Work

MedMind AI at #SMM4H-HeaRD 2026: Data Extraction and Generation Using Prompt Engineering and Structured Outputs (Tasks 1–6)

Year: 2026

Type: conference-paper

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-miniand gpt-5.4)can accurately extract and classify crucial health information when constrained by strict output schemas.Notably, our hybrid approach achieved the best overall performance among all participating systems for Task 2 (Insomnia Detection).

Source: Social Media Mining for Health Research and Applications Workshop and Shared Tasks (SMM4H)

DOI: 10.18653/v1/2026.smm4h-1.31

Authors: Aatish Pradhan, Brian M. Habersberger

Language: English

Topic: Mental Health via Writing

Subfield: Social Psychology

Field: Psychology

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W7166809753

FWCI:

Cites:

Cited by:

Related to: 10