Explainable AI reveals tissue pathology and psychosocial drivers of opioid prescription for non-specific chronic low back pain.

Effective management of non-specific chronic lower back pain (ns-cLBP) requires nuanced prescription decisions within evolving guidelines for conservative treatment. This study developed comprehensive LBP patient profiles from electronic medical records (EMR), integrating clinical charts (demographics, social determinants, diagnoses, medications) and radiology reports (MRI-confirmed diagnoses) to predict pharmacological management strategies. One-vs-one and one-vs-rest classification frameworks systematically evaluated treatment decisions across three prescriptions: no medication, NSAIDs, and opioids. Real-world complexity and heterogeneity in ns-cLBP management was reflected in modest yet clinically meaningful performance metrics (balanced accuracy = 0.58, AUC = 0.62, F1-score = 0.42). Chart-documented diagnoses marginally outperformed MRI-reported pathology as predictors, though this difference was within the range of variability, which suggests the importance of diagnoses informed by patient-reported symptoms in shaping treatment pathways. SHAP feature importance analysis identified consistent predictors (year_at_first_imaging) and variable factors (spinal_stenosis, disc_pathology, race_ethnicity, negative_psych_state, osteoarthritis_osteoarthrosis) in prescriptions, with higher associations observed in those with anxiety or depression, partnered individuals and females. By leveraging explainable AI, this study quantifies the interplay between biological and psychosocial drivers of prescribing decisions, offering a transparent, data-driven monitoring tool for understanding in chronic pain care. These findings demonstrate the potential of multi-modal EMR data and interpretable models to guide more personalized, equitable ns-cLBP management and opioid prescriptions.