@inproceedings{ntanavaras-etal-2026-synthetic,
    title = "A Synthetic Conversational Dataset for Type 2 Diabetes Management",
    author = "Ntanavaras, Stergios  and
      de Boer, Maaike  and
      Vossen, Piek T.J.M.",
    editor = "Gupta, Deepak  and
      Thompson, Paul  and
      Ananiadou, Sophia  and
      Demner-Fushman, Dina",
    booktitle = "Proceedings of the Third Workshop on Patient-Oriented Language Processing ({CL}4{H}ealth) @ {LREC} 2026",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/test-year-match/2026.cl4health-1.16/",
    doi = "10.63317/3cbhekpxj33y",
    pages = "171--181",
    abstract = "Access to real patient-doctor conversations in the medical domain is often restricted due to privacy concerns, making it difficult to build robust conversational AI systems. To address this, we present a novel methodology for generating a high-quality synthetic dataset designed for conversational triple extraction in Type 2 Diabetes management. Using structured prompting with GPT-4, we generated 16 demographically and medically diverse diabetic personas, and 256 multi-turn conversations between these personas and a caretaker agent, simulating realistic and context-rich interactions. The conversations incorporate critical properties such as personalization, empathy, contextual awareness, and medically grounded advice, as validated through both LLM-based and human expert evaluations. These synthetic conversations are further annotated with Subject-Predicate-Object (SPO) labels at the token level, integrating both manual and LLM-automated methods, forming the foundation for downstream tasks like triple extraction. Our work demonstrates the feasibility of using generative AI to simulate healthcare conversations at scale, offering a solution for data-scarce domains."
}