Neuropathic Pain Scale Based Clustering for Subgroup Analysis in Pain Medicine

Abstract

Neuropathic pain (NeuP) is often more difficult to treat than other types of chronic pain. The ability to predict outcomes in NeuP, such as response to specific therapies and return to work, would have tremendous value to both patients and society. In this work, we propose an adaptive clustering algorithm using the Neuropathic Pain Scale (NPS) to develop a set of standard patient templates. These templates may be useful in studying treatment response in NeuP. The approach is evaluated on 108 subjects' baseline data and results demonstrate the efficacy of utilizing neuropathic pain scale (NPS) metrics and our proposed method.