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Prediction of blood glucose levels in diabetic patients using a hybrid AI technique
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
1999 (English)In: Computers and biomedical research, ISSN 0010-4809, E-ISSN 1090-2368, Vol. 32, no 2, 132-144 p.Article in journal (Refereed) Published
Abstract [en]

One of the problems in the management of the diabetic patient is to balance the dose of insulin without exactly knowing how the patient's blood glucose concentration will respond. Being able to predict the blood glucose level would simplify the management. This paper describes an attempt to predict blood glucose levels using a hybrid AI technique combining the principal component method and neural networks. With this approach, no complicated models or algorithms need be considered. The results obtained from this fairly simple model show a correlation coefficient of 0.76 between the observed and the predicted values during the first 15 days of prediction. By using this technique, all the factors affecting this patient's blood glucose level are considered, since they are integrated in the data collected during this time period. It must be emphasized that the present method results in an individual model, valid for that particular patient under a limited period of time. However, the method itself has general validity, since the blood glucose variations over time have similar properties in any diabetic patient.

Place, publisher, year, edition, pages
1999. Vol. 32, no 2, 132-144 p.
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:liu:diva-29804DOI: 10.1006/cbmr.1998.1506Local ID: 15216OAI: oai:DiVA.org:liu-29804DiVA: diva2:250622
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2017-12-13Bibliographically approved
In thesis
1. Medical data time series analyses using AI techniques
Open this publication in new window or tab >>Medical data time series analyses using AI techniques
2002 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In many areas of medicine, we collect serial measurements of different variables as they change over time. Changes in a time series is often reflective of a change in a patient's status and may be helpful in guiding the appropriate therapy. As we tend to collect data more data as well as more frequently and for longer periods of time, the amount of information available to the clinician grows. Even though there is and has been a siguificant development in medical technology, the management of these growing anaounts of data still requires manual interpretation in many cases.

Typically, the information collected from serial measurements of data is used to establish a trend, which may be related to a patient's progress or deterioration. Isolated measurements of a variable also contiibute to a better understanding of a patient's status. The physician ahnost always manually interprets this type of infonnation. Time series may contain other types of information that may not be as easily be interpreted this way.

This thesis describes a number of cases where other types of information have been extracted from medical data time series. Time series may contain information that can be used for prediction as well as categorization (diagnosis). Except from the potential of improving patient care, this information is also helpful in expanding the understanding of the processes involved in each example.

The methods used in this thesis include principal component analysis, neural networks, cross-correlation analysis and the wavelet transform. By applying these methods to analyse the information in the various time series, adaptable and effective tools for an increased understanding of these problems may be created. This may contribute to better patient management by providing the clinician with improved decision support.

Place, publisher, year, edition, pages
Linköping: Linköpings universitet, 2002. 49 p.
Series
Linköping University Medical Dissertations, ISSN 0345-0082 ; 726
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-29437 (URN)14783 (Local ID)91-7373-164-1 (ISBN)14783 (Archive number)14783 (OAI)
Public defence
2002-04-15, Högdahlsalen, Universitetssjukhuset, Linköping, 13:00 (Swedish)
Opponent
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2012-09-14Bibliographically approved

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