JMIR Publications

ABSTRACT

Background:

The subjective report of pain remains the gold standard for assessing symptoms in chronic pain patients and response to analgesics. This subjectivity underscores the importance of understanding patients’ personal narrative, as they offer the accurate representation of the illness experience.

Objective:

In this pilot study involving 19 patients with chronic low-back pain (CLBP), we applied emerging tools from natural language processing (NLP) to derive quantitative measures that capture patients’ narratives of their pain.

Methods:

Patients’ narratives were collected during recorded semi-structured interviews in which patients 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 like the numerical rating scale and visual analogue scale.

Results:

First, we show that patients’ utterances are significantly closer in semantic space to anchor sentences derived from validated pain questionnaires than to their antithetical counterparts. Furthermore, we demonstrate that the semantic distances between patients’ utterances and anchor sentences related to quality of life are strongly correlated with reported CLBP intensity on the numerical rating and visual analogue scales respectively. Consistently, we observe significant differences between individuals with low versus high pain.

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 application.