Work

DNT at #SMM4H–HeaRD 2026: Leveraging BERT-based Encoders and LLMs for Medical Information Extraction

Year: 2026

Type: conference-paper

Abstract: This paper presents our systems for two tasks at #SMM4H-HeaRD 2026.For Task 1 (multilingual Adverse Drug Event detection), we fine-tune BERT-based multilingual models (In-foXLM and XLM-RoBERTa) and Qwen3.5-9B with ensemble methods, achieving 0.8584 macro F1 on the development set and 0.5304 F1 on unseen Farsi.For Task 7 (span detection of ClinicalImpacts and SocialImpacts in opioid narratives), DeBERTa-Large with simplified labeling achieves the best test performance (0.583 relaxed F1, 0.500 strict F1).Our analysis shows that LLMs excel on known languages in Task 1, while transformer-based models with simplified labeling generalize better for NER tasks.

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

DOI: 10.18653/v1/2026.smm4h-1.7

Authors: Doan Nhat Tien, Thìn Đặng Văn

Institution: Vietnam National University Ho Chi Minh City

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W7166890064