Identifying Medication Abuse and Adverse Effects from Tweets: University of Michigan at #SMM4H 2020
V.G.Vinod Vydiswaran | Deahan Yu | Xinyan Zhao | Ermioni Carr | Jonathan Martindale | Jingcheng Xiao | Noha Ghannam | Matteo Althoen | Alexis Castellanos | Neel Patel | Daniel Vasquez
Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task
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.
- V.G.Vinod Vydiswaran 1
- Deahan Yu 1
- Xinyan Zhao 1
- Jonathan Martindale 1
- Jingcheng Xiao 1
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