  Objectives: This study aims to systematically review the literature on the
computational processing of the language of pain, or pain narratives, whether
generated by patients or physicians, identifying current trends and challenges.
Methods: Following the PRISMA guidelines, a comprehensive literature search was
conducted to select relevant studies on the computational processing of the
language of pain and answer pre-defined research questions. Data extraction and
synthesis were performed to categorize selected studies according to their
primary purpose and outcome, patient and pain population, textual data,
computational methodology, and outcome targets. Results: Physician-generated
language of pain, specifically from clinical notes, was the most used data.
Tasks included patient diagnosis and triaging, identification of pain mentions,
treatment response prediction, biomedical entity extraction, correlation of
linguistic features with clinical states, and lexico-semantic analysis of pain
narratives. Only one study included previous linguistic knowledge on pain
utterances in their experimental setup. Most studies targeted their outcomes
for physicians, either directly as clinical tools or as indirect knowledge. The
least targeted stage of clinical pain care was self-management, in which
patients are most involved. Affective and sociocultural dimensions were the
least studied domains. Only one study measured how physician performance on
clinical tasks improved with the inclusion of the proposed algorithm.
Discussion: This review found that future research should focus on analyzing
patient-generated language of pain, developing patient-centered resources for
self-management and patient-empowerment, exploring affective and sociocultural
aspects of pain, and measuring improvements in physician performance when aided
by the proposed tools.
