{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:37:48Z","timestamp":1761176268957,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Clinical chronic pain assessment often relies on standardized self-report questionnaires, which constrain patients\u2019 narratives and are frequently reported as difficult to interpret. Adapting these questionnaires across populations and cultures is costly and time-consuming, and minor ad-hoc adaptations can have major consequences in scoring validity. In this study, we investigated whether patients\u2019 personal descriptions of their pain, i.e., their language of pain, can predict scores from standard clinical self-report measures, including assessments of pain intensity, positive and negative affect, catastrophizing, depression, and anxiety. These are crucial dimensions of the chronic pain experience. To model these predictive relations, we developed a suite of NLP pipelines, including psycholinguistic feature extraction, language model encoding, and large language model prompting, emphasizing locally hosted solutions addressing privacy concerns w.r.t. clinical data. We systematically evaluated the robustness and generalization of these pipelines across three datasets of language of pain, encompassing transcriptions of chronic pain interviews varying in topics, format, length, language, and chronic pain conditions, mirroring challenges in the clinical practice. Our results and analyses systematize the strengths and limitations of each pipeline and lay the foundation for selecting predictive models based on clinical targets and evaluation scenarios.<\/jats:p>","DOI":"10.3233\/faia251310","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:57:53Z","timestamp":1761127073000},"source":"Crossref","is-referenced-by-count":0,"title":["Language as a Predictor of Multiple Clinical Chronic Pain Assessments"],"prefix":"10.3233","author":[{"given":"Diogo A.P.","family":"Nunes","sequence":"first","affiliation":[{"name":"INESC-ID, Instituto Superior T\u00e9cnico, Universidade de Lisboa, Portugal"}]},{"given":"Joana","family":"Ferreira-Gomes","sequence":"additional","affiliation":[{"name":"i3s, Faculdade de Medicina, Universidade do Porto, Portugal"}]},{"given":"Fani","family":"Neto","sequence":"additional","affiliation":[{"name":"i3s, Faculdade de Medicina, Universidade do Porto, Portugal"}]},{"given":"A. Vania","family":"Apkarian","sequence":"additional","affiliation":[{"name":"Northwestern University Feinberg School of Medicine, USA"}]},{"given":"Paulo","family":"Branco","sequence":"additional","affiliation":[{"name":"Northwestern University Feinberg School of Medicine, USA"}]},{"given":"David","family":"Martins de Matos","sequence":"additional","affiliation":[{"name":"INESC-ID, Instituto Superior T\u00e9cnico, Universidade de Lisboa, Portugal"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251310","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:57:53Z","timestamp":1761127073000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251310"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251310","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}