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Categorization and analysis of pain and activity in patients with low back pain using neural network technique
Department of Anesthesiology, Örebro Medical Center Hospital, Örebro, Sweden.
Department of Anesthesiology, Mayo Clinic and Mayo Foundation, Rochester, Minnesota.
2002 (English)In: Journal of medical systems, ISSN 0148-5598, E-ISSN 1573-689X, Vol. 26, no 4, 337-347 p.Article in journal (Refereed) Published
Abstract [en]

Low back pain represents a significant medical problem, both in its prevalence and its cost to society. Most episodes of acute low back pain resolve without significant long-term functional impact. However, a minority of patients experience extended chronic pain and disability. In this paper, we have explored new techniques of patient assessment that may prospectively identify this minority of patients at risk of developing poor outcomes. We studied 15 patients with acute low back pain and 25 patients with chronic low back pain over 4 month's time. Patients monitored their pain and activity levels continuously over the first 3 weeks. Pain and functional status were assessed at baseline and at 3 weeks following enrollment. Follow-up assessment of functional status and progress were performed at 2 and 4 months. The pain and activity levels were categorized using a self-organizing-map neural network. A back-propagation neural network was trained with the categorization and outcome data. There was a good correlation between the true and predicted values for general health (r = 0.96, p < 0.01) and mental health (r = 0.80, p < 0.01). No significant correlation was found if activity and pain data were not entered into the analysis. Our results show that neural network techniques can be applied effectively to categorizing patients with acute and chronic low back pain. It is our hope that future research will allow these categorizations to be tied to prognostic and therapeutic decisions in patients who present with episodes of back pain.

Place, publisher, year, edition, pages
2002. Vol. 26, no 4, 337-347 p.
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:liu:diva-29551DOI: 10.1023/A:1015820804859Local ID: 14922OAI: oai:DiVA.org:liu-29551DiVA: diva2:250367
Note

On the day of the defence day the status of this article was accepted.

Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2012-09-14Bibliographically 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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