Classification, Extraction, and Normalization : CASIA _ U nisound Team at the Social Media Mining for Health 2021 Shared Tasks (2021)

Abstract: This is the system description of the CASIA_Unisound team for Task 1, Task 7b, and Task 8 of the sixth Social Media Mining for Health Applications (SMM4H) shared task in 2021. Targeting on deal with two shared challenges, the colloquial text and the imbalance annotation, among those tasks, we apply a customized pre-trained language model and propose various training strategies. Experimental results show the effectiveness of our system. Moreover, we got an F1-score of 0.87 in task 8, which is the highest among all participates.

## introduction enormous data in social media has drawn much attention in medical applications. with the rapid development of health language processing, effective systems in mining health information from social media were built to assist pharmacy, diagnosis, nursing, and so on @xcite @xcite . the health language processing lab at the university of pennsylvania organized the social media mining for health applications (smm4h) shared task 2021 (mag), which provided an opportunity for fair competition among state-of-the-art health information mining systems customized in the social media domain. we participated in task 1, subtask b of task 7, and task 8. task 1 consists of three subtasks in a cascade manner: (1) identifying whether a tweet mentions adverse drug effect; (2) mark the exact position that mentions ade in the tweet; (3) normalization ade mentions to standard terms. subtask b of task 7 @xcite is designed to identify professions and occupations (profner) in spanish tweets during the covid-19 outbreak. task 8 is targeting the classification of self-reported breast cancer posts on twitter. the ubiquitous two challenges of all the smm4h shared tasks are (1) how to properly model the colloquial text in tweets; (2) avoid prediction bias caused by learning from unbalanced annotated data. the tweet's text, mixing with informal spelling, various emojis, usernames mentioned, and hyperlinks, will hinder the real semantic comprehension by a common pre-trained language model. meanwhile, medical concepts are imbalanced in the real world due to the imbalanced morbidity of various diseases, and this phenomenon is also reflected in social media data. training with imbalanced data will induce the model to pay much attention to the major classes and neglect the tail classes, which hinders the model's robustness and generalization. to address the challenges above, we utilize a language model pre-trained on tweet data as the backbone and introduce multiple data construction methods in the training process. in the following, we will describe our methods and corresponding experiments for each task separately. at last, we summary this competition and discuss future directions. ## task 1: english ade tweets mining adverse drug effect (ade) is among the leading cause of morbidity and mortality. the collection of those adverse effects is crucial in prescribing and new drug research. this task's objective is to find the tweet containing ade, locate the span, and finally map the span to concepts in standard terms. ## task 8: self-reported patient detection the adverse patient-centered outcomes (pcos) caused by hormone therapy would lead to breast cancer patients discontinuing their long-term treatments @xcite . the research on pcos is beneficial to reducing the risk of cancer recurrence. however, pcos are not detectable through laboratory tests and are sparsely documented in electronic health records. social media is a promising resource, and we can extract pcos from the tweet with breast cancer self-reporting @xcite . first and foremost, the pco extraction system requires the accurate detection of selfreported breast cancer patients. this task's objective is to identify tweets in the self-reports category. in this dataset, the training set consists of 3513 tweets (898 self-report and 2615 non-relevant tweets), the validation set consists of 302 tweets (77 self-report and 225 non-relevant tweets), and the test set consists of 1204 tweets. ## conclusion and future work this work explores various customized methods in tasks of classification, extraction, and normalization of health information from social media. we have empirically evaluated different variants of our system and demonstrated the effectiveness of the proposed methods. as future work, we intend to introduce the medical domain's knowledge graph to improve our system further.