Turning Patients’ Open-Ended Narratives of Chronic Pain Into Quantitative Measures: Natural Language Processing Study

Background Subjective report of pain remains the gold standard for assessing symptoms in patients with chronic pain and their response to analgesics. This subjectivity underscores the importance of understanding patients’ personal narratives, as they offer an accurate representation of the illness experience. Objective In this pilot study involving 20 patients with chronic low back pain (CLBP), we applied emerging tools from natural language processing (NLP) to derive quantitative measures that captured patients’ pain narratives. Methods Patients’ narratives were collected during recorded semistructured interviews in which they spoke about their lives in general and their experiences with CLBP. Given that NLP is a novel approach in this field, our goal was to demonstrate its ability to extract measures that relate to commonly used tools, such as validated pain questionnaires and rating scales, including the numerical rating scale and visual analog scale. Results First, we showed that patients’ utterances were significantly closer in semantic space to anchor sentences derived from validated pain questionnaires than to their antithetical counterparts. Furthermore, we found that the semantic distances between patients’ utterances and anchor sentences related to quality of life were strongly correlated with reported CLBP intensity on the numerical rating and visual analog scales. Consistently, we observed significant differences between individuals with low and high pain levels. Conclusions Although our small sample size limits the generalizability of these findings, the results provide preliminary evidence that NLP can be used to quantify the subjective experience of chronic pain and may hold promise for clinical applications.