be reformulated for this representation. Some classifiers can be reinvented as data completion methods. In this work the Decision Tree, Nearest Neighbour, and Naive Bayesian methods are demonstrated to have the required aptness. An approach is implemented whereby the entered missing values are not necessarily a close match of the true data; however, they intend to cause the least hindrance for classification. The proposed techniques find their application particularly in medical diagnostics. Where clinical data represents a number of related conditions, taking Cartesian product of class values of the underlying sub-problems allows narrowing down of the selection of missing value substitutes. Real-world data examples, some publically available, are enlisted for testing. The proposed and benchmark methods are compared by classifying the data before and after missing value imputation, indicating a significant improvement.
Novel data mining techniques for incompleted clinical data in diabetes management
- Jelinek, Herbert, Yatsko, Andrew, Stranieri, Andrew, Venkatraman, Sitalakshmi
- Authors: Jelinek, Herbert , Yatsko, Andrew , Stranieri, Andrew , Venkatraman, Sitalakshmi
- Date: 2014
- Type: Text , Journal article
- Relation: British Journal of Applied Science & Technology Vol. 4, no. 33 (2014), p. 4591-4606
- Relation: https://doi.org/10.9734/BJAST/2014/11744
- Full Text:
- Reviewed:
- Description: An important part of health care involves upkeep and interpretation of medical databases containing patient records for clinical decision making, diagnosis and follow-up treatment. Missing clinical entries make it difficult to apply data mining algorithms for clinical decision support. This study demonstrates that higher predictive accuracy is possible using conventional data mining algorithms if missing values are dealt with appropriately. We propose a novel algorithm using a convolution of sub-problems to stage a super problem, where classes are defined by Cartesian Product of class values of the underlying problems, and Incomplete Information Dismissal and Data Completion techniques are applied for reducing features and imputing missing values. Predictive accuracies using Decision Branch, Nearest Neighborhood and Naïve Bayesian classifiers were compared to predict diabetes, cardiovascular disease and hypertension. Data is derived from Diabetes Screening Complications Research Initiative (DiScRi) conducted at a regional Australian university involving more than 2400 patient records with more than one hundred clinical risk factors (attributes). The results show substantial improvements in the accuracy achieved with each classifier for an effective diagnosis of diabetes, cardiovascular disease and hypertension as compared to those achieved without substituting missing values. The gain in improvement is 7% for diabetes, 21% for cardiovascular disease and 24% for hypertension, and our integrated novel approach has resulted in more than 90% accuracy for the diagnosis of any of the three conditions. This work advances data mining research towards achieving an integrated and holistic management of diabetes. - See more at: http://www.sciencedomain.org/abstract.php?iid=670&id=5&aid=6128#.VCSxDfmSx8E
- Authors: Jelinek, Herbert , Yatsko, Andrew , Stranieri, Andrew , Venkatraman, Sitalakshmi
- Date: 2014
- Type: Text , Journal article
- Relation: British Journal of Applied Science & Technology Vol. 4, no. 33 (2014), p. 4591-4606
- Relation: https://doi.org/10.9734/BJAST/2014/11744
- Full Text:
- Reviewed:
- Description: An important part of health care involves upkeep and interpretation of medical databases containing patient records for clinical decision making, diagnosis and follow-up treatment. Missing clinical entries make it difficult to apply data mining algorithms for clinical decision support. This study demonstrates that higher predictive accuracy is possible using conventional data mining algorithms if missing values are dealt with appropriately. We propose a novel algorithm using a convolution of sub-problems to stage a super problem, where classes are defined by Cartesian Product of class values of the underlying problems, and Incomplete Information Dismissal and Data Completion techniques are applied for reducing features and imputing missing values. Predictive accuracies using Decision Branch, Nearest Neighborhood and Naïve Bayesian classifiers were compared to predict diabetes, cardiovascular disease and hypertension. Data is derived from Diabetes Screening Complications Research Initiative (DiScRi) conducted at a regional Australian university involving more than 2400 patient records with more than one hundred clinical risk factors (attributes). The results show substantial improvements in the accuracy achieved with each classifier for an effective diagnosis of diabetes, cardiovascular disease and hypertension as compared to those achieved without substituting missing values. The gain in improvement is 7% for diabetes, 21% for cardiovascular disease and 24% for hypertension, and our integrated novel approach has resulted in more than 90% accuracy for the diagnosis of any of the three conditions. This work advances data mining research towards achieving an integrated and holistic management of diabetes. - See more at: http://www.sciencedomain.org/abstract.php?iid=670&id=5&aid=6128#.VCSxDfmSx8E
Performance evaluation of the dependable properties of a body area wireless sensor network
- Balasubramanian, Venki, Stranieri, Andrew
- Authors: Balasubramanian, Venki , Stranieri, Andrew
- Date: 2014
- Type: Text , Conference paper
- Relation: 2014 International Conference on Reliabilty, Optimization, & Information Technology (Icroit 2014); Faridabad, India; 6th-8th February 2014 p. 229-234
- Full Text:
- Reviewed:
- Description: Body Area Wireless Sensor Networks (BAWSNs) are self-organizing networks capable of monitoring health intrinsic data of a patient. BAWSNs extended with a health care application can be used to perform medical assessments by remotely monitoring patients. The accuracy of medical assessments fundamentally depends on the correctness of the data received from the BAWSN. However, data errors may arise at the sensor or during transmission across the wireless sensor network. Therefore, it is imperative to measure the health intrinsic data of a patient precisely. The formulated measurable properties in our work precisely measure the performance of the BAWSN in a remote Healthcare Monitoring Application (HMA). In this paper, we collated various performances using the measurable properties in our real-time test-bed and presented a comprehensive evaluation of these properties in a BAWSN.
- Authors: Balasubramanian, Venki , Stranieri, Andrew
- Date: 2014
- Type: Text , Conference paper
- Relation: 2014 International Conference on Reliabilty, Optimization, & Information Technology (Icroit 2014); Faridabad, India; 6th-8th February 2014 p. 229-234
- Full Text:
- Reviewed:
- Description: Body Area Wireless Sensor Networks (BAWSNs) are self-organizing networks capable of monitoring health intrinsic data of a patient. BAWSNs extended with a health care application can be used to perform medical assessments by remotely monitoring patients. The accuracy of medical assessments fundamentally depends on the correctness of the data received from the BAWSN. However, data errors may arise at the sensor or during transmission across the wireless sensor network. Therefore, it is imperative to measure the health intrinsic data of a patient precisely. The formulated measurable properties in our work precisely measure the performance of the BAWSN in a remote Healthcare Monitoring Application (HMA). In this paper, we collated various performances using the measurable properties in our real-time test-bed and presented a comprehensive evaluation of these properties in a BAWSN.
Visual character N-grams for classification and retrieval of radiological images
- Kulkarni, Pradnya, Stranieri, Andrew, Kulkarni, Siddhivinayak, Ugon, Julien, Mittal, Manish
- Authors: Kulkarni, Pradnya , Stranieri, Andrew , Kulkarni, Siddhivinayak , Ugon, Julien , Mittal, Manish
- Date: 2014
- Type: Text , Journal article
- Relation: International Journal of Multimedia & Its Applications Vol. 6, no. 2 (April 2014), p. 35-49
- Full Text:
- Reviewed:
- Description: Diagnostic radiology struggles to maintain high interpretation accuracy. Retrieval of past similar cases would help the inexperienced radiologist in the interpretation process. Character n-gram model has been effective in text retrieval context in languages such as Chinese where there are no clear word boundaries. We propose the use of visual character n-gram model for representation of image for classification and retrieval purposes. Regions of interests in mammographic images are represented with the character n-gram features. These features are then used as input to back-propagation neural network for classification of regions into normal and abnormal categories. Experiments on miniMIAS database show that character n-gram features are useful in classifying the regions into normal and abnormal categories. Promising classification accuracies are observed (83.33%) for fatty background tissue warranting further investigation. We argue that Classifying regions of interests would reduce the number of comparisons necessary for finding similar images from the database and hence would reduce the time required for retrieval of past similar cases.
- Authors: Kulkarni, Pradnya , Stranieri, Andrew , Kulkarni, Siddhivinayak , Ugon, Julien , Mittal, Manish
- Date: 2014
- Type: Text , Journal article
- Relation: International Journal of Multimedia & Its Applications Vol. 6, no. 2 (April 2014), p. 35-49
- Full Text:
- Reviewed:
- Description: Diagnostic radiology struggles to maintain high interpretation accuracy. Retrieval of past similar cases would help the inexperienced radiologist in the interpretation process. Character n-gram model has been effective in text retrieval context in languages such as Chinese where there are no clear word boundaries. We propose the use of visual character n-gram model for representation of image for classification and retrieval purposes. Regions of interests in mammographic images are represented with the character n-gram features. These features are then used as input to back-propagation neural network for classification of regions into normal and abnormal categories. Experiments on miniMIAS database show that character n-gram features are useful in classifying the regions into normal and abnormal categories. Promising classification accuracies are observed (83.33%) for fatty background tissue warranting further investigation. We argue that Classifying regions of interests would reduce the number of comparisons necessary for finding similar images from the database and hence would reduce the time required for retrieval of past similar cases.
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
- Full Text:
- Reviewed:
- 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
- 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
- Full Text:
- Reviewed:
- 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
CWDM: A case-based diabetes management web system
- Nguyen, Linh Hoang, Sun, Zhaohao, Stranieri, Andrew, Firmin, Sally
- Authors: Nguyen, Linh Hoang , Sun, Zhaohao , Stranieri, Andrew , Firmin, Sally
- Date: 2013
- Type: Text , Conference paper
- Relation: 24th Australasian Conference on Information Systems, 4-6th December, 2013 p. 1-10
- Full Text:
- Reviewed:
- Description: Treatment refers to the therapy to treat a disease or a health issue. Treatment in this situation is similar to medical treatment which mainly uses medicines in an attempt to relieve the pain or even stop the disease. However, medicines themselves could not entirely cure the disease (in this case, diabetes), the patients will need more intervention which will be introduced in the next section. In most of documents for diabetic treatment, insulin therapy may be the main factor, however it would seem that diabetic patient needs more than just insulin. Therefore, TCM – traditional Chinese medicine – is recommended in the diabetic treatment as a lot of its remedies not only adjust insulin but also maintain good health for the patients. This section presents some of the TCM remedies to treat diabetes. As mentioned, diabetic patients are treated by lifestyle intervention and insulin therapy according to their diabetic status. The prevalence of diabetes and its complications leads to the requirement of treatment and care plan. Guidelines for T2D treatment indicated the following primary areas: lifestyle improvement which involves at least two and half hours of physical operations every week, dietary plan which decreases the fat intake, and weight management which requires weight loss approximately 7% of the baseline weight; cardiovascular risk factor reduction by managing blood pressure, cholesterol level, control smoking status, hypertension; and blood glucose management such as mono-therapy methods using oral medications to reduce A1c levels (Ripsin, Kang, & Urban, 2009). Self-monitoring of blood glucose levels for T2D treatment is also suggested. The self-monitoring of blood glucose method is recommended because it could enhance the patients’ self-consciousness of managing their diabetic status and require greater behaviours, responsibilities and efforts. Besides, this method is cost-effective in long term for diabetic complications treatment (Szymborska-Kajaneka, Psureka, Heseb, & Strojek, 2009). Another related study recommended that for T2D patients who are using insulin, self-monitoring of blood glucose should be carried out daily at least three times; and for patients without insulin usage the frequency of blood glucose self-monitoring should be adjusted individually (Varanauskiene, 2008). Both studies indicate that there have been controversies whether self-monitoring of blood glucose is useful for T2D patients without insulin treatment. We recommend traditional Chinese medicine (TCM) as the major medicine for treating diabetes according to a report of natural Chinese medicines (Li, Zheng, Bukuru, & Kimpe, 2004) which indicates the results from many cases in various research and medical activities.
- Authors: Nguyen, Linh Hoang , Sun, Zhaohao , Stranieri, Andrew , Firmin, Sally
- Date: 2013
- Type: Text , Conference paper
- Relation: 24th Australasian Conference on Information Systems, 4-6th December, 2013 p. 1-10
- Full Text:
- Reviewed:
- Description: Treatment refers to the therapy to treat a disease or a health issue. Treatment in this situation is similar to medical treatment which mainly uses medicines in an attempt to relieve the pain or even stop the disease. However, medicines themselves could not entirely cure the disease (in this case, diabetes), the patients will need more intervention which will be introduced in the next section. In most of documents for diabetic treatment, insulin therapy may be the main factor, however it would seem that diabetic patient needs more than just insulin. Therefore, TCM – traditional Chinese medicine – is recommended in the diabetic treatment as a lot of its remedies not only adjust insulin but also maintain good health for the patients. This section presents some of the TCM remedies to treat diabetes. As mentioned, diabetic patients are treated by lifestyle intervention and insulin therapy according to their diabetic status. The prevalence of diabetes and its complications leads to the requirement of treatment and care plan. Guidelines for T2D treatment indicated the following primary areas: lifestyle improvement which involves at least two and half hours of physical operations every week, dietary plan which decreases the fat intake, and weight management which requires weight loss approximately 7% of the baseline weight; cardiovascular risk factor reduction by managing blood pressure, cholesterol level, control smoking status, hypertension; and blood glucose management such as mono-therapy methods using oral medications to reduce A1c levels (Ripsin, Kang, & Urban, 2009). Self-monitoring of blood glucose levels for T2D treatment is also suggested. The self-monitoring of blood glucose method is recommended because it could enhance the patients’ self-consciousness of managing their diabetic status and require greater behaviours, responsibilities and efforts. Besides, this method is cost-effective in long term for diabetic complications treatment (Szymborska-Kajaneka, Psureka, Heseb, & Strojek, 2009). Another related study recommended that for T2D patients who are using insulin, self-monitoring of blood glucose should be carried out daily at least three times; and for patients without insulin usage the frequency of blood glucose self-monitoring should be adjusted individually (Varanauskiene, 2008). Both studies indicate that there have been controversies whether self-monitoring of blood glucose is useful for T2D patients without insulin treatment. We recommend traditional Chinese medicine (TCM) as the major medicine for treating diabetes according to a report of natural Chinese medicines (Li, Zheng, Bukuru, & Kimpe, 2004) which indicates the results from many cases in various research and medical activities.