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    A hypoglycemic episode diagnosis system based on neural networks for Type 1 diabetes mellitus

    186254_186254.pdf (1.433Mb)
    Access Status
    Open access
    Authors
    Chan, Kit Yan
    Ling, Sai
    Nguyen, Hung
    Jiang, Frank
    Date
    2012
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Chan, Kit Yan and Ling, Sai Ho and Nguyen, Hung and Jiang, Frank. 2012. A hypoglycemic episode diagnosis system based on neural networks for Type 1 diabetes mellitus, in IEEE Congress on Evolutionary Computation, Jun 10-15 2012, pp. 2046-2051. Brisbane, Qld: IEEE.
    Source Title
    Proceedings of the IEEE Congress on Evolutionary Computation
    Source Conference
    IEEE Congress on Evolutionary Computation
    DOI
    10.1109/CEC.2012.6256604
    ISBN
    978-1-4673-1508-1
    Remarks

    Copyright © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    URI
    http://hdl.handle.net/20.500.11937/48161
    Collection
    • Curtin Research Publications
    Abstract

    Hypoglycemia (or low blood glucose) is dangerous for Type 1 diabetes mellitus (T1DM) patients, as this can cause unconsciousness or even death. However, it is impossible to monitor the hypoglycemia by measuring patients’ blood glucose levels all the time, especially at night. In this paper, a hypoglycemic episode diagnosis system is proposed to determine T1DM patients’ blood glucose levels based on these patients’ physiological parameters which can be measured online. It can be used not only to diagnose hypoglycemic episodes in T1DM patients, but also to generate a set of rules, which describe the domains of physiological parameters that lead to hypoglycemic episodes. The hypoglycemic episode diagnosis system addresses the limitations of the traditional neural network approaches which cannot generate implicit information. The performance of the proposed hypoglycemic episode diagnosis system is evaluated by using real T1DM patients’ data sets collected from the Department of Health, Government of Western Australia, Australia. Results show that satisfactory diagnosis accuracy can be obtained. Also, explicit knowledge can be produced such that the deficiency of traditional neural networks can be overcome. A clear understanding of how they perform diagnosis can be indicated.

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    • Diagnosis of hypoglycemic episodes using a neural network based rule discovery system
      Chan, Kit Yan; Ling, S.; Dillon, Tharam; Nguyen, H. (2011)
      Hypoglycemia or low blood glucose is dangerous and can result in unconsciousness, seizures and even death for Type 1 diabetes mellitus (T1DM) patients. Based on the T1DM patients’ physiological parameters, corrected QT ...
    • Classification of hypoglycemic episodes for Type 1 diabetes mellitus based on neural networks
      Chan, Kit Yan; Ling, S.H.; Dillon, Tharam S.; Nguyen, H. (2010)
      Hypoglycemia is dangerous for Type 1 diabetes mellitus (T1DM) patients. Based on the physiological parameters, we have developed a classification unit with hybridizing the approaches of neural networks and genetic algorithm ...
    • The Quality of Routinely Collected Data: Using the "Principal Diagnosis" in Emergency Department Databases as an Example
      Liaw, S.; Chen, Huei-yang; Maneze, D.; Taggart, J.; Dennis, S.; Vagholkar, S.; Bunker, J. (2012)
      Objectives: This paper aims to estimate the reliability of using “principal diagnosis” to identify people with diabetes mellitus (DM), cardiovascular diseases (CVD), and asthma or chronic obstructive pulmonary disease ...
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