Categorization and Analysis of Pain and Activity in Patients with Low Back Pain Using a Neural Network Technique

  • Authors:
  • John J. Liszka-Hackzell;David P. Martin

  • Affiliations:
  • Department of Anesthesiology, Orebro Medical Center Hospital, S-701 85 Orebro, Sweden;Department of Anesthesiology, Mayo Clinic and Mayo Foundation, Rochester, Minnesota 55905

  • Venue:
  • Journal of Medical Systems
  • Year:
  • 2002

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Abstract

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 r =0.80, p