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

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Health & Medical Research · Depth: Expert, short

Summary

A system for binary insomnia classification from MIMIC-III clinical notes, developed for Subtask 1 of the SMM4H HeaRD 2026 Task 2, utilizes OpenAI GPT-5.2 with Chain-of-Thought (CoT) prompting. This LLM-based approach integrates three strategies: fixed 8-shot prompting, dynamic retrieval using semantic embeddings, and self-consistency voting. The system also incorporates rule-based criteria, combining symptom patterns like difficulty sleeping and daytime impairment with primary and secondary insomnia medication indicators. Its top configuration, Self-Consistency Voting, achieved a 95.67% weighted F1 on validation and an 82.35% F1 on the official test set, outperforming the 81.25% F1 of the baseline. This test score substantially exceeded the task's mean (68.05%) and median (70.37%). Key contributions include explicit comorbidity exclusion prompting and context-aware nursing note handling.

Key takeaway

For NLP Engineers developing clinical classification systems, integrating advanced LLM techniques is critical. Your teams should consider Chain-of-Thought prompting with models like OpenAI GPT-5.2, augmented by self-consistency voting and rule-based criteria, to achieve high diagnostic accuracy. This approach significantly outperforms baseline methods for tasks like binary insomnia classification, offering a robust strategy for handling complex medical conditions and improving recall from unstructured clinical notes.

Key insights

LLMs with Chain-of-Thought prompting and self-consistency voting significantly enhance binary insomnia classification from clinical notes.

Principles

• Self-consistency boosts LLM recall.
• Rule-based criteria refine LLM predictions.
• Explicit comorbidity exclusion improves accuracy.

Method

The method combines OpenAI GPT-5.2 with Chain-of-Thought prompting, 8-shot examples, dynamic semantic retrieval, and self-consistency voting. It integrates rule-based criteria for symptom patterns and medication indicators.

In practice

• Apply CoT prompting for clinical text.
• Integrate self-consistency for robust recall.
• Use rule-based logic for specific diagnoses.

Topics

• Large Language Models
• Chain-of-Thought Prompting
• Clinical NLP
• Insomnia Classification
• MIMIC-III
• Self-Consistency Voting

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.