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Turning Patients' Open-Ended Narratives of Chronic Pain Into Quantitative Measures: Natural Language Processing Study.

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Titel: Turning Patients' Open-Ended Narratives of Chronic Pain Into Quantitative Measures: Natural Language Processing Study.
Autoren: Norel R; IBM (United States), Yorktown Heights, NY, United States., Gewandter J; Department of Anesthesiology and Perioperative Medicine, School of Medicine and Dentistry, University of Rochester, Rochester, NY, United States., Zhang Z; Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC, United States., Tahsin A; Department of Psychiatry, University of Rochester, Rochester, NY, United States., Abdallah CG; Menninger Department of Psychiatry & Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States., Markman J; Eli Lilly (United States), Chestnut Hill, MA, United States., Duan Z; Department of Psychiatry, University of Rochester, Rochester, NY, United States., Cecchi G; IBM (United States), Yorktown Heights, NY, United States., Geha P; Department of Psychiatry, University of Rochester, Rochester, NY, United States.
Quelle: JMIR human factors [JMIR Hum Factors] 2025 Nov 25; Vol. 12, pp. e80269. Date of Electronic Publication: 2025 Nov 25.
Publikationsart: Journal Article
Sprache: English
Info zur Zeitschrift: Publisher: JMIR Publications Inc Country of Publication: Canada NLM ID: 101666561 Publication Model: Electronic Cited Medium: Internet ISSN: 2292-9495 (Electronic) Linking ISSN: 22929495 NLM ISO Abbreviation: JMIR Hum Factors Subsets: MEDLINE
Imprint Name(s): Original Publication: Toronto : JMIR Publications Inc, [2014]-
MeSH-Schlagworte: Natural Language Processing* , Chronic Pain*/psychology , Chronic Pain*/diagnosis , Pain Measurement*/methods , Narration* , Low Back Pain*/psychology, Humans ; Female ; Male ; Middle Aged ; Adult ; Pilot Projects ; Surveys and Questionnaires ; Aged ; Quality of Life
Abstract: Background: Subjective report of pain remains the gold standard for assessing symptoms in patients with chronic pain and their response to analgesics. This subjectivity underscores the importance of understanding patients' personal narratives, as they offer an accurate representation of the illness experience.
Objective: In this pilot study involving 20 patients with chronic low back pain (CLBP), we applied emerging tools from natural language processing (NLP) to derive quantitative measures that captured patients' pain narratives.
Methods: Patients' narratives were collected during recorded semistructured interviews in which they 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, including the numerical rating scale and visual analog scale.
Results: First, we showed that patients' utterances were significantly closer in semantic space to anchor sentences derived from validated pain questionnaires than to their antithetical counterparts. Furthermore, we found that the semantic distances between patients' utterances and anchor sentences related to quality of life were strongly correlated with reported CLBP intensity on the numerical rating and visual analog scales. Consistently, we observed significant differences between individuals with low and high pain levels.
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 applications.
(©Raquel Norel, Jennifer Gewandter, Zhengwu Zhang, Anika Tahsin, Chadi G Abdallah, John Markman, Zhiyao Duan, Guillermo Cecchi, Paul Geha. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 25.11.2025.)
Contributed Indexing: Keywords: chronic pain; digital phenotyping; natural language processing; pain narratives; semantic distance
Entry Date(s): Date Created: 20251125 Date Completed: 20251125 Latest Revision: 20251125
Update Code: 20251126
DOI: 10.2196/80269
PMID: 41290220
Datenbank: MEDLINE
Beschreibung
Abstract:Background: Subjective report of pain remains the gold standard for assessing symptoms in patients with chronic pain and their response to analgesics. This subjectivity underscores the importance of understanding patients' personal narratives, as they offer an accurate representation of the illness experience.<br />Objective: In this pilot study involving 20 patients with chronic low back pain (CLBP), we applied emerging tools from natural language processing (NLP) to derive quantitative measures that captured patients' pain narratives.<br />Methods: Patients' narratives were collected during recorded semistructured interviews in which they 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, including the numerical rating scale and visual analog scale.<br />Results: First, we showed that patients' utterances were significantly closer in semantic space to anchor sentences derived from validated pain questionnaires than to their antithetical counterparts. Furthermore, we found that the semantic distances between patients' utterances and anchor sentences related to quality of life were strongly correlated with reported CLBP intensity on the numerical rating and visual analog scales. Consistently, we observed significant differences between individuals with low and high pain levels.<br />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 applications.<br /> (©Raquel Norel, Jennifer Gewandter, Zhengwu Zhang, Anika Tahsin, Chadi G Abdallah, John Markman, Zhiyao Duan, Guillermo Cecchi, Paul Geha. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 25.11.2025.)
ISSN:2292-9495
DOI:10.2196/80269