Chronic Pain Patient Narratives Allow for the Estimation of Current Pain Intensity

We demonstrate a proof-of-concept for the analysis of the language of chronic pain for pain intensity estimation. Importantly, we show that focus on specific words/themes is especially correlated with specific pain intensity categories. We interviewed chronic pain patients and collected demographic...

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Bibliographic Details
Main Authors: Nunes, Diogo A.P., Ferreira-Gomes, Joana, Oliveira, Daniela, Vaz, Carlos, Pimenta, Sofia, Neto, Fani, de Matos, David Martins
Format: Conference Proceeding
Language:English
Subjects:
chronic musculoskeletal pain
Current measurement
Estimation
Musculoskeletal system
natural language processing
Pain
pain intensity estimation
patient narratives
Support vector machines
Visualization
Weight measurement
Online Access:Request full text
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Summary:We demonstrate a proof-of-concept for the analysis of the language of chronic pain for pain intensity estimation. Importantly, we show that focus on specific words/themes is especially correlated with specific pain intensity categories. We interviewed chronic pain patients and collected demographic and clinical data. 65 patients (40 females), averaging \mathbf{56.4} \pm \mathbf{12.7} years of age, participated in the study. Patients reported their current pain intensity on a Visual Analogue Scale, which we discretized into 3 classes: mild, moderate, and severe pain. We extracted language features from the transcribed interview of each patient and used them to classify their pain intensity category. We measured performance with the weighted \mathbf{F}_{\mathbf{1}} score. Finally, we analyzed potential confounding variables for internal validity. The best performing model was the Support Vector Machine with an Early Fusion of select language features, with an \mathbf{F}_{\mathbf{1}} of 0.60, improving 39.5% upon the baseline. Patients with mild pain focused more on verbs, whilst moderate and severe pain patients focused on adverbs, and nouns and adjectives, respectively. We show that language features from patient narratives indeed convey information relevant for pain intensity estimation, and that our models can take advantage of that.
ISSN:2372-9198
DOI:10.1109/CBMS58004.2023.00306