Work

BIT@UA at #SMM4H 2024 Tasks 1 and 5: finding adverse drug events and children’s medical disorders in English tweets

Year: 2024

Type: conference-paper

Abstract: In this paper, we present our proposed systems, for Tasks 1 and 5 of the #SMM4H-2024 shared task (Social Media Mining for Health), responsible for identifying health-related aspects in English social media text.Task 1 consisted of identifying text spans mentioning adverse drug events and linking them to unique identifiers from the medical terminology MedDRA, whereas in Task 5 the aim was to distinguish tweets that report a user having a child with a medical disorder from tweets that merely mention a disorder.For Task 1, our system, composed of a pretrained RoBERTa model and a random forest classifier, achieved 0.397 and 0.295 entity recognition and normalization F1-scores respectively.In Task 5, we obtained a 0.840 F1-score using a pre-trained BERT model.

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

DOI: 10.18653/v1/2024.smm4h-1.37

Authors: Luis Afonso, João Almeida, Rui Antunes, José Oliveira

Institution: University of Aveiro

Language: English

Topic: Pharmacovigilance and Adverse Drug Reactions

Subfield: Toxicology

Field: Pharmacology, Toxicology and Pharmaceutics

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: hybrid

Funder: European Federation of Pharmaceutical Industries and Associations