2026· Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks· pp. 211-215· 1 citation· 15 references
TL;DR
This paper addresses Task 1 on adverse drug event (ADE) detection and Task 8 on Mul-tiClinNER at SMM4H-HeaRD 2026 and targets clinical NER for English text.
Abstract
In this paper, we address Task 1 on adverse drug event (ADE) detection and Task 8 on Mul-tiClinNER at SMM4H-HeaRD 2026. ADE detection is formulated as a multilingual binary classification problem over social media posts spanning German, French, Russian, English, Mandarin and Japanese, with zero-shot on Farsi. Using XLM-RoBERTa-Large with a dual-pooling head, combined with stratified sampling, language-conditioned inputs, translation-based augmentation, and calibrated ensembling, our model achieves a macro F1 score of 0.6088, surpassing both the competition mean (0.5465) and median (0.5798). Our work in MultiClinNER targets clinical NER for English text. Using GLiNER-large with sliding-window inference, query augmentation, and calibrated thresholds, it achieves strict F1 scores of 0.7591 (Disease), 0.7263 (Procedure), and 0.6733 (Symptom), outperforming a Pub-MedBERT baseline across all entities.
This study investigates the English-to-Turkish clinical translation performance of three large language models (Aya-23, Seed-X-Instruct, Gemma-3) using zero/few-shot prompting and QLoRA fine-tuning. Our three-stage setup evaluates: (1) general translation after FLORES-devtest training, (2) medical domain adaptation on...
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Synergizing a curated domain-specific knowledge base with LLMs via a RAG architecture is an effective strategy for accurately identifying ADEs in unstructured Chinese clinical notes, providing a foundational open-source benchmark and a robust technical framework to advance pharmacovigilance, drug safety research, and c...
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Alation studies reveal a strong synergy between POS features and CRF decoding, with the combined approach yielding the best single-model performance, and ensembling models trained with different objectives improves both overall F1 score and discontinuous MWE scores, demonstrating the importance of training diversity fo...
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Overall, the proposed reformulation module produces more natural and clinically relevant Arabic questions and can be plugged into Arabic clinical QA pipelines as a measurable, tunable front-end.
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The technical feasibility of text-based distractor selection rate prediction is established and the performance landscape across model categories is characterized, with potential application scenarios requiring future validation.
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A morphological barrier is shown: while pivot-based adaptation facilitates transfer for distant languages through script alignment, it induces negative transfer in morphologically complex agglutinative languages like Gondi.
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