Classification of patients with pain based on neuropathic pain symptoms: comparison of an artificial neural network against an established scoring system.
Wider use of pain assessment tools that are specifically designed for certain types of pain--such as neuropathic pain--contribute an increasing amount of information which in turn offers the opportunity to employ advanced methods of data analysis. In this manuscript, we present the results of a study where we employed artificial neural networks (ANNs) in an analysis of pain descriptors with the goal of determining how an approach that uses a specific symptoms-based tool would perform with data from the real world of clinical practice. We also used traditional statistics approaches in the form of established scoring systems as well as logistic regression analysis for the purpose of comparison. Our results confirm the clinical experience that groups of pain descriptors rather than single items differentiate between patients with neuropathic and non-neuropathic pain. The accuracy obtained by ANN analysis was only slightly higher than that of the traditional approaches, indicating the absence of nonlinear relationships in this dataset. Data analysis with ANNs provides a framework that extends what current approaches offer, especially for dynamic data, such as the rating of pain descriptors over time.
Behrman
Michael
M
Department of Mathematics, University of Wisconsin--Madison, Madison, WI, USA.
Linder
Roland
R
Assadi
Amir H
AH
Stacey
Brett R
BR
Backonja
Misha-Miroslav
MM
D000328:Adult; D000368:Aged; D000465:Algorithms; D000704:Analysis of Variance; D005260:Female; D006801:Humans; D007257:Information Theory; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D010146:Pain / Q000145:classification* / Q000209:etiology*; D010147:Pain Measurement / Q000379:methods*; D010523:Peripheral Nervous System Diseases / Q000145:classification* / Q000150:complications*; D015203:Reproducibility of Results