An Intelligent Clinical Decision Support System for Analyzing Neuromusculoskeletal Disorders

Nigar Şen Köktaş, Neşe Yalabik, Güneş Yavuzer

2008

Abstract

This study presents a clinical decision support system for detecting and further analyzing neuromusculoskeletal disorders using both clinical and gait data. The system is composed of a database storing disease characteristics, symptoms and gait data of the subjects, a combined pattern classifier that processes the data and user friendly interfaces. Data is mainly obtained through Computerized Gait Analysis, which can be defined as numerical representation of the mechanical measurements of human walking patterns. The decision support system uses mainly a combined classifier to incorporate the different types of data for better accuracy. A decision tree is developed with Multilayer Perceptrons at the leaves. The system is planned to be used for various neuromusculoskeletal disorders such as Cerebral Palsy (CP), stroke, and Osteoarthritis (OA). First experiments are performed with OA. Subjects are classified into four OA-severity categories, formed in accordance with the Kellgren-Lawrence scale: “Normal”, “Mild”, “Moderate”, and “Severe”. A classification accuracy of 80% is achieved on the test set. To complete the system, a patient follow-up mechanism is also designed.

References

  1. G. Barton, P. Lisboa, A. Lees, S. Attfield, “Gait quality assessment using self-organizing artificial neural networks”, Gait and Posture, vol. 25, pp. 374-379, 2007
  2. M. Kohle , D. Merkl , J. Kastner, “Clinical gait analysis by neural networks: issues and experiences”, in Proceedings of the 10th IEEE Symposium on Computer-Based Medical Systems 1997, pp.138
  3. N. Sen Koktas, N. Yalabik, G. Yavuzer, “Ensemble Classifiers for Medical Diagnosis of Knee Osteoarthritis Using Gait Data”, in Proceeding of IEEE International Conference on Machine Learning and Applications, 2006, pp. 225-230
  4. R. K. Begg, M. Palaniswami, B. Owen, “Support Vector Machines for Automated Gait Classification”, IEEE Transactions On Biomedical Engineering, vol. 52, pp. 828-838, 2005
  5. L. I. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms. WileyInterscience, 2004
  6. J. Kittler, M. Hatef, R. P. W. Duin, J. Matas, “On Combining Classifiers”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, pp. 226-239, 1998
  7. H. Gök, S. Ergin, G. Yavuzer, ”Kinetic and kinematic characteristics of gait in patients with medial knee arthritis” , Acta Orthop Scand, vol. 73, pp. 647-652, 2002
  8. K. J. Deluzio, J. L. Astephen, “Biomechanical features of gait waveform data associated with knee osteoarthritis: An application of principal component analysis”, Gait and Posture, vol. 25, pp. 86-93, 2007
  9. Kaufman, K., Hughes, C., Morrey, B., Morrey, M., An, K., “Gait characteristics of patients with knee osteoarthritis”, Journal of Biomechanics,, vol. 34, pp. 907-915, 2001
  10. Anil K. Jain , Robert P. W. Duin , Jianchang Mao, “Statistical Pattern Recognition: A Review”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, pp. 4- 37, 2000
  11. R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification. John Wiley and Sons, New York, 2001
  12. R.P.W Duin, PRTOOLS (version 4). A Matlab toolbox for pattern recognition. Pattern Recognition Group, Delft University of Technology, February 2004
  13. Decision Support Systems, January 2008, http://www.openclinical.org/dss.html
  14. R. A. Miller, F.E. Masarie, “Use of the Quick Medical Reference (QMR) program as a tool for medical education”, Methods of Information in Medicine, vol. 28, pp.340-5, 1989
  15. DXPlain, January 2008, http://lcs.mgh.harvard.edu/projects/dxplain.html
  16. E. Coiera. The Guide to Health Informatics (2nd Edition). Arnold, London, October 2003.
  17. K. Kawamoto, “Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success”, BMJ, vol. 330, pp. 330- 765, 2005
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Paper Citation


in Harvard Style

Şen Köktaş N., Yalabik N. and Yavuzer G. (2008). An Intelligent Clinical Decision Support System for Analyzing Neuromusculoskeletal Disorders . In Proceedings of the 8th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2008) ISBN 978-989-8111-42-5, pages 29-37. DOI: 10.5220/0001732700290037


in Bibtex Style

@conference{pris08,
author={Nigar Şen Köktaş and Neşe Yalabik and Güneş Yavuzer},
title={An Intelligent Clinical Decision Support System for Analyzing Neuromusculoskeletal Disorders},
booktitle={Proceedings of the 8th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2008)},
year={2008},
pages={29-37},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001732700290037},
isbn={978-989-8111-42-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2008)
TI - An Intelligent Clinical Decision Support System for Analyzing Neuromusculoskeletal Disorders
SN - 978-989-8111-42-5
AU - Şen Köktaş N.
AU - Yalabik N.
AU - Yavuzer G.
PY - 2008
SP - 29
EP - 37
DO - 10.5220/0001732700290037