  Chronic pain is a multi-dimensional experience, and pain intensity plays an
important part, impacting the patients emotional balance, psychology, and
behaviour. Standard self-reporting tools, such as the Visual Analogue Scale for
pain, fail to capture this burden. Moreover, this type of tools is susceptible
to a degree of subjectivity, dependent on the patients clear understanding of
how to use it, social biases, and their ability to translate a complex
experience to a scale. To overcome these and other self-reporting challenges,
pain intensity estimation has been previously studied based on facial
expressions, electroencephalograms, brain imaging, and autonomic features.
However, to the best of our knowledge, it has never been attempted to base this
estimation on the patient narratives of the personal experience of chronic
pain, which is what we propose in this work. Indeed, in the clinical assessment
and management of chronic pain, verbal communication is essential to convey
information to physicians that would otherwise not be easily accessible through
standard reporting tools, since language, sociocultural, and psychosocial
variables are intertwined. We show that language features from patient
narratives indeed convey information relevant for pain intensity estimation,
and that our computational models can take advantage of that. Specifically, our
results show that patients with mild pain focus more on the use of verbs,
whilst moderate and severe pain patients focus on adverbs, and nouns and
adjectives, respectively, and that these differences allow for the distinction
between these three pain classes.

Computation and Language