ID: 214701 Chronic Pain Variability and Pain-Activity Correlations Differ Between Patients With and Without Prior Back Surgery

Patient baseline diagnoses/behaviors are thought to influence outcomes and quality of life during SCS therapy1,2,3 however, interaction of these factors is not well understood. Machine learning is used to identify relationships between parameters of multi-factor datasets via patient clustering. Here we apply an unsupervised machine learning technique to clinical data to identify patient-clustering factors and derive clinically relevant relationships between baseline patient factors and outcomes.