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Analysis of the information content in sonoclot data and reconstruction of coagulation test variables
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
Department of Anesthesiology and Intensive Care, Örebro Medical Center Hospital, Örebro, Sweden.
2002 (English)In: Journal of medical systems, ISSN 0148-5598, E-ISSN 1573-689X, Vol. 26, no 1, 1-8 p.Article in journal (Refereed) Published
Abstract [en]

The Sonoclot Coagulation AnalyzerTM is a viscoelastometer used for in vitro analysis of the coagulation process from the start of fibrin formation, through polymerization of the fibrin monomer, platelet interaction, and eventually to clot retraction and lysis. In this paper, we have analyzed series of Sonoclot curves and simultaneously obtained coagulation tests (APT, PT, Fibrinogen, Platelet Count, and D-dimer) from patients who underwent total hip replacements (THA). By using the Principal Component Analysis method (PCA), we found that the most important coagulation test variables as reflected in the Sonoclot signature, are Platelet Count, PT, and Fibrinogen. Also, by using a Back-Propagation Neural Network (BP), we were able to reconstruct the coagulation variables Platelet Count, PT, and Fibrinogen from the Sonoclot curve with a reasonable accuracy. This would also indicate that these three coagulation test variables are most important in determining the appearance of the Sonoclot signature.

Place, publisher, year, edition, pages
2002. Vol. 26, no 1, 1-8 p.
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:liu:diva-29552DOI: 10.1023/A:1013007202250Local ID: 14923OAI: oai:DiVA.org:liu-29552DiVA: diva2:250368
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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