Identifying Medication Abuse and Adverse Effects from Tweets: U niversity of M ichigan at # SMM 4 H 2020 (2020)

Abstract: The team from the University of Michigan participated in three tasks in the Social Media Mining for Health Applications (#SMM4H) 2020 shared tasks – on detecting mentions of adverse effects (Task 2), extracting and normalizing them (Task 3), and detecting mentions of medication abuse (Task 4). Our approaches relied on a combination of traditional machine learning and deep learning models. On Tasks 2 and 4, our submitted runs performed at or above the task average.

## introduction the social media mining for health applications (#smm4h) shared task 2020 provided a unique hands-on experience for graduate students and researchers to apply concepts in text cleaning, natural language processing, and health informatics on standardized research tasks. the tasks are motivated by challenges in using social media data for health research, including informal, colloquial expressions and misspelling of clinical concepts, data sparsity, noise, and ambiguity @xcite . this year, a team of students from the university of michigan participated in three tasks -on classification of tweets that report adverse effects (task 2 english), extraction and normalization of adverse effects (task 3), and characterization of chatter related to prescription medication abuse (task 4). we submitted three runs for each of these tasks. this paper describes our approach in developing and training the components for our participation, along with the results over validation and test data sets. we participated in the previous iteration of this task in #smm4h 2019, and proposed two support vector machine (svm) models and a bidirectional long short-term memory (lstm) model @xcite . we followed up on our participation during the post evaluation phase to further develop our approaches and proposed a neural network (nn) model called the collocated lstm with attentive pooling and aggregated representation (clapa) @xcite , that performed better than the three official submissions. based on those insights, this year we proposed three nn models, viz. a multi-stage convolutional neural network model, the clapa model, and a bert-augmented clapa model. ## task 4: characterizing prescription medication abuse in tweets this multi-class classification task involves distinguishing, among tweets that mention at least one prescription opioid, benzodiazepine, atypical anti-psychotic, central nervous system stimulant, or gaba analogue, tweets that report potential abuse/misuse (annotated as "a"), non-abuse/-misuse consumption (annotated as "c"), merely mention medications (annotated as "m"), or are unrelated (annotated as "u") @xcite . the training and test sets consist of 13,172 and 3,271 tweets, respectively. the evaluation metric was set as the f1 score for the potential abuse/misuse class. so, we reformulate the task as a binary classification task of distinguishing potential abuse/misuse tweets (class "a") from non-abuse tweets (union of classes "c", "m", and "u"). ## results the performance of our submitted runs for all three tasks on validation and test sets are summarized in ## conclusion our approach for participating in the #smm4h 2020 shared tasks focused on building on our previous efforts in addressing related tasks. we participated in three tasks and experimented with a combination of traditional machine learning and deep learning models. two of the submitted runs performed at or above the average task performance. additional experiments are planned to further improve the recall, and thereby the overall performance, of our proposed models for these tasks.