BIT @ UA at # SMM 4 H 2024 Tasks 1 and 5: finding adverse drug events and children’s medical disorders in E nglish tweets (2024)

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 pre-trained 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.

## introduction social media text, such as tweets from twitter, holds a vast amount of textual information and can be a solid source for clinical findings @xcite . several research initiatives have been pursued to promote the development of data mining solutions from social media to foster healthier lives @xcite . text from social media has been used by biomedical nlp (natural language processing) researchers for different purposes including sentiment analysis @xcite , disease normalization @xcite , suicide attempt prediction @xcite , and classification of depression users @xcite . the 9th social media mining for health research and applications (#smm4h-2024) workshop continues this research endeavor promoting seven different tasks @xcite . in this work, we describe our participation in the #smm4h 2024 shared task where we present our developed sys-tems for tasks 1 and 5, both consisting in mining english tweets from twitter. in sections 2 and 3 we present the datasets in use and the methodology followed, and discuss the results obtained during the official challenge for tasks 1 and 5 respectively. finally, we draw some conclusions in section 4 and present future lines of research. in task 1, participants had to develop systems to automatically identify mentions of adverse drug events (ades) and link them to unique identifiers from the standard terminology meddra @xcite . past research work also explored the identification of ades from medical case reports and social media @xcite . ## conclusions in this work, we presented machine learning models to detect ades (task 1) and users reporting having children with medical disorders in english tweets (task 5). we obtained more competitive results in task 1 being slightly above the median. in task 5, our classification f1-score was 6.1 percentage points below the median result demonstrating that our approach still holds great potential for improvement. in task 1, the most relevant aspect to enhance would be for our system to be able to link ades to identifiers that were not seen during the training phase. such system should be able to consult the full meddra terminology and normalize any ade mention to the respective identifier. we also hypothesize that the adoption of the bio tagging scheme for ner (task 1) could be beneficial and a more careful hyperparameter optimization through grid search could improve results on both tasks. from our experiments, we conclude that bertbased models achieved the best performance in entity recognition and document classification proving to be on par with the state-of-the-art.