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        <title>Prestige at #SMM4H-HeaRD 2026: Binary Insomnia Classification from Clinical Notes Using LLMs with Chain-of-Thought Reasoning</title>
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            <namePart type="given">Guillermo</namePart>
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            <namePart type="given">Graciela</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Prestige at #SMM4H-HeaRD 2026: Binary Insomnia Classification from Clinical Notes Using LLMs with Chain-of-Thought Reasoning
%A Komolafe, Oyindolapo O.
%Y Lopez-Garcia, Guillermo
%Y Gonzalez-Hernandez, Graciela
%S Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, United States
%@ 979-8-89176-432-3
%F komolafe-2026-prestige
%X 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.
%U https://aclanthology.org/2026.smm4h-1.5/
%P 23-27

Markdown (Informal)

[Prestige at #SMM4H-HeaRD 2026: Binary Insomnia Classification from Clinical Notes Using LLMs with Chain-of-Thought Reasoning](https://aclanthology.org/2026.smm4h-1.5/) (Komolafe, SMM4H 2026)

• Prestige at #SMM4H-HeaRD 2026: Binary Insomnia Classification from Clinical Notes Using LLMs with Chain-of-Thought Reasoning (Komolafe, SMM4H 2026)

ACL

• Oyindolapo O. Komolafe. 2026. Prestige at #SMM4H-HeaRD 2026: Binary Insomnia Classification from Clinical Notes Using LLMs with Chain-of-Thought Reasoning. In Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks, pages 23–27, San Diego, United States. Association for Computational Linguistics.