Empirical investigation of consensus clustering for large ECG data sets
- Authors: Kelarev, Andrei , Stranieri, Andrew , Yearwood, John , Jelinek, Herbert
- Date: 2012
- Type: Text , Conference proceedings
- Full Text: false
- Description: This article investigates a novel machine learning approach applying consensus clustering in conjunction with classification for the data mining of very large and highly dimensional ECG data sets. To obtain robust and stable clusterings, consensus functions can be applied for clustering ensembles combining a multitude of independent initial clusterings. Direct applications of consensus functions to highly dimensional ECG data sets remain computationally expensive and impracticable. We introduce a multistage scheme including various procedures for dimensionality reduction, consensus clustering of randomized samples, followed by the use of a fast supervised classification algorithm. Applying the Hybrid Bipartite Graph Formulation combined with rank ordering and SMO we obtained an area under the receiver operating curve of 0.987. The performance of the classification algorithm at the final stage is crucial for the effectiveness of this technique. It can be regarded as an indication of the reliability, quality and stability of the combined consensus clustering. © 2012 IEEE.
Empirical investigation of multi-tier ensembles for the detection of cardiac autonomic neuropathy using subsets of the Ewing features
- Authors: Abawajy, Jemal , Kelarev, Andrei , Stranieri, Andrew , Jelinek, Herbert
- Date: 2012
- Type: Text , Conference proceedings
- Full Text:
- Description: This article is devoted to an empirical investigation of performance of several new large multi-tier ensembles for the detection of cardiac autonomic neuropathy (CAN) in diabetes patients using sub-sets of the Ewing features. We used new data collected by the diabetes screening research initiative (DiScRi) project, which is more than ten times larger than the data set originally used by Ewing in the investigation of CAN. The results show that new multi-tier ensembles achieved better performance compared with the outcomes published in the literature previously. The best accuracy 97.74% of the detection of CAN has been achieved by the novel multi-tier combination of AdaBoost and Bagging, where AdaBoost is used at the top tier and Bagging is used at the middle tier, for the set consisting of the following four Ewing features: the deep breathing heart rate change, the Valsalva manoeuvre heart rate change, the hand grip blood pressure change and the lying to standing blood pressure change.
Empirical study of decision trees and ensemble classifiers for monitoring of diabetes patients in pervasive healthcare
- Authors: Kelarev, Andrei , Stranieri, Andrew , Yearwood, John , Jelinek, Herbert
- Date: 2012
- Type: Text , Conference proceedings
- Full Text: false
- Description: Diabetes is a condition requiring continuous everyday monitoring of health related tests. To monitor specific clinical complications one has to find a small set of features to be collected from the sensors and efficient resource-aware algorithms for their processing. This article is concerned with the detection and monitoring of cardiovascular autonomic neuropathy, CAN, in diabetes patients. Using a small set of features identified previously, we carry out an empirical investigation and comparison of several ensemble methods based on decision trees for a novel application of the processing of sensor data from diabetes patients for pervasive health monitoring of CAN. Our experiments relied on an extensive database collected by the Diabetes Complications Screening Research Initiative at Charles Sturt University and concentrated on the particular task of the detection and monitoring of cardiovascular autonomic neuropathy. Most of the features in the database can now be collected using wearable sensors. Our experiments included several essential ensemble methods, a few more advanced and recent techniques, and a novel consensus function. The results show that our novel application of the decision trees in ensemble classifiers for the detection and monitoring of CAN in diabetes patients achieved better performance parameters compared with the outcomes obtained previously in the literature. © 2012 IEEE.
- Description: 2003009675
High definition 3D telemedicine: The next frontier?
- Authors: Stranieri, Andrew , Collmann, Richard , Borda, Ann
- Date: 2012
- Type: Text , Conference proceedings
- Relation: Studies in Health Technology and Informatics, 182, p.133-41.
- Full Text:
- Description: Evidence from the literature indicates that the degree of immersion often referred to as the "sense of being there" experienced by clinicians and patients is a factor in the success of tele-health installations. High definition and 3D telemedicine offers a compelling mechanism to achieve a sense of immersion and contribute to an enhanced quality of use. This article surveys HD3D trials in tele-health and concludes that the way HD3D is integrated into telemedicine depends on the clinical, organisational and technological context. In some settings real time HD3D is not so desirable whereas asynchronous transmission of HD3D images and videos is highly desirable. © 2012 The authors and IOS Press.
Improving classifications for cardiac autonomic neuropathy using multi-level ensemble classifiers and feature selection based on random forest
- Authors: Kelarev, Andrei , Stranieri, Andrew , Abawajy, Jemal , Yearwood, John , Jelinek, Herbert
- Date: 2012
- Type: Text , Conference paper
- Relation: Tenth Australasian Data Mining Conference Vol. 134, p. 93-101
- Full Text: false
- Reviewed:
- Description: This paper is devoted to empirical investigation of novel multi-level ensemble meta classifiers for the detection and monitoring of progression of cardiac autonomic neuropathy, CAN, in diabetes patients. Our experiments relied on an extensive database and concentrated on ensembles of ensembles, or multi-level meta classifiers, for the classification of cardiac autonomic neuropathy progression. First, we carried out a thorough investigation comparing the performance of various base classifiers for several known sets of the most essential features in this database and determined that Random Forest significantly and consistently outperforms all other base classifiers in this new application. Second, we used feature selection and ranking implemented in Random Forest. It was able to identify a new set of features, which has turned out better than all other sets considered for this large and well-known database previously. Random Forest remained the very best classifier for the new set of features too. Third, we investigated meta classifiers and new multi-level meta classifiers based on Random Forest, which have improved its performance. The results obtained show that novel multi-level meta classifiers achieved further improvement and obtained new outcomes that are significantly better compared with the outcomes published in the literature previously for cardiac autonomic neuropathy.
Insights from jurisprudence for machine learning in law
- Authors: Stranieri, Andrew , Zeleznikow, John
- Date: 2012
- Type: Text , Book chapter
- Relation: Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques p. 85-98
- Full Text: false
- Reviewed:
- Description: The central theme of this chapter is that the application of machine learning to data in the legal domain involves considerations that derive from jurisprudential assumptions about the nature of legal reasoning. Jurisprudence provides a unique resource for machine learning in that, for over one hundred years, significant thinkers have advanced concepts including open texture and discretion. These concepts inform and guide applications of machine learning to law.
Rule-based classifiers and meta classifiers for identification of cardiac autonomic neuropathy progression
- Authors: Jelinek, Herbert , Kelarev, Andrei , Stranieri, Andrew , Yearwood, John
- Date: 2012
- Type: Text , Journal article
- Relation: International Journal of Information Science and Computer Mathematics Vol. 5, no. 2 (2012), p. 49-53
- Full Text:
- Reviewed:
- Description: We investigate and compare several rule-based classifiers and meta classifiers in their ability to obtain multi-class classifications of cardiac autonomic neuropathy (CAN) and its progression. The best results obtained in our experiments are significantly better than the outcomes published previously in the literature for analogous CAN identification tasks or simpler binary classification tasks.
TEA : A generic framework for decision making in web services
- Authors: Sun, Zhaohao , Meredith, Grant , Stranieri, Andrew
- Date: 2012
- Type: Text , Journal article
- Relation: International Journal of Systems and Service-Oriented Engineering (IJSSOE) Vol. 3, no. 3 (2012), p. 41-63
- Full Text: false
- Reviewed:
- Description: This paper proposes TEA: a generic framework for decision making in web services, which integrates the environment (6 Ps) of decision making, the behaviors (6 Cs) of decision makers, and inner activities (another 6 Ps) of decision makers. This framework unifies what the decision makers can "eye" (the above-mentioned first 6Ps), should "think" (the above-mentioned another 6 Ps) and "act" (6 Cs), whenever making decisions in web services. The paper also examines interrelationships among the first 6 Ps, 6 Cs, and another 6Ps, and their influences on decision making in web services. The proposed approach will facilitate research and development of decision making and decision support systems in web services.
The role of emotional intelligence on the resolution of disputes involving the electronic health record
- Authors: Bellucci, Emilia , Venkatraman, Sitalakshmi , Muecke, Nial , Stranieri, Andrew
- Date: 2012
- Type: Text , Conference paper
- Relation: Fifth Australasian workshop on health informatics and knowledge management p. 3-12
- Full Text: false
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Unification of electronic health records and holistic medicine
- Authors: Venkatraman, Sitalakshmi , Stranieri, Andrew
- Date: 2012
- Type: Text , Journal article
- Relation: ICHM 2012 Vol. , no. (2012), p.53-59
- Full Text: false
- Reviewed:
- Description: Recent trends in the increasing use of complementary and alternative medicine (CAM) as "holistic medicine" by patients in technologically advanced nations have prompted the need to integrate their CAM information into their Electronic health records (EHR). Studies indicate that over 70% of the public in Australia used at least one form of CAM that includes nutritional products such as vitamins, supplements, and herbal medicines, and alternate medicines such as homoeopathic, Ayurvedic and Chinese medicines. There is also a growing acceptance of CAM among healthcare providers, and patients are increasingly visiting CAM practitioners. In this paper, we argue that by unifying patients' information about their CAM history along with their EHR, the healthcare quality and accuracy of measurements could be improved, and we identify six key benefits for healthcare and CAM practitioners as well as consumers. On the other hand we also foresee certain issues, such as availability of electronic data and standardised practice of different forms of CAM, and we have unearthed six main issues that require prime attention. We discuss these issues and provide recommendations for the way to go forward in integrating automated CAM software components into EHR systems.
Water allocation argument tree (WAAT): A tool for facilitating public participation in water allocation decisions
- Authors: Graymore, Michelle , Stranieri, Andrew , McRae-Williams, Pamela , Mays, Heather , Lehmann, La Vergne , Thoms, Gavin , Yearwood, John
- Date: 2012
- Type: Text , Book
- Full Text: false
- Reviewed:
A case for the re-use of community reasoning
- Authors: Stranieri, Andrew , Yearwood, John
- Date: 2011
- Type: Text , Book chapter
- Relation: Technologies for supporting reasoning communities and collaborative decision making: Cooperative approaches p.
- Full Text: false
- Reviewed:
- Description: In software engineering, the re-use concept is a design principle that improves efficency, quality and maintainability by ensuring that software artifacts are developed once and re-used may times. In an analogous way, a group's reasoning can be imagined to be re-used by that or another group to enhance efficiency, transparency and consistency in decison-making. However, the re-use of reasoning is difficult to achieve because group reasoning cannot easily be captured and the way in which a group reasoning artifact is subsequently used is not obvious. This chapter explores the case for the re-use of community reasoning and concludes that individuals can benefit from a representation of a previous groups's coalesced reasoning to be modeled and the scheme to represent the reasoning have been selected to suit the task. The authors contend that specifying the future community like to re-use the reasoning, called the intended audience, informs a decision regarding whether an exercise aimed at coalescing a group's reasoning is best performed verbally, in writing or with the use of more structured schemes such as Argument visualization.
A reasoning community perspective on deliberate democracy
- Authors: Yearwood, John , Stranieri, Andrew
- Date: 2011
- Type: Text , Book chapter
- Relation: Technologies for supporting reasoning communities and collaborative decision making: Cooperative approaches p.237-246
- Full Text: false
- Reviewed:
- Description: This chapter describes some of the current approaches to delibertative democracy and the considers them from the perspective of a reasoning community framework. This approach highlights important tasks, process and structures that can be used to enhance the process of groups engaging in deliberative democracy approaches. In particular it focuses attention on the potential for technologies to support groups in achieving broad agreed structured reasoning bases that capture the scope of an issue from multiple perspectives.
A reasoning framework for decision making in water allocation: a tree for water
- Authors: Graymore, Michelle , Mays, Heather , Stranieri, Andrew , Lehmann, La Vergne , McRae-Williams, Pamela , Thoms, Gavin , Yearwood, John
- Date: 2011
- Type: Text , Conference paper
- Relation: Paper presented at International Conference on Integrated Water Management 2011
- Full Text: false
- Reviewed:
Data Mining and Analytics 2011: Proceedings of the Ninth Australasian Data Mining Conference
- Authors: Vamplew, Peter , Stranieri, Andrew , Ong, Kok-Leong , Christen, Peter , Kennedy, Paul
- Date: 2011
- Type: Text , Edited book
- Full Text: false
Decision support based needs assessment for cancer patients
- Authors: Stranieri, Andrew , Kulkarni, Siddhivinayak , Macfadyen, Alyx , Love, Anthony , Vaughan, Stephen
- Date: 2011
- Type: Text , Conference paper
- Relation: Australasian workshop on health informatics and knowledge management (HIKM)
- Full Text: false
- Reviewed:
- Description: Regular assessment of wellness or quality of life for patients throughout a cancer journey is important so as to identify aspects of life that could lead to distress and impede recovery or acceptance. The emerging trends in assessment are to deploy validated, quality of life instruments on touchscreen computers in medical waiting rooms. However, these add to workload of health care professionals and can be impersonal for patients to use. In this article, an alternate approach is presented that involves a decision support system with natural dialogue that elicits the patient's specific context in a far finer grained manner than is possible with questionnaire based instruments. The system includes a model of heuristics that health care professionals in a locality use to make inferences regarding a patient's quality of life and avenues for referral.
- Description: E1
Does the Delphi process lead to increased accuracy in group-based judgmental forecasts or does it simply induce consensus amongst judgmental forecasters?
- Authors: Bolger, Fergus , Stranieri, Andrew , Wright, George , Yearwood, John
- Date: 2011
- Type: Text , Journal article
- Relation: Technological Forecasting and Social Change Vol. , no. (2011), p.
- Full Text: false
- Reviewed:
- Description: We investigate the relative impact of internal Delphi process factors - including panelists' degree of confidence, expertise, majority/minority positioning - and an external factor, richness of feedback - on opinion change and subsequent accuracy of judgmental forecasts. We found that panelists who had low confidence in their judgmental forecast and/or who were in a minority were more likely to change their opinion than those who were more confident and/or in a majority. The addition of rationales, or reasons, to the numeric feedback had little impact upon panelists' final forecasts, despite the quality of panelists' rationales being significantly positively correlated with accurate forecasts and thus of potential use to aid forecast improvement over Delphi rounds. Rather, the effect of rationales was similar to that of confidence: to pull panelists towards the majority opinion regardless of its correctness. We conclude that majority opinion is the strongest influence on panelists' opinion change in both the 'standard' Delphi, and Delphi-with-reasons. We make some suggestions for improved variants of the Delphi-with-reasons technique that should help reduce majority influence and thereby permit reasoned arguments to exert their proper pull on opinion change, resulting in forecast accuracy improvements over Delphi rounds. © 2011.
Fault-tolerant data aggregation scheme for monitoring of critical events in grid based healthcare sensor networks
- Authors: Saeed, Ather , Stranieri, Andrew , Dazeley, Richard
- Date: 2011
- Type: Text , Conference paper
- Relation: Paper presented at 19th High Peformance Computing Symposium (HPC 2011) part of SCS Spring Simulation Multiconference (SpringSim'11)
- Full Text:
- Reviewed:
- Description: Wireless sensor devices are used for monitoring patients with serious medical conditions. Communication of content-sensitive and context sensitive datasets is crucial for the survival of patients so that informed decisions can be made. The main limitation of sensor devices is that they work on a fixed threshold to notify the relevant Healthcare Professional (HP) about the seriousness of a patient’s current state. Further, these sensor devices have limited processor, memory capabilities and battery. A new grid-based information monitoring architecture is proposed to address the issues of data loss and timely dissemination of critical information to the relevant HP. The proposed approach provides an opportunity to efficiently aggregate datasets of interest by reducing network overhead and minimizing data latency. To narrow down the problem domain, in-network processing of datasets with Grid monitoring capabilities is proposed for the efficient execution of the computational, resource and data intensive tasks. Interactive wireless sensor networks do not guarantee that data gathered from the heterogeneous sources will always arrive at the sink (base) node, but the proposed aggregation technique will provide a fault tolerant solution to the timely notification of a patient’s critical state. Experimental results received are encouraging and clearly show a reduction in the network latency rate.
Feature selection using misclassification counts
- Authors: Bagirov, Adil , Yatsko, Andrew , Stranieri, Andrew
- Date: 2011
- Type: Conference proceedings , Unpublished work
- Relation: Proceedings of the 9th Australasian Data Mining Conference (AusDM 2011), 51-62. Conferences in Research and Practice in Information Technology (CRPIT), Vol. 121.
- Full Text:
- Description: Dimensionality reduction of the problem space through detection and removal of variables, contributing little or not at all to classification, is able to relieve the computational load and instance acquisition effort, considering all the data attributes accessed each time around. The approach to feature selection in this paper is based on the concept of coherent accumulation of data about class centers with respect to coordinates of informative features. Ranking is done on the degree to which different variables exhibit random characteristics. The results are being verified using the Nearest Neighbor classifier. This also helps to address the feature irrelevance and redundancy, what ranking does not immediately decide. Additionally, feature ranking methods from different independent sources are called in for the direct comparison.
- Description: Dimensionality reduction of the problem space through detection and removal of variables, contributing little or not at all to classification, is able to relieve the computational load and the data acquisition effort, considering all data components being accessed each time around. The approach to feature selection in this paper is based on the concept of coherent accumulation of data about class centers with respect to coordinates of informative features. Ranking is done on the degree, to which different variables exhibit random characteristics. The results are being verified using the Nearest Neighbor classifier. This also helps to address the feature irrelevance, what ranking does not immediately decide. Additionally, feature ranking methods available from different independent sources are called in for direct comparison.
Technologies for supporting reasoning communities and collaborative decision making: Cooperative approaches
- Authors: Yearwood, John , Stranieri, Andrew
- Date: 2011
- Type: Text , Book
- Full Text: false
- Reviewed:
- Description: The information age has enabled unprecedented levels of data to be collected and stored. At the same time, society and organizations have become increasingly complex. Consequently, decisions in many facets have become increasingly complex but have the potential to be better informed. Technologies for Supporting Reasoning Communities and Collaborative Decision Making: Cooperative Approaches includes chapters from diverse fields of enquiry including decision science, political science, argumentation, knowledge management, cognitive psychology and business intelligence. Each chapter illustrates a perspective on group reasoning that ultimately aims to lead to a greater understanding of reasoning communities and inform technological developments.