Literature DB >> 8187464

Time series analysis and control of blood glucose levels in diabetic patients.

T Deutsch1, E D Lehmann, E R Carson, A V Roudsari, K D Hopkins, P H Sönksen.   

Abstract

This paper describes features of a computer-based decision support system which is being developed to assist in the management of insulin-dependent diabetic patients. The clinical context is the provision of advice on the adjustment of the basic insulin regimen such as occurs at regular visits to the clinician. The integrated system combines data processing and interpretation, generation of qualitative advice and testing the implications of that advice using a glucose/insulin dynamic simulator. The two major features described in this paper are time series analysis of blood glucose data, and their interpretation in relation to the provision of advice for controlling the patient's blood glucose level. It is demonstrated that two approaches may be adopted in such time series analysis: an intuitive approach, manipulating symbolic representations of the data, and formal time series methods which decompose the series into clinically related components.

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Year:  1994        PMID: 8187464     DOI: 10.1016/0169-2607(94)90053-1

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN: 0169-2607            Impact factor:   5.428


  5 in total

Review 1.  Practical use of self-monitoring of blood glucose data.

Authors:  Barry H Ginsberg
Journal:  J Diabetes Sci Technol       Date:  2013-03-01

2.  Data mining technologies for blood glucose and diabetes management.

Authors:  Riccardo Bellazzi; Ameen Abu-Hanna
Journal:  J Diabetes Sci Technol       Date:  2009-05-01

3.  Distributed intelligent data analysis in diabetic patient management.

Authors:  R Bellazzi; C Larizza; A Riva; A Mira; S Fiocchi; M Stefanelli
Journal:  Proc AMIA Annu Fall Symp       Date:  1996

4.  Mining biomedical time series by combining structural analysis and temporal abstractions.

Authors:  R Bellazzi; P Magni; C Larizza; G De Nicolao; A Riva; M Stefanelli
Journal:  Proc AMIA Symp       Date:  1998

Review 5.  "Smart" continuous glucose monitoring sensors: on-line signal processing issues.

Authors:  Giovanni Sparacino; Andrea Facchinetti; Claudio Cobelli
Journal:  Sensors (Basel)       Date:  2010-07-12       Impact factor: 3.576

  5 in total

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