@inproceedings{pradhan-habersberger-2026-medmind,
    title = "{M}ed{M}ind {AI} at {\#}{SMM}4{H}-{H}ea{RD} 2026: Data Extraction and Generation Using Prompt Engineering and Structured Outputs (Tasks 1{--}6)",
    author = "Pradhan, Aatish  and
      Habersberger, Brian M.",
    editor = "Lopez-Garcia, Guillermo  and
      Gonzalez-Hernandez, Graciela",
    booktitle = "Proceedings of the 11th Social Media Mining for Health Research and Applications ({SMM}4{H}-{H}ea{RD} 2026) Workshop and Shared Tasks",
    month = jul,
    year = "2026",
    address = "San Diego, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.smm4h-1.31/",
    pages = "187--205",
    ISBN = "979-8-89176-432-3",
    abstract = "Six tasks from the SMM4H{--}HeaRD 2026 workshop were addressed with task-specific large-language-model (LLM) pipelines relying on prompt engineering, strict structured (JSON) responses, and deterministic rule sets. The pipelines utilize no task-specific fine-tuning and can be adapted across diverse clinical and social media data. This study demonstrates that general-purpose LLMs (gpt-5.4-mini and gpt-5.4) can accurately extract and classify crucial health information when constrained by strict output schemas. Notably, our hybrid approachachieved the best overall performance among all participating systems for Task 2 (Insomnia Detection)."
}