Chronic pain patient narratives allow for the estimation of current pain intensity

• Type: Article in International Conference Proceedings Book
• Date: 2023

Authors

Nunes, DAP (Author) Other; Ferreira Gomes, J (Author) Other; Oliveira, D (Author) Other; Carlos Vaz (Author) FMUP; Pimenta, S (Author) FMUP; Neto F.L. (Author) FMUP; de Matos, DM (Author) Other

Conference proceedings International

Title: Proceedings - IEEE Symposium on Computer-Based Medical Systems

Pages: 716-719

36th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2023

L¿Aquila, 22 June 2023 through 24 June 2023

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-00X-EQ0

DOI: 10.1109/cbms58004.2023.00306

Abstract (EN): 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 56.4 +/- 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 F-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 F-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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4