E dinburgh_ UCL _ H ealth@ SMM 4 H ’22: From Glove to Flair for handling imbalanced healthcare corpora related to Adverse Drug Events, Change in medication and self-reporting vaccination (2022)

Abstract: This paper reports on the performance of Edinburgh_UCL_Health’s models in the Social Media Mining for Health (SMM4H) 2022 shared tasks. Our team participated in the tasks related to the Identification of Adverse Drug Events (ADEs), the classification of change in medication (change-med) and the classification of self-report of vaccination (self-vaccine). Our best performing models are based on DeepADEMiner (with respective F1= 0.64, 0.62 and 0.39 for ADE identification), on a GloVe model trained on Twitter (with F1=0.11 for the change-med) and finally on a stack embedding including a layer of Glove embedding and two layers of Flair embedding (with F1= 0.77 for self-report).

## introduction identification of healthcare-related topics from social media is considered meaningful work in society. therefore, it is attracting attention, particularly from pharmaceutical companies who want to know what people (i.e. patients) think and report about their products. technically, it requires the ability to apply natural language processing (nlp) techniques for the automatic collection, extraction, representation, analysis and validation of social media data such as twitter and reddit posts. this paper summarises our work in the social media mining for health applications (#smm4h) workshop @xcite , including identification of ades (task 1), classification of change in medication regimen in tweets (task 2) and classification of tweets indicating self-reported covid-19 vaccination. the first task splits into three further sub-tasks of classification of english tweets reporting ades (task 1a), extraction of ade spans in tweets (task 1b) and normalization of these colloquial mentions to their standard concept identifiers (ids) in the meddra vocabulary (task 1c) @xcite . all of these are explained in detail in section 2. ## conclusion in this work, we explore the use of different models associated with different techniques for dealing with imbalanced healthcare-related social media corpora. we observed that the best technique relied on the use of deep learning models such as roberta or flair, under-sampling and the adjustment of the weight loss. in the future, we plan to explore more techniques for handling imbalanced datasets, augmenting the training corpus and relying on more embedding models.