PAINT: A Framework for Multilingual Pain Representation from Medical Licensing Exams to Real-World Clinical Texts

Key points

• Pain is one of the most common and clinically important symptoms, reflecting disease onset, progression, and treatment response.1
• However, pain is difficult to accurately capture and interpret from medical text, as its expression varies across languages, documentation types, and patient demographics, and is often embedded in terminology, negation, temporality, and contextual descriptions.1
• To capture multilingual pain representations, we leverage large language models (LLMs) and adopt an ensemble strategy to improve performance.1
• We release the WorldMedQA-Pain ensemble labels, the extraction pipeline, validation scripts, and clinician annotation sheets for publicly licensed corpora, providing a foundation for pain-centered clinical NLP and for studying pain documentation across languages and healthcare contexts.1

Sources (1)

• [1]PAINT: A Framework for Multilingual Pain Representation from Medical Licensing Exams to Real-World Clinical Texts
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 12:28 PM

  In this study, we introduce PAINT, a multilingual framework for studying pain representation across medical exams and real-world clinical texts.

  Pain is one of the most common and clinically important symptoms, reflecting disease onset, progression, and treatment response.

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