@inproceedings{komolafe-2026-prestige,
    title = "Prestige at {\#}{SMM}4{H}-{H}ea{RD} 2026: Binary Insomnia Classification from Clinical Notes Using {LLM}s with Chain-of-Thought Reasoning",
    author = "Komolafe, Oyindolapo O.",
    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-1.5/",
    pages = "23--27",
    ISBN = "979-8-89176-432-3",
    abstract = "This paper describes our system for Subtask 1 of the SMM4H HeaRD 2026 Task 2, which is an LLM-based system for binary insomnia classification from MIMIC-III clinical notes using OpenAI GPT-5.2 with chain-of-thought (CoT) prompting. Our approach implements three strategies: baseline fixed 8-shot prompting, dynamic retrieval using semantic embeddings, and self-consistency voting. The system applies rule-based criteria combining symptom patterns (difficulty sleeping and daytime impairment) with medication indicators (primary and secondary insomnia medications).Our best configuration (Self-Consistency Voting) achieved 95.67{\%} weighted F1 on validation and 82.35{\%} F1 on the official test set , outperforming the Baseline (81.25{\%} F1). Notably, our test F1-score of 82.35{\%} substantially exceeded the task mean (68.05{\%}) and median (70.37{\%}) across all participating teams. Key contributions include explicit comorbidity exclusion prompting, context-aware nursing note handling, logical constraint enforcement for prediction consistency, and a comparative analysis demonstrating that self-consistency improves recall at moderate computational cost."
}