Categorization and analysis of pain and activity in patients with low back pain using a neural network technique.

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 ofpatients 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.