@inproceedings{elyazori-etal-2025-capturing,
    title = "Capturing Patients' Lived Experiences with Chronic Pain through Motivational Interviewing and Information Extraction",
    author = "Elyazori, Hadeel R A  and
      Abdulrazzaq, Rusul  and
      Al Shawi, Hana  and
      Amouzou, Isaac  and
      King, Patrick  and
      Manns, Syleah  and
      Popal, Mahdia  and
      Patel, Zarna  and
      Destefano, Secili  and
      Shah, Jay  and
      Gerber, Naomi  and
      Sikdar, Siddhartha  and
      Lee, Seiyon  and
      Acuna, Samuel  and
      Lybarger, Kevin",
    editor = "Ananiadou, Sophia  and
      Demner-Fushman, Dina  and
      Gupta, Deepak  and
      Thompson, Paul",
    booktitle = "Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health)",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.cl4health-1.28/",
    pages = "321--330",
    ISBN = "979-8-89176-238-1",
    abstract = "Chronic pain affects millions, yet traditional assessments often fail to capture patients' lived experiences comprehensively. In this study, we used a Motivational Interviewing framework to conduct semi-structured interviews with eleven adults experiencing chronic pain and then applied Natural Language Processing (NLP) to their narratives. We developed an annotation schema that integrates the International Classification of Functioning, Disability, and Health (ICF) with Aspect-Based Sentiment Analysis (ABSA) to convert unstructured narratives into structured representations of key patient experience dimensions. Furthermore, we evaluated whether Large Language Models (LLMs) can automatically extract information using this schema. Our findings advance scalable, patient-centered approaches to chronic pain assessment, paving the way for more effective, data-driven management strategies."
}