Abstract
Introduction: Advances in digital health have expanded the view into a chronic pain patient’s experience and health-related outcomes through objective data from wearables and subjective data through questionnaires. A patient’s subjective response to therapy can be captured through a pre-determined discrete scale or in free-form feedback using the patient’s own words (i.e., free-text). Whereas the free-text response is intrinsically difficult to quantify, the forced choice of discrete scales represents a limited solution that dispenses with the nuances that natural language affords. To overcome this limitation, we implemented an Artificial Intelligence (AI) approach using Natural Language Processing (NLP) that includes aspects related to the daily pain experience, mood, alertness, activity, sleep, as well as those related specifically to Spinal Cord Stimulation (SCS) therapy recommendations that were created using personalized AI models1.
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